NONNAVIGATIONAL SPATIAL MEMORY PROCESSING IN THE MEDIAL TEMPORAL LOBE AND GOAL-DIRECTED BEHAVIOR IN RHESUS MACAQUES
A Dissertation submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Pharmacology
By
Elyssa M. LaFlamme, B.A.
Washington, DC February 7, 2020
Copyright 2020 by Elyssa M. LaFlamme All Rights Reserved
ii
NONNAVIGATIONAL SPATIAL MEMORY PROCESSING IN THE MEDIAL TEMPORAL LOBE AND GOAL-DIRECTED BEHAVIOR IN RHESUS MACAQUES
Elyssa M. LaFlamme, B.A.
Thesis Advisor: Ludise Malkova, Ph.D., Patrick A. Forcelli, Ph.D.
, ABSTRACT
The hippocampus and cortical areas of the medial temporal lobe have a critical role in navigational spatial memory, as has been well-established in rodent models.
However, nonnavigational spatial memory is more difficult to evaluate in rodents, and it is unknown whether it shares the same neural substrates. Nonhuman primates offer a more suitable translational model. The Hamilton Search Task, adapted for monkeys, is a behavioral assay for nonnavigational spatial memory in which animals are required to track self-generated selections from a linear array of boxes baited with hidden food rewards. Perfect performance entails visiting each box only once without repeating. This task is sensitive to loss of hippocampal function. Experiment 1 investigated the hypothesis that pharmacological inactivation of the parahippocampal cortex would also impair task performance, as the parahippocampal cortex is a major source of spatial input to the hippocampus.
Bilateral microinfusion of glutamate receptor antagonist kynurenic acid in the parahippocampal cortex, but not unilateral microinfusion, profoundly and selectively impaired nonnavigational spatial memory across long delays.
Furthermore, contralateral inactivation of the parahippocampal cortex in one hemisphere and the hippocampus in the opposite hemisphere also impaired
iii performance, indicating that the hippocampal-parahippocampal pathway is critical for nonnavigational memory. Experiment 2 investigated the hypothesis that cholinergic blockade in the hippocampus would also impair performance, but in fact, bilateral microinfusion of neither nicotinic antagonist mecamylamine nor muscarinic antagonist scopolamine affected Hamilton Search Task performance.
In rodent models, extended training in stimulus-reward paradigms can prevent animals from shifting their behavior to reflect changes in reward value. This represents a shift away from goal-directed decision-making into instrumental, habit-directed behavior, which makes it a useful experimental model for the overreliance on habit often found in addiction and other neuropsychiatric disorders.
However, this loss of devaluation effect after overtraining has been difficult to reproduce in humans. Experiment 3 investigated the effect of extended training with concurrent visual discriminations on reinforcer devaluation in rhesus macaques. Not only was reinforcer devaluation unaffected by overtraining for hundreds of trials, but this study offered evidence that extended training may even correct for biases produced by attentional habit.
vi ACKNOWLEDGMENTS
My sincere and boundless gratitude…
To my mentors, Drs. Ludise Malkova and Patrick Forcelli, for your patience and trust and for all the ways you helped me to grow as a scientist. You gave me the best possible grad school experience, and any success I find will be thanks to you. To our veterinarians, Drs. Patricia Foley and Robin Tucker, for keeping the monkeys happy and healthy and for teaching me how to do the same. To my support team in the Translational Biomedical Sciences Program, especially Dr. Kathryn Sandberg and Emily Bujold, for your unwavering encouragement and mentorship and for always pushing me one more step forward. To the best labmate of all time, Hannah Waguespack, for the cakes and the company, for facing the grind with me head-on, and for making me laugh even on the hardest days. To my lab elders, Drs. Brittany Aguilar and Catherine Elorette, for teaching me everything I know about monkeys and for being the kind of team (and podcast club) I was excited to work with every day. To my science soulmate, Meezah Ehtesham, for always reminding me why I love research. To my best friend, Lindsey Osburnsen, for being my home base, my greatest support, and my inspiration for almost a decade; and to Goose, who (probably) can’t read but deserves to be thanked anyway for gracing me with his presence. To my parents, Sheri Diffely and Jamie LaFlamme, and my sister, May LaFlamme, for a foundation of unconditional support and indefatigable humor. Once upon a time, you let me believe I could fly, and I did. To my grandma, Linda Diffely, for a lifetime supply of cookies, books, theater, laughter, and unconditional support. You have always been my greatest role model and my number one champion. And to my grandpa, Bob Diffely, for all the bedtime stories about outer space and quantum physics and for giving me my first science books. You are the reason I fell in love with science in the first place and the reason I believed in myself in a world still trying to convince us that science is not for girls. You are dearly missed.
“Our prime obligation to ourselves is to make the unknown known. We are on a journey to keep an appointment with whatever we are.” ~Gene Roddenberry
vii TABLE OF CONTENTS
Chapter I: The Parahippocampal Cortex and Hippocampal-Parahippocampal Interactions in Nonnavigational Spatial Memory ...... 1
1.1 Introduction ...... 1
1.2 Methods ...... 40
1.3 Results ...... 52
1.4 Discussion ...... 70
Chapter II: Hippocampal Cholinergic Transmission in Nonnavigational Spatial Memory ...... 80
2.1 Introduction ...... 80
2.2 Methods ...... 87
2.3 Results ...... 90
2.4 Discussion ...... 94
Chapter III: Reinforcer Devaluation and Habit Formation by Extended Training ...... 97
3.1 Introduction ...... 97
3.2 Methods ...... 105
3.3 Results ...... 115
3.4 Discussion ...... 123
Chapter IV: General Discussion ...... 128
4.1 Discussion ...... 128
References ...... 137
v LIST OF FIGURES
Figure 1.1: Microinfusion site targeting and verification ...... 54
Figure 1.2: Hamilton Search Task performance after bilateral inactivation of the parahippocampal cortex ...... 59
Figure 1.3: Hamilton Search Task performance after bilateral inactivation of the hippocampus ...... 62
Figure 1.4: Hamilton Search Task performance after crossed-inactivation of the contralateral hippocampus and parahippocampal cortex ...... 64
Figure 1.5: Hamilton Search Task performance after unilateral inactivation of the parahippocampal cortex ...... 67
Figure 1.6: Hamilton Search Task performance after unilateral inactivation of the hippocampus ...... 68
Figure 2.1: Hamilton Search Task performance after cholinergic receptor blockade in the hippocampus ...... 93
Figure 3.1: Reinforcer preference across probe sessions ...... 117
Figure 3.2: Devaluation index as a function of training cycle and relative object history ...... 119
Figure 3.3: Devaluation index as a function of cumulative training ...... 120
Figure 3.4: Difference score as a function of training cycle and relative object history ...... 121
vi LIST OF TABLES
Table 1.1: Number of Hamilton Search Task runs per experimental condition ...... 56
Table 2.1: Number of Hamilton Search Task runs per experimental condition ...... 91
Table 3.1: Review of concurrent visual discrimination training in reinforcer devaluation tasks in monkeys ...... 107
Table 3.2: Number of LR and HR exposures for each testing cycle ...... 116
vii LIST OF ABBREVIATIONS
