Memories of a lifetime: Contribution of amyloid-beta and NMDA- receptors to memory formation, stability, and malleability.
Peter S. B. Finnie
Department of Psychology
McGill University, Montreal
August, 2014
A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of Doctorate of Philosophy in Psychology.
© Peter S. B. Finnie, 2014
Contents.
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Contents.
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iv List of Figures.
2.7.1. Figure 1 - A!42 levels are elevated in BLA 90 minutes after strong training. 159
2.7.2. Figure 2 - Infusion of LY-450139 or BACE1 inhibitor after strong training does 160 not affect memory expression, but allows memory to undergo reconsolidation.
2.7.3. Figure 3 - Post-training LY-450139 infusion does not prevent reduced synaptic 161 NR2B expression observed 48h after strong training.
2.7.4. Figure 4 - Post-reactivation LY-450139 enhances memory for weak training, 162 whereas BACE1 inhibitor prevents the induction of reconsolidation.
2.7.5. Figure 5 - Figure 5. Representative cannula placements. 163
3.7.1. Figure 1. Systems consolidation and reconsolidation of the AP5-insensitive 202 learning effect.
3.7.2. Figure 2. PepR845A infused into d+vHC impairs Training1 memory retention, but 203
Training2 is only AP5-sensitive when LTM1 is omitted.
3.7.3. Figure 3. Post- Training1 infusion of pepR845A/SCR-pepR845A into ACC. 204
3.7.4. Figure 4. Prior exposure to context or footshock alone is insufficient to induce 205 AP5-insensitive learning.
3.7.5. Figure 5. Only rats given a similar conditioning protocol during Training1 and 206
Training2 exhibit AP5-insensitive second learning.
3.7.6. Figure 6. cFos-positive neurons in ACC and hippocampus following similar versus 207 different Training2.
3.9.1. Supplementary Figure 1. Infusing pepR845A, but not ZIP into dHC 24h after 214 contextual fear conditioning impairs memory retention.
List of Figures.
3.9.2. Supplementary Figure 2. Short or long exposure to Context2 is not sufficient to 215 engage AP5-insensitive second learning mechanisms.
3.9.3. Supplementary Figure 3. 1 or 3 immediate shocks in Context1 or 2 immediate 216 shocks in Context2 is not sufficient to engage AP5-insensitive second learning mechanisms. 3.9.4. Supplementary Figure 4. One- and two-phase contextual fear conditioning each 217 produce reliable fear responding during LTM1 test.
3.9.5. Supplementary Figure. 5. Weak, strong, or long CFC as Training1 can switch the 218 mechanisms of Training2.
3.9.6. Supplementary Figure 6. Specificity and selectivity of two-phase fear 219 conditioning protocol.
3.9.7. Supplementary Figure 7. Representative cannula placements. 220
Appendix Figure 1. Infusion of pepR845A into dHC 24h after contextual fear 339 conditioning impairs memory retention when tested 30 days later.
Appendix Figure 2. Infusion of ANI into dHC+vHC following contextual fear 340 conditioning in Context2 does not impair retention of a previously acquired Context1 fear memory.
vi Abstract.
Memory provides a record of past experience that can help an organism predict future events and thus guide behaviour. To remain accurate, memories must be adaptable to changes in the environment, yet those that are critical to survival of the organism should not be readily overwritten by anomalous events. Thus, the neuronal representation encoding each memory trace likely requires a fine balance of stability versus malleability. Memory reconsolidation is a process by which the stabilized alterations of neural connectivity that retain a memory can be destabilized and modified following retrieval, and likely mediates malleability. Consistent with the stability/malleability hypothesis, stronger memories acquired through extensive experience are often resistant to destabilization unless novel features are presented during retrieval.
The first chapter of this thesis reviews a variety of means by which the brain could selectively stabilize memories, including changes to synaptic plasticity mechanisms that initiate memory destabilization (i.e. reduced NMDA-receptor expression). Specifically it will be proposed that experience-dependent changes to plasticity mechanisms (“metaplasticity”) observed during developmental critical periods might continue throughout the lifespan, in order to regulate memory stabilization. It then discusses how distinct memory systems may encode the general features of multiple experiences independently of unique details of each episode. In this way each event may be encoded in detail, but common features could be gradually identified across experiences allowing them to be selectively stabilized against alteration.
The second chapter proposes that the continuation of developmental metaplasticity processes throughout life may contribute to the common age-related cognitive pathology,
Abstract.
Alzheimer’s disease (AD). This study tests the involvement of an AD-associated peptide, amyloid-beta, in the processes by which memories are stabilized against interference and also destabilized following their reactivation. It is reported that in normal adult rats, inhibiting amyloid-beta production after strong auditory fear conditioning allows memory of this training to subsequently undergo reconsolidation when it otherwise would not. It is also demonstrated that inhibition of enzymes that generate amyloid-beta can influence memory reconsolidation processes occurring after retrieval.
The third chapter explores how prior experience with a similar event may alter learning systems used by the brain. It is observed that when a rat has prior knowledge of a specific fear conditioning procedure this can abolish the requirement for dorsal hippocampal NMDA- receptors when it acquires fear to a second new context. However, disrupting long-term memory in anterior cingulate cortex reinstates the requirement for hippocampal NMDA- receptors without impairing expression of the conditioned fear response to the first context.
These results suggest that the brain may detect when a common conditioning sequence occurs during multiple training tasks, which can be stored independently of the representation of each specific contextual fear association.
Together, these studies provide two examples of experience-dependent changes to the mechanisms of learning. This work begins to elucidate how memories for related events might be integrated at both synaptic and structural levels to form complex knowledge structure, and how this might lead to pathology over time.
viii Résumé.
La mémoire fournie un répertoire d’expériences passées qui peut aider un organisme à prédire des événements futurs, et par conséquent guider son comportement. Afin de demeurer précis, les souvenirs doivent pouvoir s’adapter aux changements de l’environnement, toutefois, ceux qui sont critiques à la survie d’un organisme ne devraient pas être facilement remplacés par des événements anormaux. Donc, la représentation neuronale qui encode chaque trace d’un souvenir exige probablement un fin équilibre entre la stabilité et la malléabilité. La reconsolidation de la mémoire est un processus par lequel les altérations des connectivités neuronales précédemment stabilisées (qui maintiennent la mémoire) peuvent être déstabilisées et modifiées suite au rappel d’un souvenir, ce qui joue possiblement un rôle dans la malléabilité. En cohérence avec l’hypothèse de la stabilité/malléabilité, des souvenirs plus intenses produits suite à des expériences considérables sont souvent résistants à la déstabilisation, à moins que de nouvelles caractéristiques soient présentées lors du rappel.
Le premier chapitre de cette thèse passe en revue une variété de façons par lesquelles le cerveau pourrait sélectivement stabiliser les souvenirs, incluant les changements aux mécanismes de plasticité synaptique qui initient la déstabilisation des souvenirs (expression diminuée des récepteurs NMDA). Spécifiquement, il y sera proposé que les changements aux mécanismes de plasticité qui sont dépendants d’expériences (« métaplasticité ») observés pendant les périodes critiques du développement puissent continuer tout au long de la vie pour réguler la stabilisation des souvenirs. Il est ensuite discuté comment différents systèmes de mémoire peuvent encoder les caractéristiques de multiples expériences indépendamment des détails uniques de chaque épisode. De cette façon, chaque événement serait encodé en détail, mais des caractéristiques communes pourraient être graduellement identifiées à travers
Résumé. diverses expériences, leur permettant ainsi d’être sélectivement stabilisées et à l’abri d’altérations.
Le second chapitre propose que la continuation des processus de métaplasticité développementale tout au long de la vie puisse contribuer à une pathologie cognitive commune liée au vieillissement, la maladie d’Alzheimer (MA). La présente étude teste l’implication d’un peptide associé à la MA, l’amyloïde bêta, dans les processus par lesquelles les souvenirs sont stabilisés contre l’interférence et aussi déstabilisés suite à leur réactivation.
Il y est montré que chez des rats adultes normaux, l’inhibition de la production d’amyloïde bêta après un conditionnement par la peur de forte intensité permet au souvenir de cet entrainement d’être subséquemment reconsolider, alors qu’autrement il ne le serait pas. Il est aussi démontré que l’inhibition des enzymes qui génère l’amyloïde bêta peut influencer les processus de reconsolidation de la mémoire qui surviennent après le rappel.
Le troisième chapitre explore comment une expérience antérieure avec un événement similaire peut altérer les systèmes d’apprentissage utilisés par le cerveau. Il a été observé que lorsqu’un rat possède une connaissance préalable d’une procédure spécifique de conditionnement par la peur, ceci peut abolir la nécessité des récepteurs NMDA de l’hippocampe dorsal lorsqu’il acquière la peur à un nouveau second contexte. Cependant, perturber la mémoire à long-terme dans le cortex cingulaire antérieur rétabli la nécessité des récepteurs NMDA de l’hippocampe dorsal sans nuire à l’expression de la réponse de peur conditionnée au premier contexte. Ces résultats suggèrent que le cerveau peut détecter quand une séquence commune de conditionnement survient pendant de multiples entrainements, lesquels peuvent être emmagasinés indépendamment de la représentation spécifique de chaque association de peur contextuelle.
x Résumé.
Ensemble, ces études amènent deux exemples de changements liés à l’expérience qui affectent les mécanismes d’apprentissage. Le présent travail commence à élucider comment les souvenirs pour des événements reliés peuvent être intégrés au niveau synaptique et structural pour former une structure complexe de connaissances, et comment ceci pourrait mener à des pathologies au fil du temps.
xi Acknowledgements.
Any project that takes as long as this thesis has inevitably enlisted the assistance of a legion of supporters. This is a brief thank you to a small number of those people.
First, to my advisor and mentor, Karim Nader, who has always encouraged me to pursue the experiments I find most fascinating and has shaped my way of thinking about the world for almost a third of my existence. Also to my graduate program committee members, Andy
Baker and Wayne Sossin, who were always happy to provide expert insight regarding my projects over the years.
I am very grateful to the many Nader lab members who have come and gone during my extended tenure for providing either technical or moral support (but usually both). To the early members, Cyrinne Ben Mamou and Mike Honsberger, for building my scientific confidence, and the (relative) newcomers, Joelle Lopez, Jane Zhang, Julian Gitelman, Shih-
Dar Chang, and Carmelo Milo, for keeping the lab together when I neglected to do so. I have also learned volumes from Virginia Migues and Bruno Moraes, who patiently taught me and re-taught me the basics of biochemistry (which is no small task). I am deeply indebted to
Karine Gamache who translated my thesis abstract and provided extensive help with biochemical procedures, but also made sure I found all the things I couldn't see, got all the things I couldn't buy, and fixed all the problems I couldn't solve. Also to Einar Einarsson, who inadvertently taught me how to be a scientist, both socially and professionally. And finally, to the "yin and yang" of the Naderhood: Oliver Hardt, who inspires me to always think about my small projects by framing them as broadly as possible, and Szu-Han Wang, who reminds me to keep the details in focus and, it seems, will remain my honours supervisor for all eternity.
Acknowledgements.
I also greatly appreciate the contributions of the many undergraduate students who have helped me during the last four years. Evan Kelso, Jordana Wynn, and Johnna Perdrizet collected pilot data that is not explicitly included in this thesis, but helped guide me to the work presented here. Also Noemi Stern has recently helped with behavioural testing. Finally,
I'm very grateful for the generous assistance of Maria Protopoulos and Elizabeth Sinclair during the projects presented in Chapters 2 and 3, respectively. Maria assisted with several of the behavioural procedures and diligently performed most of the histology for the amyloid- beta study. Liz collected the initial pepR845A pilot data and assisted with surgery, histology, and behaviour for several other experiments. These studies would not have been completed without their help.
I will forever cherish the many friends I’ve latched on to during my time in graduate school, to whom I owe what's left of my sanity. My wonderful officemates, Maliha, Andrew,
Elia, Colin, and Selma helped keep me grounded. My lab sister, Georgina Archbold, consistently instilled the knowledge that I was not alone in the challenges I encountered over the course of my PhD. Andrea Chen provided me with a calm, accepting place to inhabit even when she was far away. Sam Goldberg lured me out of my shell, and Josh and Zena helped keep me out here. Michelle Leybman offered unconditional, unwavering support, and showed me that my scientific view of the world is not the only empirical perspective. And Mike Klein and Seth Davis helped me to think through and not think through my troubles, respectively.
I owe the most overdue thanks to the old guard. Suneye Koohsari was a friend in a new city when I had no one else, and has inexplicably stuck around ever since. Christopher J.
Martin has taught me to embrace heated debate - the truest route to genuine friendship - and his counterpart, Simon C. Baker, has demonstrated the value of listening without speaking.
My parents, Elizabeth and John, have supported me in every way I've needed - even long
xiii Acknowledgements. after it was their job to do so. There is no end to the gratitude I feel for their willingness to stand by my lifetime of decisions to always take the long road. And my grandmother, Dorothy
Barss, donated her pure interest in my life. Our long conversations about the mind and its idiosyncrasies led me to my current conceptualization of memory as a whole.
Finally, I cannot possibly begin to express my appreciation to Lara Pierce, who - when times were most bleak - kept me from disappearing altogether. Her already prolific scientific career has directly inspired many ideas I have presented in this thesis, and her extensive editing has transformed them into something vaguely comprehensible. She has given me the gift of always knowing I have an accomplice at my side, even when no one else understands what I’m trying to say.
Thank you all.
xiv Statement of Original Contribution.
The research findings reported in this dissertation are presented in two yet unpublished manuscripts. The manuscript comprising Chapter 2 is prepared for submission to the journal eLife. This is one of the first studies to explore the relationship between memory reconsolidation and the Alzheimer's-associated peptide amyloid-beta (Ohno 2009;
AlvarezRuiz and Carrillo-Mora 2013), and the first to investigate if amyloid-beta can control whether or not a memory will be labilized/destabilized by reactivation. It is also the first to show that a pharmacological manipulation at the time of memory formation can alter whether or not a memory will subsequently undergo reconsolidation following reactivation. This is critical given that the strength of memory encoding will often dictate stability (Sevenster et al.
2013). It provides a new model for how pharmacological treatments can augment stability of a memory independently of the strength of its encoding. Critically, this study is also the first to propose that changes to the way memory is encoded as knowledge accumulates over a lifetime could contribute to the pathogenesis of Alzheimer's disease, instead of the opposite perspective generally assumed in the field.
The manuscript presented in Chapter 3 is prepared for submission to Nature
Communications. Although many studies have now investigated the phenomenon of experience-dependent, NMDAR-independent learning using an array of tasks, it is not yet clear what the animal learns during a first training session that contributes to the change in learning mechanisms. This is a critical step towards unveiling when and how the brain identifies already familiar components of ongoing experience, by which it may construct a knowledge base of the world. This is the first study to report that knowledge of the temporal sequence of a fear conditioning procedure can selectively eliminate the requirement for
Statement of Original Contribution.
NMDA-receptors in the dorsal hippocampus during the encoding of subsequent tasks with a similar training procedure. It is also the first to show that this memory for the training procedure is mediated by anterior cingulate cortex (ACC), and can be retained or disrupted independently of the conditioned fear response to the previously shocked context. Finally, it is the first study to report a loss of memory retention when a peptide that induces removal of synaptic AMPA-receptors (TAT-pepR845A, see Migues et al. 2014) is infused into ACC.
xvi Contribution of Authors.
I originally developed and designed the study presented in Chapter 2 under the supervision of Dr. Nader, and have performed most of the behavioural experimentation, biochemistry (with assistance from Bruno Moraes, Johnna Perdrizet, and Noemi Stern), data analysis, and manuscript preparation. Maria Protopoulos performed much of the tissue histology and assisted with behavioural procedures for the experiments displayed in Figures
2b and 4b.
The study in Chapter 3 was originally developed by Dr. Szu-Han Wang, Dr. Nader, and myself. I have designed and conducted most of the experiments, except Experiments 1 and 4, which I performed with Dr. Wang, and the experiment in Supplementary Figure 1b, which was primarily collected by Elizabeth Sinclair. Elizabeth also assisted with surgery, tissue histology, immunohistochemisty, and data analysis. Karine Gamache performed the animal perfusions and assisted with the immunohistochemistry procedures. Dr. Nader, Dr. Wang, and Karine Gamache have also assisted in preparation of the manuscript
Manuscripts resulting from this doctoral research program.
(* these authors contributed equally to this work.)
Finnie, P. S. B., Protopoulos, M., & Nader, K. (In preparation). Amyloid-beta contributes to
fear memory stabilization and destabilization. (See Chapter 2).
Finnie, P. S. B., Wang, S.-H., Sinclair, E., Gamache, K., & Nader, K. (In preparation). Task
knowledge depends on cingulate cortex and lifts hippocampal NMDA-receptor
requirement for contextual fear conditioning. (See Chapter 3).
Migues, P. V., Hardt, O., Finnie, P. S. B., Wang, Y. W., & Nader, K. (2014). The
maintenance of long-term memory in the hippocampus depends on the interaction
between N-ethylmaleimide-sensitive factor and GluA2. Hippocampus.
doi:10.1002/hipo.22295
Nader, K., Hardt, O., Einarsson, E. Ö., & Finnie, P. S. B. (2013). The Dynamic Nature of
Memory. In C. M. Alberini (Ed.), Memory Reconsolidation (pp. 15–41). Academic Press:
London, UK.
Finnie, P. S. B., & Nader, K. (2012). The role of metaplasticity mechanisms in regulating
memory destabilization and reconsolidation. Neuroscience & Biobehavioral Reviews, 36(7),
1667–1707.
Wang, S.-H.*, Finnie, P. S. B.*, Hardt, O., & Nader, K. (2012). Dorsal hippocampus is
necessary for novel learning but sufficient for subsequent similar learning.
Hippocampus, 22(11), 2157–2170.
Commonly used abbreviations.
A! - Amyloid-beta ACC - Anterior Cingulate Cortex AD – Alzheimer’s disease "7-nAChR - Alpha-7 nicotinic acetylcholine receptor. AMPAR - "-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor. ANI - Anisomycin (protein synthesis inhibitor) AP5 (also D-, L-, and DL-AP5) - (D and/or L)-2-amino-5-phosphonopentanoate (a competitive NMDAR antagonist) BLA - Basolateral Amygdala CaMKII - Calcium- and calmodulin-dependent protein kinase II CPFE - Context Pre-exposure Facilitation Effect (2-phase contextual fear conditioning) DG – Dentate gyrus (hippocampal structure) dHC - Dorsal Hippocampus DMTP - Delayed matching-to-place (a water maze task in which animals must remember the location of a platform over short inter-trial intervals, but with a location that changes each day EphB2 - Ephrin type-B receptor 2 GluA1/GluA2/GluA3 - AMPAR subunits A1/A2/A3 (aka GluR1/2/3) ICV - Intra-cerebroventricular (infusion into brain ventricles) L-VGCC – L-type voltage-gated calcium channel LY – Gamma-secretase inhibitor, LY-450139 PKA - Protein kinase A (aka cAMP-dependent protein kinase) PKM# - Protein Kinase M zeta PR-LTM – Post-reactivation long-term memory test MK-801 - Dizoclipine (Uncompetitive NMDAR antagonist) MTL - Medial Temporal Lobe NMDAR - N-methyl-D-aspartate receptor NR2A/NR2B - NMDAR subunits (aka GluN2A/B) NSF - N-Ethylmaleimide-Sensitive factor vHC - Ventral Hippocampus ZIP – PKM# inhibitor
Running head: Chapter 1 – General Introduction
Chapter 1
General Introduction
Chapter 1 – General Introduction
1.1. MEMORY PERSISTENCE AND MALLEABILITY.
The brain has a difficult job. Amid the myriad incomprehensible acts of sensation and perception, motor control, and homeostasis it must also perform the most unbelievable feat of all: intersecting both past and future in the present moment. This miraculous facet that is memory allows organisms to use their experience of the past as navigation towards the best future course of action. The most critical memories - those that shape perceptions and influence daily survival - must also persist indefinitely. A duckling must somehow, in the first brief moments of its life, rapidly learn the visual features of its mother (or maternal substitute;
Lorenz 1937) and remember them into adulthood (Nakamori et al. 2013). This imprinting improves the chance of survival of the duckling by motivating it to remain in the safe presence of its mother.
Retaining a record of what came before can also help predict what will happen next. It should generally be biologically adaptive to avoid a particular cave where a predator was once encountered. Yet in most instances this persistent record of the past must be flexible; adapting to the changing environment (Dudai 2009). Learning that apples can be found hanging from branches of a tree helps accelerate our search for nourishment, but becomes a liability come winter when time spent searching its limbs is increasingly detrimental to survival. A memory strategy that draws on the most recent experience will adapt to such changes, but is also not the most adaptive system, as finding that the last apple has been picked does not mean new apples will never grow on the tree again. Thus the brain must also work out which memories most reliably predict the world over time, and selectively preserve these against loss.
So how does the brain indefinitely maintain important information, yet modify this set of predictions when they are no longer valid? Some have proposed that once fully stored
2 Chapter 1 – General Introduction memory is rarely, if ever, replaced or overwritten by new information (McGaugh 2000;
Alberini 2011). This model typically predicts the existence of a mechanism that allows newly encoded information to dominate, inhibit, or add on to existing, irrelevant memory traces, while leaving the original encoding intact. This has been studied most intensively in the field of behavioural extinction, where it has long been understood that repeated unreinforced exposure to a previously conditioned stimulus (CS) will only transiently inhibit its ability to elicit a conditioned response (CR), which can recover over time or following reminders
(Pavlov 1927). This theory has been bolstered by a variety of studies showing that some morphological changes in the brain induced by initial learning can persist following extinction
(Vetere et al. 2011b), and the dominant memory that is expressed is dictated by the distinct neural circuit that is activated at retrieval (Herry et al. 2008).
Of course, a memory system that encodes and maintains every new experience as an independent memory trace should necessarily be resource-intensive and likely prone to interference at the time of retrieval. Therefore, others have proposed models of memory malleability that involve updating the content of memories via alterations to the neuronal storage medium (see Bruel-Jungerman et al. 2007; Dudai 2012). Changes in the environment would thus be stored by modifying the physical encoding of an existing memory trace, likely through a process known as reconsolidation (see Lee 2009). However, physically altering previously encoded memories risks interfering with those most critical to our survival. For such a system to function adaptively, the brain must also be capable of selectively protecting some memories over others. In addition to relying on emotional valence (see McGaugh 2000), one manner in which the brain may preserve memories could result from the repeated exposure to features common across multiple experiences. Those parts of experience that are
3 Chapter 1 – General Introduction repeatedly encountered may thus be strengthened. Such a system may allow new experiences to be encoded over and within a framework of existing knowledge, which may influence both the information extracted from a given event and the biological systems mediating its storage into memory (Bartlett 1932; Bergman and Roediger 1999; Tse et al. 2007; Wang and Morris
2010). The work presented in this thesis is aimed at exploring neural mechanisms that protect memories against modification, and how existing pieces of knowledge can influence how we encode related information.
In this general introduction I will survey the parameters and proposed functions of reconsolidation and what it tells us about the brain’s ability to gradually modify existing memories. I will then summarize evidence that memory is subserved by the potentiation and depression of synaptic connections in the brain, describing one mechanism in particular (the
"-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor; AMPAR) that is thought to mediate synaptic strength, and how it might be allow memory to persist over time. The critical role of N-methyl-D-aspartate receptors (NMDARs) in initiating both synaptic plasticity and memory formation will be discussed, along with mechanisms by which experience-dependent changes may alter both the function of these receptors and their contribution to learning. This will be approached from the perspective that memory encoding may change with time, and thus the contribution of NMDA-receptors may also transition somewhat predictably across the lifespan. This leads into a discussion of one particular protein that has been implicated in age-related pathology – amyloid-! (A!) – and how the actions of this small peptide could contribute to the malleability of memories. Finally, I will discuss how the brain might formulate rules about the world during repetition of similar events to facilitate learning.
4 Chapter 1 – General Introduction
In the first study (Chapter 2) I test if inhibition of A! at the time of learning or at the time of retrieval can influence memory malleability. In the second study (Chapter 3) I identify what shared components of two distinct behavioural experiences cause a fundamental change in the mechanisms used to learn the second task. Thus this thesis aims to characterize how the repetition of experience can shape and maintain knowledge.
1.2. RECONSOLIDATION AS A MODEL OF MEMORY MALLEABILITY AND STABILITY.
In recent years it has become increasingly clear that previously acquired memories can be modified by experience (Lewis and Lewis 1979; Nader et al. 2000; Sara 2000) through processes that alter the neural substrates of the initial encoding (Lee et al. 2012; Doyère et al.
2007; Rose and Rankin. 2006).
After acquisition, memory is gradually stabilized from a labile to a fixed state via a process known as cellular consolidation (Müller and Pilzecker 1900; McGaugh 1966; 2000). While in the labile state a memory is vulnerable to disruption by an array of amnesic agents (from electroconvulsive shock to inhibition of protein synthesis), and this period during which it is sensitive to disruption is used to define the consolidation phase (Duncan 1949; Gerard 1949;
McGaugh 1966; Barondes and Cohen 1968). By this measure, cellular consolidation is typically found to be completed within hours for most types of memory in many animal species (see Duncan 1949; McGaugh 2000).
It was originally thought that, once consolidated, memory remained fixed in the brain and resistant to alteration (Glickman 1961). This opinion was challenged intermittently during the
1960s and 70s by evidence of fully consolidated memories that would again show sensitivity to
5 Chapter 1 – General Introduction amnesic treatments during a brief period after their retrieval (Schneider and Sherman 1968;
Misanin et al. 1968; Lewis and Lewis 1979). Although such claims were swiftly dismissed by pillars of consolidation theory (Dawson and McGaugh 1969; Squire et al. 1976), renewed interest in the late 1990s revived the idea that consolidated memory could be destabilized or labilized by retrieval, requiring subsequent protein synthesis-dependent restabilization or
“reconsolidation” processes (Przybyslawski and Sara 1997; Sara 2000; Nader et al. 2000).
Such phenomena have now been observed using a wide variety of amnesic treatments, behavioural tasks, and animal species (see Nader and Einarsson 2010 for a partial list).
Through the characterization of both the kinds of events that induce reconsolidation and the conditions under which it does not occur, several functional roles for this process have been identified. Generally, reconsolidation is thought to allow for the strengthening or alteration of memory with experience. For example, when multiple training trials are given across days, rats progressively exhibit improving performance of a task (i.e. faster localization of a hidden platform in a water maze, or increased freezing response to a specific context in which footshocks are delivered), and protein synthesis inhibitors applied after a later training trial can impair performance acquired during the previous trial(s) (Rodríguez-Ortiz et al.
2005; Lee 2008; 2010; Morris et al. 2006). Thus enhancement of memory strength may engage and even require reconsolidation processes. Infusing drugs that prevent the initiation of reconsolidation (such as an inhibitor of protein degradation injected into the hippocampus) likewise prevents the strengthening of contextual fear memory (Lee 2008), which will be further discussed in subsequent sections. Altering the conditions of a task, such as the moving the location of a hidden water maze platform, can also induce reconsolidation (Rossato et al.
2006b; Haubrich et al. 2014). Thus, updating the content of a memory with newly acquired
6 Chapter 1 – General Introduction information may depend on this process.
And yet, memory for a given behavioural task (i.e. fear conditioning or spatial reference water maze) is not always observed to undergo reconsolidation when reactivated by re-exposing the animal to the trained stimuli. One common finding is that stronger training procedures (typically repeated learning trials) result in memories that are less likely to undergo reconsolidation following reinforced or unreinforced reactivation trials (Suzuki et al.
2004; Rodríguez-Ortiz et al. 2005). A leading model of reconsolidation explains such results by positing that as a memory approaches a theoretical “learning asymptote” across experience
(at which point no additional strengthening will improve predictive validity or task performance), reconsolidation should no longer occur unless relevant novel information is also presented (Morris et al. 2006; Sevenster et al. 2013; Osan et al. 2011; Winters et al. 2009; Lee
2010; Finnie and Nader 2012). This is fundamentally based on the Rescorla-Wagner model of associative learning (Rescorla and Wagner 1972) and Wagner’s ‘Sometimes Opponent
Process” (SOP) theory (Wagner 1981), which essentially suggest that animals should only learn when experience deviates from predictions. By applying SOP theory to the model of reconsolidation it can be hypothesized that an existing memory will only be reverted to the unstable, malleable state when it does not accurately predict an ongoing event, thus warranting a re-structuring of its content.
These theories have recently been formally supported using an ingenious reconsolidation procedure allowing mismatch between expectations and reality to be empirically defined, with the assumption being that this provides the novelty required for induction of reconsolidation (Sevenster et al. 2013). Human subjects experienced a visual fear conditioning task consisting of three training trials (either one or all of which were
7 Chapter 1 – General Introduction reinforced), followed by a single reinforced or unreinforced reactivation trial. It was observed that both a change in participants’ expectancy of electric shock and the sensitivity of their memory to an amnesic agent (the beta-adrenegic antagonist, propranolol) required that reactivation deviated from predicted outcomes. Specifically, a weak fear memory (i.e., one formed via trials with inconsistent reinforcement) was sensitive to disruption when reactivation was reinforced, but a strong memory (i.e., one that received consistent reinforcement) was not. However, the strong memory was disrupted when the reactivation stimulus was unreinforced, as this provided novel information about the visual stimulus.
Together these findings indicate that a mismatch between expectations and an experienced outcome at reactivation was necessary for the induction of reconsolidation.
The ability of a reactivation procedure to enhance the strength of a given memory is not the only source of novelty capable of inducing reconsolidation. Other work has demonstrated that a strong reconsolidation-resistant memory for the location of specific objects within an environment can be labilized by re-exposing the animal to a subtly altered environment (Winters et al. 2009). Similarly, a very strong fear conditioning memory that does not undergo reconsolidation following additional training trials can be destabilized by changing when during these trials an aversive footshock is administered (Díaz-Mataix et al.
2013). Behavioural novelty effects have been inhibited using pharmacological treatments that reduce endogenous neurobiological novelty signals, such as dopamine release (see Lisman and
Grace 2005), thus preventing novelty-induced reconsolidation (Reichelt et al. 2013).
Conversely, behavioural novelty effects have reportedly been mimicked using metabotropic acetylcholine or endocannabinoid receptor agonists, causing a strong memory to undergo reconsolidation in the absence of new information at the time of reactivation (see Flavell et al.
8 Chapter 1 – General Introduction
2013 for a summary; but also Lee and Flavell 2014). In conclusion, it appears that a memory can be stored in a resilient, reconsolidation-resistant state following strong or repeated training trials. Once in this state a retrieval cue that deviates from previously learned predictions is necessary to induce lability. This inclusion of novel information likely triggers additional activation of neuronal plasticity signals, such as dopamine or acetylcholine release, which are capable of destabilizing a memory that is otherwise resistant to modification.
The same conceptualization may also explain the documented resistance of certain older memories to undergo reconsolidation after retrieval (Milekic and Alberini 2002; Suzuki et al.
2004; Eisenberg and Dudai 2004; Frankland et al. 2006). This model would postulate that as time passes memories typically do not passively wait for retrieval, like a photograph or a page in a notebook. Instead they likely continue to change over time, being either progressively forgotten (Hardt et al. 2013; 2014) or more resiliently stored (Frankland et al. 2006; Alberini
2011). It is possible that such processes occur during sleep when memories may be reactivated or replayed (Skaggs and McNaughton 1996), in order to help strengthen (Rasch et al. 2007;
Diekelmann et al. 2011), integrate (Wagner et al. 2004; Lewis and Durrant 2011), or purge
(Saletin and Walker 2012; van der Helm et al. 2011) information, as appropriate. This may also occur as memory is retrieved during waking experience, with those memories most frequently reactivated being selectively strengthened (see Inda et al. 2011; Kim et al. 2014).
However, strength and age of a memory are not the only identified boundary conditions to reconsolidation. While novelty seems to be important for initiation of the reconsolidation process, there must be a ‘novelty threshold’ beyond which an existing memory ceases to be labilized and updated by reactivation. At this point, the dissimilar reactivation stimuli might instead be encoded via de novo memory formation. This has been most evident in the case of
9 Chapter 1 – General Introduction extinction training, where the strength of a previously learned behavioural response (such as conditioned freezing to an auditory tone previously paired with footshock) is weakened by extensive unreinforced exposure to the conditioned stimulus. It is well documented that extinction training is not mediated by “unlearning” or “erasure” of the existing memory, as the extinguished response can re-emerge after exposure to the trained stimulus in a new environment (“renewal”), re-exposure to the reinforcing stimulus (“reinstatement”), or simply following long time intervals (“spontaneous recovery”; see Bouton and Moody 2004). Instead extinction is likely encoded, at least partially, by the formation of additional inhibitory memory traces. With regards to memory reconsolidation, it has been found that an extended memory reactivation session (i.e. a long exposure to a feared context) often fails to induce reconsolidation and instead reduces responding via extinction (Eisenberg et al. 2003; Suzuki et al. 2004; but see Duvarci et al., 2006 for conflicting results). Thus, it has been concluded that beyond a certain point, novelty engages the encoding of a new memory instead of updating an existing one (Merlo et al. 2014).
Some interesting exceptions have arisen when such procedures are combined, as when a standard reactivation trial is given shortly (~1 hour) before extinction training (Monfils et al.
2009; Schiller et al. 2010). This protocol, referred to as “post-reactivation extinction”, can persistently inhibit a fear memory in a manner that is resistant to reinstatement/renewal/spontaneous recovery, and is instead mediated by reconsolidation mechanisms (Oyarzún et al. 2012; Rao-Ruiz et al. 2011; Clem and Huganir 2010; Jarome et al. 2012; though also see Costanzi et al. 2011). Thus it appears that the process of retrieval destabilizes the memory, which allows its neural encoding to be irreversibly modified by extinction training within a brief time-window, and causes persistent suppression or even
10 Chapter 1 – General Introduction
“erasure” of the learned response (Balducci et al. 2010a; Clem and Huganir 2010).
If an overarching rule derived from this literature proposes that a high degree of novelty should engage new memory formation, a moderate degree should strengthen (or modify) an existing memory, and no novelty should go unencoded via long-term memory (Sevenster et al.
2013; Osan et al. 2011), then one hypothetical implication is that the brain may increasingly accumulate “strong”, semi-orthogonalized memories over time (even assuming non- pathological processes of forgetting). With age and experience it seems likely that this accumulation must, therefore, necessarily change how the brain encodes and retains memories. Although it may become functionally impossible to dissociate new memory encoding from the updating of intertwined, inter-connected memory networks (also see Dudai
2012), in theory this accumulation of knowledge may lead to a predominance of updating processes with age. As will be discussed in Section 1.6, fundamental properties of the environment may be gradually extracted due to repetition across experience, thus - barring major environmental changes - the brain should progressively acquire a more accurate predictive system. Our anecdotal experience of age as being associated with resistance to change may, therefore, be due to a memory-retention system that (perhaps due to limited resources) increasingly fits ongoing experience into existing knowledge structure rather than encoding anew (see Wilson et al. 2006 for a related discussion). Hence, the conceptualization of memory guiding this thesis assumes that age-related changes to memory processing may not typically be a symptom of gradual decay or degeneration, but instead a symptom of the way a lifetime of memories are processed and updated within a finite storage system. I will return to this idea in Section 1.5 of this chapter.
11 Chapter 1 – General Introduction
1.3. AMPA-RECEPTOR CONTRIBUTION TO SYNAPTIC STRENGTH AND MEMORY.
For decades an assumption underpinning much of the field of neuroscience has been that persistent changes in behavioural responding are mediated by changes to synaptic efficacy: the strength of communication between neurons (Bliss and Collingridge 1993; Bliss and Lomo
1973). Although the universal validity of this assumption has been contested (see Neves et al.
2008; Barnes 1995 for discussion), recent work has provided tentative evidence for a causal role of synaptic long-term potentiation (LTP) and depression (LTD) of auditory thalamic inputs to amygdala in memory formation and loss, respectively (Nabavi et al. 2014). Many forms of LTP and LTD are thought to be initiated, respectively, by a large or small increase in post-synaptic intracellular calcium, as can occur when NMDARs in the cell membrane are activated by glutamate released from a pre-synaptic neuron (see Lüscher and Malenka 2012).
Historically, evidence that these forms of synaptic plasticity mediate memory have relied on observations of mimicry or occlusion (Cooke and Bear 2014). Mimicry has been demonstrated through the observation that memory formation can evoke LTP- or LTD-like changes in the brain. For instance, Whitlock and colleagues (2006) reported that one-trial inhibitory avoidance training caused potentiation of synaptic transmission in the CA1 region of hippocampus, by measuring field excitatory potentials after learning. Likewise, after rats learned a motor skill task with one paw, field potentials were found to be increased in the contralateral but not ipsilateral motor cortex (Rioult-Pedotti et al. 1998).
LTD mimicry is less readily apparent, but it has been found that low-frequency stimulation of the hippocampus can induce LTD when it is applied as a rat explores a new environment containing several salient objects, but not when it explores a familiar environment or a novel environment containing no objects (which instead evokes potentiation;
12 Chapter 1 – General Introduction
Manahan-Vaughan and Braunewell 1999; Kemp and Manahan-Vaughan 2004; Kemp and
Manahan-Vaughan 2007). Thus this exploration of the spatial location of objects appears to facilitate LTD.
A combination of such work has indicated that under some conditions the increase in potentiation of amygdala synapses can be partially reversed by extinction training (Quirk et al. 1995; Clem and Huganir 2010; Mao et al. 2006), and that behavioural extinction can be mimicked by electrophysiological induction of depotentiation (Lin et al. 2003). This indicates that replacing a behavioural training experience with experimental induction of a neurophysiological correlate can be sufficient to produce a comparable behavioural output.
Studies of occlusion have typically involved training animals to perform a task and then attempting to induce LTP or LTD via an established protocol. For example, specifically following contextual fear conditioning (but not exposure to the context alone), the amplitude of in vitro hippocampal LTP induced by saturating high-frequency stimulation was found to be inhibited (Sacchetti et al. 2002). In the study by Whitlock and colleagues discussed above, populations of cells that exhibited LTP-like changes after behavioural training exhibited a lesser ability to undergo LTP following high-frequency stimulation. Furthermore, after the reactivation of a consolidated auditory fear memory it has been found that induction of depotentiation, via a procedure dependent on group-1 metabotropic glutamate receptors
(mGluR1s), is prevented at a thalamus-lateral amygdala synapse in ex vivo tissue (Kim et al.
2010; Lee et al. 2011).
In an inverse manner, saturation of LTP in the hippocampus via electrical stimulation has also been found to occlude associative memory formation (Moser et al. 1998). Similarly, LTP
13 Chapter 1 – General Introduction induction at Schaffer collaterals of the hippocampus can prevent the acquisition, extinction, recall, and reconditioning of trace eye-blink conditioning in mice when applied shortly before these behavioural procedures (Gruart et al. 2006). Thus it appears that LTP/LTD and learning can both mimic and occlude one another, providing strong correlative evidence that learning and memory are at least partially mediated by changes to synaptic strength.
Using optogenetic techniques, the Malinow group has recently provided causal support that coordinated synaptic potentiation and depotentiation of a discrete population of neurons can produce and ‘erase’ memory, respectively (Nabavi et al. 2014). By optically stimulating auditory thalamic inputs to lateral amygdala neurons (which had been engineered to transgenically express light-sensitive opsins) at the same time as delivery of footshock to the behaving animals, these animals came to exhibit a fear response to optical stimulation alone.
Using an LTD-induction protocol in these same neurons, it was found that expression of the memory could be suppressed (or “extinguished”), and that the induction of LTP could again reinstate the conditioned fear response. Thus, it is becoming increasingly clear that LTP and
LTD can serve specific information-encoding functions, and are the likely endogenous mechanisms by which the brain forms memories.
1.3.1. Synaptic AMPA-receptor insertion, maintenance, and endocytosis.
Although a vast array of molecular and cellular changes can alter the strength of synaptic connections, at excitatory synapses a primary mechanism involves changes to the number of post-synaptic "-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs), which mediate a majority of fast glutamatergic neurotransmission in the brain (Malenka and
Bear 2004). An increase in AMPAR expression at the synapse will generally increase synaptic strength and is observed after LTP induction (Hayashi et al. 2000; see Malenka and Nicoll
14 Chapter 1 – General Introduction
1999; Song and Huganir 2002), while a decrease in expression can reduce synaptic strength, as is observed in the hippocampus after LTD induction (Carroll et al. 1999; Ehlers 2000).
These processes do not rely on a simple binary change to the number of synaptic
AMPARs, however. AMPARs are heterotetramers composed of distinct subunits, GluA1-
GluA4, each with distinct molecular binding partners, trafficking mechanisms, and kinetic properties (Shi et al. 2001; Malinow and Malenka 2002; Song and Huganir 2002). It has been observed that after activation of synaptic plasticity mechanisms, phosphorylation of GluA1 at
Serine845 (Ser845) or PDZ domain of its carboxy-terminus by cAMP-dependent protein kinase (PKA) or calcium- and calmodulin-dependent protein kinase II (CaMKII), respectively, can promote synaptic and perisynaptic insertion and stability of GluA1- containing AMPARs, which may be gradually replaced with GluA2-containing AMPARs via constitutive recycling (Ehlers 2000; Shi et al. 2001; Hayashi et al. 2000; Oh et al. 2006; Choi et al. 2000; He et al. 2009; Esteban et al. 2003). PKA phosphorylation at Ser845 sites may be important for early phases of LTP and LTD, possibly "priming" the AMPAR for plasticity
(Esteban et al. 2003; Seol et al. 2007), and activity-dependent dephosphorylation of Ser845 may be involved in subsequently removing GluA1-containing AMPARs during depotentiation or LTD (Ehlers 2000; but see Lee et al. 2010a).
A small proportion of these AMPARs in the adult rodent forebrain lack GluA2-subunits
(most of which are GluA1 homomers; see Wenthold et al. 1996; Lu et al. 2007; Liu and Zukin
2007), and as a result are calcium-permeable (CP) and inwardly-rectifying (Hollmann et al.
1991; Bowie and Mayer 1995). The functional expression of these CP-AMPARs is transiently increased in an activity-dependent manner (Thiagarajan et al. 2005; Isaac et al. 2007), potentially being inserted to mediate the initial phases of LTP (Plant et al. 2006) and
15 Chapter 1 – General Introduction selectively removed during LTD (Clem and Huganir 2010; Bellone and Lüscher 2006). Thus
CP-AMPARs could also provide an additional source of calcium influx for plasticity induction
(Wiltgen et al. 2010a) and, as will be discussed, their insertion and removal could also allow for rapid modifications to synaptic strength. Of relevance, it has been observed that GluA1 knockout mice express deficient early LTP but normal late LTP in the hippocampus, suggesting that these processes are dissociable and GluA2 insertion may not always require
GluA1 insertion, but instead can occur via a slower constitutive process (Romberg et al.
2009).
Despite the importance of the GluA1-subunit, most AMPARs in the adult forebrain contain GluA2 (Burnashev et al. 1992), which may more persistently maintain synaptic strength (Sacktor 2011). GluA2-containing AMPARs are more gradually trafficked to the membrane and stabilized at the synapse by several complex intracellular mechanisms (Song and Huganir 2002; Song et al. 1998; Sacktor 2011). GluA2 subunits can be delivered directly to the synapse, but this appears to occur in a continual manner (Shi et al. 2001) that is likely regulated by their binding to N-ethylmaleimide sensitive fusion protein (NSF) and an autonomously active protein kinase C isoform, protein kinase M# (PKM#; Sacktor 2011). In response to LTP-inducing stimulation, synthesis of the PKM# protein can be induced, which is both necessary and sufficient for LTP maintenance (Ling et al. 2002; Yao et al. 2008; Kelly et al. 2007). Through an interaction with protein kinase C" interacting protein (PICK1), an intracellular molecule involved in synaptic endocytosis of AMPARs, PKM# can cause NSF to drive GluA2-AMPARs from an extrasynaptic reserve pool, moving them to the synapse to induce LTP (Yao et al. 2008). It is thought that NSF binding interferes with the direct interaction between GluA2 and PICK1 (Hanley et al. 2002). Once at the synapse, GluA2 will
16 Chapter 1 – General Introduction then be stabilized by interactions with structural proteins, where PKM# will continue to facilitate the NSF-mediated block of PICK1 and also inhibit binding of another protein, guanine-nucleotide exchange factor brefeldin-resistant Arf-GEF 2 (BRAG2), involved in the active endocytosis "process (Sacktor 2011). If PKM# is inhibited by the application of a small peptide that mimics the regulatory pseudosubstrate, zeta-inhibitory peptide (ZIP), or if NSF binding is disrupted by GluA2 c-terminal-mimicking interference peptides (G2CT, pep2m, or pepR845A), then synaptic GluA2 expression can return back to basal levels in a manner resembling depotentiation/LTD (Lüscher et al. 1999; Nishimune et al. 1998; Osten et al. 1998;
Song et al. 1998; Lee et al. 2002; Luthi et al. 1999; Noel et al. 1999; Serrano et al. 2005). This reversal of LTP by ZIP can be prevented by applying another peptide, GluA23y, which mimics another binding region of the GluA2 C-terminal and inhibits endocytosis, likely by reducing BRAG2 binding (Migues et al. 2010). Of importance, GluA23y can also inhibit induction of LTD, as it prevents the synaptic removal of GluR2-containing receptors
(Ahmadian et al. 2004).
In summary, it appears that regulation of AMPAR insertion and removal is critical for plasticity induction and long-term maintenance of synaptic changes.
1.3.2. AMPAR regulation during memory formation and maintenance.
Learning has also been widely observed to influence the phosphorylation and synaptic expression of AMPARs in a variety of species and brain regions. For instance, phosphorylation of GluA1 at Ser845 increases immediately after training and can be necessary for memory formation (Hu et al. 2007; Ferretti et al. 2014) or the persistent suppression of contextual fear by post-reactivation extinction training (Clem and Huganir 2010). Given that
Ser845 phosphorylation might be important for GluA1 trafficking, it is not surprising that
17 Chapter 1 – General Introduction behavioural tasks such as associative fear conditioning have been found to increase the total number of GluA1 subunits and GluA1 surface expression in amygdala both 1 and 24 hours after training (Mao et al. 2006). Interestingly, when given one hour after training, memory extinction can persistently reduce fear expression and reverse the increased GluA1 expression, but when given 24 hours after training reduced fear expression spontaneously returns over time and there is no concomitant reduction in GluA1 expression. However, if a plasticity-facilitating drug (NMDAR-agonist D-cycloserine; DCS) or memory retrieval is given shortly before extinction, persistent fear reduction is observed along with reduced
GluA1 expression (Mao et al. 2006; Clem and Huganir 2010).
CP-AMPARs (predominantly GluA1 homomers in adult mammals) can specifically be inserted into the synapse after behavioural experience, and can persist for hours (Hong et al.
2013) or days (Clem and Huganir 2010). This has also been observed in infralimbic cortex after the extinction of fear conditioning, in a manner that depends on metabotropic glutamate receptor 5 (mGluR5; Sepulveda-Orengo et al. 2013). Furthermore, a large minority of lateral amygdala synapses exhibit increased functional GluA1-containing AMPAR expression
(including CP-AMPARs) 3 hours after fear conditioning, and preventing receptor insertion in a small percentage of neurons has been reported to impair memory formation (Rumpel et al.
2005). As will be discussed below, some have proposed that this CP-AMPAR insertion during learning not only enhances induction of calcium-dependent plasticity processes, but the instability of GluA1-homomers at the synapse may permit the rapid internalization of
AMPAR that mediates the reversal of learning that is possible in the hours immediately following memory acquisition or reactivation (Clem and Huganir 2010; Descalzi et al. 2012).
As after LTP induction (Plant et al. 2006), there may be a gradual replacement of GluA2-
18 Chapter 1 – General Introduction lacking with GluA2-containing AMPARs after learning (Takahashi et al. 2003; McCormack et al. 2006), presumably serving to preserve the strength of synaptic transmission and help retain memory in a consolidated form (Hong et al. 2013). For instance, in the aforementioned
Whitlock study, synaptoneurosomal expression of AMPARs (GluA1 and GluA2) in the hippocampus was increased 30 minutes after inhibitory avoidance training. Matsuo and colleagues (2008) observed increased expression of GluA1 in mature spines of hippocampal
CA1 region within two hours of fear conditioning that dissipated between 24 and 72h. Migues and colleagues (2010) found that by two days after training, synaptic GluA2 (but not GluA1) expression in basolateral amygdala was greater than untrained controls and correlated with the strength of expression of a consolidated auditory fear memory. As mentioned above, Hong and colleagues (2013) observed an increase in GluA2-lacking CP-AMPAR (inwardly rectifying) currents in lateral amygdala 5 minutes and 12 hours after auditory fear conditioning that had dissipated by 24 hours, while overall AMPAR activity remained elevated for at least a week, likely mediated by GluA2-containing receptors (but see Rao-Ruiz et al. 2011 for differing results). Clem and Huganir (2010) reported that CP-AMPAR currents at lateral amygdala synapses were unchanged 2 hours after auditory fear conditioning, but were potentiated at 12, 24, and 48 hours, before returning to baseline within
7 days of training. However, overall AMPAR currents remained potentiated for at least 7 days (but see Rao-Ruiz et al. 2011 for distinct results). Thus is appears that increasing synaptic GluA2 expression may be primarily responsible for mediating long-lasting forms of memory.
However, a long-standing dilemma resulting from most molecular models of memory is that any form of plasticity occurring at a synapse during memory encoding (i.e. LTP/LTD-
19 Chapter 1 – General Introduction like processes) will have to persist so as to retain learned information (Sossin 2008). Given the rapid turnover of many synaptic membrane proteins, including AMPARs (on the order of hours-to-days; Archibald et al. 1998; Huh and Wenthold 1999), it has long been postulated that some type of persistent mechanism must be capable of restoring the strength of synaptic connections as their components gradually decay or are removed. As PKM# is known to promote its own synthesis in a positive-feedback loop (Westmark et al. 2010), this previously discussed model of synaptic GluA2 maintenance provides an ideal mechanism for memory persistence by perpetuating a cycle of AMPAR endocytosis and replacement (Sacktor 2011).
Indeed, inhibiting PKM# by injecting ZIP, or overexpression of a dominant-negative mutation of PKM#, has been widely observed to impair the retention of consolidated memories - even if they are very remote (Serrano et al. 2008; Migues et al. 2010; Shema et al.
2011; Hardt et al. 2010b; Li et al. 2010). This was first observed when ZIP infused into rat dorsal hippocampus after an active avoidance memory had reached a fully-consolidated state impaired its retention when tested two hours or even one week later (Pastalkova et al. 2006).
Since this seminal finding, the amnesic effect of ZIP has been replicated for many behavioural tasks in several species, including mice (Li et al. 2010) and aplysia (Cai et al. 2011), which is indicative of an evolutionarily conserved mechanism that may be a fundamental feature of neuronal plasticity (Sacktor 2012). Although it has recently been argued that PKM# might not actually be necessary for memory maintenance, as PKM#-lacking transgenic mice still exhibit memory impairments following ZIP infusion that must be due to its effects on a different molecular target (Volk et al. 2013; Lee et al. 2013), there is now evidence for potential functional compensation by other related protein kinase C isoforms (see Ren et al.
2013a). Therefore, PKM# remains the most viable molecular mechanism for the persistence of
20 Chapter 1 – General Introduction memory, and provides an explanation for how the rapid turnover of synaptic components does not result in amnesia.
Converging evidence also confirms the importance of the NSF-GluA2 interaction for long- term memory maintenance. As with ZIP, infusing peptides into the dorsal hippocampus that mimic the NSF binding site on GluA2 (pep2m and pepR845A) can impair the retention of consolidated object location memories without disrupting memory acquisition (Migues et al.
2014). Surprisingly, despite the effectiveness of ZIP and pep2m at blocking long-term memory retention, and the effectiveness of pepR845A (which more specifically mimics the
NSF binding site) in LTP maintenance (Lee et al. 2002), several initial studies reported that pepR845A only disrupts the acquisition, but not the maintenance of consolidated memories
(Joels and Lamprecht 2010; Wang et al. 2013). From these studies it was not clear why this peptide was ineffective in blocking memory. However, we have recently reported that pepR845A infused into the dorsal hippocampus does not affect the consolidation of newly acquired information, and can instead impair retention of consolidated object location and contextual fear conditioning memories (Migues et al. 2014). We also demonstrated that this effect does not recover over multiple tests across weeks (Migues et al. 2014), and is still evident when tested one month after infusion (see Appendix Figure 1). This has been further confirmed in the study comprising Chapter 3 of this dissertation, where we report evidence for retention impairments of contextual fear memory following pepR845A infusions into either the hippocampus or anterior cingulate cortex (ACC). Furthermore, recent work indicates that pepR845A also impairs fear extinction memories when infused into infralimbic cortex of rats, thus causing a return of previously extinguished conditioned fear responding
(Archbold 2013).
21 Chapter 1 – General Introduction
ZIP and pepR845A are each known to impair LTP maintenance by promoting GluA2- endocytosis, which is also required to block the retention of consolidated memories. Infusing
GluA23y before administration of ZIP or pepR845A into the amygdala or dorsal hippocampus (Migues et al. 2010; 2014) leads to intact memory performance, indicating that both PKM# and NSF preserve memory by interfering with a process that actively removes
GluA2 from the synapse (Sacktor 2011). This has led some authors to propose that neurons are engaged in a continual process of regulated synaptic decay back to basal states, suggesting that the memories they encode may gradually fade if synaptic connections are not periodically rejuvenated via reactivation or re-induction of plasticity (Hardt et al. 2014). Indeed, more unpublished work from our lab strongly indicates that twice-daily application of GluA23y during the retention of rapidly forgotten object location and fear extinction memories can maintain these memories for weeks (Hardt et al., personal communication).