28, area entorhinal cortex 35, area medial division of the perirhinal cortex 36, area lateral division of the perirhinal cortex 36c caudal division of area 36 in the perirhinal cortex 36cl caudolateral subdivision of area 36 in the perirhinal cortex 36cm caudomedial subdivision of area 36 in the perirhinal cortex 36d dorsal division of area 36 in the perirhinal cortex 36r rostral division of area 36 in the perirhinal cortex 36rl rostrolateral subdivision of area 36 in the perirhinal cortex 36rm rostromedial subdivision of area 36 in the perirhinal cortex Aß amyloid-ß AALAC Association for Assessment and Accreditation of Laboratory Animal Care AD Alzheimer’s disease AMPA α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid AN, subject a male rhesus macaque used for Aims 1 and 2 ANOVA analysis of variance AP-7 2-amino-7-phosphonoheptanoic acid, an NMDA receptor antagonist CA1-3 Cornu ammonis subregions of the hippocampus DMS delayed matching-to-sample task DNML delayed nonmatching-to-location task DNMS delayed nonmatching-to-sample task EC caudal division of the entorhinal cortex ECL caudal limiting field of the entorhinal cortex EI intermediate division of the entorhinal cortex ELc caudolateral division of the entorhinal cortex ELr rostrolateral division of the entorhinal cortex EO olfactory division of the entorhinal cortex EP, patient a human lesion patient ER rostral division of the entorhinal cortex fMRI functional magnetic resonance imaging FR a male rhesus macaque used for Aim 3 GABAA γ-aminobutyric acid receptor subtype A HH combination of high-repetition devalued and nondevalued objects HL devalued high-repetition object paired with nondevalued low repetition object HM, patient Henry Molaison, a human lesion patient HR high-repetition IACUC institutional animal care and use committee IST invisible sensor task KYNA kynurenic acid, a glutamate receptor antagonist LH devalued low-repetition object paired with nondevalued high
viii repetition object LL combination of low-repetition devalued and nondevalued objects LO, subject a male rhesus macaque used for Aim 1 LR low-repetition M1-5 muscarinic acetylcholine receptor subtypes 1-5 mAChR muscarinic acetylcholine receptor MTL medial temporal lobe MR, patient a human lesion patient MRI magnetic resonance imaging MUS muscimol, a GABAA receptor agonist nAChR nicotinic acetylcholine receptor NB, patient a human lesion patient NBQX 2,3-dioxo-6-nitro-7-sulfamoyl-benzo[f]quinoxaline, an AMPA receptor antagonist NMDA N-methyl-D-aspartate OCD obsessive compulsive disorder OD, subject a male rhesus macaque used for Aims 1, 2, and 3 PAM positive allosteric modulator PPA parahippocampal place area RA, subject a male rhesus macaque used for Aims 1 and 3 RE, subject a male rhesus macaque used for Aim 2 ROUT robust regression and outlier removal test SA a male rhesus macaque used for Aim 3 SAL physiological saline SCOP scopolamine, a muscarinic cholinergic receptor antagonist SL, subject a male rhesus macaque used for Aim 1 TE area within the inferotemporal cortex TEO area within the inferotemporal cortex TF lateral division of the parahippocampal cortex TFl lateral subdivision of the parahippocampal TF region TFm medial subdivision of the parahippocampal TF region TH medial division of the parahippocampal cortex THIP 4,5,6,7-tetrahydroisoxazolo[4,5-c]pyridin-3-ol, a GABAA receptor agonist TPT auditory association area of the superior temporal gyrus V4 area of the visual cortex VML visuomotor learning task VPC visual paired comparison task WGTA Wisconsin General Testing Apparatus YO, subject a male rhesus macaque used for Aim 1 YR, patient a human lesion patient
ix
Chapter I: The Parahippocampal Cortex and Hippocampal-Parahippocampal Interactions in Nonnavigational Spatial Memory
1.1 INTRODUCTION
1.1.1 Introduction to Memory and the Medial Temporal Lobe
In 1953, Henry Molaison was treated for a severe seizure disorder with a bilateral medial temporal lobectomy and consequently suffered profound and permanent amnesia. When the first case study on “patient H.M.” was published
(Scoville & Milner 1957), it launched decades of research on the medial temporal lobe (MTL), its subregions, and its role in processing memory. H.M. and many similar cases to follow demonstrated significant retrograde amnesia (i.e. the loss of autobiographical memory prior to the surgery) as well as total anterograde amnesia (i.e. the inability to form new long-term memories), such that he lived, literally, in the present moment and had no autobiographical memory beyond the previous few minutes. Other areas of his memory were unaffected, however, including semantic knowledge (i.e. concepts and facts) and procedural memory
(i.e. how to perform practiced actions, such as playing the piano). His lifelong contributions to research were as valuable and interesting for the cognitive and mnemonic capabilities that were spared by his lesions as they were for his impairments.
1 As in H.M., surgical and organic lesions in human subjects are rarely restricted to a single brain region, so associated deficits cannot be directly attributed to a specific area. Neuroplasticity and compensatory adaptations also complicate structure-function assessment even for the most selective lesions. Furthermore, after H.M.’s death in 2008, his brain was re-analyzed and his lesions were discovered to be far more extensive than previously believed (Augustinack et al.
2014), which recontextualized the 50 years of data collected and published on his cognitive and behavioral profile. Complications like these have necessitated the use of animal models for more controlled, specific investigation into MTL function to supplement and clarify findings from human cases. More than most other fields, this body of research has relied heavily on nonhuman primate models to produce translational cognitive and behavioral data, particularly in capacities that are difficult or impossible to assess in rodents (e.g. nonnavigational spatial memory, or spatially-informed memory that does not require real-world or virtual locomotion).
With ever-improving models and technologies, collective understanding of the
MTL has evolved and refined enormously over time. One early theory (O’Keefe &
Dostrovsky 1971; O’Keefe & Nadel 1978; O’Keefe & Speakman 1987) proposed that the hippocampus primarily acts as a spatial map to represent the local environment in microstructure. Another theory (Cohen & Squire 1980;
Eichenbaum 2000) implicated the MTL specifically and exclusively in long-term declarative memory (i.e. anything that can be consciously recalled), although other proponents included only episodic memory (i.e. experienced events) and excluded
2 semantic memory from the MTL domain (Wood et al. 1982; adopted from Tulving
1972). An attempt to combine the spatially-oriented and memory-oriented hypotheses suggested that the MTL instead encodes all attended experience
(Morris & Frey 1997). For decades, it was debated whether MTL subregions are all functionally equivalent (Squire et al. 2004) or whether they are specialized for different aspects of perception, learning, and memory (Murray & Wise 2004;
Murray et al. 2007).
A more recent theory (Lavenex & Amaral 2000) is an extension of the dual- stream hypothesis for visual processing (Mishkin et al. 1983; Goodale & Milner
1992). It describes one pathway for object feature information from the occipitotemporal cortex through the striate cortex and inferotemporal cortex (the ventral “what” stream) that projects into the MTL primarily through its perirhinal cortical area, and a second pathway that projects spatial information from the occipitotemporal cortex through the striate cortex and posterior parietal cortex (the dorsal “where” stream) and into the MTL primarily through its parahippocampal cortical area. Both information streams are projected from the perirhinal and parahippocampal cortices to the entorhinal cortex and, from there, to the hippocampus, where complex, multimodal associative memories are encoded and retrieved. This associative hierarchy model is, of course, an oversimplification of the vastly nuanced systems interacting with and within the MTL, but it serves as a useful framework for contextualizing the unique roles of the subregions. The
3 following sections will outline evidence from humans, monkeys, and rodents for the contributions of each major MTL division in memory and related processes.
1.1.2 Medial Temporal Lobe Structures
In monkeys as well as humans, the MTL is comprised of the hippocampal formation (Ammon’s horn, dentate gyrus, and subiculum); the pre-, pro-, post-, and parasubiculum; and the entorhinal, perirhinal, and parahippocampal cortices positioned along the ventromedial surface of the brain surrounding the rhinal sulcus.
Hippocampus. The hippocampus is named after the seahorse (Greek hippo,
‘horse,’ and kampos, ‘sea monster’) for its spiral structure; its CA1-3 subfields, together referred to as Ammon’s horn, are also a reference to the curvature of a ram’s horn (Latin cornu Ammonis, ‘horn of Ammon,’ an Egyptian god with a ram’s head; Duvernoy 2005). In primates, it is an elongated structure along the rostrocaudal axis of the brain and makes up the medial wall of the inferior horn of the lateral ventricle in each hemisphere. Positioned around the spiral from centermost outward are, in order, the dentate gyrus, CA3, CA2, and CA1 fields;
CA3 runs along the dorsal side of the hippocampus, and CA1 and CA2 run along the lateral side (Ding 2013). Altogether, the subicular complex (subiculum, presubiculum, prosubiculum, postsubiculum, and parasubiculum) separates the hippocampus proper from the entorhinal cortex, although the subiculum itself is generally considered a part of the hippocampal formation (Scharfman et al. 2000;
4 Witter 2010) and is separated from CA1 by the prosubiculum (Rosene & Van
Hoesen 1987; Ding 2013). The hippocampus also has a major tract of nerve fibers connecting it to various regions in the brain: the individual tracts in each hemisphere are called the fimbriae, while the part that connects the bilateral hippocampi through the corpus collosum is called the fornix.
Entorhinal cortex. The macaque entorhinal cortex encompasses Brodmann’s
(1909) area 28 and has been divided into seven subfields based on cytoarchitecture (Amaral et al. 1987; Insausti et al. 1987). The rostral-most olfactory division EO, making up 12.5% of the total surface area, borders the ventral side of the piriform cortex and receives direct input from the olfactory bulb; it is caudolaterally bordered by the rostral division ER, which makes up 19% of the total surface area between the rostral end of EO and the rostrolateral division ELr. ER also extends caudally to the intermediate division EI. ELr runs along the medial surface of the rhinal sulcus and borders area 35 of the perirhinal cortex, with the
ER-ELr boundary running parallel to the rhinal sulcus. Caudolateral division ELc is medially bordered by EI and laterally by the fundus of the rhinal sulcus. Together,
ELr and ELc make up only 9.2% of the total surface area. EI is a central transition region bordered laterally by ELc, rostromedially by EO and ER, and caudally by the parasubiculum and caudal division EC, and it is the largest subfield with claim to
27.4% of the total surface area. Covering 17.7% of the surface area, EC is situated between the rhinal sulcus and area 36 of the perirhinal cortex laterally and, medially, by the parasubiculum and the caudal limiting field, division ECL. ECL
5 represents 14.3% of the surface area at the caudal-most end of the entorhinal cortex, bordered medially by the parasubiculum and laterally by area 36.
Perirhinal cortex. The macaque perirhinal cortex has two major subdivisions: the smaller medial area 35 and the larger lateral area 36 (Insausti et al. 1987;
Suzuki & Amaral 1994b). Area 35 follows the fundus along the full length of the rhinal sulcus as it rings the piriform cortex. Area 36 is further divided into dorsal
(36d), rostral (36r, with rostromedial 36rm and rostrolateral 36rl), and caudal (36c, with caudomedial 36cm and caudolateral 36cl) areas. At the most rostrodorsal end, 36d comprises a third of the temporal pole; 36r is caudally adjacent to 36d and bounded by inferotemporal area TE by von Bonin and Bailey’s classification
(1947); and the largest division, making up the caudal end of the perirhinal cortex,
36c is medially bounded by entorhinal divisions EI and EC and laterally bounded by parahippocampal division TF. Areas 35, 36rm, 36rl, 36cm, and 36cl of the perirhinal cortex are highly inter- and intraconnected, but area 36d is less strongly associated with the other regions (Lavenex et al. 2004).
Parahippocampal cortex. The entire rostrocaudal extent of the macaque parahippocampal cortex surrounds ventral portions of the hippocampal formation: rostrally, it is bounded by the visual cortical area V4; caudally, by the entorhinal and perirhinal cortices; laterally, by interotemporal area TE; and medially, by the parasubiculum and presubiculum (Insausti et al. 1987; Suzuki & Amaral 1994a,
2003). It has two major divisions: the smaller medial area TH, adjacent to presubiculum, and the larger lateral area TF, which is primarily bordered by area
6 TE and can be subdivided into medial TFm and lateral TFl. TH, TFm, and TFl are highly inter- and intraconnected (Lavenex et al. 2004).
1.1.3 Medial Temporal Lobe Connectivity
Perirhinal connectivity. Area 36 of the perirhinal cortex and, to a lesser degree, area 35 receive unimodal input from temporal lobe visual areas TE, TEO, and V4 (Webster et al. 1991; Martin-Elkins & Horel 1992; Suzuki & Amaral 1994a;
Saleem & Tanaka 1996) with even stronger feedback projections preferential for
TE and TEO (Lavenex et al. 2002). It also shares reciprocal connections with multimodal association areas in the insular and cingulate cortices, but not with the retrosplenial cortex (Suzuki & Amaral 1994a; Lavenex et al. 2002; Aggleton et al.
2012). Although the orbitofrontal cortex projects to the perirhinal cortex, there is low reciprocity (Amaral 1994a; Lavenex et al. 2002). The perirhinal cortex also has strong projections to the olfactory tubercle (Friedman et al. 2002). Among the perirhinal subdivisions, area 36c is most strongly associated with the parahippocampal cortex through the anterior portion of TF (Suzuki & Amaral
1994a; Lavenex et al. 2004). These are primarily feedback projections, with a lesser feedforward connection from TF to area 36r.
Parahippocampal connectivity. The parahippocampal cortex has highly reciprocal connections to multimodal association cortices, including the superior temporal sulcus and insular, cingulate, retrosplenial, and posterior parietal cortices
(Jones & Powell 1970; Seltzer & Pandya 1976; Vogt & Pandya 1987; Anderson et
7 al. 1990; Suzuki & Amaral 1994a; Lavenex et al. 2002; Blatt et al. 2003). It is also the recipient of unimodal input: the auditory association area TPT of the superior temporal gyrus projects reciprocally to area TH (Van Hoesen 1980; Tranel et al.
1988; Suzuki & Amaral 1994a; Lavenex et al. 2002), and caudal visual areas in
TE, TEO, and V4 project to area TF (Webster et al. 1991; Martin-Elkins & Horel
1992; Suzuki & Amaral 1994a; Blatt et al. 2003), with preferential reciprocation with V4 (Lavenex et al. 2002). Other projections originate from the orbitofrontal cortex (Carmichael & Price 1995) and other frontal lobe areas that, in humans, are associated with functions including attention, planning, semantic and perceptual processing, inductive and deductive reasoning, calculation, working memory, and recognition (Suzuki & Amaral 1994a).