1.3.3. AMPAR-based model for memory maintenance and updating.
Of course, memory persistence makes up only one of the requirements for an adaptive memory system, as laid out in the opening sections of this thesis. Being able to encode, maintain, and retrieve a memory is beneficial, but without the capacity to modify which of many related memories will be expressed it should be more difficult to efficiently respond to a changing environment (Dudai 2009). Thus, Section 1.3.3 will present a model to explain how regulation of AMPAR-mediated transmission may control the updating of memory when novel information is experienced at the time of retrieval.
In the previous sections I proposed that a consolidated memory remains in a stable state via the actions of PKM# and NSF, which maintain synaptic GluA2-containing AMPAR expression. Therefore, in order to initiate updating of the memory, these stability mechanisms
22 Chapter 1 – General Introduction may have to be released, thus permitting synaptic remodeling (Shao et al. 2012; Yoshii et al.
2011). In agreement with this prediction, it has been observed that shortly after retrieval of conditioned fear memories, GluA2-containing AMPARs are rapidly removed from the synapse and replaced by GluA1-containing (and perhaps GluA2-lacking) AMPARs - a process which could play dual roles in the destabilization of memory (Rao-Ruiz et al. 2011;
Hong et al. 2013; Clem and Huganir 2010). Blocking the first step in this process (synaptic
GluA2 endocytosis) has been found to prevent the labilization/destabilization of memory (Yu et al. 2013; Hong et al. 2013). The rapid insertion of CP-AMPARs may then be required to modulate the strength of the synaptic connections that encode the memory (Clem and
Huganir 2010).
First, as a consolidated memory is reactivated, activation of plasticity-induction mechanisms (including NMDA-receptors, discussed in Section 1.4) might encourage GluA2- maintenance molecules like NSF and PKM# to disengage, or the GluA2-PKM#-NSF complex to be trafficked out of the synapse for degradation (Sacktor 2012). If a memory is strongly encoded, inhibition of GluA2 endocytosis by NSF and/or PKM# could resist destabilization at retrieval, and only with appropriate reactivation cues might this inhibition on plasticity be lifted, allowing for labilization. Indeed, PKM# mRNA appears to wait in a repressed state in dendrites under basal conditions (Hernandez et al. 2003; Muslimov et al. 2004), and is locally translated into PKM# protein following synaptic activity (as occurs after LTP induction
Hernandez et al. 2003; Kelly et al. 2007). However, consistent behavioural training (i.e. 25 spatial water maze training trials) may also boost PKM# mRNA transcription (Klur et al.
2009), tentatively suggesting that additional PKM# production may be required to mediate the more resilient encoding of long-lasting memories. Relatedly, monkeys who exhibited both
23 Chapter 1 – General Introduction faster acquisition of a delayed non-matching to sample task and increased task accuracy thereafter displayed more dendritic spines containing both GluA2 and PKM#, perhaps imbuing memory-encoding synapses with enhanced stability (Hara et al. 2012). In older monkeys there was also a positive correlation between recognition memory accuracy and the density of GluA2 expression in those spines that coexpressed GluA2 and PKM#.
Furthermore, virally overexpressing PKM# in a cortical brain region (the insular cortex) has been found to enhance consolidated conditioned taste aversion memory in rats, and even to restore memories that have degraded over time (Shema et al. 2011). This work suggests that increasing levels of PKM# could actually serve to ‘hyperconsolidate’ or ‘hyperstabilize’ memories, preventing their decay and perhaps their alteration after retrieval, although this has not yet been directly tested using a reconsolidation task.
However, some preliminary support may be derived from the finding that a strongly- encoded conditioned place preference memory for a drug-of-abuse was only impaired by ZIP infusions into nucleus accumbens when they followed a reactivation session (Crespo et al.
2012). This hints that only in combination are these manipulations sufficient to overcome this resilient stabilized memory state, perhaps via mechanisms that converge to induce GluA2 endocytosis. An endogenous system that removes PKM# or NSF, perhaps via proteolytic degradation of these proteins (Hrabetova and Sacktor 1996; 2001; Sacktor 2012), may be involved in relieving the inhibition of AMPAR endocytosis after reactivation. For example, in the nucleus accumbens core an increase in NSF polyubiquitination (which primes proteins for degradation) and a subsequent decrease in NSF protein has been observed shortly after reactivation of a conditioned place preference memory (Ren et al. 2013b). When these researchers suppressed protein degradation in nucleus accumbens by infusing the proteasome
24 Chapter 1 – General Introduction inhibitor lactacystin, it prevented both memory destabilization and the downregulation of synaptic NSF and GluA2 caused when anisomycin or extinction training were given after reactivation.
At a neuronal level, protein degradation by the proteasome has been demonstrated to be necessary to “tag” weakly stimulated synapses in a manner that enables them to subsequently capture the gene transcription/translation products of strong stimulation at another nearby synapse (see Frey and Morris 1997 for a review of synaptic tagging), leading to facilitated long-term potentiation (Cai et al. 2010). Based on the findings of Ren and colleagues, this initial degradation of proteins likely includes the NSF-PKM#-GluA2 complex, which could be necessary to affix a new marker/tag at an activated synapse that allows long-lasting storage processes to be engaged (Sajikumar and Korte 2011). Together these findings provide a tentative link between NSF ubiquitination/proteolysis and memory updating. When conditions are adequate to overcome the maintenance of GluA2-containing AMPARs by NSF and PKM# (causing their synaptic removal), this might be sufficient to initiate memory destabilization and permit updating (Hong et al. 2013).
As will be proposed in Chapter 2, it is also possible that memory strengthening and weakening (and their respective physiological correlates) can be initiated via partially independent, dissociable destabilization processes. Rao-Ruiz and colleagues (2011) reported that blocking GluA2 endocytosis via hippocampal infusion of GluA23y before or after reactivation of a contextual fear memory caused a subsequent enhancement of memory expression, yet GluA23y also blocked the persistent erasure of fear memory that occurred when extinction training followed reactivation. Thus, when reactivation occurs in the presence of GluA23y, memory strengthening may still be possible (as the process of AMPAR
25 Chapter 1 – General Introduction insertion may be intact) but memory weakening may not (as existing GluA2-containing receptors cannot be removed). But while the suppression of post-reactivation Glu2- endocytosis by GluA23y prevents a decrease in hippocampal GluA2/AMPAR currents observed 1 hour after reactivation, it also blocks an increase that occurs 7 hours later. Instead, these endocytosis-deficient synapses exhibit a slower decay of AMPAR-mediated currents, possibly indicative of a greater relative contribution of GluA1-containing receptors when
GluA2-subunits cannot be properly removed (see Jonas 2000; Lu et al. 2009).
Conversely, Hong and colleagues (2013) recently reported that in lateral amygdala, synaptic GluA2-containing AMPARs must be removed in order to increase the number of
GluA2-lacking AMPARs after retrieval of an auditory fear memory. When endocytosis was blocked with GluA23y at memory reactivation, the increase in CP-AMPAR activity otherwise observed 5 minutes later was prevented, with no effect on overall AMPAR-mediated currents.
Pre-reactivation GluA23y infusion also prevented the post-reactivation amnesia caused by either CP-AMPAR blocker or protein synthesis inhibitor. Thus the activation of just-inserted
CP-AMPARs seems to be necessary for the restabilization of labilized fear memory, though this study did not assess if these receptors may also contribute to the plasticity signal necessary for destabilization itself.
Using post-reactivation extinction procedures, however, it has been observed that GluA1
Ser845 phosphorylation and CP-AMPAR activity may contribute to the ability to persistently modify memory, likely via reconsolidation (Monfils et al. 2009; Clem and Huganir 2010). In the minutes after retrieval of fear conditioning memories there can be an increase of PKA- phosphorylated GluA1 at Ser845 in lateral amygdala, associated with the return of the memory to a labile state that allows it to be updated (Monfils et al. 2009; Jarome et al. 2012;
26 Chapter 1 – General Introduction
Kim et al. 2014). Suggesting a role in the rapid removal of these Ser845-phosphorylated
GluA1-homomers, a procedure proposed to “erase” fear memory (by giving extinction one hour after reactivation) has been observed to cause dephosphorylation back to baseline (non- reactivated) levels (Monfils et al. 2009). This is similar to the observed phosphorylation of
Ser845 shortly after LTP induction, and dephosphorylation after LTD induction (Lee et al.
2000). A detailed regional and temporal analysis of GluA1 phosphorylation at Ser845 has recently revealed that levels peak after 30 minutes in basal amygdala, hippocampal CA3, and medial prefrontal cortex of animals that received fear conditioning and then memory reactivation, relative to each procedure individually (Fukushima et al. 2014). It is known that
Ser845 PKA phosphorylation can regulate GluA1/CP-AMPAR stability (He et al. 2009), so this step might be critical for the change in synaptic strength observed after memory retrieval.
Indeed, mice with a mutation of GluA1 Ser845 that prevents its phosphorylation by PKA acquired fear conditioning normally, but did not exhibit persistent fear memory suppression
(i.e. “erasure”) when memory extinction was given after reactivation (Clem and Huganir
2010). They also did not exhibit the increase in CP-AMPAR function that was observed in normal animals after fear conditioning. Thus, the phosphorylation of GluA1 by PKA may permit CP-AMPARs to more persistently remain at the synapse (Ehlers 2000; Esteban et al.
2003), and their dephosphorylation and removal may mediate rapid memory erasure that is possible in a brief window after reactivation.
As it is quite evident that receptor expression at the synapse changes at retrieval, some of these findings might predict that memory destabilization could be associated with transient depotentiation of potentiated connections (or vice versa). The Rao-Ruiz study discussed above reported a decreased synaptic expression of GluA1, GluA2, and GluA3 1 hour after
27 Chapter 1 – General Introduction reactivation, and GluA2 and GluA3 expression remained low after 4 hours, but GluA2 expression was dramatically elevated by 7 hours. Accordingly, there was a concomitant decrease in AMPAR mediated currents at 1 hour post-reactivation, but an increase at 7 hours.
Clarke and colleagues (2010) similarly reported in mice that acquisition of an object recognition task was associated with enhanced field excitatory post-synaptic potentials in
CA3-CA1 synapses of the hippocampus 6 hours after training. When animals were tested for their object memory 24 hours later, by presenting one novel and one familiar object (animals tend to explore the novel item they do not recognize more than the familiar item they remember), they continued to exhibit synaptic facilitation during exploration. But this facilitation dropped off rapidly after training, with mice showing significant depotentiation by
1.5 hours and repotentiation again by 3 hours post-training. Thus a transient period of synaptic depotentiation following reactivation may reflect synaptic destabilization and/or weakening of existing memory before it can be updated with newly encountered information and the synaptic structure can be restabilized.
Further supporting this position, in animals trained to perform a hidden-platform spatial water maze task, moving the platform for several trials (reversal learning) selectively facilitated induction of LTD in the hippocampus 1.5 hours later (Dong et al. 2013). Reversal learning is known to induce reconsolidation (Rossato et al. 2006a), suggesting that this depotentiation was involved in destabilizing the synapses encoding the reactivated memory.
Accordingly, both LTD and reversal learning could be prevented by the application of
GluA23y, suggesting that endocytosis of GluA2-AMPARs was necessary for both processes
(Dong et al. 2013).
The behavioural induction of reconsolidation was also found to occlude the
28 Chapter 1 – General Introduction pharmacological induction of mGluR1-dependent depotentiation in ex vivo tissue prepared immediately after reactivation (Kim et al. 2010; Lee et al. 2011). This suggests that a form of depotentiation may already have been engaged by the behavioural experience, which was apparently halted at the time of brain removal (unless rescued by norepinephrine treatment
Lee et al. 2011). With a longer delay between reactivation and brain extraction (1+ hours) these researchers were able to induce depotentiation via mGluR1 stimulation, suggesting that the prior occlusion could have been lifted via repotentiation of the synapse in the intact animal. But when brain extraction occurred immediately after reactivation and mGluR1 stimulation was instead delayed, depotentiation was not inducible.
Together this work indicates that in the intact animal, synapses may engage mechanisms of synaptic depotentiation/depression after memory reactivation, and only thereafter may they readily undergo bidirectional plasticity. This may explain why extinction given immediately after reactivation (essentially an extended extinction session) may not always cause depotentiation of AMPAR currents in basal/lateral amygdala (Hong et al. 2009; though see
Quirk et al. 1995; Kim et al. 2007), but when extinction is delayed to 1 hour after reactivation it can reverse AMPAR potentiation (Clem and Huganir 2010). Synapses should also have to repotentiate in order to maintain or even strengthen long-term memory expression, perhaps initially via replacement of removed GluA2-containing AMPARs with those including GluA1
(see Hong et al. 2013). This could be reflected in LTP-like changes, as 3 hours after auditory fear memory is reactivated additional potentiation of excitatory field potentials has been observed (Doyère et al. 2007), which could be related to the finding that unreinforced reactivation can, itself, enhance memory expression (Inda et al. 2011). At present it is not yet clear if synaptic depotentiation and depression, per se, always occur after memory
29 Chapter 1 – General Introduction reactivation, but it is evident that mechanisms associated with depression/depotentiation may contribute to the memory destabilization process.
In summary, AMPAR-mediated LTP and LTD are likely the neural mechanisms that mediate memory, requiring the association of PKM# and NSF with GluA2 to maintain synaptic efficacy changes. In the case of strongly-encoded memories, I proposed that these memory maintenance proteins may also suppress subsequent modification of synaptic efficacy following retrieved. However, I hypothesized that when a reactivation cue is sufficiently novel the associated plasticity induction signals may be sufficient to remove or degrade these stability proteins, allowing remodeling of synaptic connections, and thus modification of the memories they encode. This provides one model for how synaptic AMPAR regulation could control the ability of memories to last over long intervals but also remain malleable in response to changes in the environment.
1.4. THE FUNCTIONS OF NMDA-RECEPTORS IN MEMORY FORMATION AND STABILIZATION.
Thus far this thesis has reviewed mechanisms that regulate synaptic strength, but has provided little description of the processes that induce synaptic plasticity and memory acquisition. For several decades NMDARs have been strongly implicated in both synaptic plasticity (Collingridge et al. 1983) and learning and memory (Morris et al. 1986; Morris
1989; Kentros et al. 1998). In reference to the last section, NMDAR activation can increase
PKM# levels (Sacktor et al. 1993; Sacktor 2011) and trafficking of AMPARs to the synapse
(Lu et al. 2001; Pickard et al. 2001; Descalzi et al. 2012) as is necessary for the induction of some types of LTP (Lüscher and Malenka 2012). This section will briefly summarize the
30 Chapter 1 – General Introduction involvement of NMDARs in synaptic plasticity and memory acquisition, followed by a survey of the literature on NMDAR-independent learning, a review of how NMDAR expression can change during development and with behavioural experience, and finally a proposal for how such changes might contribute to memory stability and malleability.
1.4.1. NMDARs and the induction of synaptic plasticity.
NMDARs are rather unique membrane receptors in that they can serve as molecular coincidence detectors (see Riedel et al. 2003). Only when glutamate binds to the extracellular ligand site at the same time that the neuron is depolarized will the receptor be activated by relieving a magnesium blockade of the ion channel (Nowak et al. 1984). Activation also requires glycine or D-serine to be bound to an allosteric modulatory site of the receptor
(Dingledine et al. 1999). Although the channel can transmit several different ions, including sodium and potassium, its role in calcium influx is its most critical feature with regards to synaptic plasticity processes. As discussed above in reference to CP-AMPARs, increasing post-synaptic intracellular calcium concentrations can activate a wide array of molecular cascades involved in gene expression and long-term synaptic plasticity (Yang et al. 1999; see
Lisman 1989; Lüscher and Malenka 2012). Generally it is thought that strong activation of
NMDARs will cause a large, rapid influx of calcium, leading to the induction of LTP, whereas a weaker and more prolonged activation of NMDARs will cause a smaller influx of calcium, leading instead to LTD (Malenka and Bear 2004). Thus some models of plasticity have proposed the existence of a modifiable threshold for bidirectional plasticity at which increases in stimulation frequency or intensity will transition from inducing synaptic depression to potentiation, and vice versa (Bienenstock et al. 1982; Cooper and Bear 2012).
This has been widely demonstrated by studies in which application of an NMDAR
31 Chapter 1 – General Introduction antagonists, such as D- or DL-(2R)-amino-5-phosphonovaleric acid (D- or DL-AP5), MK-
801, and CPP, during chemical or electrophysiological neuronal stimulation protocols can block induction of some forms of LTP (Bliss and Collingridge 1993; Malenka and Nicoll
1999), LTD (Dudek and Bear 1992), and LTP reversal (called depotentiation; Fujii et al.
1991) in neurons from many species and brain regions. However, post-induction application of the antagonist (i.e. after neuronal stimulation) typically has little or no effect on the maintenance of AMPAR-mediated LTP (Muller et al. 1988; Davies and Collingridge 1989).
Although many other membrane channels and receptors are capable of inducing types of LTP and LTD, NMDAR-mediated plasticity is by far the most studied and most ubiquitous form throughout the mammalian forebrain (Collingridge et al. 1983; but see Lüscher and Malenka
2012; Riedel et al. 2003; and Malenka and Bear 2004 for reviews).
1.4.2. NMDARs are critical during the acquisition of long-term memory.
The demonstrated requirement for NMDARs in plasticity induction during the early
1980s motivated several groups to probe their involvement in learning and memory (see
Riedel et al. 2003; Morris 2013 for historical perspectives). Over the subsequent decades
NMDARs have been elaborately investigated, yet the fundamental cognitive functions of these receptors have not yet been fully elucidated. This is partially due to a number of experimental inconsistencies, which will be discussed in the following section. However I will first summarize the general functional roles that NMDARs are thought to play in memory.
In a series of pioneering studies it was reported that NMDARs are necessary for the initiation of long-term memory formation. Morris and colleagues (1986) observed a severe memory acquisition deficit when rats were administered the NMDAR antagonist DL-AP5 via chronic intracerebroventricular (ICV) infusion throughout training of a hippocampus-
32 Chapter 1 – General Introduction dependent task (the hidden-platform spatial reference water maze; Morris et al. 1986).
Importantly, infusion of this antagonist after acquisition of training had no effect (Morris
1989). Furthermore, the same doses of antagonist that impaired memory were also found to prevent LTP induction in hippocampal neurons (Davis et al. 1992), correlatively indicating that there could be a direct relationship between learning and synaptic plasticity. These observed memory acquisition impairments following ICV infusion also appeared to be specific to spatial/contextual learning, as non-spatial tasks (such as the visual discrimination water- maze, in which the hidden platform that allows animals to escape the pool was marked by one of two distinct objects) could be acquired normally in rats receiving AP5 (Morris 1989).
These findings were replicated with both chronic ICV infusion of D-AP5 as well as local infusion of DL-AP5 directly into the hippocampus (Morris 1989).
A flurry of subsequent studies using a diverse array of tasks revealed that, at the time of learning, NMDARs may not actually be necessary for the acquisition of short-term forms of memory, but are required for their long-term retention (Kim et al. 1991; 1992; Kim and
McGaugh 1992; Gewirtz and Davis 1997; Steele and Morris 1999; Santini et al. 2001; though see Walker and Davis 2000 for detailed dissection of the effect). It was proposed that the period of NMDAR-independent short-term memory lasts just a few minutes before an expression impairment emerges (see Kim et al. 1992). For instance, rats infused with DL-AP5 into the dorsal hippocampus prior to contextual fear conditioning showed a normal fear response (freezing) immediately after the shock, however this response dissipated completely within four minutes, while control animals continued to express freezing (Kim et al. 1992).
Similarly, using a 'delayed matching-to-place' (DMTP) water maze task (in which animals explored a pool to find a hidden escape platform and then retrieved this information after a
33 Chapter 1 – General Introduction variable interval) it was demonstrated that AP5-treated rats could maintain spatial memory independently of NMDARs for 15 seconds but not 2 hours (Steele and Morris 1999). Finally, a stable pattern of hippocampal “place cell” activity that typically forms during initial exposure to an environment was observed despite animals being injected with an NMDAR antagonist. However, the persistence of this mapping over hours was impaired (Kentros et al.
1998). Together, this work tentatively indicates that NMDARs are necessary for the induction of LTP and the encoding of information for long-term retention, but not for the initial acquisition, retrieval, or expression of short-term memories.
Further refinement of the role of NMDARs in memory formation came from work showing that antagonists applied before testing frequently did not interfere with the expression/retrieval of memories acquired hours or days earlier (Miserendino et al. 1990;
Venable and Kelly 1990; Gewirtz and Davis 1997; Walker and Davis 2000; Bast et al. 2005).
For example, relatively low doses of DL-AP5 infused into BLA before rats were trained with a fear potentiated acoustic startle procedure, wherein the presentation of a fear-conditioned tone increases a 'startle' response to a loud sound, prevented memory acquisition as tested the next day (Miserendino et al. 1990). Importantly, this was not due to a reduction in the salience of the light or footshock stimuli themselves, as low-dose AP5 infusion before testing had no effect on memory expression. This hypothesis was further confirmed using a second- order conditioning procedure (Gewirtz and Davis 1997), which involved associating a specific tone with footshock, then repeatedly presenting the shocked tone and a visual stimulus together, causing the visual stimulus itself to subsequently elicit a fear response (see Rizley and Rescorla 1972 for a similar design). Of note, it was found that acquisition of fear to the unshocked visual stimulus was blocked by AP5 infusion, even though AP5 was not present
34 Chapter 1 – General Introduction during the tone-footshock training trials, indicating that amygdalar NMDARs likely mediate the fear association. Critically, infusing DL-AP5 into amygdala before a final testing session was actually observed to mildly enhance the fear potentiated startle response to the tone, suggesting that these receptors are not critical for fear expression.
Subsequent work confirmed that NMDAR antagonists applied after training also typically have no effect on long-term memory retention (Staubli et al. 1989; Morris 1989; Danysz and
Parsons 2012; Liang et al. 1994b; Maren et al. 1996; Gould and Gould 2007; Gould et al.
2002; Fanselow and Kim 1994; Fanselow et al. 1994), and instead only impair memory when infused immediately prior to training. Danysz and colleagues were the first to report such a dissociation by demonstrating that pre-training ICV application of higher doses of AP5 prevented acquisition of a passive inhibitory avoidance task (in which rats are trained with shock to avoid one section of a two-section chamber), but post-training injection had no effect on the length of time it took for rats to re-enter the shocked chamber when tested 24 hours later. Using a standard spatial reference memory version of the water maze task, Kim and colleagues (2011) found that hippocampal infusion of AP5 caused a memory deficit when given before but not after training each day. Comparably, Maren and colleagues (1996) found that DL-AP5 infused directly into BLA before training impaired the acquisition of a contextual fear conditioning task when tested in the same context 24 hours later. However, post-training infusion had no effect on the long-term retention of the fear association.
Congruently, AP5 application after the induction of LTP in rat hippocampal slices typically has no effect on the persistence/expression of LTP (Davies and Collingridge 1989). Thus,
NMDAR activity is not required for the maintenance of long-term synaptic changes.
35 Chapter 1 – General Introduction
Together this diverse set of findings indicate that NMDARs are often necessary to trigger the initiation of long-term memory storage, but are typically not required for the consolidation or retrieval of this information thereafter. Although a considerable number of studies have reported NMDAR antagonist-induced consolidation and retrieval impairments (see
Jerusalinsky et al. 1992; Liang et al. 1994a; Maren and Fanselow 1995; Maren et al. 1996;
Lee and Kim 1998; Fendt 2001; Lindquist and Brown 2004; Lee et al. 2001), many of these effects are likely attributable to non-specific effects of these drugs in specific regions of the brain. For instance, studies reporting impairments induced by post-training or pre-testing application of NMDAR antagonists have often used amygdala-dependent tasks and/or local infusion of DL-AP5 (Maren et al. 1996; Lee and Kim 1998; Fendt 2001; Lee et al. 2001;
Lindquist and Brown 2004). However, several electrophysiology studies reported that DL-
AP5 applied to amygdala neurons can impair basal synaptic transmission (Chapman and
Bellavance 1992; Maren and Fanselow 1995; Li et al. 1995). In fact, Matus-Amat and colleagues (2007) recently reported that the L-isoform of AP5 itself (comprising one half of the DL-AP5 treatment used in many of the aforementioned studies) may block synaptic transmission in BLA, perhaps even independently of NMDARs (see Chapman and
Bellavance 1992). These authors observed that infusion of D-AP5 into BLA impaired acquisition but not retrieval of a contextual fear association, while DL- and L-AP5 were both observed to disrupt fear expression. DL-AP5 infused into the dorsal hippocampus did not impair memory expression, suggesting that the non-specific effect of L-AP5 may be limited to
BLA neurons. This is consistent with the initial observations that L-AP5 had no apparent effect on synaptic function in hippocampal neurons (Morris et al. 1986; Davies and
Collingridge 1989), which was an important consideration for the study I report in Chapter 3 of this thesis.
36 Chapter 1 – General Introduction
Relatedly, several reports around the time of the initial studies by Morris and colleagues demonstrated that in some brain structures NMDAR antagonists could frequently have extraneous effects on motivation and sensorimotor function (Keith and Rudy 1990;
Hargreaves and Cain 1992). For example, some studies reported reduced muscle tone (Turski et al. 1985), motor control (Davies and Watkins 1982), and evidence of ataxia (Tricklebank et al. 1989). Others observed evidence of anxiolytic effects (Dunn et al. 1989) and perceptual abnormalities that might reduce the sensitivity of animals to detect or discriminate non-salient stimuli due to sensory distortions (Staubli et al. 1989). As many initial studies of hippocampus-dependent learning used systemic or ICV injections of NMDAR antagonists, it is possible that behavioural deficits were a result of drug effects in extra-hippocampal regions.
For instance, in the study by Danysz and colleagues (1988) discussed above, chronic ICV
AP5 injections during training also impaired the acquisition/expression of short-term working memory (i.e. remembering from which arms in a radial maze the rat had already retrieved food). However, this may have been the result of impaired sensorimotor function as the rats exhibited some degree of ataxia and other behavioural abnormalities. Relatedly, several other studies have reported memory impairments caused by both pre- and post-training injections of
MK-801 (de Lima et al. 2005; Ceretta et al. 2008). However, this drug has been reported to produce stereotypy and neurotoxicity at doses close to those required to disrupt LTP in the hippocampus (Olney et al. 1991; Morris 2013; Tiedtke et al. 1990; Wu et al. 2005).
Partially in an effort to minimize these confounding effects, some researchers began investigating whether familiarizing animals to the demands of a behavioural task could relieve some of the deficits resulting from sensorimotor disruptions, and thus reveal the underlying contribution of NMDARs to learning (Morris 1989; Bannerman et al. 1995; Saucier and Cain
37 Chapter 1 – General Introduction
1995). In the specific case of the water maze task it was hypothesized that if a rat had experience with swimming, navigating based on extra-maze cues, searching for the hidden escape platform, and climbing out of the water, this could facilitate the performance of these behaviours when an NMDAR antagonist was on board during training, and thus deconfound the effects on spatial learning (see Bannerman et al. 2006 for discussion). However, these studies revealed unexpected and discrepant results. As will be discussed in the next sections, they incited intense debate over which cognitive abilities truly require hippocampal
NMDARs, with some proposing a specific requirement in spatial learning (Morris et al.
2013), and others invoking a general contribution to response selection among multiple related memories (Bannerman et al. 2006; 2012), possibly via the process of pattern separation (McHugh et al. 2007). Still others found evidence that hippocampal NMDARs are not required for spatial memory formation at all, instead ruling that antagonists must simply cause sensorimotor deficits that are reduced by prior training and otherwise obscure intact learning ability (Saucier and Cain 1995; Saucier et al. 1996; Cain et al. 1997; Hoh et al. 1999).
However, considerable evidence has now accumulated to indicate that experience with a behavioural task can lift the requirement for NMDARs (in some brain regions) when learning a new version of the same task (Bannerman et al. 1995; Saucier and Cain 1995; Sanders and
Fanselow 2003; Hardt et al. 2009; Wiltgen et al. 2010a; Wang et al. 2012a; Laurent et al. 2008;
Li and Richardson 2013; Dragoi and Tonegawa 2013; Inglis et al. 2013). As is discussed next, such procedures have been used to demonstrate which elements of learning specifically require NMDARs, and which can be mediated by other mechanisms.
1.4.3. Experience-dependent, NMDAR-independent spatial learning.
Coincidental papers in the journal Nature first identified situations in which spatial
38 Chapter 1 – General Introduction learning could occur via NMDAR-independent mechanisms. Morris and colleagues
(Bannerman et al. 1995) demonstrated that chronic ICV infusions of the NMDAR antagonist
D-AP5 impaired the gradual acquisition of a spatial reference memory water maze task in which animals had to find and remember the location of a submerged platform hidden at a fixed spot in the pool. However, in rats that had previously successfully acquired this task over many days, training in a new water maze (housed in another laboratory with distinct spatial cues and a different platform location) was acquired normally in the presence of AP5.
However, AP5 impaired the induction of LTP in dentate gyrus neurons regardless of pre- training, although memory acquisition still required hippocampal function, as observed when the hippocampus was lesioned prior to training in the second maze. Thus the requirement for
NMDAR-dependent plasticity in the hippocampus seemed to be eliminated by prior experience. This effect was not merely due to generalization between the training environments, as all rats performed at chance levels upon placement into the second pool and could still locate the platform in the first pool following training in the second. Most remarkably, AP5-insensitivity did not occur when rats had received prior "non-spatial" training in a water maze task that required the rats to find a platform that moved on each trial in a room lacking spatial navigation cues. This suggested that familiarity with the behavioural performance of the task (such as swimming and platform escape) did not alleviate the deficit, and so sensorimotor accounts of NMDAR antagonist-induced memory deficits were improbable.
This surprising result tempted researchers toward the interpretation that NMDARs are not, in fact, necessary for spatial learning. Yet the diminished effectiveness of AP5 was observed only after prior spatial learning, leading Bannerman and colleagues to conclude that
39 Chapter 1 – General Introduction
NMDARs may serve to encode rules or strategies specific to the spatial task (and broadly applicable to subsequent similar tasks). Thus the rats’ existing knowledge could ameliorate the effect of AP5, potentially indicating that NMDARs are not always necessary to modify an existing spatial map with new features or to associate the new platform location with a particular part of that representation.
Saucier and Cain (1995) similarly reported that an NMDAR antagonist (injected systemically) failed to impair acquisition of a second water maze task, however this was observed following training that was non-spatial in nature. Thus the theory of Cain and colleagues hinged on the assumption that the aforementioned sensorimotor deficits caused by
NMDAR-antagonists could be functionally eliminated during extensive familiarization with non-spatial aspects of the behavioural task (Saucier and Cain 1995; Cain et al. 1996). Once the rat adjusted to the general demands of the task it could overcome impaired sensorimotor processes, revealing no specific effect of NMDAR blockade on spatial learning ability. In their final attack on the hypothesis that a spatial strategy acquired during a first task lifts the requirement for NMDARs, Hoh, Cain, and colleagues (1999) injected AP5 before both non- spatial pre-training and subsequent spatial water maze training, finding that rats could still acquire the reference memory task. However, this still did not rule out the possibility that prior formation of a spatial strategy was important for NMDAR-independent water maze learning. Problematically, most of the experiments performed by the Cain lab were disadvantaged by the use of the same water maze for non-spatial and spatial training sessions, differentiated only by the use of curtains surrounding the pool. Thus it is possible that the animals may have acquired some subtle spatial representation of the environment during
“non-spatial” pre-training sessions that could be applied thereafter (see Morris 2013).
40 Chapter 1 – General Introduction
A somewhat convincing resolution of the cognitive verses sensorimotor impairment controversy has only emerged in the last several years (Inglis et al. 2013; Morris et al. 2013), nearly two decades after the initial reports of NMDAR-independent learning by the Morris and Cain labs. It is now apparent that at high doses, chronic AP5 infusion can cause sensorimotor impairments along with apparent ‘cognitive’ learning deficits, but at lower concentrations with infusions directly into the hippocampus the measurable sensorimotor deficits largely disappear, leaving a dissociable impairment to spatial learning (Inglis et al.
2013). The lower concentration used in these studies (10mM) is close to that used in our study presented in Chapter 3 (12.7mM), which is far below the concentration at which sensorimotor deficits became apparent (20mM and 30mM). Although direct comparison with our studies is not entirely informative, as Inglis and colleagues used chronic slow infusion of
AP5 via mini-pump rather than our rapid acute dosing before training, it is worth noting that we only rarely observed markedly abnormal behavior after infusion. Other tasks aimed at minimizing the effects of sensorimotor deficits (i.e. wherein the rat learns which of two visible platforms allow it to escape) also revealed impairing effects of chronic ICV infusion of AP5
(Morris et al. 2013). Importantly, this study demonstrated that some sensorimotor deficits induced by NMDAR antagonists may actually be a consequence of a spatial learning deficit, and not vice versa, as across trials on each day of water maze training the rats’ behavioural deficits (such as reduced swimming speed and increased thigmotaxis) worsened rather than improved (see Morris 2013 for discussion).
Yet other debates have continued to emerge regarding the nature of cognitive functions mediated by hippocampal NMDARs. Bannerman himself now maintains that the fundamental cause of water maze performance deficits after loss of NMDAR function is
41 Chapter 1 – General Introduction impaired response selection, not learning ability (Bannerman et al. 2006; 2012; Taylor et al.
2014; Bannerman et al. 2014). He and his colleagues have proposed that animals acquire a strategy during the first training session that - when consistent with the demands of the second task - can aid in encoding (and thus eliminate the requirement for NMDARs). But when inconsistent (i.e. when the rat has previously learned a non-spatial search strategy or a reversed platform location), this strategy can be detrimental (requiring some type of response inhibition or selection of a new response at the time of memory retrieval). These researchers have proposed that such selection between competing response options requires functional
NMDARs in the hippocampus. From this conceptualization emerged the paradoxical prediction that the more extensive the pre-training with an inconsistent solution (i.e. non- spatial navigation or reversed platform location), the more strongly the task-specific behavioural strategy would be encoded and thus the greater impairment that should be observed when NMDARs are absent during subsequent spatial water maze training with a new platform location (Bannerman et al. 2006). This possibility has not yet been directly tested, but is consistent with the failure of rats with impaired NMDAR function to rapidly learn new platform locations in a previously trained environment (Morris et al. 1990; Steele and Morris 1999).
Instead, Bannerman and colleagues have offered an alternative test of these predictions using mice with selective elimination of NMDARs in two hippocampal sub-regions: dentate gyrus (DG) and CA1 (Bannerman et al. 2012; Taylor et al. 2014). These animals showed normal incremental learning of a spatial reference memory water maze task, but impaired reversal learning (navigating to a new platform location). In a novel task, transgenic and wild- type mice were pre-trained in a water maze to find a platform that was hidden under a specific
42 Chapter 1 – General Introduction marker that moved on each trial. During subsequent training, the platform remained in a fixed location but two identical markers were inserted into the pool and the mice had to learn, based on spatial navigational cues in the room, at which marked location the platform had been placed. After extensive training, when the mice were started at a position equidistant from each marker (or when the markers were removed entirely) all mice exhibited knowledge of the spatial location of the platform. However, when they began at a position closer to the decoy marker, the CA1+DG NMDAR knockout mice preferentially approached this incorrect location, as though they were not relying on spatial knowledge and/or could not inhibit the behavioural response to approach the nearest marker. Thus it was proposed that animals with impaired DG+CA1 NMDAR function might fail to use environmental knowledge to guide appropriate behaviour at the time of retrieval, but otherwise show normal gradual learning of this spatial task.
This is consistent with the previously discussed finding that AP5-treated rats fail to remember a fixed daily platform location across trials in a DMTP water maze task, despite extensive pre-training (Steele and Morris 1999). It also matches work by Nakazawa and colleagues (2003) using the same task, showing that naïve transgenic mice lacking NMDARs in hippocampal CA3 initially show improvement in performance over days (thus learning some procedural aspects of the task that are applicable to new platform locations across trials each day), but once this learning rule or strategy has been acquired they fail to utilize it to encode information during the first exposure to a new platform location each day. Mice lacking CA3 NMDARs similarly fail to rapidly form a representation of a novel context that can be associated with a subsequent footshock (i.e. they show a deficit when the interval between placement into a context and shock administration is 20 or 40 seconds, but not
43 Chapter 1 – General Introduction longer; McHugh and Tonegawa 2009). Together this work has begun to elude to the fact that hippocampal NMDARs are not critical for spatial learning (which might be slowly mediated by other brain regions) but are indispensible for rapid one-trial formation of spatial and contextual representations.
This might relate to the noted deficits of mice lacking NMDARs in specific regions of the hippocampus during tasks that require so-called pattern separation abilities (McHugh et al.
2007). Pattern separation is the ability to encode and retrieve discrete memories for events sharing overlapping features. It was observed that learning to distinguish or discriminate between similar environments requires NDMARs in the dentate gyrus of hippocampus
(McHugh et al. 2007). Thus mice lacking NMDARs in dentate gyrus might also be expected to fail to encode information in a manner that allows them to properly orthogonalize and distinguish between discrete training trials of a given task, like the DMTP water maze (see
Nakazawa et al. 2004).
When considered in conjunction, this work leaves few lines of recourse for the hypothesis that NMDAR-dependent hippocampal plasticity is invariably required for spatial learning. As will be discussed in Section 1.6, considerable work has also amassed to suggest that even rats with virtually complete hippocampal lesions can acquire complex spatial navigational information about environments when given extensive training (i.e. Winocur et al. 2005). One speculative interpretation consistent with all the aforementioned data is that the hippocampus, as hypothesized so extensively in human cognitive neuroscience, is particularly important for the inherently rapid formation of discrete autobiographical
“episodic” memories (Tulving and Markowitsch 1998). In animals this is sometimes modeled using “episodic-like” tasks requiring encoding of trial-specific “what-when-where” information
44 Chapter 1 – General Introduction that can retrieve events based on the unique time, sequence, and location of their occurrence
(Ergorul and Eichenbaum 2004; Eichenbaum et al. 2012; Clayton and Dickinson 1998).
Indeed, it is with this sort of memory formation that animals lacking hippocampal NMDAR function may be most impaired (Jensen and Lisman 1996; Huerta et al. 2000; Rajji et al. 2006;
Place et al. 2012). Conversely, through gradual training (or with prior similar experience), it might be possible for other brain regions to incrementally encode “trial-independent” information about invariant aspects of a task (see Olton et al. 1979; Nakazawa et al. 2004).
The difficulty during hippocampal NMDAR antagonism may thus lie in encoding episodic- like information orthogonally, allowing separation of information from each trial in a useful way (i.e. encoding the unique sequence of complex stimuli comprising an event that dissociates it from prior events). When this fails due to NMDAR dysfunction in the hippocampus, the brain may compensate by using other systems that preferentially encode statistical regularities versus unique attributes of experiences (see McClelland et al. 1995;
O'Reilly and Rudy 2001 for discussion). This may lead to perseverance and interference during memory recall when the animal must respond discriminately based on spatially similar contexts (McHugh et al. 2007), visually similar arms in a maze (Engelhardt et al. 2008), or recency of incompatible outcomes experienced in a single environment (Morris et al. 1990;
Warburton et al. 2013).
For example, the aforementioned impairment of pattern separation abilities observed in
DG NMDAR knockout mice appears not to result from a failure to rapidly acquire fear of a context in which footshocks were repeatedly delivered, but rather to slowed inhibition of generalized fear to the distinct features of a similar context in which footshocks were never experienced, possibly due to an orthogonalization deficit (McHugh et al. 2007). Instead these
45 Chapter 1 – General Introduction mice may have to rely on other brain systems to systematically identify which unique features of the shocked context specifically predict shock, and which unique features of the unshocked context signal safety.
In the recent Bannerman studies (Bannerman et al. 2012; Taylor et al. 2014), the transgenic mice were, of course, lacking hippocampal NMDARs throughout experimentation, including during extensive pre-training with the platform hidden under a single beacon that moved on each trial, training with dual beacons, and testing. Thus impaired encoding of the recency of training sessions (when and where each beacon was experienced, as well as which was associated with platform escape) may make these memories less resilient against interference at retrieval. This could provide an explanation for why mice lacking NMDARs in
CA1+DG acquired and performed the dual-beacon task like wild-type animals except on trials when they started nearer the decoy beacon – a condition which may exacerbate a deficit in recalling when they had previously experienced a beacon in the same approximate location.
Mice lacking DG+CA1 NMDARs also fail to acquire a radial arm maze task in which they have to remember which three out of six arms are consistently baited with food across trials (Bannerman et al. 2012). One possibility is that hippocampal NMDARs are fundamentally necessary to bind the similar visual attributes of these arms (when the mouse views each entrance from the central choice compartment), with reward that is experienced only later when the mouse has walked to the end of a baited arm. When NMDARs are missing, an over-reliance on trial-independent learning systems may cause the features common to all arms to dominate control over behaviour. In this way, a mouse lacking functional hippocampal NMDARs may still be able to gradually encode the allocentric radial maze environment, a representation of each arm, and reward at the end of an arm, but
46 Chapter 1 – General Introduction struggle to remember which trial-specific sequence of these events led to reward when faced with a choice about which visually-similar arm to enter on later tests. This interpretation is also consistent with the impairing effects of ICV infusions of NMDAR antagonists or
DG+CA1 NMDAR knockout on spatial water maze reversal learning (Morris et al. 1990;
Taylor et al. 2014), as the brain may have to actively dissociate the new trials from previous encounters in order to properly learn the location of a moved hidden platform. When this process is disrupted, subsequent performance could be impaired by the fact that the animal cannot remember when the platform was seen in each location (see Warburton et al. 2013 for related findings). This could likewise explain why animals with deficient hippocampal
NMDAR function struggle to remember which of two arms they have just visited in a T- maze, amongst other episodic-like abilities (Tonkiss and Rawlins 1991; Nakazawa et al. 2003;
Niewoehner et al. 2007).
Bannerman’s claim that such behavioural impairments are due not to a memory formation deficit but rather response-selection interference at the time of retrieval are further challenged by one study showing that rats given hippocampal infusion of AP5 failed to form a short- lasting memory for which of two sand-wells contained food of a specific flavour, as revealed when subsequently cued with the a food pellet of the same flavour from a start location that was nearer the decoy well (Day et al. 2003). Critically, AP5 infusion prior to testing 20 minutes later had no effect. Of note, extensive training over many trials produced a memory that could still be retrieved when AMPARs in the hippocampus were inactivated, suggesting that this highly familiar spatial memory might be mediated by an extra-hippocampal structure.
Ingenious studies by Dragoi and Tonegawa (2011; 2013) have recently provided additional insight into the function of NMDARs in the hippocampus. It was found that
47 Chapter 1 – General Introduction sequences of hippocampal “place cells” that will be activated as a rat first traverses a linear track environment are partially pre-determined, and exhibit so-called “pre-play” during sleep prior to initial exposure to the environment (Dragoi and Tonegawa 2011). On first exposure to the linear environment the pre-played neuronal representation is adjusted in a manner requiring NMDARs in the CA3 region of the hippocampus, thus forming a stable neuronal mapping of this specific environment. However, subsequently encoding a representation of an extension of the existing track (by attaching a second linear track perpendicularly to the first) did not require CA3 NMDARs, and encoding of a detached linear track (with reward similarly presented at the end of the track) required NMDARs more transiently than in naive animals. Specifically, the encoding of the first event seemed to pre-establish a hippocampal encoding space that permitted the second task to be fit into the pre-existing representation.
One feasible extension of this result is that the brain forms a predicted parameter space for future experience, into which matching features of subsequent events can be rapidly fit even when NMDARs are knocked out.
Accordingly, it was observed that mice lacking CA3 NMDARs acquired a spatial alternation task (in which mice learned that food would be located in the opposite arm of a T- maze on each trial) more slowly than wild-type animals. But after successful acquisition of the first task via prolonged training, all mice acquired a second version of the task (using the same alternation procedure but a distinct apparatus shape) equally well. Dragoi and Tonegawa reasoned that these results are indicative of the animal having gradually formed a general
“schema” or flexible rule during the first task (also see Tse et al. 2007) that functionally anticipates the potential requirement for encoding of similar tasks in the future. Interestingly, the establishment of such a schematic rule appears to be much faster with intact hippocampal
48 Chapter 1 – General Introduction
NMDAR activity, whereas subsequently using this rule to acquire another similar task occurs at the same rate without CA3 NMDARs. These authors proposed that in their place,
NMDARs in extra-hippocampal brain regions might be engaged to integrate new information with the existing schematic knowledge (consistent with related observations; Tse et al. 2011;
Wang et al. 2012b). Further discussion of gradual rule learning mediated by extra- hippocampal brain regions will be provided in Section 1.6, and is the basis for the study presented in Chapter 3.
In reference to the original observation of NMDAR-independent learning by Bannerman,
Morris, and colleagues (1995), it is possible that the first task establishes knowledge for a general sequence of events involved in solving the spatial reference water maze. Based on the work by Dragoi and Tonegawa, I propose that this is mediated in the brain by a pre- established hippocampal network of neurons prepared to encode tasks sharing a similar progression of events. Thus rats entering the second pool arrive with knowledge that is compatible to perform and encode the task. Not surprisingly they appear to acquire this task more rapidly than naïve rats (see Bannerman et al. 1995). When hippocampal NMDARs are blocked, the pre-established scaffolding for encoding may permit linkages to be formed between representations of features of the new environment, potentially mediated by extra- hippocampal regions where NMDARs are still active (see Niewoehner et al. 2007). Rats returned to the first pool for reversal training, however, begin with an incompatible response that can interfere with learning a new sequence of directions necessary to reach the platform
(Morris et al. 1990). When NMDARs are blocked during reversal, the rat continues to perseverate on the old platform location, indicating that they continue to rely on trial-
49 Chapter 1 – General Introduction independent information stored during the initial training trials, and are slower to encode a new navigational sequence.
1.4.4. NMDAR-independent contextual fear conditioning.
Although the diverse literature presented in Section 1.4.3 can be difficult to reconcile, one evident conclusion is that the use of complex spatial navigation tasks may not be the most ideal approach to studying the role of hippocampal NMDARs in learning. Formation of these memories almost always requires extensive training, without which they do not persist over time (Steele and Morris 1999; Day et al. 2003). This is problematic, as storage of persistent memories for trial-specific stimulus conjunctions or sequences of events seems to be the process most critically requiring hippocampal NMDAR activity (Rajji et al. 2006).
To avoid some of the confounds that may emerge due to gradual acquisition of spatial tasks via extra-hippocampal regions, other researchers turned to more rapidly-acquired procedures to investigate the role of hippocampal NMDARs in memory acquisition. Soon it was demonstrated that NMDAR-independent learning phenomena existed for the acquisition of conditioned fear memories (Roesler et al. 1998; Sanders and Fanselow 2003; Hardt et al.
2009; Wiltgen et al. 2010a; Tayler et al. 2011; Wiltgen et al. 2011; Wang et al. 2012a). Of note, this effect was observed primarily using intrahippocampal infusions of NMDAR antagonists. NMDARs in basal and lateral amygdala, on the other hand, were found to remain necessary regardless of training history, as DL-AP5 infusions into this region have been observed to block both a first and second fear conditioning task (Lee and Kim 1998;
Laurent and Westbrook 2009). Although this work is not yet conclusive, as DL-AP5 could cause disruption of basal neurotransmission in amygdala (Maren and Fanselow 1995; Li et al.
1995; Laurent and Westbrook 2009), these results tentatively indicate that amygdala may
50 Chapter 1 – General Introduction always require NMDARs to form new fear associations. It also hints that the experience- dependent change in requirement for NMDAR activity is a phenomenon that may be limited to tasks relying on the hippocampus.
Sanders and Fanselow (2003) first developed the protocol for NMDAR-independent fear learning, documenting that rats receiving footshocks in a first novel context were impaired in their acquisition of a conditioned fear response by pre-training infusions of AP5 into the dorsal hippocampus. However, five days later the same rats demonstrated intact acquisition of fear conditioning to a second distinct context following AP5 infusion. Thus it appeared that prior conditioning could lift the necessity for hippocampal NMDARs, even in rats whose memory of the first training had been impaired by AP5. However, this last point was based on a result that was indicative of a slight impairment in rats given AP5 before both training sessions. It was also difficult to interpret, as it suggested that animals failing to properly acquire the first task were still insensitive to AP5 before the second (also see Hoh et al. 1999).
With this in mind, work in our lab began to reveal that the reduced requirement for
NMDAR activity during fear conditioning of a second context was dependent on intact storage of a prior contextual fear conditioning memory (Hardt et al. 2009). In contrast to the results by Sanders and Fanselow, in this study it was found that animals given AP5 infusions into the dorsal hippocampus before each of two contextual fear conditioning sessions were unable to acquire either task. However, rats given control vehicle infusion before the first training were unimpaired by AP5 given before the second training. Moreover, it was observed that impairing consolidation of the first task by post-training infusions of anisomycin also caused acquisition of the second task to be insensitive to AP5. These findings indicated that when memory for the first task was disrupted, the brain treated the second training as though
51 Chapter 1 – General Introduction it was the first, requiring NMDAR activity in the hippocampus.
To further support this position, Hardt and colleagues showed that animals given extinction training after the first fear conditioning task could still acquire the second task when treated with AP5. Given that extinction training is thought to occur by inhibiting a response, and not by erasing or unlearning the initial memory (Pavlov 1927; Konorski 1967), it was interpreted that intact storage (but not intact retrieval) of the first contextual fear memory is necessary for NMDAR-independent learning. Importantly, AP5-treated rats did not merely fail to discriminate between the two training contexts, as they showed little generalized fear when placed into the second training chamber. This is consistent with the fact that rats can initially form a hippocampal place-cell mapping of a new environment without
NMDAR activity, although this mapping rapidly decays (Kentros et al. 1998). In the presence of NMDAR antagonist, animals placed into the second chamber are able to determine that they are not in the previously trained context, but will perhaps be unable to encode useful trial-specific information about the event that can allow proper retrieval later. However, when components of the second training event are conserved from the prior task, perhaps strengthening of these shared features may allow intact fear conditioning in rats given AP5.
This model was subsequently used to study memory processes in very young (post-natal day 17) rats (Li and Richardson 2013). It is thought that because of an under-developed memory system many animals species exhibit infantile amnesia for early-life events due to rapid forgetting (Campbell and Campbell 1962). Thus rat pups were trained to fear a specific cue (white noise), and it was found that the NMDAR-dependent memory for this task was forgotten over the subsequent 14 days as indicated by near-complete loss of the conditioned fear response. At this time-point the rats were 're-trained' to fear either the same stimulus or a
52 Chapter 1 – General Introduction novel visual stimulus. In both cases the systemic injection of NMDAR antagonist, MK-801, did not impair memory acquisition. This work indicates that these "forgotten" memories are not truly lost in infant rats, as they can influence later memory encoding. An intriguing finding was that with a shorter inter-training interval (such that animals that did not forget the first training before the second), NMDAR activity was required to learn the novel visual conditioning task. This potentially indicates that formation of a specific cued fear response requires NMDARs, and when rats "forget" the first conditioning over time it is actually due to a loss of specificity of the memory (also see Anderson and Riccio 2005). In this way, the effect of amnesia on subsequent learning mechanisms is likely due to the generalization between the conditioning stimuli used during each training, and hence the second task may simply strengthen the generalized residual memory of the first training (perhaps via a reconsolidation-like processes; see Winocur et al. 2009 for related findings).
Several studies by Wiltgen and colleagues tell a related story, indicating that memory for unrelated events (i.e. exploration of a dissimilar context or water maze training) does not subsequently engage NMDAR-independent contextual fear conditioning, but memory for related events (i.e. exploration of a similar context or prior fear conditioning) does engage
NMDAR-independent conditioning (Tayler et al. 2011; Wiltgen et al. 2011). In this way they propose, like Morris and colleagues (Inglis et al. 2013), that NMDAR-independent learning could be partially due to a form of generalization between the representations of similar training environments that occurs when the second task is acquired in the presence of
NMDAR antagonists. Perhaps a representation of the first environment, encoded by
NMDARs in the hippocampus (i.e. Kentros et al. 1998), is used as a sort of scaffolding to mediate formation of new associations during a second related event. Tayler and colleagues
53 Chapter 1 – General Introduction
(2011) showed that, relative to unactivated hippocampal neurons, cells affixed with a persistent marker of activation during training in a first context showed no less reduction of plasticity protein, Arc, following CPP injection before training in a second context. As these researchers observed that the second training induced normal Arc expression in the hippocampus and that lesions of the dorsal hippocampus impaired the second training task, they proposed that plasticity in this structure remains necessary for learning in an NMDAR- and Arc-independent manner. As one example, in place of NMDAR activity, CP-AMPARs might contribute to the acquisition of a second contextual fear conditioning task (Wiltgen et al. 2010a). However, from these results it is also possible that the hippocampus does not require plasticity at all to acquire second training in the presence of NMDAR antagonist, and instead simply mediates the retrieval of the memory for previous training. Moreover, Wiltgen and colleagues have primarily used systemic application of NMDAR antagonist, which if sufficiently effective, should also impair acquisition of the second fear conditioning task by blocking amygdalar NMDARs (Lee and Kim 1998; Laurent and Westbrook 2009).
We address these possibilities further in Chapter 3, but did so also in a recent study where a variety of pharmacological manipulations were applied selectively to the dorsal, ventral, or dorsal+ventral hippocampus either before or after fear conditioning of a second novel context
(Wang et al. 2012a). It was observed that complete inactivation of either dorsal or ventral hippocampus with pre-training infusion of muscimol, or consolidation blockade via post- training anisomycin, impaired acquisition of a first but not a second conditioning task.
However, pre-training inactivation of both dorsal and ventral hippocampus with muscimol impaired acquisition of the memory for the second conditioning, as did pre-training muscimol into dorsal and post-training anisomycin into the ventral hippocampus. This indicates that
54 Chapter 1 – General Introduction neither pole of the hippocampus is necessary for the second learning, but either is sufficient and mediates not just memory retrieval but memory formation/consolidation.
While this work shows that dorsal hippocampal NMDARs are no longer necessary for experienced learning, this does not indicate whether or not NMDAR function actually changes between a first and second training session in the absence of antagonists. An alternative possibility is that another plasticity mechanism can compensate for NMDAR- blockade in an experience-dependent manner (perhaps a metaplasticity-like change, as discussed in the next section), but NMDARs would be the default mechanism when no antagonist is administered. One indication of altered NMDAR function after learning is that a systemically injected agonist of these receptors, D-cycloserine (DCS), can enhance memory for a first extinction session, but not a second ‘re-extinction’ session after re-training of fear
(Langton and Richardson 2008). Changes to NMDAR activity during prior learning
(mediated by cellular localization, subunit composition, overall expression, or modification of modulatory molecules bound to the receptors) could feasibly bias encoding away from dorsal hippocampal circuits and route it towards cortical systems that already encode the first experience, via network attractor states (see Fanselow 2010 for a related discussion). Indeed, like sensory cortices, the hippocampus exhibits activity- and experience-dependent regulation of the expression, localization, and subunit composition of NMDARs (Rao and Craig 1997;
Pérez-Otaño et al. 2005; see Shipton and Paulsen 2014; Xu et al. 2009; Lee et al. 2010b).
Collectively the work summarized in Section 1.4 has led most researchers to conclude that the contribution of NMDARs to memory formation is principally within a brief window of acquisition, and serves to initiate processes necessary for storage of long-term memory (see
Riedel et al. 2003 for discussion). Although there may be situations in which antagonists are
55 Chapter 1 – General Introduction effective when given after memory acquisition or even at retrieval, this may often be attributable to non-specific effects of the drug or injection route. Furthermore, their selective role in the initiation of memory formation does not mean that NMDARs will be critical each time a similar task is to be learned. Instead, common features of multiple experiences may reduce the reliance on these receptors. As will become apparent in the following sections, it also does not rule out some contribution of NMDARs to the processing and retention of memories over long intervals (Villarreal et al. 2002; Shinohara and Hata 2014), including when memory is reactivated during experience (Przybyslawski and Sara 1997) or perhaps even during sleep (Kavalali et al. 2011; Diekelmann et al. 2011). Next I will discuss how endogenous changes to NMDARs could contribute to another form of experience-dependent learning: memory reconsolidation. I propose that modulation of the expression or kinetic properties of these receptors during learning may alter whether or not a memory can be modified by experience.
1.4.5. NMDAR composition, trafficking, and memory.
The discussion so far has largely ignored a critical feature of NMDARs. Like AMPARs, each NMDAR is composed of four subunits. Each receptor is a heterotetramer consisting of two NR1 (GluN1) subunits and two NR2 (GluN2) subunits, A-D (Rosenmund et al. 1998).
NR3 (GluN3) subunits also exist, but these are beyond the scope of this discussion. NR1 and
NR2 subunits combine in diheteromeric conformations (i.e. NR1/NR2A and NR1/NR2B), but in the adult brain may typically take a triheteromeric form (NR1/NR2A/NR2B) (Rauner and Köhr 2011). Each of these subunits confers distinct and complex kinetic properties to an
NMDAR. It is typically assumed that relative to NR2A, the NR2B-subunit confers a higher affinity for glutamate, a much lower opening probability, but longer decay/deactivation
56 Chapter 1 – General Introduction kinetics, which may endow it with a superior ability to summate temporally offset inputs received by a neuron, thus facilitating plasticity induction (Flint et al. 1997; Cull-Candy and
Leszkiewicz 2004; Monyer et al. 1994; Vicini et al. 1998). NR2B has also been found to transmit a higher proportion of calcium versus other ions (Sobczyk et al. 2005; Erreger et al.
2005). Distinct regional and subcellular localization, as well as developmentally-regulated expression patterns, have been well documented. Specifically, there is little or no NR2A but high levels of NR2B protein prenatally, but a downregulation of NR2B throughout post-natal developmental periods that results in greatly diminished expression that is primarily limited to forebrain (Monyer et al. 1994; Law et al. 2003; Wang et al. 1995). As will be discussed, this process is thought to be developmentally pre-determined (Liu et al. 2004b) yet at least partially dependent on experience (Barria and Malinow 2002).
Despite decades of study the field has yet to reach consensus regarding the distinct roles of NR2A and NR2B subunits in bidirectional synaptic plasticity. While some groups posit that NR2Bs preferentially induce LTD and NR2As primarily mediate LTP (Dalton et al.
2012; Massey et al. 2004), others propose the opposite (Paoletti et al. 2013; Gardoni et al.
2009; Morishita et al. 2007). Some inconsistencies may be due to regional differences in function, developmental expression changes, in vivo versus in vitro neuronal preparations, or the use of pharmacological versus transgenic manipulations (Dalton et al. 2012; Paoletti et al.
2013; Massey et al. 2004; Morishita et al. 2007), but in general no overarching rules have been discerned. It has been proposed that because of their longer deactivation kinetics, NR2B- containing NMDARs may allow plasticity induction via relatively temporally distributed inputs to a neuron (Ewald et al. 2008). This is because NR2B may allow for greater temporal summation of calcium currents sufficient for plasticity induction, whereas NR2A requires
57 Chapter 1 – General Introduction more rapid stimulation to reach plasticity thresholds (Philpot et al. 2001; 2007). Thus, under some conditions, inputs that are less precisely coincident may be able to activate calcium influx via NR2B-containing NMDARs, possibly promoting LTP induction. Indeed this may sometimes be the case, particularly at inactive synapses with low NR2A to NR2B ratios (Lee et al. 2010b). However, in theory this may also mean that relatively temporally spaced inputs should frequently cause activation of NR2Bs alone (without activating NR2As), producing calcium transients that are smaller and thus more likely to engage LTD. Given that NR2A- containing NMDARs have a greater opening probability after glutamate binding and are highly expressed at the synapse of adult animals, these may provide the bulk of the calcium signal necessary for LTP in adults (see Paoletti et al. 2013). For instance, using low-frequency electrophysiological stimulation capable of inducing LTD, NR2B-containing NMDARs have been found to mediate much of the ion flux, whereas a high-frequency tetanic stimulation protocol used to induce LTP causes NR2A-containing receptors to transfer a majority of the charge (Erreger et al. 2005). NR2As may thus preferentially allow for refinement and saturation of LTP based on rapid, strong, temporally-coincident inputs, although under these conditions NR2B-containing NMDARs at the synapse are still likely to be activated and should contribute by modulating the direction and magnitude of plasticity induction (see
Kirkwood and Bear 1995 for a related discussion).
The distinct contribution of NR2A and NR2B to plasticity processes is not limited to their distinct ion channel kinetics, however. Their interaction with specific intracellular molecules may, in fact, be the most critical feature of each NR2 subunit (see Shipton and Paulsen 2014).
Most studied has been the preferential binding of the critical plasticity protein, CaMKII, to the intracellular c-terminal of the NR2B subunit (Barria and Malinow 2005; Mayadevi et al.
58 Chapter 1 – General Introduction
2002). Although this binding can happen in a number of different ways, one may allow calcium influx via the NR2B-containing receptor to persistently activate CaMKII by engaging its autophosphorylation (Bayer et al. 2001; Strack and Colbran 1998; Strack et al. 2000). This autophosphorylated state may be important or even critical for induction of LTP in some cases (Zhou et al. 2007).
In fact it has been found that expressing the NR2A c-terminal itself can prevent the induction of LTP, whereas the NR2B c-terminal may promote LTP (Foster et al. 2010).
Specifically, in mature hippocampal slices knockdown of NR2B expression has been found to prevent LTP induction (even though NR2B activation was not normally required for LTP in this preparation). When these absent NR2Bs were replaced with a variety of chimeric NR2 subunits, they observed that expressing NR2A containing the c-terminal of NR2B rescued
LTP while expressing NR2B containing the NR2A c-terminal did not. Critically, overexpressing NR2A alone did not rescue LTP, although expressing NR2A lacking part or all of its c-terminal did restore LTP. Thus it appears that the NR2A c-terminal can itself inhibit the induction of LTP (even though NR2A activity was selectively required), and that the NR2B c-terminal might instead be important to recruit intracellular signaling molecules, likely including CaMKII.
Relatedly, NR2A c-terminal expression in the developing hippocampus may inhibit the formation and growth of synaptic spines, while the NR2B c-terminal may facilitate the synaptogenesis and maturation of spines in a manner requiring CaMKII binding (Gambrill and Barria 2011). Moreover, binding of CaMKII to NR2B may even maintain LTP, as disrupting this association could partially impair the maintenance of saturated LTP, allowing additional LTP to be induced (Sanhueza et al. 2011; but see Halt et al. 2012 for differing
59 Chapter 1 – General Introduction results). Finally, and of relevance to the next section, although the binding of activated
CaMKII to NR2B may promote the formation of new synapses, this binding can actually reduce synaptic NR2B expression (Sanz-Clemente et al. 2013). When part of the NR2B c- terminal was mutated to mimic a homologous portion of the NR2A tail, this led to reduced phosphorylation by casein kinase 2 (which is involved in removal of synaptic NR2B- containing NMDARs). Accordingly, NR2B content was diminished at remaining synapses, suggesting that activity can induce binding of CaMKII and NR2B, resulting in NR2B downregulation (as will be discussed below). Together this work strongly suggests that NR2A and NR2B subunits are important for plasticity induction, but each can contribute to LTP and LTD in complex fashion, depending on the experimental conditions.
1.4.6. NMDAR contribution to memory stabilization, destabilization, and restabilization.
Some authors have attempted to provide general rules for the contribution of each NR2 subunit to memory formation: for example NR2As for memory encoding and NR2Bs for extinction or unlearning (i.e. Dalton et al. 2012). However, given that no consistent relationship has been established for either LTP/LTD and memory or LTP/LTD and the
NR2 subunits, it might be optimistic to assume that NR2A and NR2B should selectively support readily dissociable aspects of cognition. Instead, I will present a model wherein experience-dependent changes to the relative expression of NR2A and NR2B may regulate the stability of memory.
The contribution of NMDARs to memory acquisition, as reviewed above, led many researchers to probe their involvement during memory reactivation. Initially some groups found that NMDARs are necessary to reconsolidate a memory following its labilization by retrieval. For the trace to persist after reactivation, NMDARs were found to be necessary to
60 Chapter 1 – General Introduction re-establish the long-term memory, as systemically applied NMDAR antagonist MK-801 impaired post-reactivation retention of behavioural tasks in both rodents and crabs
(Przybyslawski and Sara 1997; Pedreira et al. 2002). But subsequent work demonstrated a specific function of NMDARs in the induction of reconsolidation in amygdala after retrieval of auditory fear memory (Ben Mamou et al. 2006). Just as D-AP5 has been found to have no effect on short-term memory expression after learning (Matus-Amat et al. 2007), rats given
D-AP5 or NR2B-selective antagonist ifenprodil immediately before reactivation showed no impairment of memory expression during the tone presentation (Ben Mamou et al. 2006).
Importantly, it was also observed that neither antagonist had an effect on retention of this fear memory 24 hours later during a post-reactivation long-term memory (PR-LTM) test. But most amazingly, the pre-reactivation infusion of either NMDAR antagonist prevented the amnestic effect of post-reactivation protein synthesis inhibition by anisomycin. This resembled the previously discussed effects of NMDAR antagonists applied pre- but not post-training, as here they appeared to prevent the initiation of plasticity required for a long-term change in memory, but not the subsequent reconsolidation/restabilization process. This was the first study to conclusively reveal that the reconsolidation process actually consists of two sequential phases: destabilization of the consolidated memory (possibly reflecting the induction phase of a LTP/LTD-like process), followed by the protein synthesis-dependent restabilization of this memory into a persistent re-consolidated state (akin to the late phases of
LTP/LTD). This provided one explanation for the behavioural observation that memory strength can limit the induction of reconsolidation (i.e. Suzuki et al. 2004), suggesting that it could result from a failure of a reactivation cue to trigger sufficient activation of synaptic plasticity mechanisms, including NR2B-containing NMDARs, that are necessary for destabilization. Many other behavioural and pharmacological manipulations have since been
61 Chapter 1 – General Introduction used to corroborate this finding of dissociable phases of memory destabilization and restabilization (i.e. Lee et al. 2008; Kim et al. 2011; Wang et al. 2009a; Winters et al. 2009;
Fukushima et al. 2014; Lee and Flavell 2014; Milton et al. 2013).
However, not all studies observed that NMDAR-antagonists could prevent memory destabilization. It has been reported using several different behavioural tasks that pre- reactivation infusion of a non-competitive NMDAR antagonist, MK-801, can impair restabilization of an array of behavioural tasks, and therefore cannot have prevented destabilization under these conditions (Przybyslawski and Sara 1997; Lee et al. 2006; Brown et al. 2008; Lee and Everitt 2008; Milton et al. 2012; Itzhak 2008; Wu et al. 2012; Winters et al. 2009; Flavell and Lee 2013; Reichelt and Lee 2012; Merlo et al. 2014). The study by Lee and colleagues (2006) is particularly difficult to reconcile given that the training procedure was highly similar to that of Ben Mamou and colleagues. Myself and others have proposed that the discrepancy could be due to distinct requirements for NR2B- and NR2A-containing
NMDAR activity during destabilization and restabilization, respectively (Finnie and Nader
2012; Flavell et al. 2013; Milton et al. 2013). Milton and colleagues confirmed this hypothesis using an auditory fear conditioning task, demonstrating that BLA infusion of the selective
NR2B antagonist ifenprodil prevented the induction of reconsolidation, and thus the amnesic effects of post-reactivation anisocmycin. However, pre-reactivation application of NR2A- preferring antagonist had no effect on destabilization, and instead caused memory impairment when rats were tested 24 hours later. It was also recently found that NR2B antagonist infused into the rat dorsal hippocampus could prevent the updating of a consolidated contextual fear memory when appetitive rewards are given in the same context, which otherwise suppressed the conditioned fear response (Haubrich et al. 2014). In general, therefore, these results
62 Chapter 1 – General Introduction suggest that a lack of NR2B activity could lead to a failure to induce memory destabilization.
Yet this interpretation does not fully account for the inability of the non-selective antagonist MK-801 to prevent memory destabilization (Lee et al. 2006; Merlo et al. 2014). A more satisfying explanation could be derived from recent reports of metabotropic NMDAR function via NR2B-subunit, which is independent of ionotropic activity and could be necessary for some forms of LTD (Nabavi et al. 2013; Kessels et al. 2013; Tamburri et al.
2013). Given that MK-801 should block only the NMDAR ion channel, while competitive antagonists like AP5 and ifenprodil should block both metabotropic and ionotropic responses to glutamate binding (Chung 2013), these antagonists may also have distinct effects on memory destabilization (see Tamburri et al. 2013). In this sense, AP5 may prevent destabilization because it prevents the activation of an intracellular cascade triggered by
NR2B conformational change, while also blocking the calcium influx via the NMDAR channel. This explanation is still consistent with the demonstration by Milton and colleagues
(2013) that NR2B and NR2A could differentially mediate destabilization and restabilization, as blocking NR2B metabotropic functions with ifenprodil should prevent induction of reconsolidation.
Since the initial report by Ben Mamou, memory destabilization has also been found to require the activation of many other molecular mechanisms, depending on the behavioural task, brain region, and species, including general neuronal activity (as blocked with tetrodotoxin; Lee and Flavell 2014), L-VGCCs (Suzuki et al. 2008; Haubrich et al. 2014) and endocannabinoid receptors (CB1; Kim et al. 2011; Suzuki et al. 2008), protein degradation by the proteasome pathway (Lee et al. 2008), the protein phosphatase calcineurin (Fukushima et al. 2014), nitric oxide (Balaban et al. 2014), ventral tegmental area dopamine D2 receptor
63 Chapter 1 – General Introduction signaling (Reichelt and Lee 2012), and, as discussed previously, CP-AMPARs (Clem and
Huganir 2010) and endocytosis of GluA2-containing AMPARs (Yu et al. 2013; Hong et al.
2013; Rao-Ruiz et al. 2011). It was also recently reported that a strong contextual fear memory that would otherwise not undergo reconsolidation following brief re-exposure to the context could be destabilized when a CB1 agonist was given before this reactivation (Lee and
Flavell 2014). Thus it seems clear that an array of plasticity induction mechanisms may be critical for memory destabilization, perhaps even in a mutually synergistic or summative manner.
1.4.7. NMDAR-mediated metaplasticity.
One intuitive hypothesis is that endogenous changes to the expression of these plasticity mechanisms following learning could be an intrinsic system to protect stronger memories against reconsolidation induction. The other side of this coin is that reactivation cues that are too brief or lacking novelty may induce insufficient activation of these plasticity mechanisms, thus failing to initiate the reconsolidation process. Therefore, changing plasticity induction mechanisms following learning could mean that a given reactivation cue may cease to engage memory reactivation, instead requiring longer (Suzuki et al. 2004) or more novel (Winters et al. 2009) cues to trigger labilization. Indeed, there is considerable evidence of changes to plasticity mechanisms in response to experience, both following acute behavioural tasks and during cortical development. Here I will focus on experience-dependent changes to NMDAR function resulting from relative expression of the NR2A and NR2B subunits.
As mentioned previously, the expression of NR2 subunits is known to change predictably throughout post-natal mammalian development (Monyer et al. 1994; Sheng et al. 1994; Liu et al. 2004b; Nakamori et al. 2010). NR2B-containing NMDARs dominate prenatally and at
64 Chapter 1 – General Introduction birth, with virtually no NR2A expression in the forebrain at these time-points. However, in rodents a transition during the first days or weeks after birth causes a dramatic increase in
NR2A and a concomitant decrease in NR2B expression, along with a declining contribution of NR2B-containing NMDARs to total NMDAR excitatory post-synaptic currents (Bellone and Nicoll 2007). Given that NR2A and NR2B can differentially engage plasticity (see
Section 1.4.5), this transition has been suggested to control processes of brain development
(Sheng et al. 1994; Wang et al. 2011).
Yet this transition does not rely exclusively on developmental programming. The induction requirements and properties of LTP and LTD can also be altered in an experience- dependent manner - a phenomenon broadly known as 'metaplasticity' (Huang et al. 1992;
Wexler and Stanton 1993; Deisseroth et al. 1995; Abraham and Bear 1996; see Abraham
2008). At a synaptic level, even brief activation can rapidly alter the subsequent induction of plasticity. This was initially demonstrated by Huang and colleagues (1992) in the Schaffer collateral pathway of hippocampal CA1 slices. When weak electrophysiological tetanic stimulation was first applied to the pathway, a subsequent strong high-frequency tetanus evoked reduced potentiation relative to that evoked when the weak priming tetanus was omitted. Importantly, strong stimulation could overcome this inhibition, suggesting that LTP is not lost but rather the induction threshold is simply raised. However, the LTP induced by suprathreshold stimulation can also be less long-lasting following priming activity (Fujii et al.
1996; Woo and Nguyen 2002). This effect may be mediated by NMDARs, as applying the antagonist AP5 during priming stimulation could prevent the inhibition of LTP. Moreover, applying NMDA itself (to activate NMDARs) could substitute for the priming stimulation.
Importantly, this suppression of LTP is not simply due to occlusion by previously induced
65 Chapter 1 – General Introduction potentiation, as a priming stimulus itself can cause little or no change to excitatory current, yet robustly inhibit LTP induction (Huang et al. 1992; Fujii et al. 1996; Xu et al. 2009). Many other studies have now replicated the fundamental finding that prior synaptic activity can augment LTP induction.
Conversely, brief priming stimulation that raises the threshold for LTP can also lower the threshold for LTD induction and/or increase its persistence (Christie and Abraham 1992;
Wang et al. 1998; Yang and Faber 1991; Wexler and Stanton 1993). This is the basic premise of the sliding bidirectional plasticity threshold model proposed by Bienenstock, Cooper, and
Monro (1982) to explain how synapses could be stabilized by adapting to their recent history of activity (thus maintaining synaptic strength within an adaptive range; see Bear 2003). For instance, Yang and colleagues (1991) found that a weak tetanus stimulation protocol could cause a gradual induction of LTP, but when preceded by prior strong priming tetanization the weak tetanus instead caused a short-lasting depression of synaptic efficacy. A stimulation protocol that induced transient LTD was also found to be much longer lasting when a strong tetanus was given 15 minutes beforehand. Similarly, Christie and colleagues (1992) demonstrated that brief, low-frequency priming facilitation of synaptic depression in dentate gyrus was NMDAR-dependent (although the LTD-induction itself did not require
NMDARs) and persisted for more than 2 hours in vivo.
In general these studies reveal that priming activation of NMDARs can often both raise the stimulation threshold and reduce the persistence of LTP, while also lowering the threshold and extending the persistence of LTD. It has been proposed that these plasticity changes could be induced by altered NMDAR function and/or expression (see Lau and Zukin 2007 for a review). For example, LTP induction in adult rat CA1 region of the hippocampus can
66 Chapter 1 – General Introduction cause an increase in NMDAR-mediated excitatory post-synaptic potentials, likely due to a rapid insertion of NR2A-containing NMDARs (Grosshans et al. 2002). Similarly, in hippocampal pyramidal neurons from young rats, an associative-LTP protocol can cause an increased contribution of NR2A-containing NMDARs to NMDAR-mediated post-synaptic currents within seconds that persists for at least an hour (Bellone and Nicoll 2007).
Subsequently applying a protocol that induces depotentiation can also cause a reversal of this
NR2A:NR2B switch, thus increasing the contribution of NR2B-containing NMDARs to synaptic transmission. One recent study revealed even more subtle metaplasticity processes occurring at the level of individual synapses (Lee et al. 2010b). It was observed that even spontaneous glutamatergic transmission was sufficient to alter the NR2A:NR2B ratio. By blocking neurotransmission with tetrodoxin there was a rapid increase in accumulation of
NR2B-subunits selectively at each silenced synapse, with no change in NR2A, in a manner that required spontaneous activation of NMDARs. Silenced synapses were also characterized by increased NMDAR currents, due to an elevated contribution of NR2B-containing receptors. Silenced synapses were thus more readily potentiated by stimulation that was normally insufficient to induce LTP. Finally, blocking NR2A or NR2B with selective inhibitors was found to alter priming effects on LTP/LTD induction (Xu et al. 2009). These researchers observed that lower-frequency priming stimulation could enhance, while higher- frequency stimulation would inhibit the induction of LTP, while the opposite was true for
LTD induction. Low-frequency priming also reduced and high-frequency priming elevated the NR2A:NR2B ratio, which positively correlated with the magnitude of LTP and negatively correlated with LTD. Applying an NR2B antagonist before plasticity induction could also functionally reverse the decreased NR2A:NR2B ratio after low-frequency priming, thus preventing the enhancement of LTP and suppression of LTD. Conversely, an NR2A-
67 Chapter 1 – General Introduction prefering antagonist prevented the high-frequency enhancement of LTD. Thus is appears that synaptic activity can rapidly change the threshold for subsequent induction of LTP and LTD, in a manner that relies on the relative expression of NR2A and NR2B subunits.
Although at the level of individual neurons and synapses metaplasticity is often transient, lasting minutes or hours (i.e. Huang et al. 1992), plasticity changes lasting days or longer have been observed in response to behavioural experiences. The most elaborately characterized is the development of ocular dominance columns in primary visual cortex (area V1). In many mammalian species, V1 is divided into larger bands of neurons that are responsive to monocular input from the contralateral eye, along with smaller bands responsive to binocular input from both ipsilateral and contralateral eyes (Caviness 1975; Dräger 1975). In juvenile animals broader regions are initially responsive to ipsilateral signals, but a maturation process causes these binocular zones to shrink via weakening of responsiveness to the ipsilateral eye
(Olson and Freeman 1980). This primarily occurs during a critical plasticity period that terminates around the fourth or fifth post-natal week in rodents, at approximately the same time that the ratio of NR2A- to NR2B-containing NMDARs in V1 markedly increases (Smith et al. 2009). Delaying visual input (binocular deprivation by rearing the animal in the dark) can temporarily delay both the critical period and the concomitant shortening of NMDAR currents (normally caused by increasing NR2A:NR2B ratio), suggesting that these changes are regulated partly by experience and partly by developmental programming (Carmignoto and Vicini 1992; Chen and Bear 2007). This can also lower the threshold for LTP (Kirkwood et al. 1995) and increase the threshold for LTD in V1 (Kirkwood et al. 1996; Philpot et al.
2003; 2007). Following dark-rearing, less than 2 hours of exposure to light can both terminate the ocular dominance plasticity critical period and rapidly increase the NR2A:NR2B ratio in
68 Chapter 1 – General Introduction
V1 (Philpot et al. 2001; Quinlan et al. 1999a; 1999b), while longer light-exposure has also been found to reverse the LTP bias (Kirkwood et al. 1996). Once the critical period has ended, adult ocular dominance plasticity is induced much less readily and is functionally distinct from juvenile forms, as will be discussed shortly (see Sato and Stryker 2008).
Temporarily or permanently removing input from one eye (monocular deprivation) can alter the responsiveness of V1 neurons to inputs emanating from each eye (Wiesel and Hubel
1963). During the developmental critical period in rodents, fewer than 4 days of deprivation typically causes weakening of responsiveness to the deprived eye (Gordon and Stryker 1996), while 4 days or more can also cause strengthening of responsiveness to the non-deprived eye
(Dräger 1978; Frenkel and Bear 2004). These distinct processes might be controlled by changes to the expression of NR2B and NR2A subunits, respectively. Indeed, Chen and Bear
(2007) observed that monocular deprivation in juvenile mice caused a significant increase in cell membrane NR2B expression after 3 days, and a decrease in NR2A expression only after 7 days in V1 cortex contralateral to the sutured eye. The increase in NR2B expression (but not decrease in NR2A) was mimicked in cortical cell cultures by blocking NMDARs or NR2B- containing receptors for a day, in a manner that required translation of new proteins. Thus, it seems that reduced visual input in juvenile animals can cause weakening of deprived neuronal inputs via a mechanism requiring elevated NR2B expression, and then strengthening of remaining inputs via a mechanism requiring reduced NR2A. This fits well with the aforementioned findings that NR2B and NR2A c-terminals can be permissive and restrictive of synaptic plasticity, respectively (Foster et al. 2010). A substantial collection of findings support the overall decline in NR2A:NR2B ratio following visual deprivation (both monocular and binocular) and the reverse following light exposure, but whether this is
69 Chapter 1 – General Introduction mediated by expression of NR2A, NR2B, or both may depend on experimental parameters
(Quinlan et al. 1999b; 1999a; Philpot et al. 2001).
Weakening and strengthening of the visual inputs to V1 binocular zone neurons have been proposed to be mediated by LTD- and LTP-like processes, respectively (Philpot et al. 2003; see Smith et al. 2009 for a review). For instance, monocular deprivation in juvenile animals can occlude LTD induction, suggesting that the weakening of deprived eye responsiveness may have already engaged some form of synaptic depression (Heynen et al. 2003; Crozier et al. 2007). Furthermore, brief monocular deprivation can itself depress synaptic transmission, downregulate AMPA GluA1 and GluA2 subunits, and also dephosphorylate GluA1 Ser845 in
V1 contralateral to the deprived eye (Heynen et al. 2003). Finally, applying peptides that block GluA2 endocytosis (G2CT or GluA23y) prevents both LTD in visual cortex and the weakening of deprived-eye responsiveness after monocular deprivation (Yoon et al. 2009;
Yang et al. 2011). Together this evidence strongly indicates that LTD-like processes mediate the weakening of responsiveness of V1 binocular zone neurons. In V1 layer 4, which is primarily studied in ocular dominance experiments, LTD is mediated by NMDAR-dependent
AMPAR endocytosis in a manner that is sensitive to the NR2A:NR2B ratio (Philpot et al.
2007; Cho et al. 2009). Mirroring the effects on ocular dominance remodeling, longer monocular deprivation can reduce the LTP induction threshold in V1, also partly in response to synaptic NR2A:NR2B expression changes (Philpot et al. 2007; Cho et al. 2009; Chen and
Bear 2007). Most tellingly, the effects of visual deprivation (decreasing NR2A:NR2B ratio) can be mimicked in transgenic mice lacking NR2A, which exhibit a loss of LTD and a reduced stimulation threshold for LTP (Cho et al. 2009; Philpot et al. 2007).
Although this model is consistent with the findings that reduced synaptic stimulation
70 Chapter 1 – General Introduction should decrease NR2A:NR2B ratio and preferentially facilitate LTP over LTD (Xu et al.
2009; Lee et al. 2010b), it is incompatible with findings that endogenous increases in the
NR2A:NR2B ratio during the V1 maturation process is associated with residual capacity for strengthening of responsiveness to inputs from the non-deprived eye but resistance to weakening of deprived eye inputs following monocular deprivation. Even despite the increase in relative expression of NR2A versus NR2B and the termination of the developmental critical period in adult animals, there are behavioural manipulations that can reinstate ocular dominance plasticity (Sawtell et al. 2003; Yashiro et al. 2005; Hofer et al. 2006b; 2006a; Sato and Stryker 2008; Baroncelli et al. 2010; Monteyand Quinlan 2011; Yang et al. 2011). In adult rodents older than two months, ocular dominance plasticity can be engaged by longer monocular deprivation (i.e. 5 instead of 3 days), but this is mediated solely by NMDAR- dependent strengthening of ipsilateral, non-deprived eye responsiveness and not weakening of deprived eye inputs (Sawtell et al. 2003). Relatedly, Yashiro and colleagues (2005) found that
10 days of dark exposure could re-engage ocular dominance plasticity and facilitate LTD induction in V1 of adult mice, but this resulted from an increase in pre-synaptic release of glutamate along with a decrease in peri/extrasynaptic (but not synaptic) NR2A:NR2B ratio.
Thus it appears that adult animals are still capable of ocular dominance plasticity, but the form of plasticity is limited and requires longer manipulations of visual input for induction.
However, it is unclear why the experience-dependent developmental increase in
NR2A:NR2B ratio (which should reduce the threshold for LTD) would be associated with a reduced propensity for weakening of the deprived eye inputs. One internally consistent explanation is that the presence of NR2A at the synapse is suppressive of LTP induction only via some stimulation protocols (Foster et al. 2010), while NR2B expression generally broadens
71 Chapter 1 – General Introduction the inputs capable of inducing plasticity (both LTP and LTD). Specifically, Guo and colleagues (2012) recently demonstrated that the reduced NR2A:NR2B ratio in V1 caused by visual deprivation could facilitate the induction of both LTP and LTD using some protocols.
Using one protocol that relied on rate of stimulation (high-frequency tetanization leading to
LTP and low-frequency to LTD) these researchers replicated the finding that visual deprivation biased plasticity towards the induction of LTP and away from LTD (Kirkwood et al. 1996; Philpot et al. 2003). However, using another protocol, which relied on timing of near-coincidental pre- and post-synaptic neuronal stimulation (called spike-timing dependent plasticity; STDP), induction of both LTP and LTD were facilitated in the deprived V1.
Relative to those exposed to light, V1 neurons from dark-exposed animals exhibited robust
LTP and LTD when longer delays were interposed between pre- and post-synaptic stimulation. Critically, this broadened window for signal integration was not observed in the presence of NR2B antagonist ifenprodil. Increased expression of the NR2A subunit has instead been proposed to permit fine-tuning of synaptic inputs by reducing the range of correlated inputs capable of evoking plasticity, due to its faster activation kinetics (see Ewald et al. 2008 for discussion). In this way, increased NR2A expression after strengthening of a synapse may require more rapid or correlated inputs to engage additional potentiation.
However, concurrent NR2B downregulation may also restrict the inputs capable of driving synaptic depotentiation or depression, thus conferring stability to the information encoded by this neuronal connection. Together these findings suggest that an elevated NR2A:NR2B ratio following experience may actually reduce plasticity induction in general, irrespective of direction.
If such metaplasticity changes occur during memory formation in general, this could help
72 Chapter 1 – General Introduction to explain why reactivation typically seems to either induce memory destabilization or not (in an all-or-nothing manner), suggestive of an overall threshold required for labilization.
Specifically, reducing the activity of some plasticity-induction mechanisms (i.e. NR2B- containing NMDARs or L-VGCCs) seems to prevent destabilization following reactivation instead of merely weakening the memory (i.e. Ben Mamou et al. 2006; Suzuki et al. 2008).
This could be because the pharmacological inhibitors used significantly narrow the window for bidirectional plasticity induction via coincidental pre- and post-synaptic activity evoked by behavioural experience.
1.4.8. Metaplasticity as a mechanism for memory stability.
What is apparent from the exquisite characterization of V1 ocular dominance column development is that experience-dependent changes to plasticity mechanisms, including the
NR2A:NR2B ratio, can alter the visual experience necessary to induce plasticity. However, even after these changes, cortical remodeling can still occur in late adulthood, indicating that this fundamental form of memory is stabilized only against some forms of experience.
Specifically, following cortical maturation, the memory for the balance of binocular visual input is stabilized in a manner that makes it less malleable to changes in the strength of signals coming from each eye, thus requiring more prolonged forms of deprivation to evoke plasticity.
This resembles the observation that strongly-trained memories can be more resistant to destabilization induced by brief reactivation, instead requiring longer re-exposure to the conditioned stimuli to induce reconsolidation (i.e. Suzuki et al. 2004). Thus it seems plausible that ocular dominance metaplasticity is reflective of a general property of memory encoding.
Indeed, similar experience-dependent developmental changes in NR2A:NR2B ratio have been observed in brain regions associated with maternal imprinting in ducklings (Nakamori et al.
73 Chapter 1 – General Introduction
2010; 2013), song-learning in songbirds (Singh et al. 2003), and mapping of individual whisker inputs to columns of barrel cortex in rodents (i.e. Mierau et al. 2004).
Furthermore, acute behavioural experiences in adult rodents can also increase the
NR2A:NR2B ratio in brain regions required for learning. For instance, a well-characterized olfactory discrimination task has been found to decrease NR2B expression in rat piriform cortex (Lebel et al. 2006). In this task one arm of a radial maze is baited with reward on each trial, and is demarcated by one of two specific odours emanating from the arm (Staubli et al.
1987; Saar et al. 1998). Rats gradually learn to discriminate these two odours during many trials, and thus reliably choose the baited arm based upon the olfactory cue after several days of training. However, once a first pair of odours has been well learned, subsequent novel pairs of odours are learned in fewer trials, indicating that the rat has acquired a general learning rule for the task. Acquisition of the learning rule is delayed considerably when NMDAR channels are blocked with MK-801, but learning to discriminate new odour pairs does not require NMDAR activity (Quinlan et al. 2004). With reference to metaplasticity, neurons in piriform cortex exhibit a reduced propensity for LTP and a lowered threshold for LTD induction via rate-dependent stimulation protocols in the days following acquisition of the learning rule (Lebel et al. 2001; Quinlan et al. 2004). Concurrently, NR2A:NR2B ratio is elevated via a reduction in NR2B expression (Quinlan et al. 2004). However, one possibility – based on the findings of Guo and colleagues (2012) using STDP – is that this change will actually restrict the types of stimuli capable of inducing both strengthening and weakening of synapses. Thus the rule about the olfactory discrimination task, which is formed via extensive training, might be protected against interference by the increased NR2A:NR2B ratio.
Returning to auditory fear conditioning, Zinebi and colleagues (2003) observed that rats
74 Chapter 1 – General Introduction exposed to very strong training (20 tone-shock pairings) had no change in NMDAR-mediated excitatory post-synaptic currents, but instead showed enhanced neurotransmitter release and reduced sensitivity to NR2B-selective antagonist, ifenprodil, at thalamico-amygdalar synapses. They also observed a significant decline in NR2A expression but an even greater reduction in NR2B expression in amygdala. Thus it appears that strong fear training increases glutamate release but reduces functional contribution of NR2B-containing NMDARs to neurotransmission.
Similarly, Wang and colleagues (2009a) found reduced expression of NR2B subunits in basolateral amygdala following strong auditory fear conditioning consisting of 10 tone-shock pairings. Given that NR2B activity is necessary for destabilization of auditory fear memories
(Ben Mamou et al. 2006; Milton et al. 2013) and strongly-encoded memories are less likely to undergo reconsolidation following their reactivation (Lee 2008; Morris et al. 2006), it was hypothesized that the downregulation of NR2B after training could confer resistance to lability. Indeed, these strong memories were not impaired by basolateral amygdala infusions of protein synthesis inhibitor after re-exposure to the feared tone, suggesting that reconsolidation was not induced. Several manipulations (hippocampal lesioning or reactivation 30 days after training) associated with a return of NR2B expression to the level of controls also caused this strong memory to undergo reconsolidation, tentatively indicating that NR2B downregulation plays a functional role in restricting memory malleability. An intuitive interpretation is that metaplasticity-like processes may be engaged following learning to regulate the reactivation stimuli necessary to destabilize a memory. More strongly encoded memories may be more stable, thus standard reactivation cues (such as brief re-exposure to the conditioned stimulus) will trigger a plasticity process insufficient to labilize a memory.
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Instead, novel features may have to be presented at reactivation to boost the plasticity- induction signal and induce reconsolidation (Sevenster et al. 2013; Winters et al. 2011).
Further supporting the idea that a change in NMDAR function after learning could be important for memory persistence, several studies have demonstrated that detailed spatial memories rapidly forgotten over days can be maintained by blocking NMDAR activity during the retention interval (Villarreal et al. 2002; Shinohara and Hata 2014). More specifically, object location memories, which normally decay over days, can persist for weeks when twice- daily infusions of NR2B-selective antagonist ifenprodil are given into the dorsal hippocampus during retention (Hardt et al., personal communication). One explanation is that spontaneous network activity (Minerbi et al. 2009) or sporadic reactivation of memory (such as by replay during sleep Rasch et al. 2007; Sara 2010; Lewis and Durrant 2011; Tononi and Cirelli 2003) could induce low-level NR2B/NMDAR activity, leading to gradual decay of memory
(Kavalali et al. 2011; Hardt et al. 2013). Thus, it has been theorized that reduced expression of NR2B-containing NMDARs after learning might promote both the stability and persistence of memory (Hardt et al. 2014; Finnie and Nader 2012). However, NMDAR metaplasticity is just one mechanism of many that could similarly regulate memory malleability (see Finnie and Nader 2012 for discussion of other potential candidates).
1.4.9. Memory stability and aging as extensions of developmental processes.
From the evidence reviewed in Section 1.4 it is evident that NMDARs are critical for many forms of plasticity induction and memory formation. However, the functional contributions of NMDARs can be altered by experience. Prior training may even engage
NMDAR-independent learning, at least in restricted regions of the brain including the dorsal hippocampus. I have proposed that alterations to relative expression levels of NR2A and
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NR2B following learning may regulate the malleability of memories, altering the forms of experience necessary to induce changes to their content and underlying neuronal encoding.
An intriguing implication of this metaplasticity-based model of memory stability is that the transition of NMDAR expression observed during development might continue throughout the life span. Thus, relative to young adults, aged animals might be expected to have reduced expression of the NR2B subunit and/or reduced NMDAR- or NR2B-dependent plasticity.
Early studies consistently revealed that older organisms (including humans) tend to have lower NMDAR expression or reduced binding of radiolabeled ligands/antagonists in many brain regions (Clark et al. 1992; Gazzaley et al. 1996; Ossowska et al. 2001; Wardas et al.
1997; Ogawa et al. 1992; Wenk et al. 1991; Castorina et al. 1994; Tamaru et al. 1991).
Accordingly, NMDAR-mediated synaptic transmission and NMDAR-dependent plasticity processes are also attenuated with age. For instance, Barnes and colleagues (1997) found that in dentate gyrus, but not CA1, NMDAR-mediated post-synaptic potentials were reduced in old rats. Along with reduced NMDAR responses, this group also observed that at perforant path inputs to dentate gyrus in aged rats, the stimulation threshold for LTP induction was elevated (Barnes et al. 2000). Using a stimulation protocol known to induce NMDAR- dependent LTP, Eckles-Smith and colleagues (2000) observed that potentiation magnitude was lower at CA1 synapses of aged rats, as was expression of the NMDAR NR1-subunit. In striatal and cortical slices obtained from aged rats there is also reduced neuronal responsiveness to NMDA or glutamate application, and differences in propensity to undergo
LTP (Baskys et al. 1990; Cepeda et al. 1996).
With plasticity induction by NMDARs reduced, other potential mechanisms, including L-
VGCCs, may compensate (Shankar et al. 1998). One study in rats found that spatial water
77 Chapter 1 – General Introduction maze learning was impaired with age, and was associated with concomitant loss of NMDAR- dependent LTP and enhancement of NMDAR-independent (L-VGCC-dependent) LTP in the hippocampus (Boric et al. 2008). In young rats learning ability was positively correlated with NMDAR-dependent LTP, whereas old rats with unimpaired learning ability exhibited increased L-VGCC-dependent LTP while impaired aged rats did not. Another similar study found that aged rats with enhanced NMDAR-independent LTD showed preserved learning ability at a multi-day water maze procedure, whereas young rats exhibited a correlation between NMDAR-dependent LTD and learning ability (Lee et al. 2005). In young rats, memory consolidation of a fixed-location water maze task has been reported to specifically require NR2B-mediated LTD processes in the hippocampus (Ge et al. 2010). Together these studies indicate that in aged animals, NMDAR-independent plasticity can compensate for
NMDAR-mediated plasticity. One possibility is that in the aged brain, NMDAR expression is suppressed at many synapses to protect the memories they encode from interference or decay
(see Hardt et al. 2013). However, it is also equally possible that this altered expression of
NMDARs serves no adaptive function, and is merely a characteristic of aging neurons.
Given that NR2B may have a greater ability to transmit calcium (Flint et al. 1997; Monyer et al. 1994; Sobczyk et al. 2005), it could be hypothesized that these reductions in NMDAR- mediated plasticity might be primarily caused by changes to NR2B-containing receptor expression. Indeed, NR2B levels in hippocampus have been found to selectively decline with age, with no significant change in NR2A or GluA2 (Clayton and Browning 2001). Similar downregulation of subunit expression was observed by Magnusson and colleagues (2002), with reduced NR1 and NR2B expression in both the hippocampus and cortex of old versus middle-aged rats. NR2A protein was also downregulated in aged hippocampus, but not aged
78 Chapter 1 – General Introduction cortex. In a subsequent study this lab found a similar decrease in NR1 and NR2B (but not
NR2A) expression with age in synaptic regions of pre-frontal/frontal cortices that was correlated with performance at a two-day reference water maze task (Magnusson et al. 2007).
Thus declining synaptic expression of NR1 and NR2B may impair learning ability, and could explain the general learning deficits observed in the aged animals. This group also found that
NR2B (but not NR1) was downregulated in synaptic fractions obtained from frontal/pre- frontal cortices of old rats, and that this decline actually appeared to begin during early post- natal development and progress consistently throughout the lifespan (Ontl et al. 2004). The recent finding that systemic injection of an NR2B-selective antagonist impairs fear conditioning only in young adult but not aged rats provides further evidence of a functional relationship between age-dependent changes in NR2B activity and memory (Mathur et al.
2009).
This is also tentatively supported by the finding that, in middle-aged rats, NR1 and NR2B expression levels in the hippocampus are positively correlated with the ability to learn a spatial water maze task. However in aged rats (with significantly lower synaptic NR1 and
NR2B expression), this relationship is negatively correlated (Zhao et al. 2009). This implies that sustained NR2B expression may exacerbate age-associated learning deficits. However, it might be argued that while aged rats with elevated NMDAR-independent plasticity processes may acquire new information more effectively, perhaps their ability to maintain this information over time could be reduced (see Hardt et al. 2013 for a relevant model). Indeed, the endogenous reduction of NMDAR activity in the hippocampus and/or cortex has been proposed to serve as a mechanism to minimize interference in the aged brain, even though this might inherently lead to a reduced ability to encode entirely new memories (Yang et al. 2008).
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However, by housing one group of rats in standard cages and another group in an enriched environment for 10 months, these authors concluded that the reduced NMDAR expression measured in old animals was only age- but not experience-dependent. Thus it was proposed that changes in plasticity do not result from a lifetime of accumulated experiences, but are instead a direct consequence of aging. However, it could be argued that this environmental enrichment condition did not provide the types of experience or opportunities for learning that would be necessary to measurably alter overall receptor expression. In fact, other experiments have found that extended environmental enrichment can extend or even reactivate ocular dominance plasticity in adult rodents (Baroncelli et al. 2010; Greifzu et al.
2014) and may actually increase NMDAR or NR2B expression (Tang et al. 2001), so this behavioural manipulation may not ideally reflect how memories are protected over time.
Interestingly, there is evidence that as adult-born dentate gyrus (DG) neurons mature
(Nakashiba et al. 2012), and more generally as animals age (Wilson et al. 2003), there is a transition from pattern separation towards pattern completion (Yassa and Stark 2011; Wilson et al. 2006; Holden and Gilbert 2012; Holden et al. 2012; Stark et al. 2010). Given that
NMDARs in DG have been theorized to mediate pattern separation (McHugh et al. 2007), the decline in this ability could be partially caused by reduced dendritic NMDAR expression/activity in DG with age (Gazzaley et al. 1996). In fact, NR2B expression in adult- born DG neurons appears to be required to discriminate similar contexts (Kheirbek et al.
2012), and young (adult-born) DG neurons appear to mediate pattern separation while older
DG neurons engage pattern completion (Nakashiba et al. 2012). An implication of such a transition would be that, with increasing age, animals (including humans) might exhibit enhanced generalization among similar experiences (Henkel et al. 1998; Parkin et al. 2001), as
80 Chapter 1 – General Introduction they would theoretically fail to orthogonalize similar episodes into discrete, non-overlapping memories (see Wilson et al. 2006). The logical extension of this conceptualization would postulate that instead of preferentially assembling distinct memories for each experience, the aging brain should tend towards fitting experiences into existing knowledge structures. But I propose an alternative conceptualization, which is that knowledge accumulated over a lifetime might induce changes in NMDAR expression and DG function that could increase the prevalence of, and preference for, attractor states (see Wills et al. 2005; Hopfield and Tank
1985; Marr 1971). In either case, throughout early life the brain may acquire many associations and contingencies that comprise a framework by which to interpret the world.
This might facilitate adaptive responding to the environment based on the prevailing conditions of prior experience (also see Tse et al. 2007). However, with age there might be an increasing tendency to fit experiences to existing contingencies and to assimilate new events into existing knowledge structure, rather than faithfully encoding new representations.
In summary, the transition from NR2B- to NR2A-dominated NMDAR composition that begins early in development appears to continue across the full lifespan. In general this may be involved in reducing the contribution made by NMDARs to plasticity induction, shifting instead to other mechanisms like L-VGCCs. It was proposed that this switch is not only due to developmental programming or incremental degeneration, but also processes used by the brain to minimize interference and decay of memories accumulated across a lifetime.
1.5. A MECHANISM OF PATHOLOGICAL AGING AND ITS INVOLVEMENT IN MEMORY.
Thus far this thesis has presented evidence that brain changes occurring during early
81 Chapter 1 – General Introduction developmental periods to regulate neuronal plasticity might continue - albeit in less conspicuous fashion - throughout the lifespan. Hypothetically this could allow for control over both memory encoding and persistence (Quinlan et al. 2004). In Section 1.4.9 I proposed that this could contribute to the reduced NMDAR/NR2B expression and NMDAR-dependent plasticity observed with age. However, it is not only normal aging processes that are associated with changes to plasticity mechanisms, but also age-related cognitive pathologies.
For instance, binding of a variety of radiolabeled NMDAR antagonists is selectively decreased in several medial temporal regions of patients with Alzheimer’s disease (AD) relative to age-matched controls, and is not simply due to neuronal loss (U$as et al. 1992).
NR1 and NR2B expression in particular have been reported to be lower in hippocampal regions of AD patients, with NR2A potentially remaining unaltered by the pathology
(Mishizen-Eberz et al. 2004; Sze et al. 2001). Some rodent models of AD, in which mice overexpress proteins thought to contribute to pathology, likewise exhibit reduced post- synaptic NR2B expression in the hippocampus, along with impaired induction of NMDAR- dependent LTP (Dewachter et al. 2009). A reasonable extension of the model presented in the previous section might thus predict that the cause of some forms of age-related cognitive decline (both pathological and non-pathological) could be a byproduct of processes that restrict plasticity induction mechanisms in order to stabilize memory (for a related conceptualization see Wilson et al. 2006). Here I will present a model by which mechanisms thought to cause pathology may regulate, or be regulated by, processes of memory formation and stabilization.
1.5.1. Alzheimer’s disease and the amyloid hypothesis.
The most commonly studied form of age-related memory pathology is AD. A leading
82 Chapter 1 – General Introduction model of its pathogenesis is the amyloid hypothesis (Hardy and Higgins 1992; Hardy and
Selkoe 2002). Although a detailed review of this theory would go well beyond the scope of this discussion, the basic model postulates that dysregulation or accumulation of a peptide called amyloid-! (A!) in the brain causes synaptic dysfunction, neuronal degeneration, and impairments to plasticity and memory (Selkoe 2002).
A! is the product of the sequential proteolytic cleavage of transmembrane amyloid precursor protein (APP) by !- then %-secretase. The first cleavage step removes the n- terminal, and the second the c-terminal of the peptide, leaving a small A! peptide that is typically 38-43 amino acids in length (see Selkoe 2001), though often 28-33 in rodents (Cai et al. 2001). The 42/43-residue peptides have been found to be the primary component of A! plaques found in many AD patients (Masters et al. 1985; Selkoe et al. 1986), hinting that this form may be preferentially involved in pathology. Although A!42 is still believed to be the pathogenic species, it is not the plaque formation that appears to be the cause of pathology, but instead the propensity of A! (particularly the 42/43 subtypes) to aggregate into soluble, lower molecular weight oligomers (i.e. Lesne et al. 2006; Walsh et al. 2002; Shankar et al.
2008; 2007; see Walsh and Selkoe 2007). It is thought that this spontaneous aggregation is induced primarily by local concentration of the peptide (Meyer-Luehmann et al. 2003), with higher concentrations promoting the formation of larger soluble oligomeric or insoluble fibrillar forms. Furthermore, this may depend on relative levels of the common A! isoforms,
A!40 and 42, with A!42 aggregating at a higher rate (Jarrett et al. 1993) and reduced
A!40:42 ratio resulting in elevated large multimeric conformations (Baysal et al. 2013;
Kuperstein et al. 2010). Hence, altered expression ratio, which may be regulated by %- secretase (Dolev et al. 2013), could contribute to synaptic failure (Kuperstein et al. 2010) and
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AD pathogenesis (see Wolfe 2007; Findeis 2007). It should also be noted that this is true for human forms of A! most commonly studied in AD research, but perhaps not rodent A!, which differs at three amino acid residues, exhibits reduced aggregation, and does not result in AD-like pathology (see Jankowsky et al. 2007 for discussion). Thus most rodent studies use transgenic animals expressing human forms of A! or APP-processing enzymes (see Crews et al. 2010). Yet some work suggests that endogenous murine A! can naturally form soluble oligomers in the brain of young animals (Marksteiner and Humpel 2008; Takeda et al. 2013).
Although A! can remain inside the cell, most often it may be secreted into the extracellular interstitial fluid (ISF) after generation, where it can aggregate as discussed and interact with other nearby cells and synapses (see Cirrito et al. 2003). Recent work has indicated that although A! secretion does not occur via regulated vesicular release (though also see Toneff et al. 2013), its production and release is evoked principally by synaptic activity (Kamenetz et al. 2003; Wei et al. 2010; Cirrito et al. 2005; 2008; Bero et al. 2011).
Blocking neuronal activation with tetrodotoxin has been observed to decrease A! formation and release, while increasing neuronal activity with picrotoxin can increase A! levels
(Kamenetz et al. 2003). Importantly, perhaps to minimize excitotoxicity, A! secretion in response to neuronal activity has also been observed to inhibit further A! release from the same synapse, and other nearby synapses. It is thought that a majority of A! (an estimated
80%) is produced within minutes-to-hours via pre-synaptic mechanisms, as evoked by inducing synaptic vesicular exocytosis in absence of depolarization (Cirrito et al. 2005). When synaptic vesicles fuse with the cell membrane and release their contents there is a mechanism that recovers this added membrane via endocytosis (Maycox et al. 1992). Given that APP is a transmembrane protein, membrane removal can cause its internalization and processing into
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A! (Marquez-Sterling et al. 1997; Cirrito et al. 2008). It was observed that blocking neuronal activity (and thus vesicular release) reduced ISF A! levels by 60% and preventing membrane endocytosis reduced it by 70%, strongly indicating that A! is primarily generated and released in response to neurotransmission. A! also accumulates in brain sub-regions that exhibit elevated neuronal activity (Bero et al. 2011), including those that show high levels of synchronized activity during rest, called the 'default-mode network' (Buckner et al. 2005;
Vlassenko et al. 2010). Levels of the peptide in the brain also increase during acute stress
(Kang et al. 2007) and in the minutes after learning (Puzzo et al. 2011). This increase after learning was not a non-specific effect, as only fear conditioning (but not exposure to the footshock or context in absense of conditioning) resulted in higher A! levels in the hippocampus. Thus it appears that neurotransmission is directly responsible for A! production, and can be increased in vivo following aversive learning.