Entorhinal connectivity. Almost two-thirds of cortical inputs to entorhinal come from the perirhinal and parahippocampal cortices (Van Hoesen & Pandya
1975a, 1975b; Van Hoesen et al. 1975; Turner et al. 1980; Insausti et al. 1987;
Suzuki & Amaral 1994b; Insausti & Amaral 2008). All divisions of perirhinal areas
35 and 36 project to the entorhinal cortex, with the strongest association in rostrolateral areas ER and ELr and the weakest in caudomedial EI and EC (Insausti et al. 1987; Suzuki & Amaral 1994b; Mohedano-Moriano et al. 2007). The parahippocampal cortex shows the reverse gradient, with both TH and TF projecting most heavily to EI and EC. The center third of the entorhinal cortex receives overlapping perirhinal and parahippocampal inputs. While parahippocampal-entorhinal projections have a high degree of reciprocity
8 throughout TH and TF, perirhinal-entorhinal projections are reciprocated more strongly in the medial areas of the perirhinal cortex than in the lateral (Kosel et al.
1982; Saunders & Rosene 1988; Suzuki & Amaral 1994b; Suzuki 1996). The entorhinal cortex receives other direct inputs from the superior temporal gyrus, striatum, amygdala, and the orbitofrontal, insular, cingulate, and retrosplenial cortices (Van Hoesen et al. 1975; Leichnetz & Astruc 1975, 1976; Turner et al.
1980; Amaral et al. 1983; Mesulam & Mufson 1982; Insausti et al. 1987;
Carmichael & Price 1995; Suzuki 1996; Mohedano-Moriano et al. 2007; Insausti &
Amaral 2008; Aggleton et al. 2012). It does not, however, receive projections from the primary auditory cortex, although it is adjacent (Insausti et al. 1987). In fact, the only direct unimodal sensory inputs are from the olfactory-processing piriform cortex (Van Hoesen & Pandya 1975a; Insausti et al. 1987; Mohedano-Moriano et al. 2008) and some minor projections from visual cortical areas V4 and TE (Martin-
Elkins & Horel 1992; Saleem & Tanaka 1996; Mohedano-Moriano et al. 2008).
Anatomical research therefore suggests that most primary cortical information must be relayed to the entorhinal cortex through the perirhinal and parahippocampal cortices. The feedback projections from the entorhinal cortex to the perirhinal and parahippocampal cortices are also relayed back through to their cortical origins (Van Hoesen 1982; Suzuki & Amaral 1994b; Muñoz & Insausti
2005). Outside the MTL, entorhinal efferents also project to the medial frontal cortex, thalamus, pulvinar, paraventricular nucleus, and nucleus accumbens
9 (Aggleton et al. 1986; Friedman et al. 2002; Muñoz & Insausti 2005; Saunders et al. 2005).
Hippocampal connectivity. By extension, the entorhinal cortex appears to serve as the main route for cortical information to reach the hippocampus (Van
Hoesen et al. 1972, 1975; Van Hoesen & Pandya 1975a, 1975b; Kosel et al. 1982;
Insausti et al. 1987; Witter et al. 1989; Suzuki & Amaral 1990, 1994a, 1994b; Witter
& Amaral 1991). This route is known as the “perforant path.” Its strongest projections are from the entorhinal areas near the rhinal sulcus to the caudolateral two-thirds of the dentate gyrus, but there are also entorhinal projections directly to
CA3 and CA1. Within the hippocampus proper, mossy fibers from the dentate gyrus project to CA3, from which Schaffer collaterals project to CA1. CA1 then projects to the subiculum, presubiculum, and parasubiculum and back to the entorhinal cortex (Saunders & Rosene 1988). In addition to their strong reciprocal connections with the hippocampus via the entorhinal cortex, the perirhinal and parahippocampal cortices also receive direct projections from the hippocampus and subiculum (Rosene & Van Hoesen 1977; Saunders & Rosene 1988; Suzuki &
Amaral 1990; Witter & Amaral 1991).
Information from the hippocampus has several exit points beyond its MTL cortical feedback pathways. First, it projects to the medial frontal cortex via the entorhinal cortex (Muñoz & Insausti 2005). Second, it projects to the medial frontal cortex, cingulate gyrus, amygdala, and retrosplenial cortex via the subiculum
(Rosene & Van Hoesen 1977; Friedman et al. 2002; Aggleton et al. 2012). Third,
10 it projects to the septal complex, prefrontal cortex, hypothalamus, and amygdala via the fornix-fimbria system (Valenstein & Nauta 1959; Poletti & Creswell 1977;
Krayniak et al. 1979; Wyss et al. 1979; Amaral & Cowan 1980; Devito 1980;
Friedman et al. 2002; Aggleton et al. 2015). Fourth, the hippocampus has direct projections to and from the thalamus, hypothalamus, amygdala, paraventricular nucleus, ventral tegmental area, retrosplenial cortex, and more (Amaral & Cowan
1980; Suzuki & Amaral 1994a; Aggleton et al. 2012). The subiculum and entorhinal cortex project to the thalamus independently from their connections with the hippocampus (Aggleton et al. 1986).
1.1.4 Hippocampal Function
Evidence from human lesion cases. Human case studies involving hippocampal lesions are perhaps most well-known for their profound examples of anterograde and retrograde amnesia. Interestingly, even in patients who are unable to encode new long-term memories or retrieve episodic memories from their life before brain damage, widespread lesions including the hippocampus do not significantly impact personality, general intelligence, semantic processing, motivation, attention, or reasoning (Scoville & Milner 1967; Penfield & Milner 1958;
Corkin 1984; Squire & Zola-Morgan 1996; Holdstock 2005; Rosenbaum et al.
2005; Steinvorth et al. 2005). Broadly, semantic memory and non-declarative memory—including procedural learning, priming, non-associative learning, and classical conditioning—are preserved even when episodic memory is severely
11 impaired (Squire & Zola-Morgan 1996; Vargha-Khadem et al. 2001; Holdstock
2005; Rosenbaum et al. 2005; Steinvorth et al. 2005). In many cases, generalized familiarity detection is also intact (Holdstock et al. 2002; Yonelinas 2002; but see
Knowlton & Squire 1995; Manns et al. 2003). One patient, Y.R., who had selective bilateral hippocampal damage demonstrated normal verbal and object recognition memory as well as normal unimodal association memory across short (up to 10- second) delays, but impaired multimodal association memory (e.g. object-place, face-voice, temporal order of a word or item) and unimodal association memory across long delays (Mayes et al. 2004a; Pascalis et al. 2004). Patients with hippocampal damage also have difficulty manufacturing details and applying associational schemas to construct coherent fictional narratives or imagined experiences (Hassabis et al. 2007; Rosenbaum et al. 2009).
Spatial memory impairment is common following hippocampal damage.
Navigation-related deficits are found in topographical memory after bilateral or unilateral MTL lesions including the hippocampus (Spiers et al. 2001a, 2001b), and so are nonnavigational deficits in object-place association memory and object configuration memory (Bohbot et al. 1998, 2000, 2002). However, hippocampal lesions are associated with only mild deficits in a spatial oddity task in which the subject has to identify one mismatching location from among a like set (Bohbot et al. 2000), suggesting relatively intact spatial discrimination and spatial relational reasoning. Patients with hippocampal lesions sparing the parahippocampal cortex use the same navigational strategies as healthy controls during spatial exploration
12 in the invisible sensor task (IST), a spatial memory task adapted from the Morris water maze for rodents, and are able to remember the location of the sensor 30 minutes after the sampling phase (Bohbot et al. 1998, 2000, 2002). They also demonstrate normal spatial working memory in a virtual adaptation of the radial arm maze and normal non-visual spatial exploration using vestibular, proprioceptive, and kinesthetic input (Bohbot et al. 1998, 2002).