1.5.2. Effects of A! on synaptic function and memory.
Once released, the documented targets of the many oligomeric A! conformations are incredibly diverse, making it challenging to formulate unified theories of their effects
(Benilova et al. 2012). A! oligomers have been observed to influence many known components of the synapse either directly or indirectly, however, it can often be difficult to discern which of these effects are byproducts of massive neuronal dysfunction induced by high levels of the peptide. In fact, when concentrations of A! remain in a normal physiological range the effects can differ dramatically from the more frequently studied effects of high A! concentrations (see Palop and Mucke 2010), as will be discussed in the next section. Thus a majority of A! studies are only somewhat informative with regards to normal, non- pathological systems. Here I will briefly summarize several common findings that are of
85 Chapter 1 – General Introduction potential relevance to the formation and stabilization of memory.
Although secretion of A! from the cell is not mediated by regulated vesicular release, the peptide has been observed to primarily localize near synaptic spines containing NMDARs and/or post-synaptic density protein-95 (see Lacor et al. 2004; 2007). Hence, the effects of A! may be exerted primarily at or around the synapse. Indeed, high concentrations of oligomeric
A!42 are most often observed to suppress the induction of NMDAR-dependent synaptic LTP
(Walsh et al. 2002; Wang et al. 2002; Klyubin et al. 2005; Shankar et al. 2007; 2008;
Dewachter et al. 2009; see Selkoe 2002), and facilitate LTD and loss of synapses (Shankar et al. 2008; Li et al. 2009; Kim et al. 2001; Tamburri et al. 2013; Kessels et al. 2013). In one landmark study, dimeric A! isolated from the brains of human AD patients strongly inhibited the induction of LTP when applied in nanomolar concentrations to the hippocampus of normal rats (Shankar et al. 2008). Using a high-frequency stimulation protocol, long-lasting
LTP was observed in control synapses, but this lasted for a much shorter time (only ~30 minutes) when treated with A!. Conversely, a low-frequency stimulation protocol induced a transient LTD at control synapses, and this persisted for much longer (at least an hour) in
A!-treated synapses. A! application also resulted in a loss of spines that depended on
NMDAR-activation, and severely impaired passive avoidance memory when infused 3
(though not 0 and 6) hours post-training. This suggests that even small A! dimers can potently alter plasticity, though this has since been found with larger soluble oligomers as well
(Lesne et al. 2006). High concentrations of A! have also been found to induce LTD in the absence of experimental stimulation, suggesting that these oligomers alone can be sufficient to initiate plasticity (i.e. Tamburri et al. 2013). Congruently, overexpression of A! can result in
AMPAR endocytosis, which is likely responsible for synaptic weakening (see Hsieh et al.
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2006; Zhang et al. 2011b).
It has been suggested that the suppression of LTP and induction of LTD by A! may result from either its preferential activation of extrasynaptic NMDARs (Li et al. 2011;
Talantova et al. 2013; see Rush and Buisson 2014; Gomes et al. 2014) or its preferential activation of the NR2B subunit itself (Hu et al. 2009; Kessels et al. 2013; Tamburri et al. 2013;
Li et al. 2009; Palop and Mucke 2010; Ferreira et al. 2012), which may frequently reside at extra-synaptic locations (Groc et al. 2004; 2006). In either case, increased pre-synaptic glutamate release or reduced clearance from the synaptic cleft (Kabogo et al. 2010; Li et al.
2009) could result in spillover capable of stimulating these peri-/extra-synaptic NMDARs
(Kervern et al. 2012), perhaps even at neighbouring synapses (see Palop and Mucke 2010).
This prolonged activation of these primarily NR2B-containing receptors may induce LTD and spine elimination (Liu et al. 2004a). Interestingly, concurrent studies revealed that A! can even selectively activate the metabotropic functions of NR2B-containing NMDARs discussed in Section 1.4.6 (Kessels et al. 2013; Tamburri et al. 2013). Blocking NR2B with a selective antagonist was found to prevent the ability of A! to induce LTD, while an NR2A-preferring antagonist had no effect.
In general, overexpression of human A! or application of high concentrations of oligomerized A!42 appears to inhibit LTP induction and facilitate or even initiate LTD, which can result in loss of synaptic connections and eventually neuronal death (see Lacor et al. 2007). These synaptic effects are thought to be the primary cause of AD-associated cognitive pathology (Shankar et al. 2008).
1.5.3. Effects and functions of endogenous A! at normal physiological concentrations.
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It is unlikely that A! exists in the brain only as a mediator of cognitive pathology. Mice with complete knockout of APP are viable and have no fewer neurons or synaptic boutons in the hippocampus, but exhibit gross impairments to memory and induction of synaptic plasticity (Phinney et al. 1999; Seabrook et al. 1999), although this could also result from non- specific effects due to loss of APP. Yet there is amassing evidence for adaptive functions of low, physiological concentrations of A! in the brain (Atwood et al. 2003; Pearson and Peers
2006; Morley et al. 2010; Castellani et al. 2009). Accordingly, some researchers have proposed that A!, when not at levels drastically higher than in normal brain tissue (estimated to be at least 35 times higher in some APP-overexpressing mice relative to wild-types; Best et al.
2005), may facilitate LTP, learning, and memory (see Puzzo and Arancio 2013 for a review).
Puzzo and colleagues have most intricately documented the effects of endogenous and low concentrations of exogenous A!. They initially observed that, within a very small concentration range, synthetic A!42 oligomers applied to hippocampal slices enhanced the potentiation induced by a standard LTP protocol (Puzzo et al. 2008; 2012). Specifically, at a very low picomolar (pM) concentration there was increased potentiation (maximally at
200pM), whereas application of high concentrations reduced potentiation, particularly in the
200+ nanomolar range often used in A! studies (see Haass and Selkoe 2007). Furthermore, infusing mice with 200pM oligomeric A! into the dorsal hippocampus enhanced the acquisition of both spatial reference water maze and contextual fear conditioning tasks.
In a more recent study it was even reported that endogenous A! is necessary in wild- type mice for proper induction of LTP and spatial/contextual memory formation in the hippocampus (Puzzo et al. 2011). In hippocampal slices, suppressing A! levels using small interfering RNA or A! antibodies reduced the magnitude of LTP induced using a theta burst
88 Chapter 1 – General Introduction electrophysiological stimulation protocol. However, the LTP deficit was rescued via co- application of 200pM oligomeric A!, and was even enhanced by a 300pM concentration.
Application of monomeric conformations of A! did not affect LTP. Likewise, mice given hippocampal infusions of either small interfering RNA or A! antibodies before (but not after) training exhibited spatial water maze and contextual fear acquisition impairments that were rescued by co-infusion of 200pM oligomerized A!. As previously observed for LTP, within a very narrow concentration range, there were even enhancing effects of A! on contextual fear memory. In mice depleted of A! via infusion of A! antibodies, application of 250pM synthetic
A! oligomers enhanced memory relative to mice receiving 200 and 300pM, or even mice receiving only control vehicle. Given that fear conditioning was also associated with rapid increases in A! levels in the hippocampus, it was concluded that small changes in its concentration likely contribute to memory acquisition, though not consolidation.
Puzzo and colleagues also reported that the enhancement of LTP by low concentrations of A! might result from increased probability of pre-synaptic vesicular release, rather than a post-synaptic mechanism (Puzzo et al. 2008; 2011). Using a neuronal stimulation protocol called post-tetanic potentiation (PTP), plasticity induction was prevented at post-synaptic neurons, allowing the effects of A! on action potential-evoked pre-synaptic neurotransmitter release to be evaluated. These experiments revealed that in the presence of low-concentration A!, glutamate release was elevated after brief bouts of electrophysiological stimulation. Abramov and colleagues (2009) similarly showed that small (approximately 1.5- fold) elevation in extracellular A!, caused by suppressing its degradation, could increase pre- synaptic neurotransmitter vesicle release in hippocampal neuron cultures. As observed by
Puzzo and colleagues, this facilitatory effect was highly concentration-dependent, with further
89 Chapter 1 – General Introduction increases or decreases in A! levels eliminating pre-synaptic enhancement.
Puzzo and colleagues demonstrated that the effects of A! were mediated by "7- nicotinic acetylcholine receptors ("7-nAChRs), as antagonism or knockout of these receptors prevented A!-evoked potentiation enhancement. Importantly, the influence of A! on "7- nAChRs has also been found to be concentration-dependent, with low (picomolar) levels having agonistic (Dougherty et al. 2003; Fodero et al. 2004) and higher levels having inhibitory effects (Grassi et al. 2003; see also Dineley et al. 2001; 2002). It may be inferred, therefore, that A! could bidirectionally influence pre-synaptic activation via "7-nAChRs, dependent on concentration. This is further supported by the findings that larger elevations in
A! levels can reduce LTP, perhaps biasing plasticity induction towards LTD (see Wang et al.
2002; Palop and Mucke 2010). In sum, this work indicates that oligomeric A! can facilitate the initiation of plasticity and memory formation, and might be assumed to influence the induction of memory reconsolidation, as will be proposed later.
Garcia-Osta and Alberini (2009) also observed that endogenous A! is critical for memory formation. Using an inhibitory avoidance task they demonstrated that pre-training infusion of an A! antibody into the hippocampus impaired memory tested both 1 and 24 hours later. Post-training infusion had no apparent effect. Subsequently it was observed that co- infusion of the antibody along with oligomerized A!42 before training prevented impairment due to antibody alone, and also that A!42 infused alone could enhance memory formation.
This pattern of memory expression also persisted during another test 5 days later. Pre-training hippocampal infusion of "7-nAChR antagonist, mecamylamine, could mimic the effects of the
A! antibody, tentatively supporting a common mechanism of action. Finally, Morley and colleagues (2010) reported that reducing functional A! via antibody or antisense
90 Chapter 1 – General Introduction oligonucleotides, or blocking its binding to unidentified sites that recognize its structure, each impaired the acquisition of an inhibitory avoidance task in wild-type mice, and also induction of LTP in the hippocampus. Conversely, applying low-doses of A!42 peptide facilitated their acquisition of both inhibitory avoidance and object recognition tasks, along with lowering the stimulation threshold for LTP induction. In conjunction, these studies clearly indicate that endogenous A! serves an adaptive role in hippocampal synaptic plasticity and memory, despite the impairments widely observed when it is overexpressed or applied at high concentrations (i.e. Nabeshima and Nitta 1994; Balducci et al. 2010b; Lesne et al. 2006). But, while memory enhanced by low concentrations of A! could potentially be more likely to persist over time, this work does not directly indicate whether it may also influence memory stabilization or destabilization. This is tested in the study presented in Chapter 2.
1.5.4. A! restricts developmental critical periods.
Very recently, Karla Shatz and other researchers have begun to formulate a model wherein A! contributes to the regulation of developmental critical periods of plasticity in sensory cortices. A first study used young (4-5 week old) transgenic mice expressing human
A! mutation to look at ocular dominance plasticity in visual cortex (William et al. 2012). Even at this early stage of development mice with these transgenes exhibited detectable human A! expression, though at this young age severe synaptic or cognitive pathologies should not yet be present (see Garcia-Alloza et al. 2006; D'Amelio et al. 2011). Nevertheless, the effects of four days of monocular deprivation at the peak of the ocular dominance critical period on cortical plasticity were perturbed in these transgenic mice. They exhibited normal or even exaggerated weakening of neuronal responsiveness to inputs coming from the deprived eye, but a deficit in both strengthening and broadening of visual cortex territory responsive to
91 Chapter 1 – General Introduction inputs coming from the non-deprived eye. Thus this work tentatively indicates that accelerated A! generation and/or oligomerization can contribute to normal processes of cortical plasticity, and may selectively limit the induction of some forms of synaptic strengthening (perhaps LTP) in favour of LTD.
It was subsequently found that ocular dominance plasticity could be altered by A! through its binding to paired immunoglobulin-like receptor B (PirB), or possibly its human homolog leukocyte immunoglobulin-like receptor B2 (LilrB2; Djurisic et al. 2013; Kim et al.
2013). Adult transgenic mice lacking PirB exhibited increased synaptic spine density, reduced spine motility, enhanced LTP, and impaired LTD in primary visual cortex relative to wild- type mice (Djurisic et al. 2013). Accordingly, in wild-type adult animals given brief monocular deprivation there was no observed increase in responsiveness to the non-deprived eye and weakening of responsiveness to the deprived eye (also see Syken et al. 2006). In stark contrast, mice lacking PirB exhibited increased cortical responsiveness to the non-deprived eye and no weakening to the deprived eye. This work suggests that expression or activation of
PirB could suppress experience-dependent strengthening and stability of synaptic connections while facilitating weakening and spine alteration.
Oligomeric A!42 was found to bind to PirB with relatively high (nanomolar) affinity
(Kim et al. 2013). As commonly observed elsewhere, application of moderately high concentrations of A! (200nM) to hippocampal slices inhibited LTP, but strikingly this was rescued in slices from adult PirB knockout mice. PirB deletion also rescued the loss of ocular dominance plasticity previously reported in AD-model mice that express human A!, as well as memory deficits for object location and recognition tasks. Together these findings suggest that
A! binding to PirB inhibits synaptic strengthening, and could be involved in terminating
92 Chapter 1 – General Introduction critical plasticity periods in cortex. Given that memory destabilization can require neuronal mechanisms involved in synaptic depression/depotentiation (Rao-Ruiz et al. 2011; Clarke et al. 2010; Hong et al. 2013 see Section 1.3.3), this could also suggest that A! binding to PirB may be involved in the initial labilization of memory following reactivation. It is not yet entirely clear how PirB expression changes during development or in response to experience, though it is upregulated following nervous system injury (i.e. ischemia, optic nerve crushing), suggesting that it may allow for the weakening of damaged synaptic connections that is required for remodeling and recovery (Wang et al. 2010; Cai et al. 2012; see Gou et al. 2014).
Thus PirB downregulation may provide another means by which the brain could maintain the stability of strongly encoded memories. Indeed, PirB activation by A! was observed to activate phosphatases including calcineurin (Kim et al. 2013), which is known to be required for post-reactivation destabilization of some forms of memory (Fukushima et al. 2014).
In a related manner, it has been proposed that A! may induce a switch in NR2-subunit composition of NMDARs (Kessels et al. 2013), similar to the transition observed during development (Sheng et al. 1994; Monyer et al. 1994) and/or in response to experience
(Quinlan et al. 1999a; Lee et al. 2010b; Chen and Bear 2007). Application or overexpression of A! is frequently observed to reduce NMDAR activity, often by reducing (synaptic) expression of NR2B (Snyder et al. 2005; Lacor et al. 2007; Kurup et al. 2010; Um et al. 2012;
Kessels et al. 2013; Hsu et al. 2014; but also see Balducci et al. 2010b; Liu et al. 2012). This is interesting given that A! is often reported to induce plasticity via NR2B activation. One parsimonious explanation is that higher concentrations of A! may activate NR2B-containing
NMDARs, causing LTD via AMPAR endocytosis but also promoting removal of
NR2B/NMDARs (Kurup et al. 2010; Dewachter et al. 2009; Snyder et al. 2005), thereby
93 Chapter 1 – General Introduction influencing subsequent plasticity induction.
Some potential targets by which A! may initiate this process include the receptor tyrosine kinase Ephrin type-B receptor 2 (EphB2; Lacor et al. 2007; Cissé et al. 2011; Simón et al. 2009) and "7-nAChR (Snyder et al. 2005). EphB2 is important for NMDAR trafficking, localization, and/or signaling (Dalva et al. 2000; Henderson et al. 2001; Takasu et al. 2002), and A! can bind with an extracellular region of EphB2 triggering its degradation
(Cissé et al. 2011). Thus secretion of A! may subsequently reduce the capacity for NMDAR- dependent plasticity (Grunwald et al. 2001). "7-nAChRs may be activated by very low concentrations of A! (Dougherty et al. 2003), leading to calcium-induced vesicular release at pre-synaptic sites, while increasing concentrations may begin to promote endocytosis of
NR2B-containing NMDARs from post-synaptic sites through a process requiring activation of calcineurin and striatal-enriched phosphatase (STEP; Snyder et al. 2005). This, too, could reduce the capacity for subsequent induction of plasticity; potentially linking A! levels with mechanisms triggering memory formation/modification.
1.5.5. A model for memory formation, stabilization, and destabilization.
In the previous section I surveyed evidence that A! is secreted by behavioural experience and can facilitate the formation of memory. Endogenous A! release may stimulate and maintain neurotransmitter release from pre-synaptic sites (see Abramov et al. 2009), and promote plasticity-induction processes involved in memory formation (Puzzo et al. 2011). Yet stronger neuronal activation may secrete larger amounts of A!, which, in addition to stimulating pre-synaptic release, could cause suppression of post-synaptic plasticity mechanisms (i.e. Lacor et al. 2007) either indirectly via high extracellular levels of neurotransmitter (see Palop and Mucke 2010), or directly by binding to components of the
94 Chapter 1 – General Introduction post-synaptic density (i.e. Kessels et al. 2013; Kim et al. 2013). An intriguing prospect is that this change in post-synaptic plasticity mechanisms could influence metaplasticity (Balducci et al. 2010b). In this way, after A! stimulation, subsequent activation of a synapse may be associated with an altered pattern of plasticity induction mechanisms. I propose that this could be part of the process used by the brain to stabilize and protect memories against reconsolidation-mediated alteration. Thus increasing A! within a physiological range might
"hyperstabilize" a memory, making it less likely to undergo reconsolidation in the future.
However, just as A! can enhance memory acquisition, it may too contribute to destabilization when a memory is subsequently reactivated, by facilitating neurotransmission or NMDAR activity. Therefore, I propose that A! could play a dual role in both consolidation and reconsolidation processes: initially facilitating the induction of (re)consolidation, then promoting the (re)stabilization of the trace.
Only two prior studies have directly evaluated the effects of A! on the process of memory reconsolidation (Ohno 2009; Álvarez-Ruíz and Carrillo-Mora 2013). Ohno and colleagues (2009) demonstrated that with age, transgenic mice expressing human A! were increasingly impaired in the acquisition of weak contextual fear conditioning. Using a stronger training protocol the aged animals could acquire the task, but their memory was impaired following reactivation, suggesting that A! overexpression selectively impaired the reconsolidation process. Yet there is an inherent limitation of studies relying on transgenic overexpression, particularly when investigating processes of memory stabilization and destabilization. For example, small increases in A! could, theoretically, enhance formation and stability of a given memory, making it more resistant to change, but this could also enhance mechanisms that induce destabilization, making it more likely that this strong memory will be
95 Chapter 1 – General Introduction labilized by retrieval. This is further exacerbated by the substantial overexpression of A! in these AD-model animals. Alvarez-Ruiz and colleagues (Álvarez-Ruíz and Carrillo-Mora
2013) avoided this confound by directly infusing A! peptide into the hippocampus after reactivation, finding that this impaired retention of object recognition memory. However, the peptide consisted of a non-oligomerized short fragment of A! (residues 25-35) infused at a very high concentration, which could have non-specific effects on synaptic plasticity.
Therefore these studies are of limited relevance to the hypothesis proposed in this thesis regarding the functions of endogenous A!. In Chapter 2 we have explored the effects of endogenously secreted A! on memory encoding by investigating if the induction of reconsolidation is altered by its generation following both memory formation and retrieval.
1.6. SYSTEMS-LEVEL DISTRIBUTED PROCESSING AND MEMORY STABILITY.
Changes to the molecular plasticity mechanisms recruited at individual synapses are not likely to be the sole mechanism responsible for selective stabilization of each memory trace. Most learning experiences consist of complex (and often unique) combinations of sensory stimuli distributed across time. In adult organisms, some parts of these experiences may be entirely novel while others may be similar to prior events. One possibility is that the neural circuits encoding each discrete component of an experience may thus be destabilized and updated independently. Frequently encountered aspects of experience may also be more resistant to destabilization. As a hypothetical example, imagine five distinct sensory stimuli,
'a', 'b', 'c', 'd', and 'e' (they could be specific odours, tastes, sounds, etc.). When an animal is exposed to particular combinations of these stimuli during repeated episodes (first 'a-b-c', then 'a-b-d', then 'a-b-e'), it may be expected that the repeatedly encountered 'a-b'
96 Chapter 1 – General Introduction combination could be selectively strengthened relative to the other stimulus pairs that were experienced only once. The unique 'a-b-c', 'a-b-d', and 'a-b-e' experiences would also likely be encoded, but perhaps in a trial-specific manner that minimizes interference resulting from the shared stimulus features. That is to say, when the animal is presented with stimulus 'e' again in the future, it may be able to recruit the complete 'a-b-e' episode without interference from the 'a-b-c 'and 'a-b-d' episodes (which reflects pattern completion and pattern separation processes, respectively; see Rudy and O'Reilly 1999). However, based on this model I will propose that these unique 'a-b-c', 'a-b-d', and 'a-b-e' associations may typically be less stable and less persistent than the repeatedly encountered 'a-b' pairing.
Such reasoning has motivated many theories regarding independent learning systems in the brain: one, a "fast-learning" system mediating unique combinations of stimuli composing individual experiences (so-called “episodic”-like memory), and another, a "slow-learning" system that gradually extracts statistical generalities encountered during multiple experiences
(sometimes referred to as "semantic"-like memory; see Marr 1971; Teyler and DiScenna 1986;
Sherry and Schacter 1987; McClelland et al. 1995; O'Reilly and Rudy 2001). In humans these have both been defined as forms of declarative memory: episodic being autobiographical in nature, and semantic thought to be acquired via episodic events, but consisting of facts and generalities of experience that are stored independently of the original source or context
(Cohen and Squire 1980). It is thought that these forms of memory must be mediated by semi- distinct memory systems, as the ability to encode and subsequently retrieve the details of each discrete experience is fundamentally incompatible with the encoding and retrieval of general rules about the world that exist in an episode-independent form (O'Reilly and Rudy 2001). As will be reviewed in this section, these inherently distinct forms of memory have typically been
97 Chapter 1 – General Introduction ascribed to hippocampal and neocortical brain structures, respectively. Based on the encoding of different components of experience via distinct brain systems, I will also propose how some components of experience may be selectively protected against modification while others could remain more malleable to environmental changes.
1.6.1. Distributed processing networks: hippocampal and extra-hippocampal memory.
The unveiling of distinct memory systems formally began with Brenda Milner's evaluation of patient H.M. more than 50 years ago. These studies emerged following the observation that older memories tended to be less impaired in cases of dense amnesia after medial temporal lobe (MTL) damage that included the hippocampus, the most famous of which was that of Henry Molaison, “patient H.M.” (Scoville and Milner 1957; 2000).
Following surgical removal of much of his MTL (see Annese et al. 2014 for a recent characterization), H.M. suffered an almost complete inability to form new episodic memories
(anterograde amnesia for personal events) along with the loss of memory for more recent experiences, but preserved memory for early life events (indicative of a temporal gradient of retrograde amnesia). The study of H.M. and other similar patients began to formalize the idea that, in addition to the stabilization of memories in the hours after acquisition (so-called cellular consolidation), there may also exist a slower process of stabilization across brain regions (systems consolidation), whereby some memories are initially dependent on the hippocampus and other temporal lobe structures, but can gradually transition to forms that are mediated somewhat independently of these brain regions (Squire 1992).
These studies of MTL lesion patients also revealed that not all facets of learning and memory are impaired equally (Warrington and McCarthy 1988). For example, priming and procedural memory were both relatively preserved, suggesting that these forms of memory
98 Chapter 1 – General Introduction were likely mediated by brain systems other than the hippocampus (Milner et al. 1968;
Gabrieli et al. 1993; see Schacter and Tulving 1994). Yet even more critically, it seemed that retrieval of semantic memories acquired even shortly prior to MTL damage could remain somewhat intact (Kensinger et al. 2001), although this could rely on the degree of sparing of extra-hippocampal cortical MTL structures (see Tulving and Markowitsch 1998; Schmolck et al. 2002). Astoundingly, not only was it found that semantic information could be retrieved, but also that knowledge believed to be acquired via episodic-like systems (e.g. the assassination of U.S. President J.F.K.) could be learned in a semantic manner by amnesics including H.M. (Skotko et al. 2004; Tulving et al. 1991), albeit at a slower rate. For instance, one amnesic patient with extensive MTL damage showed improvement in the retention of novel three-word sentences studied repeatedly over 12 weeks. However, performance never reached the level of control subjects, nor was it present in a consciously declarable manner
(Bayley and Squire 2002). Moreover, such learning is likely to be inflexible. Another MTL patient showed gradual learning of novel three-word sentences, however this learning did not generalize to semantically related sentences, as was observed in control subjects (Stark et al.
2005). The interpretation was that factual information initially acquired in a declarative manner by healthy subjects might be slowly encoded via non-hippocampal systems in a non- declarative manner by patients with extensive MTL lesions. Thus the hippocampus may be somewhat dispensable for the gradual acquisition or retrieval of general semantic knowledge.
The emerging view at the time was that MTL could be important for rapid learning of one-time episodes, but perhaps even to bind together all forms of detailed conjunctive stimulus combinations. For example, it was observed that detailed spatial or contextual memories could only be formed and expressed via the hippocampus (Bayley and Squire
99 Chapter 1 – General Introduction
2005). Yet in MTL patients there was some evidence of preserved retrieval of very remote spatial memories (such as the topographical layout of the patient's childhood neighbourhood) but not encoding of new spatial representations (such as the patient's current neighbourhoood; Teng and Squire 1999). Even more inconsistencies emerged after reports that amnesic MTL patients, including H.M., showed some evidence of spatial learning of highly familiar places (Corkin 2002; Rosenbaum et al. 2000). For instance, H.M. could give the address and draw the layout of the home he moved into several years after his MTL removal, despite lack of knowledge of his current residence (see Corkin 2002). In this regard it was posited that the hippocampus may only be required to bind the sensory details of environments and episodic-like events, but not the overall spatial topography when encoded very gradually and/or beginning early in life (Rosenbaum et al. 2000).
These findings further indicated that preserved mnemonic abilities may be mediated by some type of semanticized representation for the gist of events (see Cermak 1984; McClelland et al. 1995; O'Reilly and Rudy 2001). In support of this idea, in instances of 'intact' episodic memory retrieval in MTL patients (for example very remote, pre-operative events) their descriptions had reduced detail relative to control subjects (Rosenbaum et al. 2008; Cipolotti et al. 2001). Even when asked to imagine hypothetical episodes, MTL-lesioned amnesics presented some inability to give the rich descriptions provided by control subjects
(Rosenbaum et al. 2009). This suggested that MTL structures might serve to bind complex representations not just in memory, but perhaps even at a perceptual level (see Barense et al.
2007). Together, this collection of research thus implies that human MTL structures are of critical importance in rapidly forming and even retrieving the intricacies of autobiographical events and spatial environments (i.e. episodic memories), however extensive experience may
100 Chapter 1 – General Introduction permit other brain regions to mediate semantic-like encoding of simplified representations of repeatedly encountered stimuli and events.
1.6.2. Hippocampal and cortical memory systems in animals.
Many of these findings from human patients have also been successfully replicated - and extended - through work with animals. Although it remains debated whether or not animals are able to form episodic memories in the same manner that humans do, it is quite clear that many species can acquire episodic-like memories that bind together the features of each experience and retain a record of which events happened when and where (Tulving
1986; Clayton and Dickinson 1998; Komorowski et al. 2009; see Eichenbaum et al. 2012).
In rodents the simplest forms of episodic-like memory have been modeled in the one- trial acquisition of contextual representations. These experiments have often used a procedure known as the context pre-exposure facilitation effect (CPFE), which requires the animal to learn about a conditioning context on one day and then form a fear response to that context the next (Rudy and O'Reilly 1999). When shock is delivered immediately after placement into a conditioning chamber, only animals pre-exposed to that specific context will acquire a conditioned fear response (Fanselow 1990; 1986). It has further been shown that formation of a mental representation of the context during pre-exposure requires an intact hippocampus
(Matus-Amat et al. 2004). Importantly, Rudy and O'Reilly (1999) observed that it was not the successive presentation of the individual features making up a context, but instead only the exposure of animals to a specific conditioning context itself that was sufficient to support conditioned fear when rats were later given immediate shock in that environment. Thus it appears that the hippocampus likely mediates the binding of stimuli or the spatial features of
101 Chapter 1 – General Introduction an environment (or an episode) into a unitary conjunctive representation. More elaborate experiments have since demonstrated that rats are capable of learning episodic-like information about which specific sensory cues predict when and where within a given environment food will be hidden, in a manner requiring the hippocampus (Day et al. 2003;
Ergorul and Eichenbaum 2004; Rajji et al. 2006).
Gradients of retrograde amnesia noted in human patients have also been reported in animals. In a seminal study using a one-phase contextual fear conditioning task in rats, Kim and Fanselow (1992) demonstrated a selective sparing of remote memories when the hippocampus was lesioned at a variable interval after training. When the lesion was applied 1 or 7 days after the completion of training, animals expressed a severe deficit in fear responding when they were returned to the conditioning context relative to control animals that received sham lesioning. However, when the lesion was applied 14 or 28 days after training both experimental and control animals expressed a similar fear response when returned to the conditioning context, indicative of preserved retention of this more remote memory. Comparable results were obtained using a socially-transmitted food preference task that requires rats to remember the scent of a particular food sampled on the breath of littermates. On subsequent tests rats will preferentially consume food matching the previously experienced scent as opposed to a novel food. Clark and colleagues (2002) found that lesioning the hippocampus one day after acquisition, but not 10 or 30 days later, impaired this food preference.
As in humans, some forms of memory in rodents are typically impaired by hippocampal lesioning regardless of age. For example, several studies have reported that disrupting hippocampal function at a variable interval after acquisition impaired performance
102 Chapter 1 – General Introduction at several different spatial tasks, even at remote time-points (Martin et al. 2005; Mumby and
Glenn 2000; Teixeira et al. 2006; Clark et al. 2005a; Broadbent et al. 2006; though also see
Maviel et al. 2004). The interpretation of such findings is that retrieval and/or expression of some forms of detailed spatial or contextual memory (such as the spatial reference water maze task) will always require the hippocampus. However, others have observed that such flat gradients of retrograde amnesia can sometimes be ameliorated by providing animals with extensive training, suggesting that even tasks requiring detailed spatial navigation could be partially maintained independently of the hippocampus when they are well-learned (Winocur et al. 2005; though also see Clark et al. 2005b). In a study by Winocur and colleagues, rats reared in a complex "village" environment could still navigate the space after hippocampal lesions, even when the identity of features within the environment were distorted, though not when the topographical arrangement was altered or the village was rotated relative to surrounding cues in the room (Winocur et al. 2005). In general this work resembles the findings in humans that spatial memories could be iteratively acquired over very extensive training, but might lack the rich environmental details rapidly stored and recovered by the hippocampus. Similar results were obtained using the aforementioned paired-associated task in which specifically flavoured cues are associated with food buried in a particular location of a large arena (Morris 2006; Day et al. 2003). This task requires hippocampal NMDAR and
AMPAR activity for acquisition of single trials, but retrieval minutes later requires only
AMPAR activity. However, "overtraining" with two paired-associates over weeks led to a persistent memory that could be retrieved after hippocampal infusion of an AMPAR antagonist. Thus, it appears that repeated training could form an extra-hippocampal memory for which food pellet flavour cue was associated with food buried at a particular location.
103 Chapter 1 – General Introduction
The detailed encoding critically mediated by the hippocampus has been the focus of many studies in rodents using a phenomenon called context generalization (Riccio et al. 1984;
Biedenkapp and Rudy 2007). In hippocampus-dependent tasks, generalization is typically studied using contextual fear conditioning. Over time the representation of the conditioning context may be degraded or simplified, such that animals often fail to discriminate the trained context from one sharing similar features or shape. One study found that at approximately the same time-point that contextual fear memories were expressed independently of the hippocampus, rats also lost the ability to discriminate between the original training context and another novel context (Wiltgen and Silva 2007). Importantly, re-exposing the animal to the original training context at a remote time-point reinstated the ability of the rat to discriminate between the contexts, indicating that environmental details could be re-learned by a process that resembles reconsolidation. This work provides correlational evidence that memories mediated by the hippocampus may typically preserve more detail than those that no longer depend on hippocampus for expression.
To confirm this hypothesis, Winocur and colleagues (2007) lesioned the hippocampus of rats shortly after contextual fear conditioning and observed not only reduced fear expression to the training context, but also an inability to discriminate between trained and untrained contexts, which mirrored the generalized fear response of unlesioned rats tested at remote time-points. Furthermore, Wiltgen and colleagues (2010b) observed more neuronal activity in the hippocampus following retrieval of recent versus remote contextual fear memories, particularly when tested in a context that was similar to the training context, but not a dissimilar context. The degree of generalization to a dissimilar context at a remote time- point could also be used as a marker for memory precision, as animals that generalized more
104 Chapter 1 – General Introduction were more impaired by hippocampal inactivation before testing in the original training context. Thus it appears that regions outside of the hippocampus are able to retain less detailed representations of remote contextual fear memories, but in animals capable of remembering more contextual details the hippocampus remains necessary for retrieval.
1.6.3. Does the anterior cingulate cortex mediate remote memory for the gist of events?
Accordingly, some groups have reported evidence of reduced hippocampal and increased cortical activation when retrieving remote versus recent spatial and contextual memories (Frankland et al. 2004; Teixeira et al. 2006; Bontempi et al. 1999). These studies have consistently reported that during retrieval of remote memories there can be increased activation of medial prefrontal and anterior cingulate cortices, along with an increased reliance on ACC for expression (see Frankland and Bontempi 2005; and Goshen et al. 2011 for a novel methodology). For instance, Frankland and colleagues (2004) showed that inactivation of ACC impaired retrieval of remote but not recent contextual fear memories.
Although Teixeira and colleagues (2006) found no change over time in the requirement for hippocampal activity for the expression of a spatial reference water maze memory, they also observed an increasing reliance on ACC, with various inactivation methods impairing performance at 1 month but not 1 day after training. Consistently, elaborate histological characterization of mice that had received contextual fear conditioning demonstrated that the retrieval of aged memories evokes strikingly elevated levels of correlated activity between many cortical regions, as well as between the hippocampus and cortex (although magnitude of hippocampal activation actually declined; Wheeler et al. 2013). This correlated activity was found to center around several primary network hubs or "nodes", including prelimbic and anterior cingulate cortices (both part of the rodent default mode network; see Lu et al 2012).
105 Chapter 1 – General Introduction
Together these findings suggested that strengthening of inter-cortical connectivity might mediate remote memory retrieval, but that the hippocampus is not completely disengaged in this process.
Recent generations of studies using intricate pharmacological and biochemical approaches increasingly support a model in which memories are not simply slowly transferred from hippocampal to cortical storage mediums. Instead this evidence indicates that cortically- mediated memories are laid-down along with the hippocampal trace, but can often require time or repetition before they can fully support retrieval. Specifically the many features of an episode could be encoded in cortex, but this may require extensive processing or experience for the brain to identify and strengthen those that are most essential to preserving the gist of experience (see Takehara-Nishiuchi and McNaughton 2008; Chau et al. 2014; Pyka and
Cheng 2014 for recent examples). In this way, the hippocampus has been proposed to serve as a sort of index to link components of memory distributed across cortical processing networks as interconnections are strengthened or established over time (Teyler and DiScenna 1986;
Teyler and Rudy 2007).
As one example, Lesburgueres and colleagues (2011) reported that chronic pharmacological inactivation of the hippocampus in the days following acquisition of a one- trial socially-transmitted food preference memory impaired retention when tested 30 days after training. However, chronic inactivation beginning two weeks after training did not impair this memory (see Winocur et al. 2007 for related findings). Chronic inactivation of obitofrontal cortex (OFC), on the other hand, impaired the retention of both recently and remotely acquired memories when tested 30 days after training. However, it was found that either an AMPAR or NMDAR antagonist infused into OFC during training did not impair
106 Chapter 1 – General Introduction memory when tested just 7 days later, but caused a substantial impairment when tested 30 days later. Thus it appears that the hippocampus may support memory expression during a period when the representations encoded by the cortex are being refined or strengthened
(requiring neuronal activity). Given that cortically-mediated representations tend to be less detailed, one possibility is that the cortical connections mediating many relevant parts of a training experience could be "tagged" via fading interactions with the hippocampus, allowing the brain to gradually identify features critical to retaining the gist of experience, so as to be converted to enduring memory traces.
However the picture is not entirely straightforward. For instance, synaptic plasticity in
ACC during acquisition of several rapidly acquired hippocampus-dependent tasks can be necessary for retrieval even days later. Einarsson and colleagues (2012) observed that blocking protein synthesis or NMDAR activity in ACC at the time of training could impair retrieval during testing just 1 and 3 days later; time-points when contextual fear expression has previously been found to rely on the hippocampus but not the ACC (Frankland et al.
2004). Similarly, Zhao and colleagues (2005) found that infusion of either an NR2B-selective antagonist or NR2B-silencing siRNA into ACC before fear conditioning could impair the freezing response to the training context 2 days later. This data is congruous with accumulating evidence that ACC activity is engaged during or shortly after learning of an array of behavioural tasks, and that memory formation can be disrupted when synaptic plasticity mechanisms are blocked at the time of learning, sometimes even when tested at recent time-points (Liu et al. 2009; Leon et al. 2010; Vetere et al. 2011a; Zhang et al. 2011a).
Overall these studies indicate that the ACC can quickly encode information that is necessary for retrieval at remote or even recent time-points. However, the ACC and other
107 Chapter 1 – General Introduction cortical regions may only be able to efficiently guide behaviour in the absence of the hippocampus once fundamentally important features of a task or a spatial environment have been gradually discerned. Indeed, the ACC has been reported to mediate a wide array of higher-order cognitive functions including the formation of rules for temporal order (Kesner
2000) or temporal sequences of stimuli (Procyk et al. 2000; Chiba et al. 1997; Steenland et al.
2012), tracking progress during sequences of behaviour orchestrated towards a goal
(Delatour and Gisquet-Verrier 2001; Ma et al. 2014; Cowen et al. 2012), encoding context that encompasses both behavioural actions and time (Hyman et al. 2012), detecting errors to learned predictions (Totah et al. 2009), facilitating learning in response to reward prediction error (Bryden et al. 2011), and/or encoding error expectancies (Carter et al. 1998). These descriptions are all inherently vague due to the lack of situational-specificity in which these cognitive abilities are recruited, which is itself suggestive that the ACC could mediate the abstraction of general rules from experience by serving as a central hub to connect diverse cortical memory networks. In agreement with this possibility, in Chapter 3 I present evidence that memories retained by the ACC may be critical to acquire a similar temporal arrangement of two fear conditioning tasks administered in distinct environments.
1.6.4. Cortical encoding as a function of time or experience?
With this point in mind, it should again be emphasized that the gradual transition towards reliance on extra-hippocampal brain regions may not be caused by the passage of time itself, but rather the repeated retrieval or replay of memories that can occur over time
(McClelland et al. 1995; O'Reilly and Rudy 2001; but see Takehara-Nishiuchi and
McNaughton 2008). Once again, this may happen during similar behavioural experiences, and perhaps even during sleep (see Lewis and Durrant 2011; Diekelmann et al. 2011; Rudoy
108 Chapter 1 – General Introduction et al. 2009). A simple theory based on the aforementioned data is that weak cortical memory traces laid-down by the initial experience may be fine-tuned as the information is replayed or reactivated, until the essential features of the event (or series of events) are extracted through a process referred to by some authors as semanticization (Winocur and Moscovitch 2011).
Evidence of such a process can be derived from one elegant study showing that rats progressively learning a cross-modal rule-shifting task displayed patterns of neural activity in medial prefrontal cortices that were replayed extensively during sleep (Peyrache et al. 2009).
The patterns that were preferentially replayed were those that had been elicited at a choice point between the two maze arms when the rat had just successfully acquired a new rule for response selection. Importantly, this neural replay appeared during synchronized bouts of firing between the hippocampus and cortex called sharp wave ripples, suggesting that this could be driven by the communication of information between these structures, specifically regarding general behavioural strategies that have just been established.
Unless an event is completely novel, such repetition of experience (either passively during rest/sleep or actively via repetition or rehearsal) should not merely derive the most statistically simple representation of the event(s), but also interleave this information with compatible extant knowledge. At a behavioural level, one seminal human study found that after reading a culturally-unfamiliar story, participants provided a narrative that became increasingly biased towards their own cultural beliefs with each re-telling (Bartlett 1932; also see Bergman and Roediger 1999). Thus with each active retrieval of the memory it was transformed to resemble existing knowledge for familiar related concepts. Interestingly, a recent study reported that human subjects repeatedly exposed to an identical object in the background of several visual scenes were subsequently better able to recognize having seen
109 Chapter 1 – General Introduction this "target" object, but were also more likely to confuse this target with a similar "lure" object than were subjects who encountered the target object only once (Reagh and Yassa 2014).
One interpretation is that subjects have a stronger memory for a repeatedly encountered object, but this repetition also promotes rapid formation of a simplified representation that dominates the detailed episodic memory for the single event. In rats a similar effect was observed using a contextual fear conditioning task in which ACC inactivation following a remote memory test facilitated their discrimination of training and novel contexts when tested again six hours later (Einarsson et al. 2014). This suggested that ACC activity otherwise suppresses a detailed memory mediated by another brain region, like the hippocampus.
However, this does not necessarily imply that remote memories are necessarily devoid of detail. Instead, extensive experience may produce a detailed semantic memory that may preserve many features of a repeated event. The aforementioned village study by Winocur and colleagues (2007) supports this assumption, as rats given continual exposure to a complex spatial environment were able to form allocentric spatial knowledge outside of the hippocampus, although this was inflexible to change (and thus more semantic-like). More direct evidence comes from Lehmann and colleagues (2009), who demonstrated that rats given repeated fear conditioning to a specific context over days formed a memory that could be retrieved without a functional hippocampus. Importantly, hippocampus-lesioned rats did not generalize to a new context, suggesting that, with sufficient training, extra-hippocampal regions could encode detailed contextual representations. Using a similar behavioural procedure, Wang and colleagues (2009b) reported that repeated training (a combination of repeated fear conditioning in one context and unreinforced exposure to another) could form a detailed memory that did not generalize over time. However, in this study hippocampal
110 Chapter 1 – General Introduction lesions applied shortly after training still impaired contextual discrimination ability, though lesions applied long after training did not impair the remote contextual discrimination ability.
Thus it appears that repeated training can both accelerate hippocampus-independent fear expression and engage encoding of detailed contextual memories by extra-hippocampal brain areas, although not under all conditions. Finally, in a recent study Lehmann and McNamara
(2011) reported that rats trained with a single contextual fear task and then given 10 brief reactivation sessions (re-exposure to the context) were less impaired by extensive post- training hippocampal lesions than were non-reactivated rats pre-exposed to the context 10 times. Thus, mere retrieval of a memory may facilitate extra-hippocampal encoding.
One possibility is that episodic, context-specific memories mediated by the hippocampus can co-exist and compete with the transformed representations mediated by the cortex, but that the detailed hippocampal trace may fade more rapidly (see O'Reilly and Rudy
2001; Wang and Morris 2010; Nadel and Hardt 2011 for related discussion), leaving only the gist of the original memory to mediate the retrieval of remote memories. This is consistent with the findings of Einarsson and colleagues discussed above, which indicate that an ACC- mediated trace may suppress a precise memory for the training context. An interesting extension of this prediction is that at a remote time-point the receding context-specific, hippocampus-dependent memory might sometimes be able to regain control of behaviour when the animal is reminded of the specifics of training. Winocur and colleagues (2009) observed that rats given contextual fear conditioning exhibited generalized freezing to both a trained and similar untrained environment when tested at a remote time-point. But re- exposing the animals to the training context shortly before testing for fear generalization could reinstate contextual discrimination. When rats had their hippocampi lesioned after re-
111 Chapter 1 – General Introduction exposure to the original training context, this impaired subsequent fear expression to both the training and generalization context. However, lesioning after re-exposure to the generalization context resulted in no impairment, but also no improvement of context discrimination.
Although it is not clear why the hippocampal lesion impaired both the context-specific and context-general memories, this work indicates that semanticized and context-specific memories can compete to mediate memory retrieval. Alvares and colleagues (2012) similarly reported that contextual fear memories periodically reactivated (via unreinforced re-exposure to the context) each week after training retained both their contextual specificity and reliance on hippocampus. This closely resembles other studies reporting that rats given a remote reminder of the original conditioning context can briefly re-engage the hippocampus in expression of a remote contextual fear memory (D&biec et al. 2002). Inhibiting protein synthesis in the hippocampus after the reminder was observed to impair the contextual fear response, in a manner referred to as “systems reconsolidation”.
Another overlapping model makes similar predictions, positing that throughout diverse experiences the brain begins to build a schematized knowledge structure in cortical regions
(Morris 2006; Tse et al. 2007). As for the transformation hypothesis, this conceptualization proposes that encoding general knowledge initially requires gradual extraction of information via the hippocampus (which could mediate the memories of each individual episode), which is transmitted to the cortex (which can slowly learn general rules of experience). But as a general schema is formed across many related episodes, new memories can be rapidly transferred via the hippocampus to cortical storage, without the gradual transformation or extraction of simplified representations.
This was demonstrated using a paired-associates task in which rats learned, over
112 Chapter 1 – General Introduction several weeks, multiple specific associations between flavoured cues and spatial locations of hidden food within a large open environment (Tse et al. 2007). On each trial the animals progressively learned to dig for reward in a particular place in the environment when cued beforehand with one of several specifically flavoured food pellets. This task was found to require an intact hippocampus for acquisition of a set of six distinct paired-associates. Yet after the rats had reached a performance asymptote for these associates over weeks of training, new flavour-location associates could be acquired in an accelerated manner (in just one trial). Lesioning the hippocampus only 48 hours after this training did not impair the original associates, nor the newly-learned associates, though it did prevent the subsequent acquisition of additional pairs. However, lesions applied 3 hours after learning the new associates impaired their retention, implying that the hippocampus is still entrenched in the initial encoding but not gradual consolidation of this new information. This rapid cortical transition is surprising given that detailed spatial memories should normally take extensive training or long time intervals to be expressed without the hippocampus (Zola-Morgan and
Squire 1990; Kim and Fanselow 1992). Thus it seems that some sort of flexible schematic or semantic-like representation of the environment and the behavioural contingencies contained therein can act as a framework on which new stimuli can be associated. However, this is not merely due to the gradual acquisition of a learning rule that expedites acquisition, as rats experienced with the paired-associates task in one environment did not more quickly learn a new version of the task in a new environment. Thus accelerated schema-mediated learning may be contextually delineated. Most importantly, these findings strongly suggest that the schematization or semanticization of memories that occurs during repeated similar experiences does not merely distill knowledge down to inflexible habitual responses (e.g. when flavour 'X' is experienced search for food nearest to landmark 'Y'; see White and
113 Chapter 1 – General Introduction
McDonald 2002), but can also lead animals to derive rules that are broadly applicable to each event (and similar yet-to-be-encountered events).
More recent work has demonstrated that the schema-based learning observed by Tse and colleagues (2007) requires prelimbic and anterior cingulate cortices during retrieval of both old and recently acquired associates, and also requires NMDAR-dependent synaptic plasticity in these regions to encode new pairs after the schema has been formed (Tse et al.
2011; Wang et al. 2012b). This indicates that these medial prefrontal structures could be responsible for both retaining schematic memories acquired over extensive training, and also for rapidly fitting new stimuli into knowledge schemas.
As discussed previously, the process by which cortical regions abstract common elements of experience may also contribute to the NMDAR-independent learning phenomena reviewed in Section 1.4.3. Inglis and colleagues (2013) have proposed that a reduced requirement for dorsal hippocampal NMDARs could be a consequence of experience-induced changes to the locus of encoding. Given that a mental map of the water maze environment has been acquired in the first training, features of the second maze that are similar (i.e. the general size and shape of the pool) could allow the rat to use cortical encoding mechanisms to acquire spatial information when hippocampal NMDARs are blocked. The rat may use the general behavioural strategies gradually acquired during the first training (perhaps requiring
NMDARs; see Bannerman et al. 1995) in order to navigate and search appropriately for the platform. As discussed in Section 1.4.3, Dragoi and Tonegawa (2013) have formulated and tested a complex model that supports this position. They suggest that prior spatial learning can develop a cortico-hippocampal schema (relying on area CA3 NMDARs) that pre- establishes cellular assemblies capable of encoding subsequent related events even in the
114 Chapter 1 – General Introduction absence of hippocampal NMDARs. In this way, learning a second T-maze alternation task was accelerated and could make use of NMDAR-independent mechanisms in hippocampal
CA3 and/or NMDAR-dependent plasticity in cortex. Chapter 3 of this thesis will directly evaluate whether NMDAR-independent contextual fear conditioning relies on a cortically- mediated semantic-like memory for prior experience.
1.6.5. Could distributed processing networks impart selective stability to memories?
The observation that some older and repeatedly activated memories can be mediated independently of hippocampus superficially resembles the commonly identified boundary conditions to reconsolidation presented in Section 1.2. The question most relevant to this thesis, then, is whether the extraction of statistical regularities or common features across experience by neocortical structures could influence the stability of memory? While some authors have proposed that diffuse encoding throughout cortical structures will make the experimental blockade of remote memory reconsolidation more difficult (i.e. Alberini 2011), this explanation is not entirely satisfying from the perspective of endogenous mechanisms mediating memory persistence and stability. These authors have invoked a "lingering consolidation" process, proposed to gradually cement information in cortical regions, after which the complete memory trace should be largely resilient to disruption or alteration. This conceptualization assumes there is not an invariant progression towards stability over time, but instead that this could be modulated by the frequency of memory retrieval/reactivation and the history of reinforcement (Inda et al. 2011; Dudai and Eisenberg 2004). I would clarify this model by stipulating that due to deviations in the content of learning episodes, repetition across time should not always stabilize the complete remote memory trace but instead should make the essential common elements more resilient against destabilization. Thus components
115 Chapter 1 – General Introduction of memory mediated by distinct brain regions might be independently labilized and updated.
Support for both perspectives comes from a study by Frankland and colleagues
(2006), which demonstrated that while recent contextual fear memories would readily undergo reconsolidation following a brief reactivation session (that was dependent on protein- synthesis in hippocampus), remote memories would not, and instead required longer reactivation cues in order to trigger reconsolidation. However, unlike for recent memory, remote memory reconsolidation was not blocked by the infusion of a protein synthesis inhibitor (anisomycin) into the dorsal hippocampus. It was also not blocked by post- reactivation infusion into the ACC. Instead, systemic post-reactivation injection was necessary, which should disrupt restabilization in all brain regions where the memory had been labilized.
One possibility is that the components of the remote memory mediated by the ACC, although known to be required for intact fear expression (Frankland et al. 2004), are not labilized by the long unreinforced reactivation session. Instead, perhaps only parts of the memory mediated by other brain regions, like the amygdala, are destabilized by remote activation (see
Wang et al. 2009a). One tentative interpretation is that following a process of systems consolidation, different properties of the contextual fear episode are stabilized to varying degrees.
It should be noted that the results of Frankland and colleagues (2006) differ from those of Einarsson and colleagues (2012) and Debiec and colleagues (2002), who observed that post-reactivation infusion of anisomycin could disrupt the reconsolidation of both recent and remote contextual fear memories when infused into the ACC and dorsal hippocampus, respectively. Thus, whether older contextual fear memories are more resistant to disruption warrants further study, but could depend on the strength of training (which differed in each
116 Chapter 1 – General Introduction of these studies).
A study by Winters and colleagues (2011) provides additional evidence that parts of a memory mediated by different brain structures may each undergo reconsolidation under dissociable reactivation conditions. They observed that previously acquired object recognition memory was destabilized by re-exposing rats to the same two objects within the same environment, by changing one object, or by altering a salient environmental feature, as evidenced by the amnestic effect of post-reactivation anisomycin infused into the perirhinal cortex (a region known to mediate memory for object identity; see Mumby and Pinel 1994;
Kornecook et al. 1999). However, using the same task, recognition of these objects was only impaired by infusions of anisomycin into the dorsal hippocampus when reactivation consisted of alteration of a salient contextual feature, but not after changing one object or giving additional training with the same objects. Thus parts of the trace mediated by the hippocampus were only labilized by one type of reactivation (altered context), indicating that elements of the memory mediated by the perirhinal cortex could undergo reconsolidation independently following the other two forms of reactivation (object re-exposure or object change).
Not only might components of the memory for a single event/environment (mediated by different brain regions) undergo reconsolidation independently, but interrelated memory traces may also be labilized independently of one another (D&biec et al. 2006). This study used a second-order fear conditioning protocol which involved pairing an auditory tone with footshock, followed by pairings of this feared tone with another distinct tone. Through this second-order association the tone that was never directly paired with shock came to elicit a conditioned fear response. It was observed that infusing anisomycin into the amygdala after
117 Chapter 1 – General Introduction reactivation of the first tone could impair fear subsequently elicited by either tone. However, when the unshocked tone was used as the reactivation cue, anisomycin blocked fear responding to this tone selectively, without reducing fear elicited by the shocked tone.