Retrograde amnesia induced by hippocampal damage is often temporally restricted; even extensive MTL lesions may not impair episodic, semantic, or spatial memory that was established early in life (Corkin 1984; Sagar et al. 1985;
Squire et al. 1993; Reed & Squire 1998; Kapur & Brooks 1999; Teng & Squire
1999; Bayley et al. 2005; Insausti et al. 2013). This supports the consolidation theory (Squire 1992; Eichenbaum 2000), which posits that the hippocampus is not so much a site for long-term memory storage, but rather an intermediary location orchestrating the activation of, and communication between, cortical areas in which the pertinent details are consolidated and from which they are recalled. With repeated re-creations of a memory representation over time, as in the case of familiar childhood memories, the synaptic pathways between cortical areas are reinforced until memory recall is eventually possible without hippocampal networking. However, the opposing multiple trace theory (Nadel & Moscovitch
1997; Westmacott et al. 2001) argues that the hippocampus does subserve direct memory storage: it posits that for all attended experiences (including retrieval of old memories), new memory traces permanently link perceptual information stored
13 in neocortical circuits to spatiotemporal and contextual information represented in the hippocampus, but as more related traces are indexed, generalizable information is more easily extracted from overlapping episodic events and, eventually, integrates into established semantic knowledge. Associated traces proliferate as a particular episodic memory is retrieved over and over, and memories with more access routes are 1) less vulnerable to hippocampal damage than single memory traces and 2) likely to have more autobiographical content transferred into semantic memory stores.
Evidence from lesion experiments in monkeys. Many of the first nonhuman primate lesion studies in the MTL attempted to recreate H.M.’s lesions and his resulting amnesia. Some of these studies did find that aspiration lesions of the hippocampus or combined hippocampus and amygdala produced deficits in visual recognition memory (Stepien et al. 1960; Mishkin 1978; Mahut et al. 1981, 1982;
Moss et al. 1981; Zola-Morgan et al. 1982, 1989a; Malamut et al. 1984; Murray &
Mishkin 1984; Saunders et al. 1984; Zola-Morgan & Squire 1985, 1986; Overman et al. 1990). In gaining access to these regions for ablation, however, fibers of passage were transected and the surrounding cortices were frequently damaged as well; in particular, the hippocampus was accessed through the underlying parahippocampal cortex. Other studies failed to identify significant recognition memory impairment after hippocampal or amygdalohippocampal lesions (Orbach et al. 1960; Mahut & Cordeau 1963; Correll & Scoville 1965b) or found recognition memory deficits produced by MTL cortical lesions sparing the hippocampus
14 (Murray & Mishkin 1986; Horel et al. 1987; Zola-Morgan et al. 1989b, 1993; Gaffan
& Murray 1992; Meunier et al. 1993; Suzuki et al. 1993; Eacott et al. 1994; Leonard et al. 1995; Buckley et al. 1997). These contradictory findings, alongside discoveries that, first, aspiration lesions of the amygdala disrupt projections between the rhinal (combined perirhinal and entorhinal) cortex and the thalamus
(Goulet et al. 1998) and second, the perirhinal cortex extends more rostrally than previously believed (Amaral et al. 1987; Insausti et al. 1987), emphasized the necessity of dissociating the effects attributed to hippocampal or amygdalohippocampal damage from the contributions of other MTL areas.
As ablation methods were refined over time, aspiration lesions were largely replaced by lesions produced by microinjection of excitotoxic agents (i.e. ibotenic acid) to kill cell bodies in the target region (Jarrard 1989). This method is less likely to damage neighboring brain regions and, critically, spares fibers of passage and vasculature at the injection site. Unfortunately, even with improved techniques, the specific role of the hippocampus in recognition memory remains controversial.
On one hand, in contrast to early findings using aspiration, ischemic (Zola-Morgan et al. 1992), or radiofrequency (Alvarez et al. (1995) lesions, Murray and Mishkin
(1998) found that monkeys with excitotoxic amygdalohippocampal lesions did not show any deficits on a standard test of visual recognition, the delayed nonmatching-to-sample task (DNMS: Gaffan 1974; Mishkin & Delacour 1975), with a memory load of up to 40 unique objects or across delays ranging from 10 seconds to 40 minutes. Nemanic et al. (2004) also reported normal DNMS
15 performance in monkeys with excitotoxic lesions restricted to the hippocampus.
On the other hand, DNMS with long delays was impaired in Beason-Held et al.
(1999) and Zola-Morgan et al. (2000), and both Nemanic et al. and Zola-Morgan et al. identified significant deficits on another recognition memory task, the visual paired comparison task (VPC: Bachevalier et al. 1993). Additionally, Beason-Held et al. demonstrated impaired memory for increasing arrays of stimuli on the color and object versions of a delayed recognition span task adapted for monkeys. A meta-analysis of these studies found a surprising inverse relationship between the extent of hippocampal damage and the degree of recognition memory impairment
(Baxter & Murray 2001b; but see Zola & Squire 2001).
Several theories have been proposed to resolve these discrepancies. Some point to differences in methodological details, such as preoperative versus postoperative training. Another posits that the inconsistencies attributed to
‘recognition memory’ actually reflect two separate processes: one involves conscious, episodic recollection while the other is a passive, generalized familiarity detection system (Mandler 1980; Jacoby & Dallas 1981; Gardiner & Parkin 1990).
Recollection may be supported by both the hippocampus and the perirhinal cortex, but of the MTL structures, only the perirhinal cortex is critical for familiarity detection (Aggleton & Brown 1999, 2006; Brown & Aggleton 2001; Eichenbaum et al. 2007; Squire et al. 2007). Depending on the delay periods and other methodological nuances, DNMS could rely on either familiarity or recollection and thus produce incongruous results after hippocampal damage.
16 In addition to familiarity detection, the monkey hippocampus is also nonessential for nonspatial visual perception (Saksida et al. 2006, 2007), for concurrent nonspatial visual discrimination learning (Malamut et al. 1984; Ridley et al. 1997), and for long-term retention of nonspatial visual discriminations
(Malamut et al. 1984; Overman et al. 1990).
As in the case with visual recognition-related deficits, early research on spatial memory in relation to hippocampal lesions was confounded by surrounding cortical damage, in particular to the parahippocampal cortex (Parkinson et al. 1988; Angeli et al. 1993). Performance on a spatial version of DNMS, the delayed nonmatching- to-location task (DNML), has had contradictory results even with selective hippocampal lesions (Murray & Mishkin 1998 vs. Alvarado & Bachevalier 2005b).
A variation of the VPC task testing memory for spatial locations (Bachevalier &
Nemanic 2008) as well as one-trial memory for locations and object-place associations (Malkova et al. 2003) are unaffected by hippocampal lesions, but memory load in a spatial version of the delayed recognition span task (Beason-
Held et al. 1999) and memory load for learning large sets of object-place associations (Belcher et al. 2006) are impaired.
Excitotoxic hippocampal lesions impair retention of nonnavigational object-in- place scene memory encoded preoperatively, regardless of temporal proximity to the intervention, but the same hippocampal lesions do not affect postoperative new scene learning (Gaffan 1994a, Mitchell et al. 2008, Froudist-Walsh et al. 2018).
There is also no apparent impairment in spatial reversal learning (Murray et al.