Therefore, under these experimental conditions only directly- but not indirectly-reactivated memories were labilized. This indicates that complex associative networks thought to make up knowledge (i.e. Anderson and Bower 1973) are not entirely labilized during the retrieval of individual elements.
D&biec’s results show that individual components of experience can undergo destabilization and reconsolidation independently, based on the reactivation cue presented.
However, the first-order tone-shock association was also strongly trained (via 8 pairings), while the second-order tone-tone association was weakly trained (via 4 pairings). Thus an alternative interpretation is that presenting the unshocked tone may have only been sufficient to labilize the weaker but not the stronger fear memory. This could mean that if an association between stimulus 'a' and 'b' is strongly encoded through many pairings, but between 'b' and
'c' is weakly encoded through few pairings, presenting stimulus 'b' alone might destabilize just the relatively malleable 'b-c' association but not the more stable 'a-b' association. Thus a logical prediction is that different parts of associative memory networks may be more resistant to reconsolidation than others, based on training strength or frequency of reactivation.
Taken one step further, if neocortex is involved in identifying statistical regularities of diverse experiences, leading to the extraction of generally applicable rules, then those regularities that are most commonly encountered in daily life may be repeatedly strengthened and increasingly stabilized in the brain (perhaps by mechanisms of metaplasticity). Tentative evidence comes from the aforementioned olfactory discrimination studies by Quinlan, Lebel
118 Chapter 1 – General Introduction and colleagues (Quinlan et al. 2004; Lebel et al. 2006), which found that acquiring a schema- like rule that accelerated acquisition of new odour pairs was associated with a decreased expression of NR2B in piriform cortex along with a switch to NMDAR-independent learning.
Based on the predictions presented in this thesis, stabilization of cortically-mediated memories via reduced NR2B expression could prevent fundamental rules from being maladaptively modified when inconsistent anomalous events are experienced. These predictions have not yet been directly tested, but such experiments could reveal how the brain preserves the knowledge that is most critical to our survival while still readily permitting malleability of other interconnected memories.
1.6.7. Summary.
This section has reviewed evidence that through repeated reactivation of a memory for an event or repeated encounters with many similar events, cortical brain regions may extract features that capture the essential attributes of experience. I proposed that such schematic knowledge may accelerate the acquisition of subsequent related experiences, and could alter the plasticity mechanisms recruited to mediate learning (such as hippocampal NMDARs).
One theoretical implication is that with diverse experience spread across weeks, months, and years, the representations of fundamental features of the world will become more strongly encoded and resiliently stored, reducing their susceptibility to destabilization and alteration.
As with repeated pairings of auditory tone and footshock, perhaps encoding these statistical regularities could result in changes to plasticity induction mechanisms, such as the downregulation of synaptic NR2B-containing NMDARs. Indeed, this could be precisely what occurs during critical periods of sensory cortex development, where repeated visual experience causes V1 neurons to acquire responsiveness to monocular inputs or selectivity for
119 Chapter 1 – General Introduction bands of light at specific orientations (Bienenstock et al. 1982; Hensch 2005), on which the rest of an organism’s visual perception abilities may be built. This could prevent maladaptive alteration of these critical memories during all but the most extreme behavioural experiences, such as chronic monocular deprivation.
1.7. SUMMARY & RATIONALE FOR STUDIES.
In this very broad introduction I have presented a comprehensive model of memory wherein the repetition of experience should promote the stabilization of memory against change. This process could serve to protect our most adaptive memories against interference when we encounter anomalous events, yet still permit an optimal degree of mnemonic flexibility when our learned predictions are consistently violated. It has been proposed that the stabilization of strong memories could be mediated by the altered expression or functional contribution of NMDARs following learning (Wang et al. 2009a). Due to the accumulation of a core set of stable memories across time, the way in which the brain encodes information may thus tend to change with age by constructing it around this existing framework. Given that rates of late-onset AD increase relatively proportionately with life expectancies (with some projections estimating that prevalence would approach 100% in a population aging to 140 years and older; see van der Flier and Scheltens 2005; Reitz et al. 2011; Kawas and Corrada
2006), I speculated that this age-related pathology could be an inevitable consequence of the way the brain maintains memories and integrates new information into an increasingly complex knowledge network. As dysregulated A! processing is the leading culprit of AD pathogenesis, it is possible that this peptide might normally contribute to the process by which memories are stabilized in the brain throughout the life span.
120 Chapter 1 – General Introduction
Therefore, the study presented in Chapter 2 was designed to test if under non-pathological conditions A! regulates memory stability. To do so, we blocked the endogenous A! proteolytic mechanisms (!- or %-secretase) in wild-type rats immediately after receiving a strong fear conditioning task known to form a reconsolidation-resistant memory. To determine if inhibiting A! generation suppressed the stabilization process, this memory was subsequently reactivated and a protein synthesis inhibitor (anisomycin) was infused, followed
24 hours later by a memory test. Post-reactivation memory disruption by anisomycin would provide evidence that A! normally contributes to memory stabilization. In a second set of experiments, A! production was manipulated at the time of reactivation, to determine if the endogenous peptide also contributes to the memory destabilization process.
However, molecular stability mechanisms at individual synapses are unlikely to account for the full gamut of memory malleability phenomena. Section 1.6 surveyed evidence of transformation in the content of memories via repeated experience, reactivation, or replay over time. It was proposed that the repetition of common features might lead to selective strengthening of these mnemonic representations, yet to confirm this hypothesis it is necessary to establish a protocol in which two events are encoded via shared knowledge. Importantly, this is different from stimulus generalization, in which animals simply fail to functionally distinguish similar conditioned stimuli (see Pearce 1987). One task known to rely on prior experience with similar events is the NMDAR-independent contextual fear conditioning procedure described in Section 1.4.4. Thus, the study presented in Chapter 3 uses the lack of reliance on dorsal hippocampal NMDARs in this procedure as an indicator that the brain has utilized some aspect of existing knowledge - obtained during prior training - to encode a new, related experience. The common features of two learning episodes were systematically
121 Chapter 1 – General Introduction manipulated to identify which were sufficient to render acquisition of the second task insensitive to NMDAR antagonist, AP5, infused into the dorsal hippocampus. Given that memory transformation/schematization processes are widely thought to involve a transition towards cortically-mediated memory, this study also tested if memory for a first training session mediated by ACC was critical to subsequently engage NMDAR-independent learning mechanisms in the dorsal hippocampus. Together these experiments are designed to characterize how the brain integrates or assimilates distinct experiences in order to build upon prior knowledge, and to provide a protocol that will allow us to explore the role of memory reconsolidation in such processes.
122 Running head: Chapter 2 – A! and memory stabilization
Chapter 2
Amyloid-! contributes to fear memory stabilization and destabilization.
Peter S.B. Finnie, Maria Protopoulos, & Karim Nader.
Department of Psychology, McGill University, 1205 Dr. Penfield Avenue
Montreal, Quebec, Canada, H3A 1B1
Chapter 2 – A and memory stabilization
2.1. ABSTRACT.
For memory to remain predictive in a changing environment it must be malleable.
Memory updating likely occurs through a process called reconsolidation. However, strong memories are less likely to undergo reconsolidation, possibly due to changes in the expression of plasticity-induction mechanisms such as the NMDA-receptor subunit NR2B. NR2B expression is reduced in some brain regions at the termination of critical plasticity periods, in aged animals, and in Alzheimer’s disease (AD). It has further been observed that amyloid-! (a peptide implicated in AD pathogenesis) can both activate NR2B-containing receptors and cause their synaptic removal. Thus this study aimed to identify whether amyloid-! might also contribute to the stability of memories. We observed in wild-type rats that amyloid-! levels are increased in amygdala following strong relative to weak auditory fear conditioning. Post- training infusion of !- or %-secretase inhibitors (which can prevent amyloid-! generation) had no effect on fear expression during a subsequent memory reactivation, but caused post- reactivation infusion of a protein synthesis inhibitor to disrupt retention of this memory – an effect that could be rescued with synthetic amyloid-! peptide. However, %-secretase inhibitor did not prevent the training-induced downregulation of NR2B. Finally, !- and %-secretase inhibitors were also found to influence the destabilization of weaker fear memories following retrieval. Together, this work suggests that an adaptive function of amyloid-! is to regulate the balance between memory stability and malleability.
2.2. INTRODUCTION.
Memory allows organisms to make predictions about their environment by preserving a record of past experience. Yet in order to functionally predict our dynamic world, memory
124 Chapter 2 – A and memory stabilization need not only persist, but also remain adaptable to changing environmental conditions (Dudai
2009). It is believed that this latter ability is accomplished when a stored memory is reactivated by a related experience. At this time, memory can enter a transient destabilized state, allowing it to be altered to incorporate new information. Once this has occurred, the updated memory is then restabilized via a protein synthesis-dependent process known as reconsolidation, which allows it to persist over time (Nader et al. 2000; Ben Mamou et al.
2006; Lee 2009). Reconsolidation theory posits that as a given task becomes more strongly encoded over repeated learning trials, each additional trial should provide less novel information and should thus be less likely to engage this memory updating process (Sevenster et al. 2013; Finnie and Nader 2012; Lee 2010). This would serve an adaptive function, as it could allow memories for consistently encountered events to be preferentially maintained.
However it remains unclear how the brain controls which memories will undergo reconsolidation following their retrieval (Ben Mamou et al. 2006; Lee et al. 2008).
One mechanism could function through the regulation of N-methyl-D-aspartate receptors (NMDARs), which play a critical role in many forms of long-lasting synaptic plasticity and memory (Bliss and Collingridge 1993; Malenka and Bear 2004). These heteromeric receptors consist of two obligatory NR1 and two NR2 (NR2A-D) subunits, and the subunit composition can influence their kinetic properties (Cull-Candy and Leszkiewicz
2004; Cull-Candy et al. 2001). For instance, relative to NR2A, the NR2B subunit may confer increased glutamate affinity and slower decay/deactivation (Vicini et al. 1998), which may enhance temporal summation of synaptic inputs (Sobczyk et al. 2005; Philpot et al. 2001;
Erreger et al. 2005) and facilitate the induction of synaptic long-term potentiation (LTP)
125 Chapter 2 – A and memory stabilization and/or long-term depression (LTD; Massey et al. 2004; Guo et al. 2012; Paoletti et al. 2013;
Dalton et al. 2012; Yashiro and Philpot 2008).
It has been suggested that the balance of memory stability versus malleability may be regulated by the relative expression of these NR2A and NR2B subunits (see Quinlan et al.
2004). During developmental periods there is an increasing expression of NR2A relative to
NR2B in much of the mammalian forebrain (Monyer et al. 1994; Sheng et al. 1994). In primary visual cortex this coincides with the termination of a period of heightened plasticity that allows for the rapid modification of ocular dominance columns (Erisir and Harris 2003;
Quinlan et al. 1999a). This is at least partly experience-dependent, as environmental factors such as binocular deprivation can delay the transition from NR2B- to NR2A-containing receptors (Quinlan et al. 1999a; Nase et al. 1999). Accordingly, several studies have demonstrated that NR2B activity is important for cortical plasticity during these critical periods (Philpot et al. 2001; Cao et al. 2007b). After critical plasticity periods have closed, the increased NR2A:NR2B ratio may restrict both the types of behavioural experience capable of inducing plasticity and the forms of plasticity that can be engaged (Sawtell et al. 2003; Sato and Stryker 2008).
There is also evidence that analogous mechanisms might control general memory processing as well, outside of critical plasticity periods. Quinlan and colleagues (Quinlan et al.
2004; Lebel et al. 2006) demonstrated that the gradual acquisition of a behavioural strategy or rule about an olfactory discrimination task resulted in reduced expression of NR2B relative to
NR2A in piriform cortex. Similarly, rats trained with a strong auditory fear conditioning paradigm (consisting of 20 pairings of tone and footshock) exhibit reduced contribution of
NR2B-containing receptors to NMDAR-mediated currents at thalamic inputs to lateral
126 Chapter 2 – A and memory stabilization amygdala (Zinebi et al. 2003). As NR2B activation in basolateral amygdala (BLA) is also known to be necessary for the induction of auditory fear memory reconsolidation (Ben
Mamou et al. 2006; Milton et al. 2013), one possibility is that the reduced expression of NR2B after strong training inhibits destabilization. Indeed, Wang and colleagues (2009a) demonstrated in rats that strong auditory fear conditioning can evoke the formation of a memory that will not readily undergo reconsolidation following unreinforced re-exposure to the feared tone, while also causing a concomitant downregulation of NR2B subunits in BLA.
One potential implication of NR2B downregulation as a mechanism of memory stability is that its developmental decline may continue (albeit in diminished fashion) as memories accumulate throughout the lifespan. Indeed, NR2B-selective antagonists are less impairing of fear conditioning memories in older mice (Mathur et al. 2009). Consistently, loss of NMDARs (predominantly the NR2B subunit) observed in the cortex (Magnusson et al.
2002; Ontl et al. 2004) and the hippocampus (Clayton and Browning 2001) of aged mice is associated with learning impairments (Magnusson et al. 2007) that can be rescued by overexpression of NR2B in the forebrain (Cao et al. 2007a). Finally, and of importance to the present study, Alzheimer’s disease (AD) has been associated with reduced NR2B expression in both human patients (Sze et al. 2001) and transgenic animal models (Dewachter et al.
2009). This tentatively hints that age-related cognitive pathology could reflect a developmental progression that begins in early life.
One of the leading models of AD pathogenesis is the amyloid hypothesis (Hardy and
Higgins 1992; Hardy and Selkoe 2002), which proposes that accumulation of amyloid-! (A!) peptides in the brain can induce synaptic dysfunction and neuronal degeneration (Selkoe
2002). These peptides are typically produced in 38-43 amino acid lengths via the sequential
127 Chapter 2 – A and memory stabilization cleavage of transmembrane !-amyloid precursor protein (APP) by enzymes !- and %-secretase
(see Thinakaran and Koo 2008). A! peptides, particularly the larger 42-43 amino acid subtypes, can aggregate into soluble oligomeric and insoluble fibrillar forms, and it is the former which is thought to principally contribute to synaptic dysfunction (i.e. Walsh et al.
2002; Lesne et al. 2006; Shankar et al. 2008; 2007). Local concentration of A! is thought to be the critical determinant of oligomerization (Meyer-Luehmann et al. 2003). Neuronal activity
(Cirrito et al. 2005; 2008; Kamenetz et al. 2003; Wei et al. 2010), which leads to cell membrane endocytosis after vesicular release (Cirrito et al. 2008; Bero et al. 2011), causes A! secretion and thus dictates extracellular A! levels which may promote aggregation. At a behavioural level, acute stress (Kang et al. 2007) and contextual fear conditioning (Puzzo et al. 2011) have each been found to transiently increase A!42 levels in hippocampal tissue.
NMDARs are one primary target of A! (Rönicke et al. 2011), and its actions have been expressed in multiple ways. For example, the surface expression of NR2B-containing
NMDARs can be reduced by A! application or overexpression, likely causing receptor endocytosis (Snyder et al. 2005; Lacor et al. 2007; Kurup et al. 2010; Um et al. 2012; Kessels et al. 2013; Hsu et al. 2014). Moreover, both NR2A and NR2B may be directly activated by
A!42 (Texidó et al. 2011). A! may also indirectly activate NMDARs by facilitating pre- synaptic release of glutamate (Kabogo et al. 2010), suppressing glutamate reuptake (Li et al.
2009), upregulating polyamines (Gomes et al. 2014; Yatin et al. 2001; Mony et al. 2011), or activating ephrin type B receptor 2 (EphB2; Cissé et al. 2011) or cellular prion protein
(Laurén et al. 2009). Several groups have also recently reported that A! can specifically activate a novel form of metabotropic signaling via NR2B-containing NMDARS (Kessels et al. 2013; Tamburri et al. 2013).
128 Chapter 2 – A and memory stabilization
Thus, the extracellular secretion of A! following behavioural experience might facilitate the induction of NMDAR-dependent plasticity, as can be necessary both for the acquisition of new memories, as well as to update existing memories via reconsolidation
(Morris 2013; Milton et al. 2013). Indeed, pre-training (but not post-training) application of very low concentrations of A!42 oligomers into the hippocampus can facilitate the formation of inhibitory avoidance, object recognition, contextual fear, and spatial water maze memories in rats (Garcia-Osta and Alberini 2009; Morley et al. 2010; Guo et al. 2012). In each of these studies it was also found that suppressing endogenous A! could impair these forms of memory. Furthermore, applying low concentrations of oligomerized A!42 enhanced LTP at hippocampal synapses, while higher concentrations inhibited LTP in favour of LTD (Puzzo et al. 2008; 2011; 2012; see Palop and Mucke 2010).
Together these converging lines of evidence led us to hypothesize that endogenous A! might serve dual functions in learning and memory. First, in addition to facilitating the induction of plasticity, endogenously secreted A! might contribute to the stabilization of memory during the consolidation process by reducing the synaptic expression of NR2B- containing NMDARs. This could, theoretically, alter the likelihood that memory would undergo reconsolidation following reactivation (Wang et al. 2009a), thus making the memory more resistant to updating. Second, we predict that reactivation of a consolidated memory should also evoke A! secretion, which could encourage its destabilization by facilitating synaptic plasticity induction through the activation of NR2B or other plasticity mechanisms.
In this study we initially tested whether strong relative to weak auditory fear conditioning evokes an increase in A!42 levels in BLA. We then inhibited A! generation in
BLA immediately after strong fear conditioning to determine if this can permit memory to
129 Chapter 2 – A and memory stabilization subsequently undergo reconsolidation when it otherwise would not. Finally, we assessed whether inhibiting A! generation at the time of reactivation can prevent the destabilization of a weak memory. Together our results indicate that in young adult rats, endogenous A! generated after learning can contribute to processes that regulate memory malleability.
2.3. RESULTS.
2.3.1. A! (x-42) levels in BLA are elevated 90 min after strong relative to weak training.
We initially aimed to determine whether strong auditory fear conditioning (10 pairings of tone and shock; 10P) – a procedure known to form a memory resistant to reconsolidation
(Wang et al. 2009a) - causes A! levels in BLA to increase 90 min after training. The 90- minute time-point was chosen based upon prior studies indicating that A! levels peak approximately 1-2 hours after neuronal activation (Cirrito et al. 2005) or behavioural experience (Kang et al. 2007), and also observations that gamma-secretase inhibition of A! is maximal approximately 1-3 hours after application (Lanz et al. 2006; Elvang et al. 2009). We compared A! levels to those measured in rats that received weak training (1 pairing of tone and shock; 1P). As in our previous study (see Wang et al. 2009a), cannulated rats were habituated to each context (training and testing) once per day for two days, and 24h later they received either 1P or 10P training, followed by infusion of %-secretase inhibitor, LY-450139
(LY) or its vehicle (VEH). The intent was not to minimize A! levels, but to prevent the increased generation of A! observed after learning. Hence, we opted to suppress an endogenous mechanisms of A! production, %-secretase, via local BLA infusion of this specific inhibitor in wild-type rats. Ninety minutes after the completion of conditioning
130 Chapter 2 – A and memory stabilization
(approximately 85 min after drug infusion), the rats were sacrificed and their BLA tissue was processed using an assay of A! (x-42; all peptides ending at the 42nd amino acid). As predicted, A! (x-42) levels were elevated in the BLA of animals that had received VEH
infusion after 10P relative to 1P training, t13=3.297, p=.012. However, rats receiving LY after
10P exhibited significantly lower A! (x-42) expression than those receiving VEH, t12=2.935, p=.024. It is conceivable that the elevated A! levels in the 10P group could contribute to the stabilization process that makes this strong fear memory reconsolidation-resistant.
2.3.2. Inhibiting A! cleavage after fear conditioning disrupts memory stabilization, but not consolidation.
To test whether this increase in A! levels contributes to the stabilization of strong fear memory we next aimed to inhibit the generation of this peptide immediately after learning by infusing LY, or an inhibitor of !-secretase. These secretases each have additional molecular targets (see Epis et al. 2012), but due to convergent effects on A! levels in the brain
(Nishitomi et al. 2006; Lanz et al. 2006) in conjunction they might allow specific and non- specific effects to be dissociated.
In the first experiment, rats were treated with LY or VEH immediately after 10
pairings of auditory tone and shock (10P) in Context1. Approximately 48h later animals were given a single tone presentation in Context2 as memory reactivation (React.), and then BLA infusions of ANI or VEH were administered. This strong fear conditioning procedure is known to not induce memory reconsolidation, as revealed by the absence of impairment by post-reactivation ANI when assessed during a post-reactivation long-term memory (PR-
LTM) test 24h later (Quinlan et al. 1999a; Wang et al. 2009; Nase et al. 1999). However, here we observed that in rats infused with LY after training the 10P memory could undergo
131 Chapter 2 – A and memory stabilization reconsolidation, as infusion of ANI after re-exposure to the tone impaired memory retention
(Fig. 2a). A three-way, one-repeated ANOVA comparing LY/VEH, ANI/VEH, and test
(React. versus PR-LTM, repeated-measure) revealed a three-way interaction, F1,30=12.618, p=.001. Further analysis indicated that conditioned freezing during React. was comparable for
all groups (all F1,30<.34, p>.563). However, there was a significant LY/VEH x ANI/VEH interaction for responding during PR-LTM, F1,30=10.05, p=.004. Tukey’s HSD post-hoc tests demonstrated that LY-ANI animals froze significantly less than all other groups during PR-
LTM (all p < .05), but freezing was comparable across these groups (all p > .7). These results indicate that endogenous A! might normally cause memory to stabilize into a reconsolidation- resistant state, but inhibiting A! with LY-450139 after training can prevent this process without altering the strength of memory exhibited during reactivation.
As %-secretase has many molecular targets (Haapasalo and Kovacs 2011), a control experiment was conducted to confirm that the LY had specifically exerted its effects on memory by reducing A! levels. In this experiment we infused oligomerized A!42 peptides
(see Methods) into BLA immediately after LY-450139 to determine if they could rescue the deficits induced by post-reactivation ANI (Fig. 1b). A schematic depiction of cannula tip placements is presented in Figure 5a, along with a representative micrograph of placements within a single rat (Fig. 5b). After 10P training all animals received infusion of LY-450139 as above, which was followed by infusion of oligomerized A!42 (A!o42) or Scrambled A!42
(Scr). After reactivation all rats then received ANI infusion. Although there was no difference in freezing during React., during PR-LTM rats given Scr were impaired relative to those given ABo42 (Fig. 1b). Violation of the homogeneity of variance assumption of ANOVA led us to conduct planned non-parametric Mann-Whitney U tests, revealing no difference in
132 Chapter 2 – A and memory stabilization distribution between groups during React (p=.142) but a significant difference during PR-
LTM test (p=.042). Therefore, as predicted, the infusion of A!o42 rescued the PR-LTM deficit observed for LY/ANI animals in Fig. 2a, indicating that LY-450139 had likely disrupted memory stabilization by reducing A! levels.
As a second control, naïve rats received the same procedure as in Fig. 1a, but instead of LY/VEH they were given infusions of !-secretase inhibitor, Inhibitor IV (BACE1inhib; see
Nishitomi et al. 2006) or VEH. This experiment replicated the pattern of results reported in
Fig. 1a. A three-way, one-repeated ANOVA comparing BACE1inhib/VEH, ANI/VEH, and
test (React. versus PR-LTM, repeated measure) revealed a three-way interaction, F1,28=5.933, p=.021 (Fig. 3c). As above, further analysis revealed that conditioned freezing during React
was comparable across groups (all F1,28<.74, p>.398), but there was a significant two-way
BACE1inhib/VEH x ANI/VEH interaction for responding during PR-LTM, F1,28=4.954, p=.034. Tukey’s HSD post-hoc tests demonstrated that BACE1inhib-ANI animals froze significantly less than all other groups during PR-LTM (all p < .05), which all froze at comparable levels (all p > .9).
Together these experiments indicate that reducing A! production via infusion of !- or
%-secretase inhibitors into BLA immediately after strong auditory fear conditioning can prevent a normal stabilization process, causing this memory to undergo reconsolidation when it is subsequently reactivated.
2.3.3. Reduced synaptic NR2B after strong fear conditioning is unaffected by LY-450139.
We predicted that A! could contribute to memory stabilization through its observed effects on synaptic NR2B expression (i.e. Kessels et al. 2013; Um et al. 2012). Therefore, we
133 Chapter 2 – A and memory stabilization next assessed the effects that conditioning strength and post-training LY450139 treatment have on the post-synaptic expression of NMDAR subunits NR1 and NR2B. The procedure was similar to Fig. 2a, except rats were trained with either 1 or 10 tone-shock pairings, and in place of a reactivation session they were sacrificed and BLA tissue was prepared for western blotting (Fig. 3). In the aforementioned study by Wang and colleagues (Wang et al. 2009a), reduced NR2B expression in whole-cell extracts was observed in 10P relative to 1P rats, and we predicted the same pattern would be detected here in the crude post-synaptic density
(PSD) fraction. We assayed NR2B and NR1, each normalized to !-tubulin, which served as a loading control. A two-way ANOVA on normalized NR2B expression, with training strength and drug treatment as between-groups factors, revealed a main effect of training strength,
F1,23=4.812, p=.039, but no main effect of drug, F1,23=.145, p=.707, and no two-way interaction,
F1,23=.856, p=.364. A two-way ANOVA on NR1 expression revealed no main effect of training strength, F1,23=.13, p=722, drug, F1,23=1.729, p=.202, and no interaction effect, F1,23=.866, p=.362. Thus, it appears that strong auditory fear conditioning downregulates NR2B at the
PSD, but post-training infusion of LY450139 does not prevent this downregulation. Thus the effects of LY-450139 displayed in Fig. 2a are likely due to a mechanism other than altered
NR2B expression.
2.3.4. Post-reactivation infusion of LY450139 enhances memory retention, but infusion of
BACE1-inhibitor prevents destabilization.
Our final hypothesis was that amyloid generated following memory retrieval could be required for the normal destabilization of memory. If so, inhibiting %- or !-secretase activity at reactivation should prevent amnesia normally induced by post-reactivation anisomycin. A preliminary experiment revealed that LY450139 infused into BLA before testing severely
134 Chapter 2 – A and memory stabilization impaired retrieval/expression of weak auditory fear conditioning (LY=15.81±5.66%,
VEH=46.15±7.96%; t29=2.852, p=.008), so in the proceeding experiments we aimed to avoid this potential confound by administering post-reactivation infusions.
All rats received 1 pairing of tone and shock (1P) and 48h later were given the reactivation procedure (a protocol known to induce memory destabilization and reconsolidation; Wang et al. 2009a). Immediately afterwards rats were randomly assigned to receive BLA infusions of either LY-450139 or VEH and then either ANI or VEH. The next day memory retention was assessed during a PR-LTM test. A three-way, one-repeated
ANOVA revealed no significant Test x LY/VEH x ANI/VEH interaction, F1,23=.165, p=.69, but significant two-way interactions of Test x LY/VEH, F1,23=10.98, p=.003, and Test x
ANI/VEH, F1,23=4.48, p=.045, as displayed in Fig. 4a. Tests of simple main effects revealed a significant difference in PR-LTM freezing scores between LY and VEH groups, F1,25=14.27, p=.001, and between ANI and VEH groups, F1,25=13.34, p=.001. There were no significant simple main effects for freezing during React. (both F1,25<1.45, p>.24).
There are two possible interpretations of these results. One possibility is that LY may have a general enhancing effect on memory when applied after reactivation, but does not reduce the sensitivity of memory to ANI. Indeed, freezing during PR-LTM exhibited by both
LY- and VEH-infused animals was impaired to a similar degree by ANI (approximately
30%). Alternatively, LY may block the induction of memory weakening while leaving the induction of memory strengthening intact. This possibility is supported by the fact that the
LY-ANI group exhibited comparable freezing to the VEH-VEH group, as will be discussed below.
135 Chapter 2 – A and memory stabilization
As %-secretase has many non-specific targets we repeated the same experiment using !- secretase inhibitor (Fig. 4b). As above, rats were given the 1P procedure then reactivation
24h later, but received post-reactivation BLA infusions of either BACE1inhib or VEH prior to infusion of either ANI or VEH. A three-way, one-repeated ANOVA revealed a significant
Test x BACE1inhib/VEH x ANI/VEH interaction, F1,32=6.79, p=.014. This was principally explained by a significant BACE1inhib/VEH x ANI/VEH interaction for PR-LTM freezing
scores, F1,32=5.2, p=.029, but there was no such interaction for freezing during React.,
F1,32=.033, p=.857. The results of Tukey’s HSD post-hoc tests supported the prediction that
BACE1inhib can prevent the impairment normally observed following post-reactivation ANI infusion, as during PR-LTM the VEH-ANI animals froze significantly less than VEH-VEH animals (p=.017), but BACE1inhib-ANI and BACE1inhib-VEH groups did not statistically differ (p=.999). Although VEH-ANI animals appeared to freeze less than BACE1inhib-ANI animals, this was not a statistically significant difference (p=.098).
Thus the effects of BACE1inhib and LY infusion into BLA after reactivation of a weak auditory fear memory are distinct. BACE1inhib appears to reduce the impairing effect of post-reactivation ANI, but unlike LY causes no enhancement of memory on its own.
2.4. DISCUSSION.
A! is widely regarded as a mechanism of cognitive pathology (Hardy and Selkoe
2002), but there is accumulating evidence that at normal physiological levels it also mediates adaptive physiological functions (Puzzo and Arancio 2013; Abramov et al. 2009; Atwood et al.
2003; Pearson and Peers 2006; Castellani et al. 2009). Here we have observed two such
136 Chapter 2 – A and memory stabilization functions in wild-type rats, with A! contributing to stabilization of memories during consolidation, and also destabilization of memories following reactivation. Thus A! may control which memories will be resistant to updating via reconsolidation.
We first demonstrated that 90 minutes after training, A! (x-42) levels are elevated in the BLA of rats that received strong relative to weak auditory fear conditioning (Fig. 1). This is in agreement with work showing that acute stress and contextual fear conditioning can each increase A! concentration in the hippocampus (Kang et al. 2007; Puzzo et al. 2011), as can synaptic vesicle release during neuronal activity (Cirrito et al. 2008; 2005). Studies have previously demonstrated that suppressing endogenous A! levels impairs memory formation, but only when given before training (Garcia-Osta and Alberini 2009). Nevertheless, here we assessed whether post-training infusion of !- and %-secretase inhibitors that are capable of reducing A! production could have subtle effects on long-term memory. In agreement with the prior findings, blocking A! production after training had no apparent effects on memory when tested during a memory reactivation session the next day (Fig. 2). However, post- reactivation infusion of anisomycin, which can disrupt reconsolidation of memories that have been destabilized by retrieval (Nader et al. 2000), induced a retention impairment in rats that had previously been given a !- or %-secretase inhibitor. This effect clearly suggests that A! contributes to a process that is not necessary for memory retention over 48h, but instead regulates the likelihood that memory can subsequently undergo reconsolidation. Superficially this behavioural effect resembles a metaplasticity-like phenomenon, wherein priming experiences can alter the threshold, direction, and/or mechanisms of synaptic plasticity without influencing synaptic strength (Abraham 2008).
137 Chapter 2 – A and memory stabilization
We also hypothesized that A! could facilitate the destabilization of consolidated memories due to its ability to increase neurotransmitter release (Palop and Mucke 2010;
Kabogo et al. 2010), activate NMDARs (Texidó et al. 2011), and open L-type voltage-gated calcium channels (L-VGCCs; Ueda et al. 1997); all of which are known to be necessary for induction of reconsolidation (Lee and Flavell 2014; Ben Mamou et al. 2006; Suzuki et al.
2008). However, when given after reactivation of a weak auditory fear memory the effects of the secretases were inconsistent. !-secretase inhibitor prevented amnesia induced by post- reactivation anisomycin, suggesting that it had blocked memory destabilization (Fig. 4a).
However, %-secretase LY-450139 did not prevent amnesia induced by post-reactivation anisomycin, and on its own enhanced memory expression during PR-LTM test (Fig. 4b). As
!-secretase has fewer plasticity-associated molecular targets than %-secretase (Epis et al.
2012), the results obtained with BACE1 inhibitor might better capture the specific mnemonic effects of diminished A! following retrieval.
Together these results provide evidence that A! can contribute to memory stabilization and destabilization, but they do not elucidate the mechanisms through which it acts. In the following discussion we will speculate on several likely candidates.
2.4.1. Mechanisms of memory stabilization.
We initially hypothesized that A! generated by learning might alter the plasticity mechanisms available to induce memory destabilization following reactivation. For example,
A! can cause the endocytosis of NR2B-containing NMDARs from the neuronal surface
(Snyder et al. 2005) and reduces NR2B-mediated NMDAR currents (Kessels et al. 2013). As
NR2B activity is necessary to destabilize consolidated auditory fear memories (Ben Mamou et al. 2006; Milton et al. 2013), it has been proposed that NR2B downregulation following
138 Chapter 2 – A and memory stabilization strong conditioning could prevent this memory from undergoing reconsolidation (Wang et al.
2009a). Here we observed a similar downregulation of NR2B in the PSD fraction of BLA tissue extracted 48h after training from animals that had received strong (10P) relative to weak (1P) fear conditioning (Fig. 3a). However, NR2B expression was comparable in rats that had received infusions of either LY-450139 or vehicle after strong training, suggesting that the mechanism by which LY-450139 inhibits reconsolidation is not via the prevention of
NR2B downregulation. This is consistent with reports that application of low concentrations of A!o42 can enhance both memory and post-tetanic potentiation (indicative of increased pre- synaptic glutamate release during action potentials), without changing post-synaptic
NMDAR currents (Puzzo et al. 2008).
Although our biochemical data suggests that post-training LY-450139 does not facilitate reconsolidation by preventing NR2B downregulation, it also does not entirely rule out NR2B endocytosis as a mechanism of memory stabilization following strong learning. We have collected preliminary evidence indicating that blocking A! generation with LY-450139 after strong training causes increased expression of a lower molecular weight (115kDa)
NR2B isoform in the PSD fraction (data not shown) that coincides with the downregulation of full-length NR2B observed here (Fig. 3a). NR2B is known to be cleaved at this length by calpain, and NR2B(115kDa)-containing NMDARs retain ionotropic functions but lack carboxy-terminus binding sites that may be important for interactions with plasticity-related proteins like calcium/calmodulin-dependent protein kinase II (Simpkins et al. 2003; El
Gaamouch et al. 2013). Hypothetically this could prevent A! and other signaling molecules from activating the metabotropic function of NR2B-containing NMDARs required to induce some forms of LTD (Kessels et al. 2013; Tamburri et al. 2013; Nabavi et al. 2013). However,
139 Chapter 2 – A and memory stabilization much more work will be needed to clarify whether NR2B cleavage can prevent the memory stabilization processes.
Alternatively, other targets through which A! might regulate the induction of synaptic plasticity include "7-nAChR (Snyder et al. 2005; Puzzo et al. 2008; Palop and Mucke 2010),
EphB2 (Cissé et al. 2011), proteasomal protein degradation (Hong et al. 2014), and/or PirB
(Kim et al. 2013).
A!42 peptide binds to "7-nAChR with astonishingly high affinity (Pettit et al. 2001;
Liu et al. 2001; Wang et al. 2000). At pre-synaptic sites low concentrations of A! can increase calcium influx via "7-nAChRs which facilitates neurotransmitter release (Dougherty et al.
2003; Fodero et al. 2004), while higher concentrations can block these channels (Grassi et al.
2003; Dineley et al. 2001; 2002a). A! may also potently influence "7-nAChR expression
(Dineley et al. 2002b; Hsiao et al. 1996; Banerjee et al. 2000; Buckingham et al. 2009), which could alter availability of these receptors during subsequent plasticity induction. Indeed, young adult mice that overexpress A! exhibit deficient contextual fear memory formation that can be overcome by increasing the strength of training (EphB2; Dineley et al. 2002b).
Interestingly, an "7-nAChR agonist has been observed to influence memory reconsolidation in a training strength-dependent manner, causing impairment following reactivation of memory for fear conditioning that used a high-intensity footshock, but enhancement when a weak-intensity footshock was used instead (Boccia et al. 2010). Thus, perhaps very strong training (as used here) generates A! that can induce a change in functional "7-nAChRs that culminates in a reduced likelihood of memory destabilization. However, there is not yet evidence that initiation of reconsolidation requires activation of nicotinic receptors in BLA.
140 Chapter 2 – A and memory stabilization
PirB, a synapse-associated transmembrane receptor, has recently been proposed to play a role in setting bidirectional synaptic plasticity thresholds for ocular dominance plasticity in visual cortex (Syken et al. 2006; Djurisic et al. 2013), but also binds AB42 with high affinity (Kim et al. 2013). Specifically, PirB expression in mice appears to limit synaptic strengthening and biases synapses towards synaptic weakening (Djurisic et al. 2013). PirB expression often increases after loss of neuronal inputs, as caused by optic nerve damage or focal ischemia (Wang et al. 2010; Cai et al. 2012; see Gou et al. 2014), which may promote the elimination of synapses rendered obsolete by severed inputs. By extension, increased neuronal activity during learning might be hypothesized to reduce PirB expression, thus suppressing the weakening of memory. Given that LTD-inducing effects of A! overexpression are not observed in mice lacking functional PirB (Kim et al. 2013), endogenous A! signaling through a PirB homolog in rats could be a strong candidate for the effects we have observed in the present study. After memory reactivation, A! release could activate available PirB receptors to facilitate induction of synaptic plasticity required for memory destabilization. This may include mechanisms primarily implicated in synaptic weakening, such as alpha-amino-3- hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) endocytosis (Hong et al.
2013; Rao-Ruiz et al. 2011), the protein phosphatase calcineurin (Fukushima et al. 2014), or protein degradation via the proteasome (Lee et al. 2008; Lee 2010; Fukushima et al. 2014).
Speculatively, stronger training protocols could cause PirB downregulation, thus limiting the ability of A! to induce memory destabilization via these mechanisms (although it should be noted that Hong and colleagues applied their treatment pre- and not post-reactivation).
However, considerable work is required to confirm these tentative predictions.
141 Chapter 2 – A and memory stabilization
Relatedly, increased A! expression reliably leads to inhibition of the protein degradation system, evidenced by the accumulation of proteins tagged for ubiquitination, which is indicative of impaired proteasomal activity (Upadhya and Hegde 2007; Hong et al.
2014). As pharmacologically suppressing proteasomal activity prevents memory destabilization after reactivation (see Kaang et al. 2011), A! secreted by strong training might also suppress this protein degradation pathway, reducing the likelihood that memory will subsequently undergo reconsolidation after retrieval.
2.4.2. Mechanisms of memory destabilization.
Here we also observed that post-reactivation infusion of !-secretase (BACE1) inhibitor prevents memory destabilization. The most straightforward interpretation is that this drug reduces A! generation, leading to less glutamate release and/or NMDAR activation
(Palop and Mucke 2010; Texidó et al. 2011), as is necessary for induction of reconsolidation.
However, NMDAR antagonists only block fear memory destabilization when infused into
BLA before, but not after reactivation (Ben Mamou et al. 2006), indicating that A! suppression by BACE1 inhibitor cannot have prevented reconsolidation by reducing
NMDAR activity. Instead, A! could have influenced downstream mechanisms known to prevent destabilization when they are blocked post-training, such as AMPA-receptor endocytosis (Yu et al. 2013; Rao-Ruiz et al. 2011), L-VGCCs or endocannabinoid receptors
(Suzuki et al. 2008).
It is well-established that increased levels of A! can induce synaptic depression via
AMPAR endocytosis (Hsieh et al. 2006; Zhang et al. 2011b; Zhao et al. 2010). Blocking
AMPAR endocytosis can prevent the synaptic removal of AMPARs after behavioural training and also spatial memory deficits caused by A! overexpression (Yiu 2012). Interestingly,
142 Chapter 2 – A and memory stabilization
AMPAR endocytosis may be induced via activation of striatal-enriched protein tyrosine phosphatase (STEP) following binding of A! to post-/peri-synaptic "7-nAChRs (Snyder et al.
2005; Palop and Mucke 2010). It can also be prevented by inhibiting calcineurin activity
(Zhao et al. 2010), which itself is necessary for memory destabilization (Fukushima et al.
2014). Yet astonishingly it is not yet known if suppressing endogenous A! can prevent the endocytosis of AMPARs, as is necessary for the induction of LTD (Ahmadian et al. 2004;
Brebner et al. 2005) and auditory fear memory reconsolidation (Hong et al. 2013).
Relatively brief application of A! peptides may both potentiate calcium influx via L-
VGCCs (Ueda et al. 1997) and chronically increase L-VGCC expression (Kim and Rhim
2011). This suggests that A! release following learning could potentially promote a switch in plasticity mechanisms, causing memory acquisition and destabilization to rely on different mediators of calcium influx (see Finnie and Nader 2012 for discussion). This could also relate to the gradual transition from NMDAR- to VGCC-dependent plasticity frequently observed with age, which is associated with preserved learning abilities in aged animals (Shankar et al.
1998; Boric et al. 2008; also see Lee et al. 2005). At the time of reactivation, A! could perhaps enhance L-VGCC-mediated calcium influx to facilitate memory destabilization, although there is not yet evidence directly indicating that activation of these channels is necessary to induce memory reconsolidation in amygdala.
In summary, our results strongly suggest that endogenously generated A! contributes to both the stabilization of strong memories into a reconsolidation-resistant state, and the destabilization of weaker memories at the time of reactivation. How it exerts these effects remains unclear, and so our future studies will focus on uncovering the molecular pathways regulating A!-mediated stabilization and destabilization.
143 Chapter 2 – A and memory stabilization
2.4.3. Memory enhancement by post-reactivation LY-450139.
The enhancement of memory observed when LY-450139 was infused after reactivation was unexpected and the mechanism is unclear (Fig. 4a). One possibility is that memory strengthening and weakening can be mediated by independent plasticity processes (see
Fukushima et al. 2014 for evidence of dissociable mechanisms of memory restabilization and strengthening). For instance, preventing the endocytosis of AMPA-receptors during unreinforced reactivation causes enhancement of fear conditioning memory, but the same treatment also prevents the persistent weakening of memory observed when extinction training is given shortly after reactivation (Rao-Ruiz et al. 2011). Hypothetically, the two major targets of %-secretase, Notch1 and A! (De Strooper et al. 1999; Mitani et al. 2012), could opposingly regulate bidirectional plasticity in this manner, with reduced A! preventing destabilization required for memory weakening and reduced Notch evoking memory strengthening (which is selectively disrupted by anisomycin). Indeed, Notch1 activity is suppressed 12h after passive avoidance learning which facilitates memory consolidation
(Conboy et al. 2007). It also limits synaptic strengthening and facilitates weakening of responsiveness of visual cortex neurons to deprived-eye inputs following monocular deprivation (Dahlhaus et al. 2008). Furthermore, Notch1-lacking mice or null mutation heterozygotes are particularly impaired at a spatial memory reversal task (Alberi et al. 2011;
Costa et al. 2003) thought to be mediated by both synaptic depression (Dong et al. 2013) and reconsolidation (Rossato et al. 2006b) in the hippocampus. If, following LY-450139 infusions, memory strengthening is engaged via suppression of Notch1 and memory weakening is inhibited via suppression of A!, this could be why anisomycin appears to selectively disrupt only the enhancement of memory (Fig. 4a), although support for this hypothesis requires additional study. Even if the enhancing effect of LY-450139 is attributable to Notch it is still
144 Chapter 2 – A and memory stabilization relevant to the discussion of A!, given that reduced amyloidogenic cleavage of APP by %- secretase can itself augment Notch activity (Roncarati et al. 2002).
2.4.4. Clinical implications.
These results provide one explanation for why most pharmacological treatments aimed at reducing A! fail to ameliorate (and can even worsen) cognitive deficits in AD patients (see
Savonenko et al. 2012), as was reported following the termination of LY-450139 phase-III clinical trials (Doody et al. 2013; Cummings 2010). We posit that this failure stems not necessarily from a misunderstanding of the effects of A! in the brain, but rather an oversimplification of how neuronal processes culminate in memory and learned behaviour.
The present study emphasizes that indiscriminately blocking mechanisms thought to impair
LTP (such as excessive A! generation) will not necessarily restore the functioning of complex memory systems. Given that stronger memories are generally more resistant to destabilization
(see Alberini 2011), rescuing LTP may improve learning and memory persistence for some behavioural tasks, but could also suppress the future updating of these memories in response to environmental changes. Furthermore, the current study reiterates the importance of using more physiological-relevant experimental manipulations of A! (rather than increasing its expression many-fold; see Best et al. 2005), which can cause extensive degeneration of synapses and neural circuits but might not accurately capture the A! dynamics exhibited during early pathogenesis in humans (Puzzo and Arancio 2013; Palop and Mucke 2010).
2.4.5. Implications to AD pathogenesis.
A speculative extension of these results is that A!-induced cognitive pathology might result from the way in which memories are processed over time. Reconsolidation theory would suggest that, across experience, the brain forms and tests predictions about the world,
145 Chapter 2 – A and memory stabilization progressively refining and strengthening those predictions that most accurately represent ongoing experience (Dudai 2009). Thus - assuming a somewhat consistent environment - with age the brain should increasingly possess a more comprehensive knowledge structure into which it may fit new events via network ‘attractor states’ imbued by pattern separation and pattern completion abilities (Wills et al. 2005; Wilson et al. 2006). This diminishing perception of novelty of events with age should be inherently associated with a decline in what may be thought of as de novo memory encoding (see Rescorla and Wagner 1972) in favour of updating existing memories or even complete assimilation of new information (Osan et al. 2011; Finnie and Nader 2012; Sevenster et al. 2013). Given the contribution of A! to memory stabilization unveiled in the current study, it is possible that its dysregulation is a natural consequence of memory persistence and encoding within a finite storage medium.
Indeed, synchronized resting-state activity throughout human brain regions known as the “default-mode network” can be associated with episodic memory retrieval (but not encoding), simulating events, or projecting oneself into hypothetical scenarios in order to predict future outcomes (Immordino-Yang et al. 2012; Buckner and Carroll 2007; Wagner et al. 2005). As neurotransmission is strongly associated with increased local A! concentrations
(see Bero et al. 2011), persistent neuronal activity associated with processing of existing knowledge may lead to local formation of pathogenic oligomers (Meyer-Luehmann et al.
2003). Accordingly, the default network regions are strongly associated with A! aggregation
(Filippini et al. 2009; Buckner et al. 2009; 2005; Vlassenko et al. 2010; Hampel 2013). Based on this model, more frequent expansion and restructuring of knowledge in response to novelty might be protective against A! accumulation and/or late-onset AD. In partial support of this idea, cognitively-demanding tasks reduce default-mode network activity (Raichle et al. 2001)
146 Chapter 2 – A and memory stabilization and level of formal education is inversely correlated with risk of developing AD (Flicker
2010). Similarly, the exposure of AD-model mice to diverse behavioural experience through environmental enrichment together with regular behavioural testing is associated with reduced A! levels and improved cognitive performance (Costa et al. 2007).
2.4.6. Dissociating the mechanisms of memory strength and stabilization.
To our knowledge this is the first study to demonstrate that non-behavioual manipulations at the time of learning can influence the susceptibility of memory to undergo reconsolidation at a later time-point. Strength of training is a well-documented boundary condition on reconsolidation (Frankland et al. 2006; Morris et al. 2006; Lee 2008), but it can be difficult to confirm that a failure to observe a memory impairment after post-reactivation amnestic treatments is not merely due to a ceiling-effect. However, our results indicate that the mechanisms mediating memory strength can be at least partially dissociated from those that regulate stability/malleability. This is not an unexpected finding, but it provides a useful behavioural model to evaluate how pharmacological treatments can influence mechanisms implicated in metaplasticity. We postulate that any experimental manipulation applied during consolidation that selectively alters the long-term expression of plasticity mechanisms required for memory destabilization has the potential to influence memory stabilization.
2.4.7. Conclusion.
In conclusion, we have demonstrated that endogenous A! may adaptively contribute to the stabilization of strong memories after their acquisition, and that the amyloidogenic secretases can contribute to the destabilization of weaker memories after their retrieval. Thus, the generation and secretion of A! following behavioural events may facilitate both the persistence of memories for these experiences and their updating following retrieval.
147 Chapter 2 – A and memory stabilization
2.5. METHODS.
2.5.1. Animal subjects.
Adult male Sprague-Dawley rats bred at Charles River Laboratories (Quebec,
Canada) were used in this study. They were housed individually in Nalgene cages containing a PVC tube, with food and water provided ad libitum. Each rat was handled for at least 2 days before stereotaxic surgery. The animals were maintained on a 12 hr light-dark cycle (07:00-
19:00 light phase). All experimentation was conducted during the light phase and followed the protocols approved by McGill University Animal Care and Use Committee and was in accordance with the Canadian Council on Animal Care guidelines.
2.5.2. Surgery.
Each rat weighed 300-350g at the time of surgery. Animals were anesthetized with a
1mL/kg IP injection of ketamine (55.55mg/mL) xylazine (3.33 mg/mL) and domitor (0.27 mg/mL) drug cocktail. For analgesia after surgery and during recovery, carprofen (5.0 mg/mL) was administered subcutaneously at 1ml/kg. Each animal was mounted on a stereotaxic apparatus (Kopf Instruments), and 22-gauge stainless steel cannulae (Plastics One,
Roanoke, VA) were bilaterally implanted in the brain targeting the basolateral amygdala
(BLA) based on Paxinos and Watson’s atlas of the rat brain (Paxinos and Watson 2007).
Coordinates for BLA cannulation were: A/P -2.8mm, L +5.1mm, D/V -8.0mm (measured from bregma), 0 degrees inclination. These were stabilized with two layers of dental cement anchored to three jewelry screws drilled into the skull. Each rat was revived with a 0.67mL/kg
148 Chapter 2 – A and memory stabilization
IP injection of antisedan (5 mg/mL). Surgeries were performed 7-10 days prior to the start of behavioural procedures.
2.5.3. Drug preparation and microinfusion.
All drugs were administered via 28- stainless steel injectors extending 1.5mm from the tip of each cannula, attached via polyethylene tubing (Intramedic #427406) to 10uL Hamilton syringes driven at 0.25 µL/min by a microinfusion pump (K.D. Scientific). %-secretase inhibitor LY-450139 (10 ug/ul, Selleck Chemicals) was dissolved in 100% dimethyl sulfoxide
(DMSO) then diluted with 4 volumes sterile physiological saline solution (PBS). 20%
DMSO in PBS served as vehicle (Lanz et al. 2006). '-secretase (BACE-1) inhibitor IV (5 ug/uL, EMB Millipore) was dissolved in 40% DMSO in PBS (Nishitomi et al. 2006).
Although these secretase inhibitors have not previously been locally injected into rat brain via microinfusion, the concentration of each was similar to that used in prior reverse microdialysis and systemic treatment studies and falls within ranges previously observed to reduce A! levels
(see Lanz et al. 2006; Nishitomi et al. 2006). Oligomerization of synthetic human A! peptides
(1-42) was based on a well-established protocol (Stine et al. 2011). Briefly, lyophilized A!42 and scrambled peptide (Anaspec Inc.) were each dissolved in hexafluoro-2-propanol (HFIP) and distributed into 45ug aliquots. HFIP was rapidly evaporated with a stream of nitrogen gas then dried under vacuum in a SpeedVac (Thermo Scientific) before storing the peptide film at -80C. The day before infusion an aliquot of peptide was brought to room temperature and dissolved in 2uL fresh, dry DMSO (Sigma-Alderich) by vortexing for 30s then sonicating for 10 mins. The solution was then diluted with 98uL Ham’s F12 nutrient mixture (Sigma) and incubated at 4C for 24h to encourage oligomerization. Immediately prior to infusion each solution (A!o42 and Scr) was further diluted in 1x PBS, bringing the peptide concentration
149 Chapter 2 – A and memory stabilization to 902pg/uL (200pM). Anisomycin (125ug/uL, Sigma-Aldrich) was dissolved in hydrogen chloride (HCl) then diluted with PBS. The pH was adjusted to 7.2-7.5 with NaOH.
2.5.4. Conditioning chambers.
Two contexts were used in all experiments. Context1 was used during fear conditioning training, and Context2 was used for reactivation and testing.
Chambers in Context1 were comprised of Coulbourn (Whitehall, PA) conditioning boxes (30cm*26cm*33cm). All four sidewalls were made of transparent Plexiglas. Consistent illumination was produced by a single light bulb located at the upper-middle of the right side wall of each chamber. Diluted vanilla scent was applied immediately prior to each training session. A digital camera was installed in front of the box for image recording and storage via freeze-frame software (Coulbourn). The experimental room remained brightly lit at all times.
Auditory tones were generated via FreezeFrame software (Coulbourn Instruments) using internal 16-bit soundcard amplified by an RCA SA-155 integrated amplifier. Footshocks were generated by a Coulbourn Instruments ‘Precision Animal Shocker’ module.
The second context (Context2) consisted of Med-Associates (St. Albans, VT) fear conditioning boxes (29cm*25cm*25cm). The sidewalls of each chamber were made of aluminum panels. A red light provided constant illumination, and a house-light flashed at 1hz.
A plastic sheet covered the floor and another was inserted to create a curved back wall in the box. Black-and-white striped wallpaper was attached to the front wall (1 inch wide/each).
Peppermint scent was sprayed before each animal was put in the box. A fan provided ambient sound. A digital camera was mounted on the ceiling and videos were recorded for later analysis. The experimental room remained dimly lit at all times. Auditory tones were
150 Chapter 2 – A and memory stabilization generated via FreezeFrame software (Coulbourn Instruments) using internal 16-bit soundcard amplified by a Med Associates ENV-225J Speaker Amplifier junction box.
2.5.5. Behavioural Procedures.
The behavioural procedures were largely as described previously (Wang et al. 2009a).
In all experiments rats were handled for three days post-surgery, then received 20 mins of habituation to each conditioning chamber per day for 2 days. On day 3 animals received
auditory fear conditioning in Context1, which consisted of either 1 pairing (1P) or 10 pairings
(10P) of tone (30s duration, 5000 Hz, 70dB) and footshock (1.5mA, 1s). The first tone was administered 180 sec after placement into the context and co-terminated with a footshock.
During 10P training there was a variable inter-pairing interval (average 3.5±2 min). In all experiments except Fig. 1 and 4, animals received microinfusion of either !- or %-secretase inhibitor/vehicle, or %-secretase inhibitor then A!o42/Scr peptide immediately after training.
For biochemical assays, rats were sacrificed either 90 min (for AB ELISA with no infusion) or 48h (for NR2B/NR1 western blots) after the completion of training. For behavioural experiments, rats received a memory reactivation session 48h after training, which consisted of a single unreinforced presentation of the conditioned tone 120 sec after placement into
Context2. One minute after the tone, each rat was removed from the chamber and infused with either anisomycin or vehicle (or in Fig. 4, with !- or %-secretase inhibitor/vehicle then anisomycin/vehicle). Twenty-four hours after reactivation, each rat received a post- reactivation long-term memory (PR-LTM) test that consisted of 3 unreinforced presentations
of the tone in Context2, with an inter-stimulus interval of 100s.
151 Chapter 2 – A and memory stabilization
Memory was assessed by quantifying the amount of time each rat spent freezing
(Blanchard and Blanchard 1969) during each unreinforced presentation of the conditioned tone.
2.5.6. Biochemical procedures.
Animals were rapidly anesthetized with isoflurane, decapitated, and brains were extracted (taking a total of <3 min). Brains were flash frozen in 2-methylbutane over dry ice, and stored at -80C until use. Amygdala samples were dissected from frozen brains using a tissue punch (1mm; Fine Science Tools).
2.5.6.1. A! quantification by ELISA.
Amyloid-beta protein levels in amygdala were quantified as described in Teich et al.
(2013). Briefly, frozen BLA punches from each rat were homogenized in approximately 10- parts homogenization buffer (20 mM Tris-HCl (pH 7.4), 250 mM Sucrose, 1 mM EDTA, 1 mM EGTA, containing Roche complete mini EDTA-free protease inhibitor cocktail) with a
Teflon Dounce homogenizer. An equal volume of 0.4% diethylamine, 100mM NaCl solution was then added and the solution was re-homogenized. Homogenates were spun at 100,000g for 1 hour in an ultracentrifuge (Beckman-Coulter Optima TLX), and the soluble supernatant fraction was collected for analysis. The DEA was neutralized with 1/10th volume of 0.5M
Tris-HCl (pH 6.8). Rodent amyloid-beta (x-42) was then measured with a high-sensitivity commercial ELISA kit (WAKO Chemicals, Japan). To obtain sufficient sample volume each solution was diluted with an equal volume (1/10th) of sample dilution buffer (~25µL), provided with the ELISA kit. 100µL of each sample was loaded, in duplicate, into each microplate well and processed as instructed by the ELISA kit.
152 Chapter 2 – A and memory stabilization
Total protein quantification. The above samples were then assayed for total protein quantification using a Thermo Scientific Pierce BCA Protein Assay Kit. This was used to calculate the concentration of amyloid-beta x-42 per µg of total protein in each sample.
2.5.6.2. Western blotting.
Subcellular fractionation. Amygdala tissue samples were homogenized in 100µL ice-cold homogenization buffer (30 mM Tris-HCl, 1mM EGTA, 4mM EDTA, pH7.4) containing protease inhibitors (Complete, Roche) using a hand-held motorize polypropylene pestle.
Homogenate was centrifuged at 500g for 10 min at 4°C. Supernatant was carefully collected and centrifuged at 100,000g for 60 min at 4°C. Supernatant was again collected and stored at
-80°C for future use. The pellet was re-suspended in an additional 50µL homogenization buffer containing 0.5% Triton-X100 and incubated for 20 min on ice, before being carefully layered over sucrose and centrifuged again at 100,000g for 60 min at 4°C. The sucrose and trion-soluble layers were removed and triton-insoluble pellet was collected and re-suspended in homogenization buffer, to serve as the crude post-synaptic density (PSD) fraction. Total protein concentration in each sample was determined using a BCA protein assay kit (Pierce), before being stored at -80°C.
Blotting procedure. Equal volumes (15 µL) of each crude-PSD sample (1µg protein/µL) were loaded into each lane and resolved used 8% SDS-polyacrylamide gel electrophoresis.
Proteins were transferred onto nitrocellulose membranes and incubated overnight (16h) at
4°C in anti-NR2B (1:250; Invitrogen), anti-NR1 (1:500; Invitrogen), or anti-!-tubulin
(1:10,000; Invitrogen) polyclonal antibodies. To quantify immunoblot bands, each membrane was incubated in ECL+ (Amersham) immunoassay detection solution for 5 min, scanned with a Storm Scanner (Molecular Dynamics), then processed and analyzed with ImageJ software
153 Chapter 2 – A and memory stabilization
(NIH). NR2B and NR1 bands were normalized to !-tubulin, which served as a loading control.
2.5.7. Statistics.
Unless otherwise stated, all behavioural experiments were analyzed using three-way mixed-design ANOVAs, with drug treatments as the between-groups factors and memory test
(REACT and PR-LTM) as a repeated factor. Significant interactions were further analyzed with two-way between-groups ANOVAs or tests of simple main effects, followed by Tukey’s
HSD post-hoc tests. Each dataset was confirmed not to violate the assumptions of ANOVA
(normality, homogeneity of variance/covariance, etc.). Western blot results were analyzed using 2-way between-groups ANOVA. ELISA results were analyzed via planned two-tailed independent-samples t-tests, with Bonferroni correction for multiple comparisons. Due to violations of the assumptions of ANOVA, the results presented in Fig. 2b were analyzed via planned non-parametric Mann-Whitney U tests, with Bonferroni correction for multiple comparisons. In all cases alpha level was 0.05.
2.5.8. Histology.
After the completion of each behavioural experiment rats were sacrificed via CO2 euthanasia and brains were rapidly extracted and stored in 10% formalin-saline, 20% sucrose solution. Brains then were cryosectioned and slide-mounted. Animals with severe tissue damage and/or cannula placements outside of BLA were excluded from analysis, as determined by an experimenter blind to group assignments.
2.6. FIGURE CAPTIONS.
154 Chapter 2 – A and memory stabilization
Figure 1. A! (x-42) levels are elevated in BLA 90 minutes after strong training.
Top panel illustrates experimental procedure. BLA-cannulated rats received either 1 or 10 pairings of auditory tone and footshock (1CS+ or 10CS+) immediately before infusion of
LY or VEH. Each rat was sacrificed 90 min following conditioning (1CS+/VEH, n=8;
10CS+/VEH, n=7; 10CS+/LY, n=7). Bottom panel displays mean concentration of A! (x-42)
(displayed in picograms per mg of total protein) ± s.e.m in BLA samples for each group obtained using ELISAe. Mean A! (x-42) levels were elevated in BLAs extracted from
10CS+/VEH relative to 1CS+/VEH and 10CS+/LY groups. * p < 0.05.
Figure 2. Infusion of LY-450139 or BACE1 inhibitor after strong training does not affect memory expression, but allows memory to undergo reconsolidation.
a) Top panel illustrates behavioural protocol. Rats received ten tone-shock pairings
(10 CS+/10P) then received BLA infusions of LY450139 (LY) or vehicle (VEH). Memory reactivation (React.) consisting of one unreinforced tone (1CS-) was given 48h later and revealed comparable freezing by all groups. Rats were randomly assigned to receive BLA anisomycin (ANI) or vehicle (VEH) immediately after React (VEH-VEH: n=10, LY-VEH: n=8, VEH-ANI: n=8, LY-ANI: n=8). Rats receiving LY-ANI exhibited significantly less freezing during PR-LTM relative to VEH-VEH, LY-VEH, and VEH-ANI rats, which all froze comparably. Data is presented as mean percent of time spent freezing during the tone(s) of each test ± s.e.m. b) Top: Behavioural protocol. The effect of LY-450139 on memory stabilization is rescued by infusion of A!. All rats received 10P then LY infusion, followed by infusion of oligomerized A!42 (A!o42, n=9) or scrambled control peptide (Scr, n=7). Freezing
155 Chapter 2 – A and memory stabilization during React. was comparable in both groups but post-reactivation ANI infusion impaired freezing during PR-LTM only in rats that had previously received Scr, relative to A!o42. c)
Top: Behavioural protocol. Rats received 10P then infusions of BACE1 inhibitor
(BACE1inhib) or VEH. Freezing during React. was comparable in all groups. Post- reactivation ANI infusion impaired freezing during PR-LTM in rats that had previously received BACE1inhib but not VEH (VEH-VEH, n=7; BACE1inhib-VEH, n=8; VEH-ANI, n=9; BACE1inhib-ANI, n=8). * p < 0.05.
Figure 3. Post-training LY-450139 infusion does not prevent reduced synaptic NR2B expression observed 48h after strong training.
Top panel illustrates experimental procedure. Rats received 1P or 10P training then infusion of either LY or VEH (1P/VEH, n=8; 1P/ANI, n=6; 10P/VEH, n=6; 10P/ANI, n=7), and were sacrificed approximately 48h later: a time-point at which 10P memory reactivation does not induce reconsolidation (see Fig. 2a). Expression of NR1- and NR2B-subunits in the crude-PSD fraction of each BLA sample was assayed via western blot, and normalized to !- tubulin loading control. Each data point is presented as a mean percentage of 1P/VEH ± s.e.m. a) Normalized NR2B expression was significantly lower in the 10P relative to 1P groups, but infusion of LY-450139 had no effect. b) Normalized NR1 expression was comparable for all groups. * p < 0.05.
156 Chapter 2 – A and memory stabilization
Figure 4. Post-reactivation LY-450139 enhances memory for weak training, whereas BACE1 inhibitor prevents the induction of reconsolidation.
Top panels illustrate behavioural protocols. All rats received 1P training, and 48h later the memory was reactivated with one unreinforced presentation of the conditioned tone (CS-
): a procedure known to induce reconsolidation (Wang et al. 2009). a) Rats were randomly assigned to receive a first post-reactivation infusion of LY or VEH before a second infusion of
ANI or VEH (VEH-VEH, n=6; LY-VEH, n=7; VEH-ANI, n=7; LY-ANI, n=7). All groups froze comparably during React. ANI impaired freezing during PR-LTM in rats that previously received either LY or VEH. However, LY-VEH rats froze significantly more than
VEH-VEH rats, suggesting the post-reactivation LY enhanced the 1P memory. b) To determine if the prior results can be attributed to the effects of LY on A! production, rats were randomly assigned to receive post-reactivation infusion of BACE1inhib or VEH before infusion of ANI or VEH (VEH-VEH, n=9; BACE1inhib-VEH, n=8; VEH-ANI, n=10;
BACE1inhib-ANI, n=9). Again, freezing during React. was comparable for all groups. As predicted, freezing during PR-LTM was impaired in VEH-ANI relative to VEH-VEH rats, however VEH-VEH, BACE1inhib-ANI, and BACE1inhib-VEH groups froze equivalently.
Thus the memory enhancement observed following LY infusion are likely due to effects on mechanisms other than A!. Data points are mean freezing during the tone(s) of each test ± s.e.m. * p < 0.05.
Figure 5. Representative cannula placements.
157 Chapter 2 – A and memory stabilization
a) Schematic diagram depicting placement of cannula tips from the experiment presented in Figure 2b, including one rat excluded from analysis due to extreme ventral positioning. Numbers on far left are the distance of each coronal slice (posterior to bregma), in millimeters. Brain schematic adapted from Paxinos and Watson (2007). b) Representative photomicrograph displaying the location of cannula tips within the BLA, from a single rat.
158 Chapter 2 – A and memory stabilization
2.7. FIGURES.
Figure 1.
159 Chapter 2 – A and memory stabilization
a 10 CS+ 48h React: 1 CS- 24h PR-LTM: 3 CS-
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160 Chapter 2 – A and memory stabilization
48h Extract BLAs for 1 CS+ or 10 CS+ Western blots LY/VEH a 1P/VEH 1P/LY 10P/VEH 10P/LY b 1P/VEH 1P/LY 10P/VEH 10P/LY
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161 Chapter 2 – A and memory stabilization
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162 Chapter 2 – A and memory stabilization
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Figure 5.
163 Running head: Chapter 3 – NMDAR-independent learning
Chapter 3
Task knowledge depends on cingulate cortex and lifts hippocampal NMDA-receptor
requirement for contextual fear conditioning.
Peter S.B. Finnie, Szu-Han Wang, Elizabeth Sinclair, Karine Gamache, & Karim Nader.
Department of Psychology, McGill University, 1205 Dr. Penfield Avenue
Montreal, Quebec, Canada, H3A 1B1
Chapter 3 – NMDAR-independent learning
3.0. PREFACE
The previous study confirms that strongly-encoded fear memories can become more resistant to destabilization, but this associative memory model is rather simplistic and reflects only how repeated pairings of tone and shock within a single session may insulate memory against alteration. Due to factors both internal and external to the organism, however, no two events will ever be identical (i.e. due to the passage of time or to state-dependent factors;
Bouton 1993; Eich 1980), and, outside of the laboratory, experiences will tend to be far more diverse. Yet the brain must be able to identify similarities and differences between events so as to recruit relevant memories to adaptively guide responding during related experiences, and also to modify their content when new information is introduced (Winters et al. 2011). The use of associative-learning procedures in which the rat is presented with ‘experimentally identical’ training trials cannot, therefore, satisfyingly inform us about how general knowledge is accrued via diverse experience.
To begin characterizing the brain's ability to extract common elements from distinct learning episodes we turned to the experience-dependent and NMDAR-independent contextual fear conditioning procedures discussed in Section 1.4.4. From this literature it is clear that during a first contextual fear conditioning session a rat can form a mental representation of the experience (presumably composed of a representation of the context, the footshock, their association, etc.) which is critical to engage NMDAR-independent learning mechanisms during a subsequent similar task (Hardt et al. 2009). However, precisely what constitutes a "similar" task is not yet empirically defined (Tayler et al. 2011; Wiltgen et al.
2011). Some recent work suggests that pre-exposure of animals to a context which shares many features of the environment used during training can be sufficient to engage NMDAR-
165 Chapter 3 – NMDAR-independent learning independent fear conditioning (Tayler et al. 2011). Contrastingly, many other findings indicate that prior conditioning in an environment that rats readily distinguish from a subsequently conditioned context is sufficient to engage NMDAR-independent learning mechanisms (Sanders and Fanselow 2003; Hardt et al. 2009; Wang et al. 2012a). Due to these inconsistencies, in the study presented in Chapter 3 we set out to systematically define what it is that a rat learns during a first learning episode that permits acquisition of a subsequent fear conditioning task during the inactivation of NMDARs in the dorsal hippocampus. We also aimed to identify brain regions involved in storing information about the first task that is necessary to subsequently engage NMDAR-independent learning.
166 Chapter 3 – NMDAR-independent learning
3.1. ABSTRACT.
The neurobiology of learning and memory formation has been studied mostly in naïve animals, yet the majority of learning in adult brains unfolds on the background of extensive prior experience. Although little is known about learning after experience, first and subsequent learning seem to be fundamentally different. For example, a first instance of contextual fear conditioning requires activation of NMDA-receptors in the dorsal hippocampus, but subsequent fear conditioning to a new context does not. It is not known how prior learning modulates the requirement for NMDA-receptor activation. Here, we show that the recruitment of NMDA-receptors in hippocampus depends not on context similarity, but similarities of the training procedure. Furthermore, disrupting long-term memory in the anterior cingulate cortex reinstates the requirement for NMDA-receptor activation in the dorsal hippocampus during second fear conditioning. These results describe how brain system interactions change after learning, and the effects on cellular processes recruited for memory formation.
3.2 INTRODUCTION.
It is now well established that the mechanisms used by the brain to encode new experiences (i.e., the activation of N-Methyl-D-aspartate receptors (NMDAR) in the dorsal hippocampus (dHC)) differ from those required for subsequent related events (Bannerman et al. 1995; Saucier and Cain 1995; Sanders and Fanselow 2003; Hardt et al. 2009; Wiltgen et al.
2010a; Wang et al. 2012a; Roesler et al. 1998; Laurent et al. 2008; Li and Richardson 2013;
Dragoi and Tonegawa 2013; de Hoz and Martin 2014; Inglis et al. 2013). For instance, the pre-training infusion of competitive NMDAR antagonist, DL-(2R)-amino-5-
167 Chapter 3 – NMDAR-independent learning phosphonovaleric acid (AP5), into the dHC of rats impairs fear conditioning of a first but not a second context (Sanders and Fanselow 2003; Hardt et al. 2009). When consolidation of the
memory for the first contextual fear conditioning task (Training1) is disrupted by infusion of a protein synthesis inhibitor into dHC, rats are unable to acquire a second similar task
(Training2) following dHC infusions of AP5, suggesting that the brain regards Training2 as a novel experience requiring NMDAR activation (Wang et al. 2012a).
While this phenomenon has been widely observed, it is still unclear what aspects of the
memory for Training1 are required to render Training2 insensitive to AP5. That is, which components of training cause the second task to be constructed around a previously stored memory (eliminating sensitivity to AP5), and which promote the creation of a new memory
(requiring activation of dHC NMDARs, and thus sensitive to AP5). Some theories propose that the hippocampus is critical for the rapid binding of stimulus features into a conjunctive context representation, but not for associating such a representation with fearful stimuli during conditioning tasks (Matus-Amat et al. 2007; Anagnostaras et al. 2001; Young et al.
1994). The prediction arising from these theories is that infusing AP5 into dHC would not
impair Training2, provided animals had previously been exposed to the training context alone
(Tayler et al. 2011). Conversely, other theories propose that the hippocampus contributes to the encoding of transient stimuli within context, like footshock (Moita et al. 2004; Chang et al.
2008; Zelikowsky et al. 2014; Lee 2010), suggesting that prior experience with contextual fear
conditioning during Training1 will be necessary for AP5 to no longer impair Training2.
Furthermore, although we have previously found that inactivation of either the dorsal
or ventral hippocampus (vHC) still allows intact acquisition of Training2 (Wang et al. 2012a), this does not elucidate whether or not these regions contribute to the retention of the
Training1 memory (or what other brain structures are involved). Previous studies have
168 Chapter 3 – NMDAR-independent learning demonstrated that after extensive hippocampus-dependent learning of multiple distinct flavour-location associations, cortical brain regions (Tse et al. 2011), including the anterior cingulate cortex (ACC; Wang et al. 2012b), can mediate accelerated encoding of new flavour- location pairs. Thus, we hypothesize that contextual fear conditioning memory acquired
during Training1 might be maintained by the ACC in order to permit acquisition of Training2 when AP5 blocks NMDA receptors in the dHC.
In light of these uncertainties, we aimed to determine the brain systems and training parameters that permit acquisition of a second contextual fear conditioning task in a manner that does not require NMDAR activity in dHC (and thus is insensitive to infusions of AP5).
In Experiment 1 we observed that 31 days after Training1, acquisition of Training2 in a second distinct context is impaired by AP5. As contextual fear memory can be expressed independently of the hippocampus one month after encoding (Kim and Fanselow 1992), this
could reflect a change in the brain regions mediating Training1. In Experiments 2 and 3 we directly investigated whether AP5-insensitive acquisition of Training2 requires that the
Training1 memory be mediated by either the hippocampus and/or ACC. We observed that memory maintained by the hippocampus is required for contextual fear expression after
Training1 but not necessarily for AP5-insensitive learning during Training2. Memory maintained by ACC, on the other hand, is not required for intact expression of Training1, but is necessary to engage AP5-insensitive learning during Training2. In Experiments 4 and 5 we evaluated which behavioural component of the Training1 conditioning task is sufficient to permit AP5-insensitive learning of Training2. We found that the switch to AP5-insensitive learning requires memory for a similar temporal arrangement of fear conditioning, while prior exposure to the conditioning context or even to a temporally-dissimilar fear conditioning task has no effect.
169 Chapter 3 – NMDAR-independent learning
We thus propose that this switch to AP5-insensitive learning mechanisms may reflect a change from encoding via de novo memory formation to encoding that is built on existing memory for the temporal arrangement of similar events.
3.3. RESULTS.
3.3.1. Experiment 1. AP5-insensitive learning state is transient, but can be reinstated with
a reminder of Training1.
In Experiments 1 and 2, we asked if the memory acquired during Training1 must be hippocampus-dependent in order for Training2 to be AP5-insensitive. Although we have previously found that impairing Training1 acquisition via inactivation of dHC+vHC can prevent NMDAR-independent learning (Wang et al. 2012a), this could also indirectly impair
the encoding process in other structures. To more directly address whether Training1 memory mediated by the hippocampus is required for NMDAR-independent learning, we took advantage of the fact that the expression of some contextual fear memories is thought to gradually become hippocampus-independent over time via a process called systems consolidation (Squire and Bayley 2007; Kim and Fanselow 1992). Such hippocampus- independent memory expression in rats is typically observed 21 or more days after contextual fear conditioning (Winocur et al. 2009; Kim and Fanselow 1992; Winocur et al. 2007). In our previous studies on AP5-insensitive learning (Wang et al. 2012a) we have used a 5-day inter-
training interval, wherein the Training1 memory should still depend on the hippocampus for expression at the time the rats experience Training2. If AP5-insensitive learning requires
Training1 memory to be expressed by the hippocampus at the time of Training2, then delaying
170 Chapter 3 – NMDAR-independent learning
Training2 to 31 days after Training1 (thereby transitioning to ‘hippocampus-independent’ expression of Training1) should cause Training2 to once again depend on NMDARs.
Using this extended procedure, freezing during the long-term memory test shortly
after fear conditioning to Context1 (LTM1, 24h after Training1) was comparable between to- be-infused drug groups (Fig. 1a), and there was very little generalization between contexts
during the pre-shock interval of Training2 (VEH=9.35±4.66%, AP5=2.23±1.98%; Mann-
Whitney U=29.5, p=.340). However, in contrast to our previous findings using a 5-day inter-
training interval, rats infused with AP5 into the dHC 31 days after Training1 froze significantly less during LTM2 relative to those given VEH (Fig. 1a). Even though these animals should still have been capable of expressing the Training1 fear memory at the remote time-point (see below), they encoded the second task as though it was a novel experience.
This tentatively suggests that for Training2 to be AP5-insensitive, the Training1 memory might have to be in a hippocampus-dependent state.
It has been observed that expression of a remote contextual fear memory can transiently return to a hippocampus-dependent state the day after rats are re-exposed to the training chamber (so-called systems reconsolidation; D&biec et al. 2002; Winocur et al. 2009).
With this in mind, we hypothesized that if Training2 is AP5-insensitive only when the
Training1 memory relies on the hippocampus, then giving a reminder of Training1 the day before Training2 might re-engage hippocampal expression and reinstate AP5-insensitive learning. Therefore, in this experiment rats received the LTM1 test 30 days after Training1, and then Training2 just 24h later (Fig. 1b). LTM1 was intended to serve as a reminder to re- engage hippocampal expression of the remote Training1 memory. LTM1 freezing was equivalent in both drug groups, but was considerably higher than in animals tested just 24h
after Training1 (Fig. 1a), possibly due to fear incubation (Eysenck 1968). However, animals
171 Chapter 3 – NMDAR-independent learning
treated with VEH or AP5 before Training2 showed similar levels of freezing during LTM2
(Fig. 1b). Thus, it appears that LTM1 given just 24h before Training2 can re-engage AP5- insensitive learning, perhaps via a process like systems reconsolidation which returns
Training1 memory expression to a hippocampus-dependent state.
3.3.2. Experiment 2. Disrupting Training1 memory maintenance in the hippocampus prevents AP5-insensitive learning, which can be reinstated by a reminder.
Although Experiment 1 indicates that a hippocampus-dependent Training1 memory trace could be necessary for AP5-insensitive subsequent learning, this could also be due to memory age itself. To dissociate these possibilities we next aimed to disrupt storage of recent
Training1 memory in the hippocampus by infusing peptides shown to impair the maintenance of long-term potentiation and long-term memory, including protein kinase-M# inhibitory peptide, ZIP (see Sacktor 2011) and pepR845A (Nishimune et al. 1998; Migues et al. 2014).
Our preliminary experiments revealed no significant effect of ZIP when it was administered
into dHC 24h after Training1 and tested 24h post-infusion (a negative effect also reported elsewhere; Serrano et al. 2008), but reliable impairment when pepR845A was infused instead
(Migues et al. 2014) (Supplementary Fig. 1).
We therefore used pepR845A to test whether maintenance of Training1 memory by the hippocampus was critical for AP5-insensitive acquisition of Training2. As we have previously found that even complete inactivation of dHC does not impair Training2 acquisition (Wang et al. 2012a), here we infused pepR845A into both the dorsal and ventral hippocampus (d+vHC).
We predicted that rats that had Training1 memory maintenance disrupted by pepR845A should have their acquisition of Training2 impaired by AP5. Rats were given d+vHC infusions of pepR845A or scrambled control peptide (Scr-pepR845A) 24h after Training1 and were
172 Chapter 3 – NMDAR-independent learning
tested 24h later. A significant impairment during LTM1 was observed in animals given pepR845A relative to Scr-pepR845A (Fig. 2a). Based on LTM1 freezing these animals were pseudorandomly assigned to approximately homogenous groups to receive infusions of AP5
or VEH immediately before Training2, which was administered 5 days after Training1. In contrast to our predictions, pepR845A-treated rats infused with AP5 before Training2 exhibited no significant impairment of LTM2 freezing. However, pepR845A-infused rats unexpectedly exhibited a moderate deficit of LTM2 freezing (Fig. 2b). Using another hippocampus-dependent task (object location memory) we have previously found that pepR845A infusion into dHC does not interfere with subsequent learning (Migues et al. 2014).
Although this cannot rule out the possibility that pepR845A may cause long-lasting memory
encoding deficits in HC, we propose that the LTM2 freezing deficit exhibited in Fig. 2b by rats previously given pepR845A could be due to a reduced baseline level of arousal resulting
from the impairment of Training1 fear memory. Most importantly, pepR845A-infused animals with impaired retention of Training1 exhibited intact acquisition of Training2 regardless of whether they received AP5 or VEH immediately beforehand.
In Experiment 1 we found that a reminder given shortly before Training2 could re- engage AP5-insensitive learning. Therefore, we reasoned that in the current experiment the
LTM1 test given 24h after pepR845A infusion might likewise reinstate AP5-insensitive learning by re-engaging the hippocampus. To test this possibility we infused pepR845A/Scr-
pepR845A into d+vHC 24h after Training1, but administered no LTM1 test. Without the reminder, we observed that rats given pepR845A after Training1 and AP5 prior to Training2 froze significantly less than all other groups (Fig. 2c). Our pilot work has previously indicated
that omitting LTM1 does not itself cause acquisition of Training2 to be sensitive to AP5 (data not shown). Thus, we conclude that Training1 memory needs to be in a hippocampus-
173 Chapter 3 – NMDAR-independent learning
dependent state to enable AP5-insensitive acquisition of Training2, which can be re- established by re-exposing the amnesic rat to the Training1 context.
3.3.3. Experiment 3. Disrupting memory maintenance in ACC after Training1 does not impair conditioned fear expression, yet re-engages AP5-sensitive learning.
The findings from Experiment 2 suggest that an extra-hippocampal brain region could
maintain the Training1 memory when memory maintenance in the hippocampus is disrupted, thus allowing the reminder to re-instantiate the AP5-insensitive learning state. One candidate region is ACC. Some studies have indicated that ACC is not required for recent contextual fear memory expression but becomes increasingly important for remote memory expression
(Frankland et al. 2004; Teixeira et al. 2006; Goshen et al. 2011). However, others have demonstrated that ACC is even required during memory formation and engaged during retrieval of recent experiences (Einarsson and Nader 2012; Steenland et al. 2012; Asok et al.
2013; Weible et al. 2012; Takashima et al. 2009; Vetere et al. 2011a; Zhang et al. 2011a; Liu et al. 2009; Leon et al. 2010; Ko et al. 2008; Cao et al. 2009; Zhuo 2009; Zhao et al. 2005;
Descalzi et al. 2012). Critically, ACC is implicated in learning/updating new associations after a prior knowledge base, called a mental schema, has been gradually established over time (Tse et al. 2011; Wang et al. 2012b).
Based on these previous findings (in which ACC is recruited during memory encoding and retrieval, but not required for recent memory expression), we predicted that infusions of
pepR845A into ACC should have no effect on expression of Training1 memory during LTM1, but could revert acquisition of Training2 to an AP5-sensitive state. Indeed, we observed no difference in freezing during LTM1 between groups treated with pepR845A and those given scr-pepR845A (Fig. 3a). However, during LTM2 we observed a small but significant
174 Chapter 3 – NMDAR-independent learning
impairment of Training2 memory in the pepR845A-AP5 group relative to pepR845A-VEH and SCR-pepR845A-AP5 groups (Fig. 3b). Thus, even 24h after encoding, the ACC appears
to maintain some aspect of the Training1 memory that is necessary for AP5-insensitive encoding of Training2 4 days later.
Together, Figs. 2 and 3 tentatively reveal a functional dissociation: pepR845A infused
into the hippocampus impairs Training1 retention but does not necessarily reinstate AP5- sensitivity, whereas pepR485A infused into ACC has no apparent effect on expression of the
Training1 memory but causes Training2 to be AP5-sensitive. However, it appears that the
Training1 memory trace must be in an active or hippocampus-dependent state (as elicited by a reminder) to enable dHC AP5-insensitive learning.
3.3.4. Experiment 4. Pre-exposure to context or footshock does not engage AP5- insensitive learning.
The aforementioned double dissociation indicates that animals able to express an intact
Training1 contextual fear response can still exhibit sensitivity to dHC-infused AP5 during
Training2 (Fig. 3), and vice versa. This potentially indicates that the mere presence of a contextual fear response is not the necessary aspect of Training1 memory that enables AP5- insensitive learning during Training2. Thus, Experiments 4-6 were aimed at identifying what a rat must experience during Training1 to engage AP5-insensitive learning.
As a first approximation we reasoned that during Training1, the rat acquires a representation of the conditioned stimulus (CS; context), the unconditioned stimulus (US; footshock), and the context-shock (CS-US) association. Here we asked whether each of these three components could be sufficient to engage AP5-insensitive learning. We systematically
modified the Training1 procedure to determine if exposure to either the CS alone or US alone
175 Chapter 3 – NMDAR-independent learning
was sufficient to allow intact learning of Training2 following dHC AP5 infusion, or whether the CS-US association was required (Experiment 5).
First, we evaluated if exposure to Context1 during Training1 (without footshocks) could induce the switch to AP5-insensitive learning. During Training1 each rat received a
4.5min exposure to Context1 (equivalent to the length of training in the previous experiments).
To maintain consistency with our standard training procedures, these animals were then given
an LTM1 test in the same context 24h later, although freezing was minimal. Animals were then pseudorandomly assigned to receive VEH and AP5 such that these groups exhibited
equivalently low mean LTM1 freezing (to-be-VEH=1.39±0.65%, to-be-AP5=1.47±0.36%, t10=0.099, p=.923). Animals infused with AP5 before Training2 froze significantly less during
LTM2 than VEH-infused rats (Fig. 4a). A comparable result was also observed for animals given short (4.5min) or long (2x30min) exposures to Context2 as Training1, prior to fear conditioning in the same context during Training2 (see Supplementary Fig. 2). These findings indicate that prior exposure to a training environment (even multiple long exposures to the
context that would subsequently serve as the CS during Training2) is insufficient to switch to
NMDAR-independent mechanisms. This finding was rather surprising, as is conflicted with prior work suggesting that animals pre-exposed to a conditioning chamber can acquire conditioned fear without NMDAR-activity in dHC (Matus-Amat et al. 2007). Hence, this
issue was probed further in Experiment 5.
We next assessed if exposure to footshock alone would be sufficient to cause intact
learning of Training2 immediately following dHC AP5 infusion. To minimize the formation of a CS representation and/or CS-US association during Training1, animals were given an immediate shock deficit procedure (Blanchard and Blanchard 1969; Fanselow 1986). Animals were placed into the conditioning chamber, shocked twice (1s inter-shock interval), and
176 Chapter 3 – NMDAR-independent learning rapidly removed (all within <20s) in order to minimize the formation of the CS representation.
Returning these rats to Context1 24h later for LTM1 elicited minimal freezing response (to-be-
VEH=1.98±1.86%, to-be-AP5=3.05±1.52%; t12=0.45, p=.662). Importantly, rats infused with
AP5 prior to Training2 showed less freezing during LTM2 than those given VEH (Fig. 4b). A similar pattern of results was observed in animals given 1 or 3 immediate shocks in Context1, or even 2 immediate shocks in Context2 as Training1 (Supplementary Fig. 3).
Together, these results indicate that rats pre-exposed to context or footshock alone still exhibit impairment of contextual fear conditioning following AP5 infusion into dHC.
3.3.5. Experiment 5. A similar conditioning procedure engages AP5-insensitive learning.
The results so far show that prior exposure to context or footshock do not permit learning via AP5-insensitive mechanisms (Fig. 4), and that a memory for contextual fear conditioning is also not always sufficient to do so (Fig. 1a and 3). Therefore we reasoned that
a more subtle aspect of the CS-US association acquired during Training1 must be critical to engage AP5-insensitive learning. One possibility is that memory for the temporal sequence of
Training1 might engage AP5-insensitive learning when the Training2 procedure is similar
(with footshock being delivered either 3 min or 24h after initial exposure to the context). Thus,
in this experiment, we combined the two Training1 components from Experiment 4, by initially exposing each animal to Context1 on a first day of training and then to immediate shock in the same context 24h later. Although either component alone is insufficient to elicit a conditioned fear response, it has been observed using a similar procedure that rats exposed to both phases can acquire a long-term contextual fear memory (Fanselow 1986) – a phenomenon known as the context pre-exposure facilitation effect (CPFE; Rudy et al. 2004).
The modified two-phase CPFE procedure implemented here was validated by an experiment
177 Chapter 3 – NMDAR-independent learning presented in Supplementary Fig. 6. It demonstrates that animals must be pre-exposed to the context in which immediate shock is delivered in order for a contextual fear response to be acquired. Furthermore, responding does not generalize to an unshocked context, even when the rats have previously experienced this context.
When the CPFE procedure served as Training1 rats exhibited reliable acquisition of fear memory, as assessed by freezing to Context1 during LTM1 (Supplementary Fig. 4a).
Nonetheless, the infusion of AP5 impaired the subsequent acquisition of Training2 when it consisted of the standard one-phase contextual fear conditioning (CFC) procedure we used in previous experiments (Fig. 5a).
To confirm the reliability of the basic effect we next replicated our original behavioural
procedure (Hardt et al. 2009; Wang et al. 2012a). Training1 and Training2 consisted of identical one-phase CFC procedures in Context1 and Context2, respectively. As observed in our prior studies, this procedure produced robust freezing during LTM1 (Supplementary Fig.
4b), and rats receiving AP5 infusion prior to Training2 exhibited no impairment during LTM2 relative to VEH controls (Fig. 5b). Even when the number of shocks administered during
Training1 was altered (to 1 or 3), Training2 was still insensitive to AP5 infusion (see
Supplementary Fig. 5a-b). Furthermore, when the CPFE procedure from Figure 5a was
combined into a long one-phase CFC procedure to serve as Training1 (12.5 mins in Context1 before delivery of two footshocks), Training2 was still AP5-insensitive (Supplementary Fig.
5c-d). Therefore, prior experience with one-phase CFC, but not two-phase CPFE, engaged
AP5-insensitive learning of subsequent one-phase CFC in an unfamiliar context.
A potential explanation for this effect is that two-phase CPFE may produce a memory
that is too weak or too fleeting to cause Training2 to become AP5-insensitive. To evaluate this possibility we reversed the order of the training procedures used in Figure 5a: one-phase CFC
178 Chapter 3 – NMDAR-independent learning
served as Training1 and two-phase CPFE served as Training2. Freezing during LTM1
(Supplemental Fig. 4c) was comparable to the previous results (Supplementary Fig. 5a-b).
With CPFE serving as Training2, we administered the AP5/VEH infusion immediately prior to the second (immediate shock) phase. In pilot experiments we first verified that NMDAR- activity is necessary to acquire contextual fear in this manner in experimentally naïve rats
(data not shown). In the current experiment we observed that animals given AP5 during the
immediate shock phase of Training2 were impaired relative to those given VEH (Fig. 5c), which resembled the results presented in Fig. 5a. Thus, AP5 infused into dHC impairs acquisition of a CS-US association during CPFE, even when rats have previously experienced
CFC in a distinct context. This general requirement for NMDAR activity in dHC during formation of a novel context-shock association is consistent with the results of several other studies (Moita et al. 2004; Chang et al. 2008).
Together, these findings suggest that a rat’s prior experience with a similar training procedure might be critical for AP5-insensitive learning. Indeed, when given two-phase
CPFE procedures during both Training1 and Training2, rats treated with either AP5 or VEH before Training2 exhibited equivalent levels of freezing during LTM2 (Fig. 5d). This rules out the possibility that the fear memory acquired when CPFE served as Training1 was simply too weak or transient to engage AP5-insensitive learning. Instead, it suggests that when a highly similar conditioning procedure is used to form a fear response to a new context, an AP5- insensitive learning mechanism is recruited. Together experiments 4 and 5 reveal that a
similar temporal arrangement of conditioning procedures used during Training1 and Training2 is the critical factor dictating the requirement for dHC NMDARs during the second task.
179 Chapter 3 – NMDAR-independent learning
3.3.6. Experiment 6. Fewer cFos-positive neurons in ACC following procedures that engage AP5-sensitive versus AP5-insensitive learning.
Thus far the results show that Training1 memory mediated by ACC is critical to engage
AP5-insensitive Training2, and that the similarity between the conditioning procedures used during each training is key for AP5-insensitive learning. Next we asked whether similar versus different training procedures engage differential brain activity patterns in ACC and
HC. To visualize brain activation we used immunohistochemistry to label neurons expressing cFos protein, as a marker of neuronal activity. Rats were trained using the same procedures as
in Experiment 5 (CFC or CPFE was used for Training1 and Training2), plus a fifth group
(homecage control) that received no training. Ninety minutes after Training2, each rat was deeply anesthetized and perfused (see methods). Frozen tissue was sliced and processed for cFos immunohistochemistry. Nuclei positive for cFos were counted in regions of interest
(ROIs) from ACC, CA1 region of dHC, and primary somatosensory (barrel) cortex (as a control region; Fig. 6a)
To compare animals receiving ‘similar’ procedures during Training1 and Training2 to those receiving ‘different’ procedures, we combined the CFC-CFC and CPFE-CPFE groups into a similar condition, and the CPFE-CFC and CFC-CPFE groups into a different condition.
In neither instance did the combined subgroups differ significantly (both p>.05 using uncorrected Mann-Whitney U planned comparisons). Based on our pepR845A findings from
Experiment 3, we predicted that the ACC might be recruited more extensively during the acquisition of similar training, so more neurons might be activated in ACC in the similar relative to different conditions. Surprisingly, the results revealed fewer cFos-positive neurons in ACC of animals in the similar relative to different condition (Fig. 6b). In CA1 and barrel
180 Chapter 3 – NMDAR-independent learning cortex we observed no differences between the similar and different groups. In all ROIs the similar and different groups each exhibited significantly more cFos-positive neurons than animals in the homecage control condition (p<.05).
3.4. DISCUSSION.
Here we have identified key factors that allow for experience-dependent dHC
NMDAR-independent learning. Only rats with prior knowledge of the general training procedure exhibited intact contextual fear conditioning following dHC infusion of AP5. This prior knowledge appears to be maintained by brain structures including ACC.
We first observed that 31 days after a first contextual fear conditioning session, the acquisition of fear to a second context again relies on NMDARs. One explanation for this
transience of AP5-insensitive learning is that over time the Training1 memory could gradually transition to a form that no longer requires the hippocampus for expression (Winocur and
Moscovitch 2011; Kim and Fanselow 1992). Therefore, when Training2 is given 31 days after
Training1, a de novo contextual fear memory might have to be encoded via dHC, requiring functional NMDARs. Indeed, when we disrupted retention of a recent Training1 memory in the hippocampus via bilateral infusion of pepR845A, acquisition of Training2 returned to an
AP5-sensitive state (Fig. 2c). However, in another group of rats, a brief memory test the day after hippocampal pepR845A infusion was found to reinstate AP5-insensitive acquisition of
Training2 (Fig. 2b). Similarly, when rats received a reminder of Training1 30 days after acquiring this task, Training2 administered 24h later also re-engaged AP5-insensitive learning mechanisms (Fig. 1b). It seems reasonable to conclude that LTM1 may refresh the memory storage system to a recent state, perhaps by re-establishing hippocampus-mediated
181 Chapter 3 – NMDAR-independent learning connections between residual elements of the trace encoded by extra-hippocampal regions
(D&biec et al. 2002; Wiltgen and Silva 2007; Winocur et al. 2009; Narayanan et al. 2007). An alternative interpretation of these findings is that memory reactivation could simply strengthen the partially degraded trace and rescue fear expression (de Hoz et al. 2004; Inda et al. 2011), thereby re-engaging the AP5-insensitive learning state. However, in a previous study we found that multiple tests did not rescue the expression of a contextual fear memory that had been impaired by pepR845A infusion (Migues et al. 2014). Therefore, we conclude
that a hippocampus-mediated Training1 memory is required for intact encoding of Training2 after AP5 infusion.
Experiments 1-3 have provided evidence of a dissociation of hippocampal and ACC function. Although animals tested 24h after hippocampal infusion of pepR845A exhibited
impaired Training1 memory expression (Fig. 2a), those receiving infusions into ACC showed no deficit (Fig. 3a). However, rats receiving infusions of pepR845A into the hippocampus
were unimpaired by AP5 infusion before Training2 (Fig. 2b), while those receiving pepR845A infusions into ACC were impaired by pre-Training2 AP5 (Fig. 3b). Because AP5-insensitive contextual fear conditioning requires prior exposure to a task with a similar procedural arrangement (Fig. 5), we infer that ACC could contribute to the maintenance of a higher- order memory for the temporal arrangement or sequence of training (Wang et al. 2012b;
Steenland et al. 2012; McClelland et al. 1995; Delatour and Gisquet-Verrier 2001; Kesner
2000; Chiba et al. 1997). The hippocampus, on the other hand, could contribute to the maintenance of a detailed context memory required for intact fear expression (Wiltgen et al.
2010b; Winocur et al. 2009). To our knowledge this is the first demonstration of such a dissociation between these components of contextual fear memory, although they resemble
182 Chapter 3 – NMDAR-independent learning episodic-like (context-specific) and semantic-like (context-general) memory systems proposed elsewhere (Winocur et al. 2009; Moscovitch et al. 2006).
A tenet of many memory theories is that the neural representations of features that are experienced during multiple events should be selectively strengthened (Wang and Morris
2010; Winocur and Moscovitch 2011; McClelland et al. 1995; Marr 1970). These models frequently ascribe to the hippocampus the ability to rapidly (yet perhaps transiently) encode and bind trial-specific details of discrete events, while some cortical regions have been proposed to mediate abstraction of higher-order trial-independent rules extracted across multiple experiences (McClelland et al. 1995; Kesner 2000; Wang et al. 2012b; Durstewitz et al. 2010; Winocur and Moscovitch 1990; Wang and Morris 2010; O'Reilly and Rudy 2001).
Cortical memory representations are likely formed immediately following a learning episode
(Lesburguères et al. 2011; Zhao et al. 2005; Einarsson and Nader 2012; Descalzi et al. 2012;
Zelikowsky et al. 2014), though their contribution to memory expression may only become apparent as the involvement of the hippocampus changes with time or experience (Winocur et al. 2009; Frankland and Bontempi 2005; Kim and Fanselow 1992; Bontempi et al. 1999;
Winocur et al. 2007; Maviel et al. 2004). The encoding of multiple similar events might increasingly strengthen these cortical representations, resulting in the reduced involvement of the hippocampus (Wang et al. 2012a; Lesburguères et al. 2011; Wang and Morris 2010; van
Kesteren et al. 2010). Indeed, the hippocampus is not always necessary for acquisition of contextual fear conditioning when learning is more gradual (Wiltgen et al. 2006; Lehmann et al. 2009; Wang et al. 2009; McHugh and Tonegawa 2009) or when animals are pre-exposed to the training context (Matus-Amat et al. 2007; Young et al. 1994). This is consistent with the view that the brain will flexibly encode fear memories via the most efficient neuronal circuits
183 Chapter 3 – NMDAR-independent learning available, which are likely altered by prior experience (Fanselow 2010). Thus, even when default learning mechanisms are disrupted, as in our experiments when AP5 is infused into
dHC prior to Training2, circuits established or strengthened during Training1 might be sufficient to acquire a conditioned fear response to Context2.
Similarly, as rats learn multiple flavour-location paired associates in a hippocampus- dependent task, they can gradually form a mental “schema” that permits accelerated learning of subsequent novel associations. This rapid encoding is likely mediated via integration with previously established networks in cortical regions (Tse et al. 2007; 2011) like ACC (Wang et al. 2012b). The same principle could also apply in our paradigm, where knowledge of the
procedural sequence of Training1 (stored in cortex) engages a different hippocampal plasticity mechanism during Training2 by recruiting elements of the existing memory. However, in both the schema (Tse et al. 2007) and NMDAR-independent learning tasks (Tayler et al. 2011;
Wang et al. 2012a) the hippocampus remains at least partially necessary during experience- dependent learning. For instance, either dHC or vHC was sufficient (but necessary) to mediate fear conditioning of a second novel context (Wang et al. 2012a). Furthermore, dHC
NMDARs are only necessary to acquire a first (but not a second) spatial water maze task
(Inglis et al. 2013), and rats’ familiarity with procedural aspects of the task can change the region of the hippocampus necessary to learn the platform location of a new watermaze from septal (dorsal) to temporal (ventral) hippocampal pole (de Hoz and Martin 2014). Thus,
NMDAR activity in dHC may not be necessary because its role in encoding the temporal
arrangement (Huerta et al. 2000; Zhang et al. 2013; Hunsaker et al. 2008) of Training2 has already been fulfilled during the prior acquisition of Training1. This fits with some alternative models of hippocampal function, which propose that this structure is important for binding
184 Chapter 3 – NMDAR-independent learning not just co-occurring contextual features but also sequences of events (Eichenbaum 2003;
MacDonald et al. 2011; Asok et al. 2013; Jensen and Lisman 1996; Weible et al. 2012;
Takashima et al. 2009; Vetere et al. 2011a).
Medial prefrontal cortical regions including the ACC could contribute to the maintenance of these temporal properties of events (Devito and Eichenbaum 2011;
Hannesson et al. 2004; Chiba et al. 1997). For instance, the ACC exhibits reduced activity during repetition of specific temporal sequences of events (Procyk et al. 2000; Weible et al.
2003), which resembles our observation of fewer cFos-positive neurons in the ACC following similar versus dissimilar training procedures (Fig. 6). Such phenomena are broadly referred to as repetition suppression of neural responses (Grill-Spector et al. 2006). When a training procedure has already been encoded perhaps fewer additional neurons are required to acquire another similar event, as storage of the new episode might simply add on to the representation of the previous experience. Relatedly, the ACC and nearby medial prefrontal structures could also process learned prediction error (Totah et al. 2009; Bryden et al. 2011), which might
increase neuronal activity when the Training2 procedure violates temporal expectations. As accumulating evidence indicates that the ACC could be a principle site for integration of new information with existing knowledge (Wang et al. 2012b), we propose that memory for the temporal arrangement of conditioning mediated by the ACC could be used to acquire subsequent experiences that have a similar configuration.
In contrast to results presented here, other studies have reported that NMDAR- independent fear conditioning merely requires recent (or even remote) pre-exposure to a similar training context (Tayler et al. 2011; Matus-Amat et al. 2007). However, the methodology of these studies and our own differ in several ways. For instance, the use of
185 Chapter 3 – NMDAR-independent learning immunohistochemistry by Tayler and colleagues (2011) necessitated the use of systemic rather than local administration of NMDAR antagonist, which could have different effects on neuronal activity in the hippocampus. Inhibition of a neuronal activity marker, activity- regulated cytoskeleton-associated (Arc) mRNA/protein, in hippocampus was observed to be much less extensive following their systemic injection of CPP (Tayler et al. 2011) than has been observed following local infusions of AP5 (Czerniawski et al. 2011; Panja et al. 2009).
Thus, if the systemic injections in this study were equally effective as local infusions, they might be expected to disrupt acquisition of the second task. Furthermore, systemic injection should also block NMDAR activity in the amygdala where it has been shown that AP5 infusion impairs both a first and second contextual fear conditioning task (Lee and Kim
1998). Perhaps residual NMDAR activity after systemic CPP injection permitted these mice to acquire contextual fear conditioning when they had previously explored a somewhat similar context.
However, these inconsistent results could also indicate that any common feature(s) might permit encoding of similar events to share components of a single trace. The elements of
Training2 that do not overlap with the Training1 trace might dictate which brain regions and molecular mechanisms are required to encode the new information experienced during the second task. In the present study we observed that the procedural sequence of fear
conditioning served as a common feature across episodes, which required Training1 memory mediated by ACC, but not NMDARs in dHC. Distinctly, Tayler and colleagues (2011) found that pre-exposure to a similar training environment permitted common contextual features to support a fear association, perhaps via a mechanism involving calcium-permeable AMPA- receptors in the hippocampus (Wiltgen et al. 2010a). Both findings may illustrate a ubiquitous
186 Chapter 3 – NMDAR-independent learning learning phenomenon that expedites encoding or links distinct episodes via any common features that are detected (Dragoi and Tonegawa 2013; Eichenbaum 2004; McClelland et al.
1995).
In conclusion, we propose that the brain extracts regularities across behavioural episodes, which can cause similar events to be encoded via distinct mechanisms. The specific mechanisms required during experience-dependent learning might be expected to vary with the type and degree of overlap between episodes. Insensitivity to AP5 infused into dHC could reflect a process by which new associative learning tasks are integrated with related knowledge about the specific temporal arrangement of conditioning, possibly maintained by cortical regions including ACC.
3.5. METHODS.
3.5.1. Subjects
Adult male Sprague-Dawley rats bred at Charles River Laboratories (Quebec,
Canada) were used in this study, and were housed individually in Nalgene cages with food and water provided ad libitum. Each was handled for at least 3 days before stereotaxic surgery.
The animals were maintained on a 12 hr light-dark cycle (07:00-19:00 light phase). All experimentation was conducted during the light phase and followed the protocols approved by McGill University Animal Care and Use Committee and was in accordance with the
Canadian Council on Animal Care guidelines.
3.5.2. Surgical Procedures.
Each rat weighed 325-400g at the time of surgery (approximately 9-12 weeks old).
187 Chapter 3 – NMDAR-independent learning
Animals were anesthetized with a 1mL/kg IP injection of ketamine (55.55mg/mL), xylazine
(3.33 mg/mL) and domitor (0.27 mg/mL) drug cocktail. For analgesia during surgery and recovery, buprenorphine (0.324 mg/mL) was administered subcutaneously at 1ml/kg. Each animal was mounted on a stereotaxic apparatus (Kopf Instruments), and stainless steel cannulae (22 or 28 gauge, Plastics One, Roanoke, VA) were bilaterally implanted in the brain targeting the regions of interest based on Paxinos and Watson’s atlas of the rat brain (Paxinos and Watson 2007). Coordinates for dHC cannulation were: A/P -3.6mm, L +/- 3.1mm, D/V -
2.6mm (measured from bregma), +10 degrees away from midline; for dHC+ACC cannulation the dHC coordinates were as above, and ACC were: A/P +2.6mm, L +/-0.7mm, D/V -1.6mm
(measured from dura), 0 degrees from midline; and for dHC+vHC cannulation the,dHC coordinates were: A/P -3.7mm, L +/- 2.25mm, D/V -2.6mm, and vHC were: A/P -6.3mm, L
+5mm, D/V -6.0mm, both 0 degrees from midline. These were stabilized with two layers of dental cement anchored to three jewelry screws drilled into the skull. Each rat was revived with a 0.67mL/kg IP injection of antisedan (5 mg/mL). Surgeries were performed 7-10 days prior to the start of behavioural training, except for Experiment 1 when surgeries were
performed 7-9 days before Training2. All procedures complied with Canadian Council on
Animal Care guidelines and were approved by the McGill Animal Care Committee.
3.5.3. Apparatus.
Two distinct training contexts were used in this study. In order to reduce generalization between the two contexts, different visual, auditory, olfactory, and textural cues were used in each, and distinct routes were taken when transporting animals from the colony.