17 1998). In contrast, hippocampal lesions do produce significant deficits in paradigms that involve making highly contextualized relational judgments, including object-in-scene and object-in-context variations of the VPC task
(Bachevalier & Nemanic 2008; Bachevalier et al. 2015), visual discrimination learning with trial-unique contexts (Doré et al. 2001), and both visuospatial and nonspatial conditional tasks (Ridley et al. 1997; Ridley et al. 2001). Spatial memory tasks that involve free movement in an open field arena, where the scene is approached from multiple viewpoints and multiplexed self-motion cues enrich the spatial context, also show significant impairment in new scene learning after hippocampectomy (Hampton et al. 2004, Lavenex et al. 2006). Similarly, configural discrimination learning in the transverse patterning task is impaired when stimuli are presented discontiguously (Alvarado & Bachevalier 2005b) but not when they are presented simultaneously as part of a single scene (Saksida et al. 2007). Hippocampal lesions are further associated with deficits on a spatial delayed alternation task (Orbach et al. 1960; Stepien et al. 1960; Pribram et al.
1962; Mahut & Cordeau 1963) for which successful performance depends on retrieval of spatial information bound to its temporal context. However, in navigational tasks, monkeys with or without hippocampal lesions use search strategies that demonstrate a preference for minimizing travel distance (e.g. foraging from the center outward), suggesting that some level of spatial reasoning is spared after hippocampectomy when memory retrieval is not required (Lavenex et al. 2006).
18 Interestingly, hippocampal lesions also reduce unlearned defensive behaviors in response to potential predators (e.g. artificial snakes; Chudasama & Murray
2004).
Evidence from intracerebral pharmacological manipulation in monkeys.
Pharmacological inactivation is a useful tool to impair both memory retrieval in the hippocampus and to block reactivation of the cortical neurons that were active during memory encoding (Tanaka et al. 2014). Consistent with lesion results
(Brasted et al. 2005), transient inactivation of the hippocampus with bilateral microinfusion of GABAA receptor agonist THIP (1.5µl, 18mM and 38mM) does not disrupt acquisition or retrieval of visuomotor associations (Yang et al. 2014).
However, bilateral microinfusion of a glutamate receptor antagonist, kynurenic acid
(KYNA: 1.5-2µl, 100mM), or the GABAA receptor agonist muscimol (MUS: 1µl,
9nmol) significantly impairs nonnavigational spatial memory in the Hamilton
Search Task (described in Methods; Forcelli et al. 2014).
Evidence from electrophysiological recording. As the body of literature on primate MTL lesions grew, a simultaneous and largely independent line of research began on the electrophysiological properties of the rodent MTL. In 1971,
O’Keefe and Dostrovsky published the first evidence for “place cells,” which are neurons that activate when the animal is in a specific location within a defined physical space; for example, in the rat hippocampus, many neurons demonstrate location-specific firing within mazes (Olton et al. 1978; McNaughton et al. 1983) or while foraging for food pellets in an open field arena (Muller et al. 1987). The
19 “place fields” of these cells—or the boundaries defining the area in which they fire, which vary in specificity from one place cell to another—may scale up and down proportionally to changes in the size of the animal’s environment, but they are not typically robust to changes in shape (Muller & Kubie 1987; O’Keefe & Burgess
1996). This discovery resulted in an early theory that the hippocampus essentially functions as a Cartesian map for internal representation of the local environment
(O’Keefe & Dostrovsky 1971; O’Keefe & Nadel 1978; O’Keefe & Speakman 1987).
However, further experiments identified neurons in the rat hippocampus that were responsive to spatial and locomotor cues beyond the animal’s allocentric coordinates, including body orientation within the space, position relative to landmarks or barriers, direction and velocity of movement, or, frequently, combinations of these cues (Olton et al. 1978; McNaughton et al. 1983; Muller &
Kubie 1987; Wiener et al. 1989; Gothard et al. 1996a, 1996b; O’Keefe & Burgess
1996; Czurko et al. 1999; Rivard et al. 2004; Pastalkova et al. 2008; Komorowski et al. 2009; Góis & Tort 2018).
Place cells were identified in the human hippocampus over 30 years after their characterization in rats (Ekstrom et al. 2003), and electrophysiology studies in nonhuman primates have identified hippocampal neurons responsive to a wide variety of spatial signals. One category demonstrates selectivity for local and distal cues, including the animal’s location in real or virtual space (Ono et al. 1993;
Matsumura et al. 1999; Hori et al. 2003, 2005; Ludvig et al. 2004; Furuya et al.
2014; Wirth et al. 2017; Hazama & Tamura 2019; Bretas et al. 2019), viewed
20 locations (Rolls et al. 1997; Robertson et al. 1998; Rolls 1999), local landmarks
(Rolls & O’Mara 1995; Georges-François et al. 1999; Wirth et al. 2017), and allocentric position or egocentric direction of visual and auditory stimuli (Rolls et al.
1989; Cahusac et al. 1989; Tamura et al. 1990, 1992; Ono et al. 1991a, 1991b,
1993). Place cells, as found in the rodent hippocampus, have scalable place fields directly influenced by the boundaries of the local environment (Furuya et al. 2014).
However, unlike rodents, monkeys depend on vision as a primary sensory modality
(Ghazanfar & Santos 2004) and have a significant population of hippocampal neurons more appropriately classified as “view cells” (Rolls et al. 1997; Rolls 1999) that respond to viewing locations external to the self. A second category of hippocampal neurons is selective for self-motion cues, including navigation along specific routes (Bretas et al. 2019), nonnavigational spatial responses (e.g. left/right or approach/withdraw arm movements) to stimuli (Miyashita et al. 1989;
Cahusac et al. 1993), linear motion or axial rotation (O’Mara et al. 1994), velocity of movement (Hazama & Tamura 2019), and even head direction or eye movement (Ringo et al. 1994; Nowicka & Ringo 2000; Ringo & Stringer 2005; Wirth et al. 2017). There are also many hippocampal cells that respond to combinations of cues and support object-place associations (Cahusac et al. 1989; Rolls et al.
1989; Eifuku et al. 1995), object-place-response associations (Eifuku et al. 1995), and location-in-scene associations (Wirth et al. 2003). These neurons are capable of updating their selectivity criteria to reflect association learning over time
(Cahusac et al. 1993).
21 In this line of research, the predominant focus on spatial cues was influenced by the limitations of the rodent model, and Eichenbaum et al. (1999) suggested that “any apparent primacy for spatial representation [in the hippocampus] is a direct consequence of the ever-present spatial regularities associated with behavioral episodes.” In addition to place cells and other neurons primed for spatial cues, hippocampal cells with entirely nonspatial selectivity criteria have also been identified across species. For example, in rats some hippocampal cells respond to specific perceptual cues (e.g. color, odor), behavioral actions (e.g. goal- approach), or cognitive events (e.g. match or non-match trials in a nonmatching- to-sample task) regardless of location (Wiener et al. 1989; Wood et al. 1999). Place cell activity may be modified directly by multimodal contextual and behavioral information (Best & Thompson 1989; Bostock et al. 1991; Save et al. 2000;
Anderson & Jeffery 2003).
The rodent hippocampus also contains “time cells” that are activated at specific moments across successive trials or task sessions and can link similar but temporally separated events (Wood et al. 1999; Pastalkova et al. 2008; MacDonald et al. 2011). A given population of time cells can simultaneously code temporal information on scales from seconds to hours (Mau et al. 2018), and gradual changes in time cell activity enable memory for event order (Manns et al. 2007) and binding of related memories in close temporal proximity (Rubin et al. 2015;
Cai et al. 2016). The temporal resolution of memory is thereby encoded on a spectrum from singular, autobiographical experiences to repeated, generalizable
22 events (Mankin et al. 2012; Komorowski et al. 2013; Eichenbaum 2014). Spatial and temporal representations are closely interlinked (Kraus et al. 2013), and even beyond coding for individual dimensions of information or for feature conjunctions, the hippocampus organizes memory into complex relational schemas that link events across time and enable the flexible expression and application of memory
(McKenzie et al. 2014; Eichenbaum 2017).