Chambers in Context1 were comprised of Coulbourn (Whitehall, PA) conditioning
188 Chapter 3 – NMDAR-independent learning boxes (30cm*26cm *33cm). All four sidewalls were made of transparent Plexiglas. Consistent illumination was produced by a single light bulb located at the upper-middle of the right side wall of each chamber. The floor was composed of parallel stainless steel bars (radius=0.25 cm,
1 cm and was on a horizontal plane, 0 degree incline). The grid floor was connected to an animal shocker unit (Coulbourn Instruments model H13-15). The intensity of electric footshock was set at 1 mA for 1 second. Diluted vanilla scent was applied immediately prior to each training session. A digital camera was installed in front of the box for image recording and storage via freeze-frame software (Coulbourn). The experimental room remained brightly lit at all times.
The second context (Context2) consisted of Med-Associates (St. Albans, VT) fear conditioning boxes (29cm*25cm *25cm). The sidewalls of each chamber were made of aluminum panels. Two lights were mounted on the right wall and an additional light was mounted on the left wall. The lights flashed alternately at a rate of 1 cycle/second. A plastic sheet was inserted to create a curved back wall in the box. Black-and-white striped wallpaper
was attached to the front wall (1 inch wide/each). The grid floor was similar to Context1 except each bar was narrower (radius=0.1 cm), had greater spacing between them (0.5 cm), and was tilted at 7 degrees. Wood-chip bedding was used to fill the ground such that it partially covered the grid floor. The intensity of electric footshock was set at 1.2 mA for 1 second (Med Associates model ENV-414S). Peppermint scent was sprayed before each animal was put in the box. A fan provided ambient sound. A digital camera was mounted on the ceiling and videos were recorded for later analysis. The experimental room remained dimly lit at all times.
We have previously observed that whether Context1 or Context2 serves as the first
189 Chapter 3 – NMDAR-independent learning training context it does not change the overall result - that is, the insensitivity of second
learning to NMDAR blockade (Hardt et al. 2009). Thus, Context2 was used during Training2 throughout this study.
3.5.4. Behavioural procedures.
3.5.4.1. General behavioural protocol: In all experiments each rat received two training
sessions. In Experiments 1-3, in the interval between Training1 and Training2 we aimed to manipulate the memory stored during Training1. In Experiments 4-6 we manipulated the learning content of Training1 and/or Training2. In all experiments animals were given
Training1 and Training2 six days apart, except in Exp. 1 when this was extended to 31 days.
Training1 was administered in Context1 (except several groups in Exp. 2 and Supplemental
Fig. 2), whereas Training2 always occurred in Context2. AP5/VEH were always infused immediately before Training2, except in Exp. 5c-d when they were given prior to the immediate shock phase of Training2. PepR845A/Scr-pepR845A infusions were given 24h after
Training1. Unless otherwise noted, each training session was followed 24h or 48h later by a
4min long-term memory (LTM) test in the same context.
3.5.4.2. One-phase contextual fear conditioning (CFC). Unless otherwise noted, one-phase conditioning consisted of a 3 min context exposure followed by 2 footshocks (30s inter-shock interval). In Supplemental Fig. 5a-b, 1 or 3 shocks were administered. The rats were removed from the context 60s after the last footshock. In Supplemental Fig. 5c-d, this one- phase conditioning was altered such that the pre-shock interval was 12 min, followed by 2 footshocks.
3.5.4.3. Immediate shock pre-exposure. In Fig. 4b and Supplemental Fig. 3, Training1
190 Chapter 3 – NMDAR-independent learning consisted of the immediate shock procedure. Animals were placed into the context and rapidly received 1, 2, or 3 footshocks with a 1s inter-shock interval. The animals were then quickly removed from the context within 10s. In these experiments the animals remained in the chamber for <20s. In Exp. 4-6, the immediate shock phase of two-phase conditioning differed slightly from this procedure (see below).
3.5.4.4. Context exposure. In Exp. 4a, Training1 consisted of context exposure. This was either 4.5min in Context1 (Fig. 4a) or Context2, or two 30 min sessions in Context2
(Supplemental Fig. 2).
In Exp. 5, the context exposure phase consisted of a 12min exploration session (as described in the CFPC section below). Animals handled rather than receiving exposure were brought to the hallway outside of the context and handled for a duration equivalent to that of animals being placed into and removed from the conditioning chamber.
3.5.4.5. Two-phase contextual fear conditioning (CFPE). In two-phase conditioning, each animal was first pre-exposed to a conditioning chamber, and the next day was given 2 immediate shocks in the same context. During pilot testing we optimized the procedure such that only animals pre-exposed to context would show a reliable conditioned fear response
(data not shown). This required both the immediate shock and the context pre-exposure procedure to differ slightly from those described above. During the context pre-exposure phase, each animal remained in its homecage as it was transported to the context on a cart.
Animals were given several minutes to acclimatize before being placed into the conditioning chamber for a 12min exposure session. Each rat was returned to the colony shortly after removal from the chamber. The next day animals were transported back to the same context
191 Chapter 3 – NMDAR-independent learning for the immediate shock session. When the two-phase conditioning was taking place in
Context1 (Experiment 5 and 6), each rat was transported to and from the context in a clear plastic mouse cage wrapped in an opaque white sheet. When two-phase conditioning was
taking place in Context2, animals were transported to the context in an opaque metal bucket containing bedding. This was done such that animals could not merely rely on transportation cues to facilitate conditioning (Rudy et al. 2002). During the immediate shock session, each animal was removed from the transportation vessel and placed into the appropriate conditioning chamber. Fifteen seconds later the rat was given two 1s, 1.3mA footshocks, with a 1s inter-shock interval. The rat was removed several seconds after the last shock, placed into the transportation vessel, and returned to the colony.
3.5.4.6. Behavioural measurement. Contextual fear memory was assessed as the percentage of time that the animal showed freezing behavior, which is defined as total immobilization except for movements required for respiration (Blanchard and Blanchard 1969; Fanselow
1990). Freezing was measured by an observer who was blind to each animal’s treatment condition but not the experimental design. Each 4-minute LTM test was divided into 30- second intervals, and the results are presented as the percentage of freezing time averaged
across all 8 intervals. Pre-shock freezing during Training2 was also assessed as the percentage of the 30s interval between 150s to 180s after initial placement into the context.
3.5.5. Group assignment.
In Experiments 1, 4, 5, and 6, animals were randomly assigned to each training group, and in Experiments 2 and 3 animals were randomly assigned to each peptide group (ZIP/Scr or pepR845A/Scr). In all experiments animals were pseudorandomly assigned to receive
192 Chapter 3 – NMDAR-independent learning
Training2 drug treatments (VEH or AP5) so as to approximately equate mean LTM1 freezing for each group.
3.5.6. Drug delivery.
All drugs were administered via 28- or 33-gauge stainless steel injectors (for hippocampus and ACC, respectively) extending +0.5mm from the tip of each cannula, attached via polyethylene tubing (Intramedic #427406) to 10uL Hamilton syringes driven at
0.4 µL/min by a K.D. Scientific microinfusion pump. NMDAR antagonist D,L-2-amino-5- phosphonopentanoic acid (AP5; Sigma) at a dose of 5µg/2µl/side was infused at a rate of
0.4µl/min. An equivalent volume of physiological saline served as vehicle control (VEH).
PepR845A (TAT(47– 57)-844KAMKVAKNPQ853 and scrambled Scr-pepR845A (TAT(47–
57)- VAKKNMAKQP (30µM; Anaspec) were dissolved in artificial cerebrospinal fluid
(aCSF, 150mM NaCl, 3mM KCl, 1.4mM CaCl2, 0.8mM MgCl2, 0.8mM Na2HPO4, and
0.2mM NaH2PO4, pH 7.4) and infused at 2µL/hemisphere in dHC, 1.25µL/cannula into d+vHC, and 0.5µL/hemisphere in ACC. The protein kinase M# (PKM#) inhibitor, Zeta
Inhibitor Peptide (ZIP) or scrambled-ZIP were dissolved in aCSF to a concentration of
20nmol/µl, and infused at a volume of 2.0µl/hemisphere. The pH-value of each solution was adjusted to 7.2-7.5 using NaOH.
3.5.7. Histology
At the end of each experiment, each brain was removed and post-fixed in 10% formalin-saline mixed with 25% sucrose solution (for cryo-protection to preserve the morphology). They were cryo-sectioned at 50µm thickness. The slides were examined by light microscopy for cannula placements by an experimenter blind to the group assignments. Only animals with injector tips bilaterally positioned within the dHC, dHC+vHC, or dHC+ACC
193 Chapter 3 – NMDAR-independent learning were included in the data analysis. Rats with substantial hippocampal and/or cortical damage were excluded from analysis.
3.5.8. Exclusion criteria.
In addition to rats removed due to technical problems (i.e. cannula blockage, incorrect cannula placement, apparatus malfunction), there were also several predefined behavioural exclusion criteria. Animals in Experiments 1, 5, and 6 that exhibited <10% freezing during
LTM1 were deemed not to have acquired Training1, and those exhibiting > 35% freezing during the pre-shock interval of Training2 to already possess a freezing response to this novel context. These exclusion criteria were included such that animals clearly demonstrated
learning during both Training1 and Training2. The very small percentage of rats that generalized between training contexts already exhibited a fear response to Context2 prior to
Training2, thus without exclusion could not be clearly distinguished from animals who were simply unimpaired by the AP5/VEH infusion. The percentage of rats excluded from each drug group was comparable in these experiments. This exclusion criterion was not included in
Experiments 2 and 3 because we predicted that pepR845A treatment would impair Training1 memory retention, and thus responding during LTM1 and potentially also contextual generalization during Training2. Finally, a total of 6 rats were excluded from versions of
Experiment 4 (Fig. 4b and Supplemental Fig. 3) because they exhibited a strong (>35%) fear
response during LTM1 following the Training1 immediate shock procedure. The aim of this experiment was to expose animals to shock during Training1 without evoking a conditioned fear response, thus animals reliably freezing during LTM1 were considered to have formed a strong context-shock association. Importantly, the patterns of results of these experiments were the same even when these animals were not excluded from analysis (data not shown).
194 Chapter 3 – NMDAR-independent learning
Immunohistochemistry.
Each animal was deeply anesthetized 90min after Training2 with the same ketamine cocktail used during surgery. The animal was then sacrificed, the brain was extracted and submerged in 4% paraformaldehyde for 4h then 20% sucrose solution for 48h (both at 4°C), before being rapidly frozen in 2-methylbutane chilled on dry ice for storage at -80°C. Each brain was then sliced on a frozen microtome at a thickness of 40µm and stored in antifreeze at
-20°C. Three slices (approximately 80µm apart) from each region of interest (ROI) were then stained for cFos. Briefly, the floating slices for each ROI from each animal were washed in
PBS then 0.3% hydrogen peroxide, then incubated in 1ml 2% BSA, 2% Normal Goat Syrum
(Vector Labs #S-1000 blocking solution) for 60min. The slices were then incubated in 1ml anti-cFos rabbit polyclonal IgG (Santa Cruz Biotech #SC-52, diluted 1:1000 in blocking solution) overnight (16h) at 4°C. They were then washed repeatedly in PBS before applying secondary antibody (biotinylated goat anti-rabbit IgG; Vector Labs, Birmingham CA #BA-
1000, diluted 1:500 in blocking solution) for 60min. Slices were again washed and incubated for 60min in Vectastain ELITE ABC reagent (Vector Labs #PK-6100). After washing, DAB
(Vector Labs #SK-4100) was then applied for 90s and promptly rinsed in PBS. The slices were slide-mounted, dehydrated, and coverslipped. Images were captured on an Olympus light microscope (IX81) at both 4x and 10x magnifications.
Both hemispheres from each slice were analyzed for all ROIs using a semi-automated counting procedure using NIH ImageJ software. Each representative 10x magnification image (cropped as necessary to fit the typical boundaries of each brain structure) was converted to a binary image based on a standardized threshold value, watershed was applied to distinguish partially overlapping cells, and particles were analyzed with a minimum cell size
195 Chapter 3 – NMDAR-independent learning of 50 pixels2 and circularity of 0.3. All semi-automated cell counts were visually inspected to identify miscounted particles. Approximately one quarter of slices were also counted manually to confirm the validity and reliability of the automated procedure. An acceptable correlation was obtained between our manual and semi-automated counts of cFos positive neurons
(Pearson’s r =-.88, p<.001).
3.5.9. Statistical analyses.
Freezing scores were analyzed using two-way between-subjects ANOVAs (with
Tukey’s post-hocs) or two-tailed independent-samples t-tests. Homogeneity of variance
(Levine’s test) and normality were evaluated to ensure the assumptions of each test were met.
In the case of violation of the homogeneity of variance assumption (Exp. 6), non-parametric
Kruskal-Wallis and Mann-Whitney U tests with Bonferroni correction were used.
Type-one error rate (") was set at 0.05 for all comparisons. Mean freezing for each group was reported as percent of assessment interval ± the standard error of the mean (s.e.m.).
3.6. FIGURE CAPTIONS.
Figure 1. Systems consolidation and reconsolidation of the AP5-insensitive learning effect.
(Top) Behavioural protocol. Training1 and Training2 consisted of one-phase contextual fear conditioning, as in prior studies (Hardt, Wang), but with a 31-day inter-training interval.
Animals were randomly assigned to receive LTM1 either 24h after Training1 (Day2) or 30 days after Training1 (Day31). Based on LTM1 freezing, animals were pseudorandomly assigned to receive VEH or AP5 before Training2 (n/group: Day2/VEH=9; Day2/AP5=9;
Day31/VEH=9; Day31/AP5=10). (a) Animals tested 24h after Training1 froze significantly
196 Chapter 3 – NMDAR-independent learning less (To-be-VEH: 44.68±7.42%; To-be-AP5: 40.25±5.62%) than those tested 30d after
Training1 (VEH: 71.88±6.9%; AP5: 69.35±5.88%). Two-way ANOVA revealed a main effect of day of LTM1 test (F1,33=18.901, p<0.0005) but not to-be-infused drug (F1,33=0.289, p=0.595), and no LTM1-day by drug interaction (F1,33=0.021, p=0.885). (b) Two-way ANOVA on LTM2 freezing revealed a main effect of LTM1-day (F1,33=4.853, p=0.035), drug (F1,33=18.790, p<0.0005), and an interaction of LTM1-day and drug (F1,33=12.846, p=0.001). Tests of simple main effects indicated that for rats given LTM1 on day 31, freezing during LTM2 was similar for VEH (70.54±5.29%) and AP5 (65.94±7.21%,) groups (F1,33=.289, p=0.594). However,
AP5-infused rats given LTM1 on Day2 were impaired relative to VEH-treated rats
(30.5±5.84% and 78.99±5.62%, respectively; F1,33=30.57, p<0.0001). *** p<0.0001. Data expressed as mean percent of LTM test spent freezing ± s.e.m.
Figure 2. PepR845A infused into d+vHC impairs Training1 memory retention, but Training2 is only AP5-sensitive when LTM1 is omitted.
(Top) Behavioural protocols. Rats were (a,b) or were not (c) given LTM1 test 24h
after infusion of pepR845A or VEH. (a) A two-way ANOVA on LTM1 freezing revealed a main effect of peptide (F1,33=13.337, p=0.001), but not to-be-infused drug (F1,33=.369, p=0.547) or an interaction of peptide and to-be-infused drug (F1,33=.021, p=.885). This indicated that rats infused with pepR845A froze significantly less (41.49±6.65%) than those given VEH
(72.22±4.97%). (b) A two-way ANOVA on LTM2 freezing revealed a main effect of peptide
(F1,33=5.629, p=0.024), but not drug (F1,33=0.054, p=0.818) nor an interaction of peptide and drug (F1,33=.39, p=.537). (c) Two-way ANOVA on LTM2 freezing of rats not given an LTM1
test revealed significant main effects of peptide (F1,34=9.474, p=.004) and drug (F1,34=8.357,
197 Chapter 3 – NMDAR-independent learning
p=.007), and an interaction of peptide and drug (F1,34=8.906, p=.005). Tests of simple main effects revealed a significant difference in LTM2 freezing between pepR845A/AP5 animals and all other groups (all p<0.001). There were no other group differences (all p>.944). * p<0.05, ** p<0.01. Data are expressed as mean percent of LTM test spent freezing ± s.e.m.
Figure 3. Post- Training1 infusion of pepR845A/SCR-pepR845A into ACC.
(Top) Behavioural protocol. Rats were infused with pepR845A or scrambled-
pepR845A into ACC 24h after Training1 and were given LTM1 test 24h later (a). Based on
LTM1 freezing animals were pseudorandomly assigned to receive dHC infusion of VEH or
AP5 prior to Training2 (n/group: SCR-pepR845A/VEH=5; SCR-pepR845A/AP5=7; pepR845A/VEH=7; pepR845A/AP5=8). (a) Two-way ANOVA revealed no main effect of
peptide (pepR845A and SCR-pepR845A; F1,23=0.064, p=0.80) or to-be-infused drug (AP5 and
VEH; F1,23=0.012, p=0.91), and no interaction of peptide and drug (F1,23=0.439, p=0.51). (b) A two-way ANOVA comparing peptide (pepR845A and SCR-pepR845A) and drug (AP5 and
VEH) for LTM2 scores revealed a peptide by drug interaction (F1,23=6.029, p=0.022). Tests of simple main effects revealed a significant difference in LTM2 freezing between pepR845A-
AP5 and pepR845A-VEH groups (F1,23=9.78, p=0.005), as well as pepR845A-AP5 and SCR- pepR845A-AP5 groups (F1,23=8.409, p=0.008), but there were no other statistically significant differences (all p>.482). * p<0.01. Data are expressed as mean percent of LTM test spent freezing ± s.e.m.
Figure 4. Prior exposure to context or footshock alone is insufficient to induce AP5- insensitive learning.
198 Chapter 3 – NMDAR-independent learning
(Top) Behavioural protocol. Rats were randomly assigned to receive either 4.5min
exposure to Context1 or an immediate shock procedure in Context1 as Training1. Freezing during LTM1 test is not shown (see results). Each group was then pseudorandomly divided to receive VEH or AP5 infusion prior to Training2 (n/group: context-VEH=6, context-AP5=6, shock-VEH=7, shock-AP5=7). (a) Animals exposed to Context1 during Training1 exhibited impaired freezing during LTM2 when infused with AP5 (20.12±6.83%) versus VEH
(57.58%±5.21%; t10=4.36, p=.001). (b) Animals exposed to two immediate shocks during
Training1 also exhibited less LTM2 freezing when infused with AP5 (32.21±9.08%) versus
VEH (65.49±6.63%; t12=3.0, p=0.012). * p<0.05, ** p<0.01. Data are expressed as mean percent of LTM2 test spent freezing ± s.e.m.
Figure 5. Only rats given a similar conditioning protocol during Training1 and Training2 exhibit AP5-insensitive second learning.
(Top) Behavioural protocol. Each rat was randomly assigned to receive either standard contextual fear conditioning (CFC) or two-phase context pre-exposure fear
conditioning (CPFE) during Training1 and during Training2. Animals from each of these four groups were pseudorandomly assigned to receive either AP5 or VEH during Training2
(n/group: CFPE/CFC-VEH=7, CFPE/CFC-AP5=8, CFC/CFC-VEH=7, CFC/CFC-AP5=8,
CFC/CPFE-VEH=9, CFC/CPFE-AP5=6, CPFE/CPFE-VEH=8, CPFE/CPFE-AP5=9). In
this experiment drug infusion was given immediately before the shock phase of Training2.
LTM1 freezing is reported in Supplementary Figure 4. Planned comparisons with Bonferroni corrections were used to compare mean LTM2 freezing of VEH and AP5 groups for each
training condition. (a) In the CPFE/CFC group, mean freezing during LTM2 was
199 Chapter 3 – NMDAR-independent learning
significantly lower for AP5 (38.01±8.86%) than VEH (76.55±6.41%) infused rats (t13=3.424, p=0.02). (b) In the CFC/CFC group, mean freezing of AP5 (61.49±7.92%) and VEH
(68.23±8.41%) rats during LTM2 was not statistically different (t13=0.584, p=1.0). (c) In the
CFC/CPFE group, mean freezing was significantly lower for AP5 (26.04±5.73%) than VEH
(59.05±7.97%) infused rats (t13=3.029, p=0.04). (d) In the CPFE/CPFE group, mean freezing of AP5 (55.35±10.56%) and VEH (52.71±10.13%) rats was not statistically different
(t15=0.179, p>1.0). * p<0.05. Data are expressed as mean percent of LTM2 test spent freezing
± s.e.m.
Figure 6. cFos-positive neurons in the ACC and hippocampus following similar versus different
Training2.
(Top) Behavioural protocol. Each rat was randomly assigned to receive either standard contextual fear conditioning (CFC) or two-phase context pre-exposure fear
conditioning (CPFE) during Training1 and during Training2, or no training (homecage control). Freezing during LTM1 was comparable across all training groups (data not shown).
Ninety mins after Training2 the rats were perfused and each brain was prepared for cFos immunohistochemistry. (a) Concerns regarding the assumptions for ANOVA led us to conduct non-parametric Kruskal-Wallis tests. For cFos positive neuron counts in ACC this test revealed significantly different mean ranks among groups ((2=14.225, p=0.001). Mann-
Whitney U post-hoc tests with Bonferroni correction revealed a significant mean rank difference between Homecage and Different groups (p=0.006), Homecage and Similar groups
(p=0.039), and, importantly, between Similar and Different groups (p=0.045).) Kruskal-
2 Wallis test of cFos counts in dorsal CA1 also revealed a significant effect (( 2=9.285, p=0.01).
200 Chapter 3 – NMDAR-independent learning
Post-hoc tests revealed a significantly different mean rank between different and homecage
(p=.027), as well as similar and homecage (p=.036) groups, but no difference between similar and different conditions (p=.609). In barrel cortex we also observed a different distribution of mean ranks across conditions ((2=11.3, p=0.004). Mann-Whitney post-hocs indicated a non- significant difference between training groups (p=1.0), although there was a significant difference for similar versus homecage (p=.009) and different versus homecage conditions
(p=0.006). (b) Schematic diagram (Paxinos and Watson 2007) and representative slices from animals in each group for each ROI (number below each slice is A/P distance from bregma).
Scale bars represent 100um for each ROI. */$ p<0.05, ** p<0.01. Data are means normalized to homecage control ± s.e.m.
201 Chapter 3 – NMDAR-independent learning
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202 Chapter 3 – NMDAR-independent learning
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205 Chapter 3 – NMDAR-independent learning
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206 Chapter 3 – NMDAR-independent learning
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Data are expressed as mean percent of LTM test spent freezing ± s.e.m. Supplementary Figure 2. Short or long exposure to Context2 is not sufficient to engage AP5- insensitive second learning mechanisms. (Top) Behavioural protocol. Rats were randomly assigned to receive as Training1 either one short 4.5min exposure or two long 30min exposures to Context2. As in our standard procedure, each rat was given LTM1 test 24h after the termination of Training1. Animals were then pseudorandomly assigned to receive VEH or AP5 immediately prior to Training2 (CFC in Context2). (a) As was to be expected, LTM1 freezing was nominal in all groups (short/VEH= 1.13±0.82%, short/AP5=2.28±1.51%; long/VEH=0.65±0.22%, long/AP5=1.85±0.92%). (b) However, VEH- and AP5-treated animals in each Training1 condition exhibited significantly different freezing responses during LTM2, as revealed by planned comparisons with Bonferroni correction (short/VEH=60.28±5.69%, 208 Chapter 3 – NMDAR-independent learning short/AP5=15.12±11.02%, t12=3.29, p=.014; long/VEH=46.42±7.72%, long/AP5=10.44±4.63%, t14=4.0, p=.002). *p<0.05, **p<0.01. Data are expressed as mean percent of LTM test spent freezing ± s.e.m. Supplementary Figure 3. 1 or 3 immediate shocks in Context1 or 2 immediate shocks in Context2 is not sufficient to engage AP5-insensitive second learning mechanisms. (Top) Behavioural protocols. During Training1 each rat received either 1 or 3 immediate footshocks in Context1 (a, b) or 2 immediate shocks in Context2 (c,d). LTM1 was administered in the same context 24h later. Based on LTM1 freezing, animals were pseudorandomly assigned to equivalent groups to receive AP5 or VEH infusions prior to CFC in Context2. (a) Bonferroni corrected planned comparisons of LTM1 scores revealed no differences in freezing between drug groups for the 1 immediate shock (VEH=3.08±1.57%, AP5=2.94±1.43%, t8=.0.068, p=1.0) and 3 immediate shock conditions (VEH=6.85±3.15%, AP5=7.72±8.75%, t13=0.195, p=1.0). (b) However, planned comparisons of LTM2 scores revealed that AP5-treated rats froze significantly less than VEH rats for both the 1-immediate shock (VEH=72.1±9.21%, AP5=35.62±8.21%, t8=2.96, p=.036) and 3-immediate shock procedures (VEH=58.21±6.61%, AP5=24.52±5.38%, t13=3.99, p=.004). (c) In rats given 2 immediate shocks in Context2 (the same context in which Training2 would later be administered), freezing during LTM1 was comparably low in to-be-VEH (3.63±1.54%) and to-be-AP5 (4.11±1.23%) groups (t12=-0.244, p=0.811). (d) Freezing during LTM2 was significantly lower for AP5 (16.76±6.87%) than VEH (52.91±10.09%) treated animals (t12=2.962, p=0.012). *p<0.05, **p<0.01. 209 Chapter 3 – NMDAR-independent learning Supplementary Figure 4. One- and two-phase contextual fear conditioning each produce reliable fear responding during LTM1 test. (Top) Behavioural protocol. As in Figure 5, each rat was randomly assigned to receive either standard contextual fear conditioning (CFC) or two-phase context pre-exposure fear conditioning (CPFE) during Training1 and during Training2. Animals from each of these four groups were pseudorandomly assigned to receive either AP5 or VEH prior to Training2 (n/group: CFPC/CFC-VEH=7, CFPC/CFC-AP5 = 8, CFC/CFC-VEH=7, CFC/CFC-AP5=8, CFC/CPFE-VEH=9, CFC/CPFE-AP5=6, CPFE/CPFE-VEH=8, CPFE/CPFE-AP5=9). Mean freezing during the LTM1 test is reported. Planned comparisons with Bonferroni correction were used to compare mean LTM1 freezing of VEH and AP5 groups for each training condition. (a) In the CPFE/CFC group, mean freezing during LTM1 was comparable for VEH (64.2±9.53%) and AP5 (63.36±7.68%) infused rats (t13=0.069, p>1.0). (b) In the CFC/CFC group, mean freezing of VEH (68.23±8.41%) and AP5 (61.49±7.92%) rats during LTM2 was not statistically different (t13=0.17, p>1.0). (c) In the CFC/CPFE group, mean freezing was statistically equivalent for AP5 (48.79±9.06%) and VEH (55.28±8.42%) infused rats (t13=0.51, p>1.0). (d) In the CPFE/CPFE group, mean freezing of VEH (60.69±9.95%) and AP5 (67.51±6.61%) rats was also not statistically different (t15=0.583, p=1.0). Data are expressed as mean percent of LTM1 test spent freezing ± s.e.m. Supplementary Figure 5. Weak, strong, or long CFC as Training1 can switch the mechanisms of Training2. (Top) Behavioural protocol. Rats were randomly assigned to receive as Training1 either one or three immediate shocks in Context1. Based on freezing during the LTM1 test 210 Chapter 3 – NMDAR-independent learning administered 24h after Training1, animals were then pseudorandomly assigned to receive VEH or AP5 immediately prior to Training2. (a) For both 1 and 3 shock conditions, LTM1 freezing was not significantly different for each drug group (1-shock/to-be-VEH= 20.63±6.88%, 1-shock/to-be-AP5=30.90±10.80%, t15=0.846, p=0.82; 3-shocks/to-be- VEH=49.85±10.15%, 3-shocks/to-be-AP5=52.09±7.83%, t11=0.178, p=1.0, Bonferroni adjusted). (b) VEH- and AP5-treated animals in each Training1 condition also exhibited similar freezing levels during LTM2, as indicated by planned comparisons with Bonferroni correction (1-shock/VEH=45.85±7.11%, 1-shock/AP5=47.31±11.69%, t15=0.109, p=1.0; 3- shock/VEH=58.84±13.05%, 3-shock/AP5=50.95±6.73%, t11=0.562, p=1.0). Data are expressed as mean percent of LTM test spent freezing ± s.e.m. In (c) and (d), the two phases used for Training1 in Fig. 5a were condensed into a single conditioning session (12 min exposure to Context1 after which two footshocks were delivered) (VEH, n=4; AP5, n=5). We found no evidence of impairment by AP5 relative to VEH. (c) Freezing during LTM1 was used to assign animals to homogeneous drug treatment groups (To-be-VEH=72.95+/-9.92%, To-be- AP5=74.83+/-4.34%; t7=0.19, p=0.86). Fear generalization was limited in both groups (VEH=6.37+/-6.36, AP5=3.47+/-2.13%) and not significantly different (t7=0.43, p=0.68). (d) Freezing during LTM2 was also equivalent (t7=0.06, p=0.95) for VEH (66.62+/-11.36%) and AP5 (65.55+/-11.79%) groups. This indicates that it was not simply the long pre-exposure to context during two-phase conditioning which prevented the switch to AP5-insensitive second learning. It appears that the brain might compare events, but NMDARs are necessary only for more salient differences in training procedures (i.e. one-trial versus two-trial conditioning). Supplementary Figure 6. Specificity and selectivity of two-phase fear conditioning protocol. 211 Chapter 3 – NMDAR-independent learning A pilot experiment was conducted to verify the parameters of the CPFE protocol used in experiments 5 and 6. It was designed to answer several main questions. Firstly, do animals require pre-exposure to the specific context in which immediate shock will subsequently be delivered, or would pre-exposure to any context rescue the deficit? Secondly, do animals pre- exposed to two contexts exhibit a comparable freezing response to those pre-exposed to just the to be-shocked context. And thirdly, will animals freeze only to the context in which they received immediate shock, or will they fail to discriminate the shocked from an unshocked context? Animals were randomly assigned to one of three groups: one that received exposure to Context1 during Training1 but only handling during Training2 (Exposure1-Handling2), one that received handling during Training1 but exposure to Context2 during Training2 (Handling1-Exposure2), and one that received exposure to Context1 during Training1 and exposure to Context2 during Training2 (Exposure1-Exposure2). Twenty-four hours after Training2, animals were placed into Context2 and two immediate footshocks were delivered as described previously. Rats in each group were then randomly assigned to be tested in one of the two contexts. A one-within, one-between two-way ANOVA revealed a significant main effect of Testing Context (F1,28=13.167, p=0.001), though no main effect of training procedure (F2,28=3.009, p=0.066). However, there was a significant Test Context by Training procedure interaction (F2,28=3.53, p=0.043). Tukey’s post-hoc comparisons revealed no differences among the training conditions for animals tested in the unshocked context (all p=0.984), but significantly less freezing to the shocked context by rats in the TH group than in the HT (p=0.035) or TT (p=0.036) groups. The HT and TT groups did not differ significantly (p=0.993). This suggests that animals a) must be pre-exposed to the context in which immediate shock will be delivered for a fear response to be acquired, and b) this fear response is expressed only when animals are tested in the shocked context, but not in the unshocked 212 Chapter 3 – NMDAR-independent learning context. Even the animals pre-exposed to both contexts showed this pattern of context discrimination and selectivity. Note that freezing response was lower in this pilot experiment than Experiments 5 and 6, which we frequently observe in uncannulated relative to cannulated rats. * p<.05. Supplementary Figure 7. Representative cannula placements. The left panel presents a schematic diagram depicting placement of cannula tips in dHC and vHC from rats used during the experiment presented in Experiment 2. The top- right panel presents a schematic diagram of cannula tips in ACC, taken from rats used in Experiment 3. Numbers on far right and left, respectively, are the distance of each coronal slice (posterior to bregma), in millimeters. Brain schematic adapted from Paxinos and Watson (2007). The middle- and bottom-right images are representative photomicrograph displaying the location of cannula tips within the dHC and vHC (middle), and ACC (bottom). 213 Chapter 3 – NMDAR-independent learning 3.9. SUPPLEMENTARY FIGURES. Training LTM 24h 24h dHC: ZIP 180s 60s SCR-ZIP 240s pep-R845A SCR-pep-R845A a ZIP/Scr-ZIPZIP/Scr ZIP b pep-R845A/Scr-pep-R845Apep R845A/Scr pep R845 est est 100 100 SCR-ZIP SCR-pep-R845A ZIP pep-R845A TM T TM T * L L 80 80 60 60 40 40 20 20 0 0 % time spent freezing during % time spent freezing during Supplementary Figure 1. 214 Chapter 3 – NMDAR-independent learning Training1 LTM1 Training2 LTM2 24h 5d 24h 270s 24h 24h 240s 180s 60s 240s 30min 30min a LTM1 b LTM2 100 To-be-VEH 100 VEH TM2 TM1 L L To-be-AP5 AP5 80 80 * 60 60 ** 40 40 20 20 0 0 4.5min Ctx2 2x30min Ctx2 % time spent freezing during 4.5min Ctx2 2x30min Ctx2 % time spent freezing during Training 1 Procedure Training 1 Procedure Supplementary Figure 2. 215 Chapter 3 – NMDAR-independent learning !"#$%$%&' (!)' !"#$%$%&* (!)* '() $* '() !$# !"# '() '("# !%"# &"# '("# !$# !"# # (!)! 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" %&'& ( %&'( &'( &'(/ !"") !"") /01231456) /01231456) % % /0123178$ /0123178$) -") -") .") .") &") &") %") %") ") ") ./0*12/342+0/5)226*+,/$7)*+,/ ./0*12/342+0/5)226*+,/$7)*+,/ / CPFC/CFC CFC/CFC # %&'& ( $ %&'( &'( !"") &'(/ !"") /01231456) % % /01231456) /0123178$ /0123178$) -") -") .") .") &") &") %") %") ") ") ./0*12/342+0/5)226*+,/$7)*+,/ ./0*12/342+0/5)226*+,/$7)*+,/ CPFC/CPFC CFC/CPFC Supplementary Figure 4. 217 Chapter 3 – NMDAR-independent learning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upplementary Figure 5. 218 Chapter 3 – NMDAR-independent learning 1+3$)$)-5 1+3$)$)-4 6789:"(73'& 012"1&'# %4 #$1 %4 #$1 #$!0 #$1 %4 '?0 '!0 #$!0 '#@=* '#@=*0 '!!" 6-7)0/8,'9:;*4<=*># &'#" :;*4<=*>'96-7)0/8,# 6-7)0/8,'96-7)0/8,#" 12" 1 &!" %!" A A $!" #!" 0 !"#$%&"'(&)#"*+&&,$)-"./+$)-" !" ()*+,-+'"./*01)23,45 ()*+,-+#".01)23,45 LTM Training Context Supplementary Figure 6. 219 Chapter 3 – NMDAR-independent learning Supplementary Figure 7. 220 Running head: Chapter 4 - General Discussion Chapter 4 General Discussion Chapter 4 - General Discussion 4.1. SUMMARY OF STUDIES. Our memory preserves a record of past events that serves to guide future behavior, but this must remain adaptable to changing experience. Although it is not yet clearly understood how or when adaptation is mediated by the formation of new memories and when it involves restructuring existing knowledge, at the very least it appears that the content of existing memory traces can be permanently altered by related experience (Dudai and Eisenberg 2004). This appears to rely on the process of memory reconsolidation. Memories are often observed to become more resilient to destabilization with both repetition (Lee 2008; Rasch et al. 2007; Diekelmann et al. 2011) and time (Frankland et al. 2006; Alberini 2011). Often these two boundaries on reconsolidation are confounded given that spontaneous retrieval during waking (Foster and Wilson 2006) or replay during sleep (Skaggs and McNaughton 1996) might gradually stabilize memories over time (Sara 2010). In general these boundaries indicate that stabilization of knowledge may be use-dependent, which may serve as an adaptive system to selectively preserve those memories that are most frequently activated to influence our behaviour. Assuming that we retain even a miniscule proportion of our experiences for an extended interval (months, years, or decades), this should theoretically lead to the accumulation of memories with time. Specifically, interleaving of experiences should gradually result in the formation of a core set of strongly encoded memories that accurately represent our world, and our daily experiences should increasingly fit (or be made to fit) into this accumulated set of predictions. This thesis aimed to explore two major predictions stemming from this conceptualization of life-long memory processing. First, that A! peptide is not exclusively an age-associated pathogen, but may be a critical component of a memory stabilization system that becomes 222 Chapter 4 - General Discussion dysregulated as our neural encoding medium saturates over time. Based on Chapter 2 it can now be concluded that the endogenous generation of A! in the rat amygdala after learning reduces the propensity of strongly-trained memories to be restructured in the future, while A! generated after retrieval can promote the restructuring of weakly-trained memories. The second prediction was that the brain can abstract and store essential features that represent experience, which can alter the encoding of subsequent events by sharing common elements. Based on the results of Chapter 3 we can conclude that an ACC-mediated memory for the temporal organization of a fear conditioning task can allow dHC NMDAR-independent mechanisms to encode a second task that has a similar training procedure. 4.2. AMYLOID-BETA PLAYS AN ADAPTIVE ROLE IN SYNAPTIC PLASTICITY AND MEMORY. The aim of the study presented in Chapter 2 was to test if A! generated during learning plays a functional role in the stabilization and destabilization of memory. This was initially motivated by reports that A! can be released by neuronal activity and stress (Kamenetz et al. 2003; Cirrito et al. 2005; Kang et al. 2007), and that concentrations of A! in the physiological range can enhance neuronal transmission, synaptic plasticity, and memory formation (Puzzo et al. 2008; Abramov et al. 2009; Garcia-Osta and Alberini 2009; Palop and Mucke 2010). Given that behavioural experiences can increase A! production, an adaptive function may be to facilitate the encoding of events. I hypothesized that behavioural tasks that are motivationally salient or require many training trials might evoke a greater increase in A!, which might contribute to the increased resistance of strong memories to destabilization/updating following reactivation (Morris et al. 2006; Lee 2010). This study was further inspired by work suggesting that at higher levels, A! can influence ocular dominance 223 Chapter 4 - General Discussion plasticity (William et al. 2012; Kim et al. 2013), activate NMDARs (Texidó et al. 2011; Li et al. 2011), and cause the loss of membrane surface/synaptic NR2B-containing NMDARs (Snyder et al. 2005; Kurup et al. 2010; Kessels et al. 2013). In conjunction, this data led us to predict that the increased A! generation that is observed as training strength increases might not simply cause a monotonic enhancement of memory stability resulting from the strengthening of encoding. Instead, A! may promote plasticity induction (e.g. the formation of memory) semi-independently of its influence on the ability of a memory to subsequently undergo plasticity (e.g. the destabilization of memory). This model is reminiscent of a metaplasticity phenomenon (Abraham and Williams 2008). 4.2.1. A! levels in the brain are elevated following aversive learning events. To test these predictions we designed a series of experiments using auditory fear conditioning, because the behavioural boundary conditions to reconsolidation for this form of memory have been previously established, as have potential molecular mechanisms that regulate these boundaries (Wang et al. 2009a). The first goal of this study was to confirm that training strength could influence A! levels in basolateral amygdala (BLA) - a region strongly implicated in fear memory formation and retention (see Maren 2001). As predicted, 90 minutes after training, uncannulated rats given 10 tone-footshock pairings had elevated measures of A! (x-42) in BLA relative to rats given 1 tone-footshock pairing. This expands upon limited and seemingly contradictory previous research that has examined A! expression following learning. In one such study, Kang and colleagues (2007) confirmed that it was possible to repeatedly collect quantifiable samples of A! in extracellular fluid in vivo following behavioural events, by using animals that over-produce A!. Young transgenic mice overexpressing APP 224 Chapter 4 - General Discussion that experienced 3 hours of restraint stress were found to have elevated expression of A! in interstitial fluid at two time points: 1 hour after stress initiation, and 8-10 hours after its termination. Interestingly, this result indicated that there might be dual waves of A! secretion that can be evoked by stressful behavioural experiences: one shortly after the start of stress, and another half a day later. If A! secretion contributes functionally to memory encoding, this finding hints that it may not be limited to the initial induction of plasticity but also to the consolidation phase. Of note, mice exposed to chronic (3 month) isolation stress were found to have A! levels that were strongly and persistently elevated in both ISF and hippocampal tissue. Results from the present study are consistent with those of Kang and colleagues, as both demonstrated that acute behavioural experiences can elicit changes in A! secretion within the following hours. Just one prior study has assessed the effects of acute behavioural experience on A! expression in the brains of wild-type rodents. This is partly because A!, especially the purportedly pathogenic A!42 isoform, naturally occurs at only very low levels in the rodent brain (Schmidt et al. 2005; Teich et al. 2013). The results of this study revealed rather different A! dynamics after behavioural training than those observed by Kang and colleagues. Puzzo and colleagues (2011) trained young adult mice with a "contextual fear conditioning" task, though it can be inferred that this was, in fact, a single-trial auditory fear conditioning procedure that elicited both auditory and contextual fear responding (see Puzzo et al. 2008 for methods). Levels of A!1-42 and x-42 (presumably cleaved by !-secretase at either the 1 or 11 residue site; see Cai et al. 2001) were both increased in hippocampal tissue extracted 1 minute, but not 5 or 30 minutes after training. Control experiments confirmed that this was likely caused by plasticity related to fear learning, as there was no post-training increase in A! levels 225 Chapter 4 - General Discussion in cerebellar tissue, and animals receiving exposure to the context, tone, or footshock alone (via an immediate shock procedure) showed no change in A! levels over baseline. The reasons for these distinct patterns of A! production in the hippocampus after aversive experiences are not entirely clear. The most likely explanation is that the behavioural manipulation used by Puzzo and colleagues was a very weak training procedure, thus A! elevations may have been too small to detect at longer delays after training. Kang and colleagues, on the other hand, used much more prolonged experiences that could be associated with greater degrees of stress and arousal. Indeed, they recorded a much more persistent and prominent elevation of A! after chronic versus acute stress. Our findings support this prediction, as we found that rats exposed to a prolonged aversive experience exhibited significantly elevated A! levels 90 minutes later, relative to those given a brief aversive experience. Despite these discrepancies, the results from Puzzo and colleagues nonetheless confirm that associative learning can evoke a transient increase in A! expression, even following weak training. 4.2.2. A! contributes to memory stabilization after learning. To determine if A! secretion observed after strong training contributes to the encoding of this event into memory, we blocked the enzymes that cleave A! from APP, !- and %- secretase. Using post-training infusions of each inhibitor, we predicted that there may be little effect on the strength of the memory when tested 24 hours later, in agreement with the findings of Garcia-Osta and Alberini (2009). However, the strong training we used here has been found to cause downregulation of NR2B-containing NMDARs in BLA 48 hours after training, which is associated with resistance of the memory to destabilization following reactivation (Wang et al. 2009a). As very high concentrations of A! have been found to 226 Chapter 4 - General Discussion suppress NR2B expression (Snyder et al. 2005; Kurup et al. 2010), we hypothesized that the large A! surge after training could contribute to this NMDAR removal. As predicted, infusion of either secretase inhibitor after training caused the strong memory to subsequently undergo reconsolidation following reactivation, when it otherwise would not. Importantly, neither treatment alone caused a reduction in the strength of the memory (as assessed by measuring the conditioned response during a subsequent test) relative to animals treated with control vehicle. The interpretation we have presented is that post-training suppression of A! inhibits a process that normally downregulates a mechanism required for subsequent destabilization (in a metaplasticity-like manner). Only one published study has attempted to pharmacologically facilitate the induction of reconsolidation in a memory that would not otherwise be destabilized following reactivation. Lee and Flavell (2014) found that infusing a CB1-receptor agonist prior to reactivation of a contextual fear memory could boost the plasticity signal evoked by re-exposure of the rat to the training context, presumably causing it to reach a threshold necessary for memory destabilization. This was observed using two different amnestic treatments applied after reactivation (IkappaB kinase inhibitor infused into the hippocampus or NMDAR inhibitor MK-801 injected systemically), which are both known to impair reconsolidation of contextual fear memories (see Lubin and Sweatt 2007; Lee and Hynds 2012; Charlier and Tirelli 2011). The assumption inherent to this study is that the conditions at the time of training (and perhaps during the retention interval) should dictate the forms of stimuli that will be required to reactivate and labilize a memory. In this case, the contextual fear conditioning procedure used does not permit memory destabilization by re-exposure to the context alone, but re- exposure is sufficient when facilitated by the endocannabinoid agonist. The results of Chapter 227 Chapter 4 - General Discussion 2 support this underlying assumption, as strong auditory fear conditioning alone produced a memory that was not destabilized by re-exposure to the tone, unless training was followed by suppression of A! in BLA. Our results also extend prior understanding by demonstrating that memory stability is not exclusively regulated by the same mechanisms as memory strength. Instead, the resistance of a memory to post-reactivation destabilization was found to be significantly diminished by application of secretase inhibitors after training, but with no significant effect on the expression of memory during reactivation. However, to convincingly dissociate memory strength from memory stability is an empirical challenge. If progressively increasing the strength of training leads to a decreasing degree of amnesia evoked by treatments applied after reactivation (i.e. Rodríguez-Ortiz et al. 2005; Morris et al. 2006), it can always be argued that the memory is still being disrupted to the same degree, but the behavioural response is at an asymptote and so impairments cannot be readily detected. As a hypothetical example, if a fear memory is mediated by AMPAR insertion at lateral amygdala synapses (see Blair et al. 2001; Rumpel et al. 2005), the retention of weak training might be more impaired by the loss of each receptor than would retention of a strong memory. In Chapter 2 we observed no significant difference in fear responding during reactivation between animals given a secretase inhibitor, that will undergo reconsolidation, and controls that will not. However the difference in fear responding elicited at reactivation by weak training memory that will undergo reconsolidation and strong training memory that will not is rather small (in our experiments approximately 20-25%, on average). Thus we cannot rule out the possibility that inhibiting A! generation is causing an undetectable reduction in memory strength that is sufficient to sway encoding from reconsolidation resistant to not. In this way, memory strength itself (for example, retention of 228 Chapter 4 - General Discussion AMPARs at the synapse) could be the sole mediator of memory stability. Yet given what is known about the neurophysiology of metaplasticity, it seems probable that the effects we have observed are due to changes in the function of plasticity mechanisms at the time of learning which inhibits the subsequent induction of reconsolidation (Hulme et al. 2013; Abraham et al. 2008). In this way, memory strength may truly be dissociated from memory malleability. In this regard, in Chapter 2 we also replicated the findings of Wang and colleagues (2009a), by demonstrating that rats given the strong 10-pairing training caused a downregulation of NR2B-subunits in the BLA, relative to rats given the weak 1-pairing training. Furthermore, we extended these findings by assaying the crude post-synaptic density fraction, suggesting that these subunits could be functional. However, we failed to confirm our hypothesis that inhibiting A! generation would prevent the downregulation of synaptic NR2B subunits following strong training. As inhibiting A! generation allowed the strong memory to subsequently undergo reconsolidation, this leads to two non-mutually exclusive interpretations. First, that NR2B downregulation after strong training does not contribute functionally to memory stabilization. However, although this is a possibility, we expect this is not the case based on the fact that NR2B activity in BLA during fear memory reactivation is necessary to induce destabilization (Ben Mamou et al. 2006; Milton et al. 2013). A second, and more likely possibility is that inhibiting A! production after strong training prevents a long-term change in the expression or activity of another plasticity induction mechanism. As this is the first study to report that pharmacological manipulations at the time of training can influence the ability of memories to undergo reconsolidation, the mechanisms targeted are not yet clear. We reviewed several prospective candidates in the discussion of Chapter 2, so they will not be reiterated here. 229 Chapter 4 - General Discussion 4.2.3. A! facilitates memory destabilization. A more surprising finding of Chapter 2 was that post-reactivation infusions of !- secretase inhibitor could interfere with the destabilization of previously consolidated memories for weak training. Given that pre- but not post-training manipulations of A! have been reliably observed to positively modulate memory acquisition (Puzzo et al. 2008; Garcia- Osta and Alberini 2009; Morley et al. 2010), it seemed feasible that this may also be the case at the time of memory retrieval. Specifically, we predicted that inhibiting endogenous A! generation immediately prior to the reactivation of a memory for weak 1-pairing auditory fear would prevent its destabilization. However, our preliminary work revealed that infusion of %- secretase inhibitor before reactivation severely disrupted retrieval/expression of memory. To avoid this potential confound we instead began these experiments by using post-reactivation infusions. Remarkably, !-secretase infusion following reactivation was found to prevent the amnesic effects of protein synthesis inhibitor given immediately afterwards. Because NR2B- containing NMDAR activity is necessary during reactivation to trigger destabilization (Ben Mamou et al. 2006), A! may not be facilitating reconsolidation by activating these receptors, as we initially hypothesized (based on Texidó et al. 2011; Kessels et al. 2013). Thus, !-secretase inhibitor joins a rapidly lengthening list of drug treatments that can prevent the destabilization of memory at the time of retrieval. In the case of auditory fear memory in BLA these mechanisms include NR2B-containing NMDAR antagonist ifenprodil (Milton et al. 2013), proteasome inhibitor !-lactacystin (Jarome et al. 2011), and GluA2 endocytosis interference peptide GluA23y (Hong et al. 2013). However there is also evidence that these specific mechanisms can prevent destabilization of different types of memory in different brain regions, suggesting that there could be considerable homogeneity in 230 Chapter 4 - General Discussion destabilization processes throughout the brain. For example, in the dorsal hippocampus NR2B activity is required to update contextual fear memories with appetitive information (Haubrich et al. 2014), and both proteasomal activity and GluA2 endocytosis are necessary for destabilization of contextual fear conditioning memories (Lee 2008; Rao-Ruiz et al. 2011). Therefore, it is not improbable that !-secretase or A! could also be a fundamental mechanism of plasticity that influences memory labilization in multiple brain regions. The second striking finding from this set of experiments was that post-reactivation infusion of %-secretase inhibitor LY-450139 produced a markedly different effect than !- secretase inhibitor. Specifically, %-secretase inhibitor alone evoked a significant enhancement of memory expression when tested 24 hours later. However, this enhancement was completely prevented in rats given anisomycin immediately after LY-450139. It is unlikely that a differing effectiveness of each drug to inhibit A! generation can exclusively account for these distinct effects. Instead, it is possible they are due to the other fragments of APP that can be preferentially produced when the activity of each secretase is disrupted (see Turner et al. 2003; Henley et al. 2009). One convincing candidate for memory enhancement following %-secretase inhibition is the reduced cleavage of APP intracellular domain (AICD) fragment. AICD should be cleaved from the APP c-terminal by %-secretase regardless of !-secretase function, and may itself have effects on plasticity, gene transcription, and memory (Ghosal et al. 2009; Müller et al. 2008). It has recently become clear that this AICD transcriptional activation is important for synaptic plasticity, though like A! it can cause memory impairments when expressed at high levels (Ghosal et al. 2009; see Borquez and Gonzalez-Billault, 2012). Reduced %-secretase activity could also lead to an accumulation of partially-cleaved sections of APP known as c- 231 Chapter 4 - General Discussion terminal fragment-" and -! (CTF" and CTF!, respectively), which are also know to influence neuronal function and memory encoding (see Turner et al. 2003; Schettini et al. 2010). However, increased CTF! generation is an unlikely candidate for memory enhancement as it has instead been reported to cause learning impairments (Tamayev et al. 2012). As little is known about the mnemonic functions of these peptides, additional work will be necessary to determine if augmented production of non-A! APP fragments can enhance memory following reactivation. However, the most likely alternative target of %-secretase inhibition, as mentioned in Chapter 2, remains Notch (De Strooper et al. 1999). Notch activity can suppress LTP and memory formation (Dahlhaus et al. 2008; Dias et al. 2014), thus inhibiting its proteolytic activation could likely facilitate long-term memory at the time of reactivation. Indeed, auditory fear conditioning causes post-translational suppression of Notch signaling in BLA, which facilitates memory consolidation (Dias et al. 2014). Although !-secretase inhibitor very likely prevents memory destabilization by reducing A! production, it could also exert its effects by increasing the accumulation of full-length APP. Uncleaved APP is thought to be important for spatial working memory (Senechal et al. 2007), as well as long-term memory formation (Doyle et al. 1990; Huber et al. 1993) and consolidation (Goguel et al. 2011). For instance, APP can control cell adhesion (Storey et al. 1999), which can be critical for late-LTP (Chun et al. 2001) and memory formation (Enevoldsen et al. 2012). As one example, increasing APP expression by inhibiting cleavage at its !-site could feasibly prevent synaptic remodeling following reactivation by increasing binding to extracellular matrix proteins (see Klier et al. 1990; Bronfman et al. 1996), which are important for retaining memory following both extinction (Gogolla et al. 2009) and reconsolidation (Brown et al. 2007). Reducing !-secretase activity can also bias APP 232 Chapter 4 - General Discussion processing towards the non-amyloidogenic pathway by providing more opportunity for "- secretase to cleave APP into CTF" and secreted APP" (sAPP"; see Vassar et al. 1999). sAPP is a particularly valid candidate as it is neurotrophic, neuroprotective (see Turner et al. 2003), and can positively modulate memory (Meziane et al. 1998; Bour et al. 2004; Ring et al. 2007; Taylor et al. 2008). Of note, in vivo LTP induction increases sAPP secretion (Fazeli et al. 1994) and exogenous application of sAPP" to hippocampal slices raises the stimulation frequency threshold for induction of both LTD and LTP while also enhancing LTP magnitude (Ishida et al. 1997). Thus inhibiting !-secretase activity could theoretically prevent memory destabilization by reducing A!, increasing sAPP", or both. One final possibility, as discussed in Chapter 2, is that both the !- and %-secretase inhibitors may prevent memory destabilization by blocking A! generation. In both experiments, rats given a post-reactivation secretase inhibitor prior to the protein synthesis inhibitor anisomycin froze comparably to rats given vehicle control treatments during each infusion. The clearest interpretation is that suppressing A! had, in fact, prevented the amnesia induced by post-reactivation anisomycin. However, in the case of the %-secretase inhibitor, some other mechanism (perhaps another APP fragment or reduced Notch activity) also serves to enhance the memory, possibly by a process that either does not require destabilization or relies on an independent form of plasticity (see Rao-Ruiz et al. 2011; Fukushima et al. 2014). In some ways this resembles the semi-dissociable induction mechanisms of synaptic LTP and LTD (see Malenka and Bear 2004), and is congruent with the overwhelming number of studies demonstrating that high concentrations of A! can facilitate/induce LTD but inhibit LTP (i.e. Wang et al. 2002; Shankar et al. 2008). Overall, the nature of the memory enhancement observed in rats treated with only %-secretase inhibitor after reactivation is 233 Chapter 4 - General Discussion unresolved, and our upcoming studies will address if directly manipulating the activity of APP fragments or the Notch pathway mediates this effect. Despite the lack of clarity regarding specific molecular targets, the study presented in Chapter 2 presents the first direct evidence that mechanisms in the amyloidogenic pathway can control memory malleability phenomena. This discussion will now turn to an interrogation of the findings presented in Chapter 3, but will return to the A! findings again in Section 4.4. There I will present an interpretation of what these studies together might tell us about the way the brain constructs knowledge across a lifetime, how this could influence age-related pathology, and speculative implications for the preventative treatment of AD. 4.3. DORSAL HIPPOCAMPAL NMDAR-INDEPENDENT LEARNING. In Chapter 3 we set out to test what an animal must learn during a first training task in order to encode a second similar task without NMDAR activity in the dorsal hippocampus (dHC). As reviewed in Section 1.4.3, even decades after the initial reports of NMDAR- independent learning considerable controversy still exists regarding the nature of the relationship between the two events that engages this change in mechanisms (Hoh et al. 1999; Morris et al. 2013; Bannerman et al. 2012). Characterizing this relationship is critical because it informs us about the types of information the brain may be capable of encoding without NMDARs in dHC, and hence the forms of learning that critically rely on these receptors. Furthermore, it intimates the manner in which the memory for one event can be molded or manipulated to aid in the encoding of another related event. These experiments were initially motivated by a prior study in our lab in which rats’ 234 Chapter 4 - General Discussion memory for a first contextual fear conditioning task was impaired by dHC infusion of anisomycin given immediately after training (Hardt et al. 2009). These infusions also caused a subsequent fear conditioning task in a distinct context to be impaired by pre-training infusion of the NMDAR antagonist, AP5. However, rats that instead had their ability to express the first fear memory inhibited by extinction training exhibited intact acquisition of the second task following AP5 infusion. For decades it has been postulated that fear extinction merely suppresses expression of the conditioned response, but the memory for the conditioned association remains at least partially intact (as has been widely postulated for decades; Pavlov 1927; Bouton and Moody 2004). Thus Hardt and colleagues concluded that intact storage of the first fear conditioning task is required for NMDAR-independent learning, though the ability to express this memory is dispensable. However, from this study it remained unclear which element of the original training memory was necessary for NMDAR-independent learning: a representation of the context, the footshock, or their conditioned association. In a study conducted during our collection of data for Chapter 3, Tayler and colleagues (2011) observed that mice merely pre-exposed to a highly similar training environment could acquire conditioned fear to a new context following systemic injection of NMDAR antagonist CPP. However, in their other experiments the results were mixed. In mice that received fear conditioning in one context, then again in a less similar environment, CPP injections impaired the second task in one experiment but not another, depending on which context had been used for first and second training. Overall it was concluded that the representation of the first environment (likely mediated by the hippocampus) could be recruited to encode the second task when NMDARs were blocked only when the contexts were sufficiently similar. However, prior exposure that consisted of an identical fear 235 Chapter 4 - General Discussion conditioning procedure in a somewhat distinct context did not always permit this to occur. Thus the nature of the relationship between the two events that engaged NMDAR- independent learning seemed to revolve around the physical similarities of the environments. Discordantly, our results in Chapter 3 using a similar procedure motivated the opposite interpretation. Using dHC infusion of AP5 throughout the study, we observed that rats pre-exposed to a distinct training context (or even to the same training context that was subsequently used for fear conditioning) were profoundly impaired by application of the antagonist before training. This was also true of rats that instead received pre-exposure consisting of immediate footshock in either context prior to fear conditioning. Even rats given both of these pre-exposure sessions on consecutive days (familiarization to the context then immediate footshock in the same environment) formed a reliable fear response to this context, yet amazingly were subsequently unaffected by AP5 infused before standard fear conditioning in another distinct context. However, rats receiving "pre-exposure" that consisted of a standard fear conditioning procedure were unimpaired by AP5 infused before receiving an identical training task in a distinct context, as reported previously (Sanders and Fanselow 2003; Hardt et al. 2009). As will be discussed at length in Section 4.3.1 below, this led us to conclude that prior exposure to a particular fear conditioning procedure, but not the conditioning context, is necessary to induce dHC NMDAR-independent learning. Some speculative explanations for the discrepancy between our findings and those of Tayler and colleagues were outlined in Chapter 3, but there is not yet a clear resolution. An additional possibility is that brain-wide suppression of NMDAR activity using systemic injections may necessitate encoding based on a shared representation of context (the result of which could be a form of post-training context generalization), as there is also not NMDAR- 236 Chapter 4 - General Discussion mediated plasticity in other regions that could compensate for hippocampal dysfunction. Conversely, our use of local infusions into dHC may allow plasticity in other brain regions, such as ACC, to selectively strengthen fear associations for the subset of features shared by both events (or the common 'gist' of these experiences). However, when the training procedures are dissimilar, perhaps the memory for the first training is not activated/retrieved during the second task, requiring de novo memory formation (which is blocked by NMDAR inactivation in dHC). Regardless, the ultimate consequence of this model is the same as that presented in Chapter 3: depending on the encoding resources available during training, the brain may, up to a point, use whichever common features it detects from prior experience to support acquisition of the task. Thus, contextual similarity may permit learning during brain- wide NMDAR blockade, while procedural similarity may permit learning during local dHC NMDAR blockade. The aforementioned explanations forwarded by Cain and colleagues (Saucier and Cain 1995; Saucier et al. 1996; Cain et al. 1996; Hoh et al. 1999), that NMDARs merely cause a sensorimotor impairment that can be alleviated or ameliorated by repeated training, cannot readily account for our results. It would be difficult to explain how one fear conditioning procedure but not another could reduce sensory or motivational deficits associated with the non-specific effects of NMDAR antagonists. Moreover, we have used local hippocampal infusions of the antagonist that should limit sensorimotor impairments, and at a concentration that induces virtually undetectable non-mnemonic effects in a majority of rats (Inglis et al. 2013; Morris et al. 2013). Thus it seems likely that in our experiments, AP5 is interfering with a learning-specific process related to synaptic plasticity in the hippocampus. Instead, our results are more consistent with the demonstrations of NMDAR- 237 Chapter 4 - General Discussion independent learning performed by Morris and colleagues. Their initial report found that rats given the same training procedure in two distinct water maze environments (requiring the animals to locate a hidden platform placed in a consistent position in the pool across days, by using spatial cues in the room for navigation) could acquire the second task during chronic ICV (Bannerman et al. 1995) or dHC infusion (Inglis et al. 2013) of AP5. This was not the case for animals pre-trained with a non-spatial version of the task in which the platform moved during each day, in an environment lacking extra-maze navigational cues. In a subsequent study, rats pre-trained on a DMTP task (in which rats learn across four daily trials the location of a platform that moves with each day) were impaired by ICV AP5 infusion prior to additional days of training in the same environment (Steele and Morris 1999). These findings suggest that NMDAR activity is particularly useful to rapidly encode a sequence or conjunction of navigational cues for how to reach the most recent platform location when it moved across days. Similarly, NMDAR activity was also required when the spatial pre-training occurred in the same context but with a different platform location (reversal learning; Morris 1989). In all of these pre-training experiments the rats had been familiarized with the demands of the task and the skills important to navigate the maze prior to infusion of AP5, but only when a similar (and not behaviourally incompatible) procedure had been previously learned was NMDAR-independent learning possible. In conjunction, this data indicates that NMDARs (primarily in the hippocampus) could be critical for specific mnemonic abilities. In the experiments by Morris, to distinguish and orthogonalize knowledge about the new location(s) of the platform in an old environment, and in our experiments, to rapidly encode trial-specific information about when to expect footshocks in a new environment (discussed next in Section 4.3.1). However, their requirement is lifted when the spatiotemporal properties of a prior experience match - and are not behaviourally conflicting 238 Chapter 4 - General Discussion with - those of an ongoing experience. This could also explain the fact that NMDAR activity in dHC has sometimes been found necessary for the suppression of conditioned responding during extinction training of a feared context (Szapiro et al. 2003), as this involves learning an inconsistent behavioural response to an identical set of cues. 4.3.1. Requirement for NMDARs, hippocampus, and ACC in temporal memory? The primary question that emerges from the above conclusion concerns which aspects of learning cannot be acquired without NMDARs in dHC, even when prior training has been given. In Chapter 3 we observed that infusion of NMDAR antagonist AP5 into dHC prevented acquisition of contextual fear in rats previously trained to fear another context using a different conditioning procedure, but not rats previously trained with a similar procedure. Although these two basic fear conditioning procedures have been used for decades and studied extensively (Fanselow 1986; 1990; Rudy et al. 2002; 2004), little is known about how the mnemonic representation of standard one-phase contextual fear conditioning differs from two-phase CPFE (in which animals are exposed first to the context then subsequently to immediate shock). At the very least our results suggest that rats encode these procedures differently - each does not rely solely on identical context-shock or conditioned fear associations. Based on our results, the simplest interpretation is that the rat also acquires knowledge about when shocks will be delivered following placement into a new environment (either 3 minutes or 24 hours later). Thus, our findings suggest that NMDARs in dHC are critical during acquisition of these new temporal sequences of stimuli or events (see Allen et al. 2014 for a detailed characterization of sequence learning in rats). Once a basic temporal configuration of fear conditioning is acquired during a first training session, memories for subsequent training sessions could be constructed in absence of dHC NMDARs by building 239 Chapter 4 - General Discussion on the existing sequence when it is again encountered. Temporal learning and memory have been primarily studied using delay and trace associative conditioning procedures. It has long been known that the hippocampus is typically not required to acquire ‘delayed’ cued fear and eyeblink conditioning (in which presentation of the conditioned stimulus, such as tone, overlaps with the presentation of the unconditioned stimulus, such as electric shock applied to the foot or eyelid respectively; see Schmaltz and Theios 1972 and Quinn et al. 2002). However, when a 'trace' interval (a short period of time) is interposed between conditioned and unconditioned stimulus presentations, this necessitates hippocampal recruitment in order to acquire the appropriate conditioned response (Moyer et al. 1990; Bangasser et al. 2006; Tseng et al. 2004). Because the hippocampus is not critical to form and express the delayed conditioned fear and eyeblink tasks, it likely mediates some aspect of temporal processing or encoding. Indeed, in rats the memory for a specific sequence of odours, but not the identity of these odours, can be impaired by hippocampal lesions, suggesting that this region encodes the temporal order of events (Fortin et al. 2002). A counter argument against this claim is that the hippocampus is actually recruited in response to increasing task difficulty, rather than the trace interval, per se (see Beylin et al. 2001). However, in this experiment the length of the conditioned stimulus was merely extended, which necessarily influences temporal properties, and thus does not rule out a hippocampal role in the encoding of temporal properties of events. Recent work at the level of the neuron has further indicated a hippocampal role in temporal processing. Indeed, certain hippocampal neurons have shown selective responsiveness to temporal aspects of a task (MacDonald et al. 2011; 2013; Shapiro 2011). Just as neurons in hippocampal CA1 can develop a pattern of firing at specific locations of a 240 Chapter 4 - General Discussion given environment (O'Keefe and Nadel 1978), it was observed that specific patterns of CA1 neurons often respond at certain times during a fixed delay of a delayed responding task. Thus it appears that these cells could form a stable "map" of repeated temporally-organized experiences, that was partially independent of the specific associative cues presented during each trial. When new delay intervals were used, most of these cells "re-timed", causing them to develop a new pattern of responding within the altered time interval. Given that persistent spatial re-mapping of CA1 cells to new environments requires NMDAR activity (Kentros et al. 1998), it is possible that re-timing to new temporal intervals might as well. Thus exposure to multiple events with similar temporal sequences may recruit the existing time-coding representation, while encoding of new sequences could require NMDAR activity. If this is the case, then retrieval of temporal sequences may also require a partially intact hippocampus in order to activate the sequence representation. This may explain our recent finding that complete inactivation of the hippocampus (dorsal and ventral) during a second conditioning task with an identical procedure could impair memory formation (Wang et al. 2012a). It has been proposed that NMDARs could mediate the association of temporally- distributed sequences of stimuli via their relatively prolonged summation of inputs (Jensen and Lisman 1996). In fact, it may be the much longer activation kinetics of NR2B-containing NMDARs (or also NR2C and NR2D) that confer enhanced temporal summation abilities (Vicini et al. 1998; Cull-Candy and Leszkiewicz 2004). Because of these properties, NMDARs in the hippocampus could be generally important for the abstraction of rules from multiple experiences (see Lyford et al. 1993), which may often (as in our task) encompass temporal information about when certain types of events occur (i.e. when repeatedly brought from the home colony and placed into a small chamber, the rat could learn to expect shock 241 Chapter 4 - General Discussion after a particular delay). When no rule can be readily abstracted across experience (i.e. when the conditioning procedures differ in terms of when a shock is administered), dHC NMDARs will again be necessary to form a new memory for the novel temporal sequence. In line with these predictions, NMDARs in the hippocampus are specifically required to acquire trace conditioning with a fixed delay between stimuli (Huerta et al. 2000; Kishimoto et al. 2006; Sakamoto et al. 2005). Huerta and colleagues (2000) found that trace auditory fear conditioning was acquired more slowly in transgenic mice lacking NMDARs in CA1 neurons, and during a long-term memory test they exhibited levels of fear responding equivalent to animals given pseudoconditioning. However, NMDAR-lacking mice acquired and expressed fear responding after delay conditioning that was comparable to wild-types. Similarly, Sakamoto and colleagues (Sakamoto et al. 2005) demonstrated that acquisition and expression of trace eyeblink conditioning was impaired in animals given pre-training dHC infusions of AP5. Finally, an inducible knockout of NMDARs (affecting virtually all CA1 pyramidal neurons though also half of cortical principal cells) during training of contextual and trace fear conditioning led to an array of behavioural impairments, including an inability to respond predictively based on the temporal properties of the conditioning procedure (Zhang et al. 2013). Thus I propose that hippocampal NMDARs are of critical importance when rapidly learning the sequence of events, as they may encode the flow of stimuli that make up behavioural episodes, including during the process of spatial navigation (Jensen and Lisman 1996). However, the memory for such sequences might also be flexibly applied to other episodes that share a similar temporal arrangement, thus allowing the abstraction of statistical temporal regularities across repeated experience, and possibly the formation of general rules. 242 Chapter 4 - General Discussion However, our results from Chapter 3 indicate that the hippocampus may not be the only region storing the temporal sequence of conditioning. Disrupting memory maintenance in the hippocampus after the first training task (with pepR845A) prevented NMDAR-independent learning of a second similar task, indicating that it had interfered with the memory for the conditioning sequence. But re-exposing the animals to the first conditioning context (though not the training sequence itself) after pepR845A infusion was found to reinstate NMDAR- independent learning. This suggests that an extra-hippocampal brain region was able to both retain the memory for the conditioning sequence, and potentially re-train the hippocampus (see Jensen and Lisman 2005 for a relevant discussion). Disrupting memory maintenance in the ACC after the first training was found to impair NMDAR-independent subsequent learning even with re-exposure to the context, suggesting that the ACC might support the temporal sequence memory. As is the case for the hippocampus, it appears that pre-training lesioning or inactivation of the ACC can selectively disrupt trace, but not delay conditioning. Trace auditory fear conditioning selectively increases neuronal activity in the ACC, and lesions can prevent acquisition of the task, but in this experiment had no effect on delayed auditory or contextual fear conditioning (Han et al. 2011). Similarly, the ACC was found to be required for trace but not delay eyeblink conditioning (Kronforst-Collins and Disterhoft 1998). Relatedly, during acquisition of trace eyeblink conditioning, the ACC initially exhibits increased neuronal responses to the conditioned stimulus that progressively decrease with training (Weible et al. 2000; 2003), in a manner that superficially resembles the reduced activation of ACC neurons that we observed during training of a familiar conditioning sequence. A recent computational model suggests that hippocampal-cortical connectivity may allow for a trace interval to be 243 Chapter 4 - General Discussion compressed into the temporal integration window of molecular plasticity mechanisms (like NMDARs) through repeated retrieval or replay of the sequence. This subsequently allows cortex to represent a temporally diffuse association independently of the hippocampus (Pyka and Cheng 2014; see Jensen and Lisman 1996). Specifically, different hippocampal loops may introduce synaptic conduction delays that allow activity in the circuit to bridge long temporal intervals, which means that cortical-hippocampal interactions could be critical to encode sequences of stimuli, though not to retrieve them after consolidation. Thus I propose that interactions of the hippocampus and ACC may be important for rapidly acquiring temporal properties of conditioned associations and, once stored, a memory for temporal sequence is likely mediated semi-independently by medial cortical structures including the ACC (Woodruff-Pak and Disterhoft 2008; also see Weible et al. 2007). As noted in Chapter 1, there is considerable evidence that the ACC and other medial prefrontal cortical regions mediate several general abilities related to the ordering and sequencing of stimuli and behaviours (see Fuster 2001). By "general" it is meant that these abilities can be observed independently of the specific sensory stimuli used to experimentally study them (i.e. Wallis et al. 2001). One detailed functional analysis of rat medial prefrontal cortices, primarily based on lesion methods, presented a hierarchy of memory abilities (Kesner 2000). Within this hierarchy, lower-order rules (such as those for reversal-learning) are mediated by more lateralized structures including agranular insula and ventrolateral orbital cortex. High-order rules (including those for object location and switching response-sets across modalities) are mediated by prelimbic and infralimbic cortices, and highest-order rules (as for temporal order) are mediated by the ACC. Although this review was framed around working memory abilities, such models assume that working memory inherently requires the 244 Chapter 4 - General Discussion use of strategies or rules acquired previously (see Wise et al. 1996). This tentatively supports the position that ACC may encode temporal sequence information, which can be utilized during the performance and encoding of subsequent similar tasks. This has been demonstrated through several experimental manipulations. For instance, using a procedure adapted from Fortin and colleagues' (2002), the aforementioned study by Devito and Eichenbaum (2011) demonstrated that rats were able to judge which of two co- presented odours occurred earlier in a sequence of five previously experienced odours. This ability did not rely on absolute recency judgments, as rats exposed to odours from two different sequences presented at different times did not show a preference. Following this, animals with lesions of either the hippocampus or medial prefrontal cortex (primarily damaging ACC, prelimbic, and infralimbic regions) were impaired in their ability to correctly choose the location of an odour based on its presentation sequence, but not in their ability to recognize a previously experienced odour. This suggests that both the hippocampus and medial prefrontal cortex mediate memory for temporal order. Similarly, rats with infralimbic and prelimbic lesions extending into the ACC were able to identify previously experienced objects, as well as the locations of identical objects, but could not judge which of two objects had been presented more recently, nor the specific locations in which two different objects had been previously presented (Barker et al. 2007). Using a spatial temporal order task, Hannesson and colleagues (2004) demonstrated that inactivating the ACC/prelimibic cortex completely eliminated a recency preference in rats that were exploring a radial maze, but not their ability to recognize which of two arms was entirely novel. Finally, in one remarkable study, neurons in another prefrontal region, the orbitofrontal cortex, were observed to fire predictively during the sequential presentation of odours that had been repeatedly 245 Chapter 4 - General Discussion experienced (Ramus et al. 2007). Thus, with extensive training, neurons responsive to a particular odour in the sequence actually began to fire before that odour was presented, but this was impaired by hippocampal lesioning. It is reasonable to assume that similar activity may also be elicited by some individual ACC neurons during exposure to familiar sequences of events. In conclusion, our results align with a literature suggesting that hippocampal-prefrontal cortex (i.e. ACC) interactions support the encoding and maintenance of temporal relationships of events (Delatour and Gisquet-Verrier 2001; Weible et al. 2007; Descalzi et al. 2012). While the hippocampus might encode episodic-like information for the order in which a specific event has occurred, allowing for disambiguation of events sharing a similar sequence (Ginther et al. 2011; Eichenbaum et al. 2012), the ACC may abstract general sequence information for use in guiding behaviour during subsequent similar events, which could form the basis of behavioural strategies or rules. 4.3.2. Is dHC NMDAR-independent learning mediated by another molecular mechanism of plasticity, or another brain region? Another question that remains unanswered by Chapter 3 concerns the molecular mechanisms that mediate NMDAR-independent learning. Shockingly, this question has gone largely untested in prior studies of NMDAR-independent learning, which have primarily investigated the behavioural conditions that induce a switch in learning mechanisms (Bannerman et al. 1995; Wiltgen et al. 2011) and the behavioural effects of NMDAR antagonists (Cain et al. 1996; Hoh et al. 1999; Morris et al. 2013). The lone exception was a study by Wiltgen and colleagues (Wiltgen et al. 2010a), 246 Chapter 4 - General Discussion which tentatively indicated that calcium-permeable (CP)-AMPARs may contribute to NMDAR-independent learning. It was found that transgenic mice lacking GluA2 subunits (meaning that all AMPARs must be GluA2-lacking, and thus calcium-permeable) exhibited enhanced forms of LTP that were likely induced by both NMDARs and CP-AMPARs. Although these mutant animals displayed large impairments of contextual fear memory consolidation, they could acquire a second fear conditioning task normally, even in the presence of NMDAR antagonist CPP. However, co-application of CPP and a CP-AMPAR antagonist before the second training led to an impairment in both mutant and wild-type mice, suggesting that CP-AMPARs may mediate NMDAR-independent learning in general. In our recent study (Wang et al. 2012a) we set out to test if, in place of NMDARs, L- VGCCs in dHC could also be engaged to acquire a second fear conditioning task. However, we observed not only that L-VGCC activity in dHC is unnecessary, but also that the dHC in general is dispensable, as pre-training inactivation of this structure by muscimol did not impair fear acquisition. These experiments indicated that either the dorsal or ventral hippocampus (vHC) must be functioning to acquire a second fear conditioning task (Wang et al. 2012a). In a manner that is inconsistent with prior studies reporting intact learning after systemic or ICV application of NMDAR antagonists (Tayler et al. 2011; Li and Richardson 2013; Bannerman et al. 1995; Morris et al. 2013), we observed that AP5 infused into both dHC and vHC before the second training session caused a mild memory deficit. This hints that the "NMDAR-independent" learning effect we observe may be caused less by a change in molecular plasticity mechanisms, and more by a change in brain regions that mediate a second similar task. However, due to the small effect size we cannot rule out the possibility that the larger total volume of AP5 infused during this experiment did not cause non-specific effects, 247 Chapter 4 - General Discussion such as spread of the drug to regions adjacent to the ventral hippocampus, including BLA (where NMDAR activity is always necessary for contextual fear conditioning; Lee and Kim 1998). Future replications will hopefully clarify if NMDAR activity in the hippocampus is entirely dispensable during a second training session. The fact that maintenance of a memory for a prior training task in the ACC is critical to allow dHC AP5-insensitive learning could suggest that this cortical region mediates encoding. However, it has not yet been confirmed if cortical plasticity is necessary to acquire a second task. Others authors, such as Dragoi and Tonegawa (2013) and Inglis and co-workers (2013), have proposed that this is likely the case, but have not provided direct evidence that extra-hippocampal structures are critically engaged during hippocampal NMDAR- independent learning. Consideration of related studies may be informative, however. For example, the repeated training of similar associative contingencies (flavour-location pairings) within a fixed environment, that has been used to model schema formation and schema-mediated learning (Tse et al. 2007; 2011; Wang et al. 2012b), is ostensibly similar to the twice-repeated pairing of context with shock we have investigated. As in our studies, Tse and colleagues (2007) initially reported that the role of the hippocampus putatively changes between the initial process by which the schema is formed and the encoding of new pairs based on an existing schema. However, as we observed for NMDAR-independent learning (Wang et al. 2012a), the hippocampus remains necessary during experience-dependent learning. Importantly, Morris and colleagues found that neuronal activity in prelimbic, retrosplenial, and anterior cingulate cortices was elevated approximately 90 minutes after rats learned a new set of paired associates based upon an existing schema, relative to rats that had merely retrieved 248 Chapter 4 - General Discussion previously-learned associates from the schema or rats that were exposed to an entirely novel set of associates that were inconsistent with the schema (Tse et al. 2011). Moreover, in another cohort of well-trained rats, AMPAR antagonist infused into prelimbic cortex (Tse et al. 2011) or ACC (Wang et al. 2012b) prevented retrieval of both the old associates and new associates acquired based on the consistent schema. Infusion of AMPAR or NMDAR antagonist before training with the new associates also prevented their retention 24 hours later. Thus, it is clear that learning based upon existing related knowledge preferentially recruits medial prefrontal cortical regions including prelimbic and ACC, and requires plasticity in these regions to form long-term memories for the related events. However, although the schema procedure and NMDAR-independent learning likely tap into similar experience-dependent mechanisms, they are not identical processes. For example, animals trained in one water maze show evidence of faster acquisition of the second task in a new environment, perhaps even when AP5 is onboard (Bannerman et al. 1995). However, despite acquiring new paired associates faster in the same environment following acquisition of a schema, rats showed no evidence of accelerated acquisition of a second paired associates task in a new environment. Furthermore, in Chapter 3 we observed that experience- dependent AP5-insensitive learning is transient. This finding is at odds with the standard conceptualization of schemas, which might generally be posited to facilitate or otherwise augment learning over very long periods (Bartlett 1932; Morris 2006). It is not entirely clear why the brain would encode a familiar episode as if it was unfamiliar following a long delay. One simple explanation is that memories could be orthogonalized over time (Bouton 1993; also see McKenzie et al. 2013), with similar events being encoded as though they are distinct merely because they are temporally separated (Aimone et al. 2006). Another interpretation is 249 Chapter 4 - General Discussion that, in our task, the memory for the training procedure is acquired in a single trial, whereas schemas for the paired associates task are gradually established across trials (Tse et al. 2007). It is conceivable that extensive training (perhaps similar fear conditioning procedures in many contexts) could produce a persistent change in learning mechanisms that would more closely resemble schemas as they are traditionally conceptualized (see Wang and Morris 2010). In this way, if the rats in our study were very familiar with the sequence of conditioning experienced over many episodes, this might provide a form of knowledge that allows for dHC NMDAR-independent learning even weeks or months later. Of relevance, it is known that rats can form a memory for a particular temporal arrangement of fear conditioning over many training trials, which is only destabilized when a reactivation cue consists of a distinct temporal arrangement (Díaz-Mataix et al. 2013). At the very least, this confirms that rats can, in fact, detect and encode temporal properties of repeated fear conditioning trials in a manner that makes a memory more resistant to disruption after retrieval. 4.3.3. Is NMDAR-independent learning mediated by memory reconsolidation? Another intuitive question stemming from Chapter 3 is whether NMDAR- independent learning builds on prior memories via reconsolidation. It seems likely that to recruit distinct learning mechanisms during the second training, the memory for the previous task must be reactivated in some manner, which could cause its reconsolidation. It would also make sense if some aspect of the existing memory were updated to include the features of the second event. However, several findings indicate that NMDAR-independent contextual fear conditioning does not induce reconsolidation. These experiments, performed by myself (reported in Figure 2) along with Lee (2008) and Tayler and colleagues (2011), have found that fear conditioning of a second novel context does not labilize the memory for prior 250 Chapter 4 - General Discussion contextual conditioning. In a pilot study I observed that the protein synthesis inhibitor anisomycin infused into both dHC and vHC after Training2 does not impair the retention of fear memory previously acquired during Training1 (see Appendix Fig. 2). In both training sessions the fear conditioning procedures were identical, but were conducted in distinct contexts (Context1 and Context2). Similarly, Tayler and colleagues (2011) reported that systemic injections of anisomycin before training could impair acquisition of either the first and second training task, but that injections before the second task did not impair the memory for the first task. This indicates that the second training had not destabilized the memory for the first task. However, neither of these experiments included a positive control to indicate that these anisomycin treatments could disrupt contextual fear memory reconsolidation using a standard reactivation protocol (as observed elsewhere; D&biec et al. 2002; Suzuki et al. 2004). As a control for another experiment, Lee (2008) reported more conclusive evidence using local dHC infusions, observing that administering fear conditioning in an already feared context, but not in a novel context, induced reconsolidation of an existing memory, which could be impaired by anisomycin. Thus training in a distinct context was possibly too dissimilar from the original event, and may have been encoded via de novo memory formation (albeit in a manner that would likely not require dHC NMDARs). Moreover, if hippocampal reconsolidation were necessary for NMDAR-independent learning then it might be expected that the very demonstration of the effect would impair the prior memory, as NMDAR antagonists are known to disrupt both water maze and contextual fear reconsolidation (Kim et al. 2011; Lee and Hynds 2012). However, it has been found that 251 Chapter 4 - General Discussion the memory for a first training task remains intact even after AP5 has been applied during a second similar task (Bannerman et al. 1995). However, in each of these experiments it is possible that reconsolidation had been induced and disrupted by anisomycin/AP5, but the behavioural measures may have been inappropriate to detect the memory impairment. As discussed at length in Section 1.6, distinct components of memories may undergo reconsolidation independently of one another (Debiec et al., 2002; Winters et al., 2011). In Chapter 3 we observed that NMDAR-independent learning requires prior exposure to a task with a similar training sequence, and was not prevented by impairing the previously acquired fear response to another context. As a result, I would predict that the second training task should not engage reconsolidation of the context representation or the conditioned fear response encoded during the prior training, but instead might engage reconsolidation of only their shared feature(s), such as the similar sequence of training. This might allow the encoding of the new training session to be integrated with the memory for the prior training. Thus, anisomycin injected systemically after Training2 would not be expected to impair expression of conditioned fear acquired during Training1 but should instead impair the memory for their shared training sequence, which should prevent NMDAR-independent acquisition of fear to a third novel context. Although this has not yet been confirmed, it would indicate that distinct functional components of a memory trace (i.e. the temporal sequence of events and the association of specific stimuli) might independently undergo reconsolidation without impairing the complete memory trace (also see D&biec et al. 2006). Furthermore, it would indicate that NMDAR-independent learning phenomena could be a consequence of a reconsolidation processes induced in restricted brain regions. The memory for shared features of events may be progressively strengthened and updated to 252 Chapter 4 - General Discussion incorporate new information, thus subtle changes across multiple related episodes may alter the accuracy of each prior memory trace. Based on our results in Chapter 3 we would predict that if such a functionally- restricted form of reconsolidation for the temporal sequence memory occurs during NMDAR- independent learning, it may be observed in neuronal circuits including the ACC. In ongoing experiments we are testing if this is, in fact, the case. It is known that reconsolidation in ACC does occur under some conditions, as Einarsson and colleagues (2012) found that retention of a contextual fear memory could be blocked by anisomycin infusion into ACC when administered after unreinforced re-exposure to the conditioning context. In summary, the behavioural protocol we have characterized in Chapter 3 provides an ideal model to test all of the aforementioned possibilities, including whether functionally- delineated reconsolidation processes can be evoked in a regionally-restricted manner. 4.3.4. Requirement for NMDARs during associative contextual fear acquisition. A related point that deserves brief discussion is our observation that (without prior similar training) NMDAR activity is required during the formation of a contextual fear association in rats pre-exposed to the conditioning context (see Fig. 5c and Supplementary Fig. 2b). Several studies have previously indicated that NMDAR activity in dHC (or even dHC activity in general) is not necessary to acquire a contextual fear memory if the rat has been pre-exposed to the conditioning context (Roesler et al. 1998; 2005; Matus-Amat et al. 2007; Young et al. 1994), or even to a similar training context (Tayler et al. 2011). Although our procedure differs in some respects to these previous studies, there is no clear explanation for the stark difference in sensitivity to NMDAR antagonists infused systemically or directly 253 Chapter 4 - General Discussion into the hippocampus before presentation of shock in rats pre-exposed to the context. One possibility is that our use of DL-AP5 may affect memory formation differently than D-AP5. Although DL-AP5 has been found to influence synaptic transmission in amygdala neurons (Li et al. 1995) and the L-isoform can impair memory retrieval when infused into amygdala (Matus-Amat et al. 2007), it does not seem to interfere with basal transmission or spatial memory formation in the hippocampus (Morris et al. 1986; Morris 1989; but also see Walker and Gold 1994). Thus we expect this likely does not account for the inconsistent findings. Another possibility is that the slightly longer interval (10 minutes) between infusion and training in the Matus-Amat study might allow for a non-hippocampal system to compensate for associative fear learning (Goshen et al. 2011). Using optogenetic tools, Goshen and colleagues have recently demonstrated that inactivation of the hippocampus during a remote contextual fear memory test, as well as during the 30 minutes prior to the test (which mimicked effects observed via pharmacological inhibition), resulted in no retrieval deficit. In contrast, inactivation that was precisely limited to the testing session impaired responding. It was demonstrated that this functional compensation likely involved ACC, as this region exhibited increased neuronal activity during the prolonged inactivation of the hippocampus. Other findings have similarly indicated that only post-training hippocampal inactivation blocks memory, likely due to compensation by other brain regions when the hippocampus is inactivated in advance of training (Maren et al. 1997; Frankland et al. 1998; see Fanselow 2010). Thus, the slightly earlier application of the drug by Matus-Amat and colleagues could have influenced compensation by other brain systems. In fact, all other prior studies reporting that dHC and/or NMDARs are not necessary to acquire fear to a pre- 254 Chapter 4 - General Discussion exposed context used treatments applied either 30+ minutes before (Young et al. 1994; Matus- Amat et al. 2004; Tayler et al. 2011) or after (Roesler et al. 2005) the footshock phase of training. A final possibility is based on the fact that Matus-Amat and colleagues (2007) used a procedure that emphasized the transportation cues as features of the environment (by repeatedly bringing the animal to and from the context during pre-exposure; also see Rudy and O'Reilly 2001). In contrast, our procedure minimized learning based on transportation method (by using a distinct transportation method only on the day of immediate shock). Thus, our task could lead to greater AP5 sensitivity because it provides only the brief seconds of context exposure before immediate shock (~15s) in which to recall the pre-exposed context and form a fear association, instead of providing extensive pre-shock transportation context cues that could facilitate learning. The procedure implemented by Matus-Amat and colleagues may, through repetition of predictive stimuli, provide ideal means of forming a memory that relies to a lesser degree on the specific context or the hippocampus, thus associative fear learning may only require NMDAR-dependent plasticity elsewhere in the brain. Our findings are consistent with another report (Chang et al. 2008), which used a behavioural design similar to that of Matus-Amat and colleagues, except that DL-AP5 was infused after animals were transported from their homecage, but immediately before they were placed into the conditioning chamber. Results from this study indicate that NMDARs in dHC are necessary for contextual fear acquisition. These findings are also somewhat consistent with several studies showing that dramatic changes in neuronal activity occur in the hippocampus following footshock delivery in a familiar context (Moita et al. 2004; Zelikowsky et al. 2014), suggesting that this structure may process the association in some 255 Chapter 4 - General Discussion way. Finally, it is known that acquiring fear to a pre-exposed context induces reconsolidation and requires activity of plasticity-associated mRNA/protein zif268 (Lee 2010), which is known to be activated by NMDAR activity (Malkani and Rosen 2001; Lee and Hynds 2012). Importantly, contextual fear memory reconsolidation has been found to require NMDAR activity in dHC (Lee et al. 2012). Together this work strongly suggests that NMDAR- mediated plasticity in dHC is involved in acquiring fear to a familiar context, thus indicating that it does not rely solely on a context-shock association formed in amygdala as proposed by Matus-Amat, Rudy, and colleagues. 4.3.5. Memory maintenance by GluA2-containing AMPARs. As discussed in Chapter 1.3, it is assumed that memory in many brain structures can be maintained, at least partially, by the regulation of AMPARs at the synapse (Sacktor 2011). It is thought that the kinases NSF and PKM# can prevent the endocytosis of GluA2-containing AMPARs from the membrane, and interfering with their interaction with the receptors using ZIP or c-terminus-mimicking peptides like G2CT, pep2m, or pepR845A can functionally disrupt memory maintenance. This study is now among the first to demonstrate that inducing GluA2 endocytosis in dHC or dHC+vHC can impair the maintenance of a consolidated contextual fear memory. Previously it was found, using ZIP infusions into dHC, that contextual fear memory is resistant to ZIP (Serrano et al. 2008). In Chapter 3 we found the same negative effect of ZIP infusions. However, many other forms of hippocampus-dependent memory are virulently impaired by ZIP, including fear memories requiring more spatially-detailed information (Hardt et al. 2010b; Pastalkova et al. 2006; Serrano et al. 2008). Thus it has been assumed that due to a limited need for spatially-precise memory, contextual fear conditioning could be 256 Chapter 4 - General Discussion mediated by another brain structure when hippocampal maintenance is blocked. However, confirming our recent report (Migues et al. 2014), in Chapter 3 we demonstrated that pepR845A infused into dHC can block contextual fear using the same procedure. The most intuitive interpretation is that pepR845A may more effectively remove synaptic AMPARs than ZIP. We expect that ongoing electrophysiology experiments with a collaborating lab will reveal the specificity of this treatment. We also provided the first demonstration that inhibiting GluA2 maintenance in ACC can impair contextual fear memory. Although other studies have found that ZIP infused into ACC can interfere with chronic pain persistence (Li et al. 2010; Xin et al. 2014), ours is the first study to confirm that pepR845A can impair a complex form of behavioural responding. Of interest, this was also the first study to show that inducing GluA2 endocytosis in a brain region can disrupt one part of a memory but not another. This subtle effect of the drug provides impetus for other studies to evaluate whether memory impairments after amnesic treatments are global, or if some components of the trace are spared. 4.4. LEARNING-RULES AND AMYLOID-BETA ACCUMULATION. In conjunction, the studies presented in Chapters 2 and 3 raise an interesting hypothetical possibility. In Chapter 3 we reasoned that memory for a specific conditioned fear association can be retained even when the memories for procedural aspects of training are impaired, and vice versa, leading to my hypothesis that with each additional similar training session the strength and stability of common features could be independently enhanced. Thus, specific rules about the world that are most broadly applicable to a diverse array of events will, by 257 Chapter 4 - General Discussion definition, be frequently activated during experience, and perhaps also during replay or simulation of events while at rest (see Buckner and Carroll 2007; Peyrache et al. 2009). Due to this extensive processing and strengthening, these important components of knowledge could be preferentially protected against interference and decay by metaplasticity processes. Such processes could include NR2B downregulation (see Lau and Zukin 2007), to prevent memory updating (Wang et al. 2009a; Haubrich et al. 2014) and forgetting (Villarreal et al. 2002; Hardt et al. 2013; Shinohara and Hata 2014), but also the A!-mediated mechanism reported in Chapter 2. Because neuronal activity and synaptic vesicular release may drive A! secretion (Cirrito et al. 2008), those neuronal populations which encode frequently activated 'general knowledge' (such as a temporal sequence common to many related events) may cause local elevations in the concentration of extracellular/interstitial A! (see Bero et al. 2011). Indeed, it has been found that brain regions exhibiting high default- mode network activity (as could be involved in rule extraction that occurs during rest) tend to accumulate A! at disproportionately high levels (see Buckner et al. 2005; 2009; Sheline et al. 2010). In humans these regions are typically reported to include medial temporal and prefrontal structures (including the hippocampus and anterior medial and posterior cingulate cortices) that are active during self-reflective thought and 'day-dreaming' (see Immordino- Yang et al. 2012). However, there is also a similarly constructed default-mode network in rats that includes ACC (Lu et al. 2012). Supporting the idea of an experience-dependent establishment of this system, infants actually show little evidence of a default-mode network that is consistently active during rest, which progressively develops throughout development towards adulthood (see Fair et al. 2009; Broyd et al. 2009; Gao et al. 2009). In Chapter 3 I reported that ACC may mediate the encoding of a general temporal characteristic of several 258 Chapter 4 - General Discussion events, which may reflect the earliest semblance of a learning rule abstracted via reactivation of memories encoded within this default-mode brain structure (see van Kesteren et al. 2010; Wang et al. 2012b). Thus, if this network is critically involved in supporting a core knowledge system, then its elaboration and refinement over years of inter-related experiences could result in dysregulation of the A!-mediated stability mechanism (Koivunen et al. 2011). Therefore, I conclude that to some extent the pathogenesis of late-onset AD may result from the brain's management of decades of interleaved memories. 4.4.1. So why is Alzheimer's disease not ubiquitous? If the onset of AD is partly a consequence of the way in which memories are maintained and integrated over a lifetime, then why do some aged individuals develop AD and others not? Although many genetic risk factors are likely to contribute to the disease independently of memory-encoding per se (see Bertram and Tanzi 2008 for a review), some may specifically influence how the brain tends to process and accumulate memories throughout life, which could promote A! pathology. In partial support of this position, at young ages individuals carrying a well-established AD risk-factor that is associated with A! aggregation in later life (the ApoE4 genotype; Kok et al. 2009; Morishima-Kawashima et al. 2000; Morris et al. 2010) already show increased activity patterns in the default-mode network during rest, and also increased hippocampal activation during a memory encoding and retrieval task (Filippini et al. 2009). Older ApoE4 carriers lacking cognitive impairment also show increased synchronized default mode network activity at rest (Westlye et al. 2011), as well as impaired deactivation of this network when the brain transitions from resting-state to encoding of episodic information (Persson et al. 2008). It has been observed that older individuals with mild cognitive impairment who showed elevated brain activity in parahippocampal cortex 259 Chapter 4 - General Discussion (part of the default network) during episodic memory processing exhibited better retention but diminished cognitive ability 2.5 years after this first assessment (Dickerson et al. 2004). This suggests that increased use of these regions throughout life could lead to their failure. Consistently, compared to non-carriers, young ApoE4 carriers show increased activity in several frontal and temporal cortical regions during performance of a memory task requiring judgments of novelty, while elderly carriers show decreased activity in the same regions relative to elderly non-carriers (Filippini et al. 2011). One possibility is that the ability to encode forms of memory mediated by these regions may decline as they become saturated or exhausted with increasing age, and this could be accelerated in ApoE4 carriers. Indeed, in elderly relative to young non-carriers, increased task-related activity within these brain regions was observed. Typically such age-related changes in brain activity are viewed as a functional compensation for gradually declining/decaying neuronal systems (i.e. Park and Reuter-Lorenz 2009; Jagust and Mormino 2011), but I argue just the opposite: that changes in encoding systems with age could be due to changes in encoding strategies required to maintain an ever-growing network of knowledge (also see Wilson et al. 2006). Specifically, older individuals may rely to a greater extent on schematization or semanticization of information into well-developed knowledge structure (Winocur and Moscovitch 2011). For instance, older adults have been observed to use judgments of familiarity over recognition of names or words they were previously presented, possibly caused by gist-based encoding due to proactive interference at the time of encoding (Jennings and Jacoby 1993). Relatedly, aging can reduce the encoding of contextual details of events, resulting in deficits in recalling the source or spatial features of previously acquired information (Uttl and Graf 1993; Light 1991). Chalfonte and Johnson (1996) found that 260 Chapter 4 - General Discussion older adults struggled to remember where items had been previously presented within a visual array, but not the identity or colour of items. However, they were less able than young adults to identify objects based on binding of identity and colour, primarily when instructed to remember the colour of each item. Thus aging may be associated with an inability to bind detailed information, which could contribute to observed episodic memory deficits (i.e. Old and Naveh-Benjamin 2008; Plancher et al. 2010). Congruently, Koutstaal and Schacter (1997) reported that the presentation of many exemplar items from a category led older adults to falsely recognize categorically-related 'lure' items during a memory test. They were also less able to recognize target items that were distinct and categorically unrelated to other studied items (suggesting an inability to encode or retrieve the details of these pictures), although they were as good as young adults at correctly identifying pictures they had previously seen when many other categorically-related images had been studied (suggesting they had encoded the common gist of these stimuli). In general this work hints that age is associated with the schematicized or semanticized encoding of the gist of experiences based on prior knowledge, at the expense of detailed episodic memory formation. I hypothesize that individuals at risk of developing AD (such as ApoE4 carriers) may rely more on this form of encoding at an earlier stage of life (see Jagust and Mormino 2011 for a related conceptualization). This could result from a tendency to perceive events and stimuli as being more similar to existing knowledge, which is consistent with several reports that middle- aged ApoE4 carriers are more likely to present 'false alarms' but not 'misses' during recognition memory tasks (claiming they had previously seen items they had not, but still recognizing objects they had actually seen; see Lind et al. 2006). In general this is not a symptom of overall memory impairment, as young and middle-aged carriers often exhibit 261 Chapter 4 - General Discussion otherwise intact or even enhanced cognitive performance (i.e. Marchant et al. 2010). Of note, young adult transgenic mice expressing ApoE4 show no detectable difference in spatial working memory ability relative to controls, but show no enhancement of either memory or hippocampal synaptogenesis in response to environmental enrichment, as observed in wild- type animals (Levi et al. 2003). As it is thought that dentate gyrus synaptogenesis is critical for pattern separation abilities (Clelland et al. 2009), this could reduce the ability of ApoE4 carriers to properly orthogonalize related events. Aged humans and mice expressing ApoE4 also have lower dendritic spine density in hippocampal dentate gyrus (Ji et al. 2003). Although there is not yet consensus on why ApoE4 expression is associated with an increased risk of AD, a leading model is that it reduces clearance of A!, which could increase the concentration and promote aggregation (Bales et al. 1999; 2009; Sharman et al. 2010). As our data suggests that A! secretion might contribute to memory stabilization and destabilization, I theorize that the way in which the ApoE4-expressing brain preferentially encodes and integrates new behavioural events with existing knowledge may be different, and could explain distinct patterns of brain activity during memory formation and at rest. In ApoeE4 carriers, a tendency towards integration or assimilation of novel information with existing knowledge may increase activity within isolated brain networks (particularly in the default mode network) involved in retention of general knowledge, which may lead to dysregulation and pathology over a lifetime. However, detailed longitudinal assessment of brain activity and memory functions will be required to identify whether patients who go on to develop AD show subtly different memory abilities in early life, perhaps including a tendency to encode and maintain differing degrees of episodic detail or a tendency to re-frame new information with reference to their existing knowledge structure (i.e. Bartlett 1932; 262 Chapter 4 - General Discussion Bergman and Roediger 1999; Attali et al. 2009). 4.4.2. Implications for Alzheimer's disease treatment. The development of treatments for AD have generally been aimed at reducing A! levels, either by blocking production or enhancing clearance (see Williams 2009; Ghosh et al. 2012). In virtually all cases these treatments are designed and tested initially in cells and animals that overexpress A! via transgenes for one or many human genetic mutations associated with familial and early-onset AD (Spires and Hyman 2005). The primary exception is animals engineered to express ApoE4, which is strongly predictive of sporadic and late-onset familial AD in humans (Saunders et al. 1993). These animal models invariably fail to capture all features of the human disease, and the progressive accumulation and aggregation of soluble and insoluble A! in these models does not entirely resemble amyloid load in AD patients, who in some cases may actually show declining levels of soluble A! with age and degeneration (Jensen et al. 1999; Giedraitis et al. 2007; Puzzo and Arancio 2013). A number of A!-reducing treatments have recently progressed to phase-II and -III clinical trials, but there has been no example of incontrovertible success, and a number of alarming failures (i.e. Cummings 2010; Doody et al. 2013; Schor 2011; Doody et al. 2014; Salloway et al. 2014). Based on the results presented in Chapter 2 and elsewhere (Garcia-Osta and Alberini 2009; Morley et al. 2010), these clinical failures may result not from a misunderstanding of A! or a misformulation of the amyloid hypothesis, but from a flawed conceptualization of memory. There is often an apparent disconnection between development of pharmacological and biochemical treatments for memory failure, and what cognitive research tells us about the formation and retention of knowledge (Schacter 1999; Hardt et al. 2010a). This binary conceptualization of mnemonic disorders often frames memory as either 263 Chapter 4 - General Discussion “better” or “worse” - abilities are either maintained or lost. But decades of cognitive neuroscience research underscores a gradual constructive process that schematizes information, interleaving and assimilating new events with prior knowledge (i.e. Bartlett 1932). To some extent, such a process might balance new encoding versus maintenance of existing memory, each at the expense of the other. In this way, facilitating processes by which new memories are stored could interfere with or overwrite existing memories by facilitating their destabilization and updating. Conversely, inhibiting plasticity processes involved in encoding may improve retention of existing memories (Villarreal et al. 2002; Shinohara and Hata 2014; Hardt et al. 2013) but sacrifice the ability to encode new information via de novo memory formation or updating (Morris et al. 1986; Przybyslawski and Sara 1997). Therefore, indiscriminately applying treatments that facilitate mechanisms of memory formation could be hypothesized to lead to abnormal retention of information by counteracting normal forgetting or decay processes (Hardt et al. 2014). It is difficult to imagine that the brain - a system apparently evolved to preserve a record of the past - is not optimally configured to maximally retain information while still sustaining the ability to efficiently encode ongoing experience over the natural lifespan of an organism. This conceptualization of A!, based on the results of Chapter 2, therefore predicts that most current treatments of late-onset AD may ultimately be swaying the balance of memory encoding versus retention in humans who have outlived the evolved "capacity" of the brain within specific realms of knowledge. Although a difficult hypothesis to confirm, this could suggest that the best hope for AD prevention may be to encourage memory processing away from ‘default’ neuronal networks, which may simply be nodes within our gradually established idiosyncratic knowledge systems 264 Chapter 4 - General Discussion (Fuster 2003; Wheeler et al. 2013). In theory this may be accomplished by maintaining diverse and varied daily experience that relies more on what I have referred to as de novo encoding, and less on strengthening of existing knowledge structure (i.e. Mainardi et al. 2014; Costa et al. 2007). Yet this conceptualization also hints at the disheartening prospect that A!- induced AD may often be an inevitable consequence of memory management over an extended lifetime. It is my hope that ingenious future research might adequately test these predictions and, with any luck, prove them wrong. 4.5. CONCLUSION. One of the major unknowns in the field of neuroscience is how the brain pieces together knowledge of the world from mere fragments it captures during individual experiences. This thesis has attempted to understand two small parts of this puzzle. First, by demonstrating that the endogenous generation of a molecule known to cause memory failure at elevated concentrations in later life can, paradoxically, facilitate the stabilization of memory. Second, by characterizing how the brain can use existing memory to encode new experiences that containing an underlying procedural similarity. From this work it was concluded that the accumulation of memories in this manner over a lifetime might lead to changes in the way that new information is encoded. A question for future studies is whether altered memory processing with age may also, paradoxically, contribute to cognitive pathology. “Life can only be understood backwards; but it must be lived forwards.” -Soren Kierkegaard 265 5. References. 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Independent-samples t-test revealed that rats infused with pepR845A (n=6) had their memory impaired relative to those infused with Scr-pepR845A (n=7), t11=2.218, p=.049. This suggests that the disruption of memory maintenance by pepR845A is persistent and does not recover over time. Data is presented as mean ± s.e.m. Alpha level was set to 0.05. * p=.05. Appendix Figure 2. Infusion of ANI into dHC+vHC following contextual fear conditioning in Context2 does not impair retention of a previously acquired Context1 fear memory. Top: A diagram of behavioural procedures. The procedures were as described in the Chapter 3 methods, except bilateral 1.5µL infusions of anisomycin (ANI; 125µg/µL, Sigma- Aldrich) or vehicle (VEH) were delivered into both dHC and vHC after Training2. Instead of subsequently testing fear in the Training2 context during LTM2, the rats were given a second test in Context1 to determine if retention of the Training1 memory had been disrupted. Due to violations of the homogeneity of variance assumption, mean freezing during each test was compared using Mann-Whitney U non-parametric tests with Bonferroni correction for multiple comparisons. These revealed no significant difference between ANI and VEH groups during both LTM1 (p=1) and LTM2 (p=1). This suggests that contextual fear conditioning in Appendix. Context2 did not induce reconsolidation of the previously acquired Context1 fear response. Data is presented as mean ± s.e.m. Alpha level was set to 0.05. APPENDIX FIGURES. Training LTM 24h 30 days dHC: pepR845A 180s 60s SCR-pepR845A 240s est 100 Scr-pepR845A pepR845A * TM T L 80 60 40 20 0 % time spent freezing during Appendix Figure 1. 339 Appendix. Training1 LTM1 Training2 LTM2 24h 5d 24h 180s 10s 240s 180s 60s 240s dHC+vHC: ANI/VEH 100 90 est 80 TM T 70 60 50 40 30 20 VEH % time spent freezing during L 10 ANI 0 LTM1 LTM2 Appendix Figure 2. 340