Similar temporal signaling has been identified in the primate hippocampus
(Watanabe & Niki 1985; Sakon et al. 2014), as have nonspatial association and recognition signaling (Wirth et al. 2009; Jutras & Buffalo 2010). Units update their selectivity criteria in response to association learning (Miyashita 1988; Sakai &
Miyashita 1991; Messinger et al. 2001; Wirth et al. 2003, 2009; Yanike et al. 2009).
In general, hippocampal units respond preferentially to familiar over novel stimuli
(Brown et al. 1987; Riches et al. 1991; Yanike et al. 2004). Additionally, in both humans (Fried et al. 1997; Kreiman et al. 2000) and monkeys (Hampson et al.
2004; Sliwa et al. 2016), there are hippocampal neurons that fire in response to entire categories of visual stimuli, such as faces, facial expressions, animal species, or natural scenes. Even more highly specialized neurons, called “concept cells,” respond to images of specific people (e.g. Jennifer Anniston) or landmarks
(e.g. Sydney Opera House) from different angles or under widely varying conditions (Quian Quiroga et al. 2005). These are not organized topographically, with neighboring neurons typically responsive to unrelated stimuli, and are
23 components of cell assemblies that represent complex concepts and subserve declarative memory (Quian Quiroga 2012).
Evidence from neuroimaging. In one of the few imaging studies that scanned awake monkeys, functional magnetic resonance imaging (fMRI) identified a categorical response in the macaque hippocampus to faces (Ku et al. 2011).
The human hippocampus is active during retrieval of spatial, temporal, and object information from recent and remote memories, both episodic and semantic
(Hayes et al. 2004; Mayes et al. 2004b; Hoscheidt et al. 2010; Nielson et al. 2015).
Although all hippocampal subunits are responsive, none behave differentially between retrieval of spatial or temporal information (Kyle et al. 2015). The hippocampus responds to temporal position of objects in a learned sequence but not in random order (Hsieh et al. 2014), and it marks both event order and duration
(Thavabalasingam et al. 2018). It is also involved in contextual discrimination and transitive inference (Greene et al. 2006), and temporal and contextual processing are closely interrelated (Ezzyat & Davachi 2014). Furthermore, the hippocampus is preferentially responsive to novel configurations and contextual associations
(Düzel et al. 2003; Pihlajamäki et al. 2004). At the highest level, the hippocampus is able to form abstract, flexible, and relational representations of spatial, temporal, and/or abstract contextual information during navigation and nonspatial, goal- directed activity (Deuker et al. 2016; Garvert et al. 2017).
24 1.1.5 Entorhinal Function
Evidence from human lesion cases. Although there are many examples of human patients with expansive lesions including significant entorhinal damage
(patient H.M., Corkin et al. 1997; patient E.P., Stefanacci et al. 2000; patient N.B.,
Bowles et al. 2007), patient M.R. is the only documented case with a selective and exclusive entorhinal lesion (Brandt et al. 2016, 2018; James et al. 2018). The lesion was localized to the lateral entorhinal cortex of her left hemisphere.
Cognitive and behavioral testing revealed that M.R. had selective impairments in object recognition memory and familiar word recognition, but intact object location memory, familiar and unfamiliar face recognition memory, and pseudoword recognition. She also experienced more frequent and vivid episodes of déjà vécu
(i.e. false, specific recollection that the present experience has happened before) compared to control subjects, but she was more resistant to stimulation of déjà vu
(i.e. a sense of false, generalized familiarity). Together, these findings implicate the lateral entorhinal cortex in familiarity-based recognition memory for specific types of stimuli, but not in episodic recollection — the reverse of hippocampal contributions to these processes.
Evidence from lesion experiments in monkeys. As primate lesion research began to distinguish between the effects of hippocampal (or amygdalo- hippocampal) ablation and the effects of damage to other areas of the MTL, initial studies on the rhinal cortex utilized combination lesions of the entorhinal and perirhinal cortices (Murray & Mishkin 1986; Gaffan & Murray 1992; Murray et al.
25 1993; Eacott et al. 1994; Higuchi & Miyashita 1996; Thornton et al. 1997; Baxter &
Murray 2001a). Meunier et al. (1993) and Leonard et al. (1995), two of the first studies to ablate the entorhinal cortex selectively, identified a mild impairment on visual recognition memory in the DNMS task compared to the significant deficits in monkeys with perirhinal lesions. Moss et al. (1981) also linked entorhinal damage to impaired performance on visual and tactile concurrent discrimination tasks. In a more spatially-grounded task, Charles et al. (2004) found evidence of retrograde amnesia for object-in-scene associations learned preoperatively, as well as impairment of new, postoperative object-in-scene learning. In all of these cases, however, the aspiration regions were not entirely restricted to the entorhinal cortex and included some damage to the neighboring perirhinal cortex. With even more selective entorhinal lesions (albeit also aspiration-induced), Buckmaster et al.
(2004) found no deficits on DNMS or object discrimination but did identify impairments in relational representation and transitive inference. Using neurotoxic lesions, Mitchell et al. (2008) reproduced the impairment in object-in-scene memory for preoperative learning, but unlike Charles et al., did not find a deficit in postoperative acquisition of new object-in-scene associations. Although entorhinal lesion studies in monkeys are still few in number, together they point to a critical role for the entorhinal cortex in processing complex relational representations and manipulations, but not in perception or discrimination. These findings are congruent with its extensive connections with multimodal association cortices.
26 Evidence from intracerebral pharmacological manipulation in monkeys.
In monkeys, pharmacological inactivation of the entorhinal cortex with bilateral infusion of the GABAA receptor agonist THIP (1.5µl; 18mM and 38mM) disrupts acquisition of novel arbitrary associations in a visuomotor learning (VML) task, but not retrieval of previously established associations (Yang et al. 2014). Neither inactivation (Yang et al. 2014) nor ablation (Brasted et al. 2005) of the hippocampus impairs acquisition or performance in the same paradigm.
Evidence from electrophysiological recording. “Grid cells” were first characterized in the entorhinal cortex of the rat in 2005 (Hafting et al. 2005).
Similar to place cells in the hippocampus, grid cells show location-selective firing, but each grid cell has multiple, separate place fields arranged like the nodes of a triangular tessellation. Cells have grid sizes with varying distances between nodes, but the geometric coordinate system indicates a spatial map microstructure within the entorhinal cortex. Grid cells are located primarily within the medial entorhinal cortex (Hafting et al. 2005; Hargreaves et al. 2005), which is the portion with strong projections from the postrhinal cortex, the rodent equivalent of the parahippocampal cortex. Unlike hippocampal place cells, grid cells also receive input from postsubicular neurons sensitive to head direction, and their grid-like spatial firing patterns support spatial computations that are not possible with place cells in isolation. Where place cell firing can support navigation to a specific location (Burgess et al. 1994), however, grid cell firing alone cannot. O’Keefe and colleagues have proposed that their original theory about the hippocampus as a
27 cognitive map (O’Keefe & Dostrovsky 1971; O’Keefe & Nadel 1978) may more appropriately describe the role of the entorhinal cortex (O’Keefe & Burgess 2005), and they suggest a network where grid cells provide path integration for place cells in the hippocampus, while feedback from place cells provides environmental input for coherent spatial orientation, binding grids to locations, and remapping across environments. This is supported by evidence that grid cells are insensitive to nonspatial environmental cues and form more erroneous representations in the absence of place cell collaboration: hippocampal ablation in the rat causes degradation of the spatial coherence of grid cell firing, reduced dispersion of firing fields, and increased influence of directional modulation (Fyhn et al. 2004). Loss of medial entorhinal input to the hippocampus also prevents place memory acquisition (Hales et al. 2014), although established hippocampal spatial representations of familiar environments are relatively stable even after medial entorhinal ablation or inactivation (Hales et al. 2014; Robinson et al. 2017).
Intermingled with grid cells are other neurons selective for head direction; velocity of movement; conjunctions of position, direction, and velocity; and proximity to boundaries (Sargolini et al. 2006; Solstad et al. 2008; Kropff et al.
2015). Many grid cells also have temporal firing fields (Kraus et al. 2015), and transient inactivation of the medial entorhinal cortex impairs temporal coding in
CA1 with a resulting impairment in memory across delays while spatial and object coding in the hippocampus remain intact (Robinson et al. 2017), indicating a specific and critical role for grid cells in orchestrating hippocampal temporal coding.
28 Nonspatial time signals in the lateral entorhinal cortex mark time on scales ranging from seconds to hours across different environmental contexts, and they can encode ongoing experience as well as timepoints in relation to temporal landmarks
(Tsao et al. 2018). Even nonspatial, nontemporal dimensions such as sound frequency are mapped by entorhinal cells (Aronov et al. 2017).
In monkeys, grid cells have hexadirectional coordinate systems for three- dimensional representations of space, and they can map based on visual exploration without locomotion (Suzuki et al. 1997; Killian et al. 2012; Wilming et al. 2018). Nodal spacing of firing fields generally correlates to the grid cell’s distance to the rhinal sulcus (Killian et al. 2012). Grid cells densely populate the posterior entorhinal cortex, whereas the anterior entorhinal cortex has more cells that respond to visual stimuli and visual recognition processes. View cells that fire according to the animal’s saccade direction or gaze position have also been identified in the primate entorhinal cortex (Killian et al. 2015; Meister & Buffalo
2018), and there are many other neuronal response properties yet to be explored.
Evidence from neuroimaging. An fMRI scan during the execution of a conditional motor associative learning task reveals equivalent signaling in the entorhinal cortex between humans and macaques for familiarity discrimination, novel stimulus presentation, task learning, and task outcome (Hargreaves et al.
2012). This is promising evidence for cross-species similarities in entorhinal function.
29 Human fMRI studies have revealed hexadirectional grid-like representations during virtual navigation (Doeller et al. 2010; Jacobs et al. 2010, 2013; Kunz et al.
2015; Horner et al. 2016; Nadasdy et al. 2017), imagined navigation (Horner et al.
2016), imagined directional views without navigation (Bellmund et al. 2016), and nonnavigational visual exploration (Nau et al. 2018; Staudigl et al. 2018). These representations demonstrate adaptive scaling and sensitivity to environmental barriers and goal direction (Nadasdy et al. 2017; Julian et al. 2018; He & Brown
2019; Shine et al. 2019), with the anterolateral entorhinal cortex more involved in distance computations based on landmarks and the posteromedial entorhinal cortex more involved in computations based on self-motion cues (Chen et al. 2019).
Interestingly, these grid-like representations are not only used to map physical, virtual, or imagined space; they also subserve navigation of olfactory space (Bao et al. 2019), navigation of abstract concept space (Constantinescu et al. 2016), social navigation (Tavares et al. 2015), and goal-directed behavior based on abstract relational knowledge using associational strength as a metric (Garvert et al. 2017). Furthermore, the anterolateral entorhinal cortex is active during retrieval of precise temporal memory (Montchal et al. 2019) and constructs holistic temporal maps of event sequences (Bellmund et al. 2019). Grid-coding is evidently engaged for both spatial and nonspatial functions across different metric scales, which supports the idea that the perforant pathway and overall MTL in humans does not function as a navigation system specifically, but as a general processor for learning, organizing, and utilizing conceptual knowledge across many domains
30 and modalities (Constantinescu et al. 2016; Bellmund et al. 2018; Mok & Love
2019). Among other quirks, this may account for cross-dimensional interference between spatial and temporal perception (Riemer et al. 2018).
1.1.6 Perirhinal Function
Evidence from human lesion cases. Some reports on patients with MTL lesions including the perirhinal cortex or patients with selective perirhinal lesions report impaired visual discrimination in tasks with high feature overlap (Barense et al. 2005; Lee et al. 2005b, 2005c; Inhoff et al. 2019), while others find no perceptual deficits regardless of visual complexity (Corkin 1984; Holdstock et al. 2000; Buffalo et al. 1998; Stark & Squire 2000; Levy et al. 2005; Shrager et al. 2006). MTL lesions including most of the perirhinal cortex but sparing the hippocampus produce a familiarity detection deficit (Bowles et al. 2007, 2016) and a delay- dependent deficit in recollection (Buffalo et al. 1998; Holdstock et al. 2000; Meunier
& Barbeau 2013; Inhoff et al. 2019), implicating the perirhinal cortex in both types of recognition memory.
Evidence from lesion experiments in monkeys. The perirhinal cortex has a definitive and critical role in visual perception in monkeys (Bussey & Saksida 2002;
Bussey et al. 2005; Buckley 2005). Like some findings in humans (Lee et al. 2005a,
2005b, 2005c), perirhinal lesions selectively impair acquisition and performance of visual discriminations with objects of high feature ambiguity or within large sets
(Saksida & Bussey 1998; Bussey et al. 2003, 2006; Alvarado & Bachevalier 2005a;
31 Saksida et al. 2007), whereas hippocampal lesions do not (Saksida et al. 2006) and may even improve performance compared to controls (Saksida et al. 2007).
In contrast, perirhinal lesions do not impair visual discrimination based on simple features, as demonstrated in the oddity task (where objects are differentiated on the basis of color, shape, or size alone) even when the discriminations are challenging (Buckley et al. 1997, 2001; Hampton & Murray 2002). Deficits are only found in discriminating complex features, such as identifying the “odd” face in a set viewed from different angles (Buckley et al. 2001). Notably, the oddity task has no overt memory component, as judgments are made on a trial-by-trial basis with no delay period over which information must be retained. Perirhinal lesions do impair acquisition of new discriminations and reversal of preoperatively learned discriminations (Buckley & Gaffan 1998b; Hampton & Murray 2002), indicating a role in learning as well.
Perirhinal ablation evokes delay-dependent (but see Ringo 1991; Baxter &
Murray 2001b) visual recognition memory in DNMS and VPC tasks (Zola-Morgan et al. 1989; Meunier et al. 1993; Eacott et al. 1994; Leonard et al. 1995; Buckley et al. 1997; Buckley & Gaffan 1998b; Buffalo et al. 2000; Malkova et al. 2001;
Nemanic et al. 2004; Alvarado & Bachevalier 2005a; Bachevalier & Nemanic 2008) and exacerbates deficits produced by hippocampal lesions (Zola-Morgan et al.
1993). Bilateral or unilateral perirhinal lesions also impair visual-visual and tactual- visual associations (Murray et al. 1993; Higuchi & Miyashita 1996; Buckley &
Gaffan 1998a; Goulet & Murray 2001). In contrast, perirhinal lesions have no
32 impact on spatial recognition memory in the DNML task regardless of delay
(Alvarado & Bachevalier 2005a). Perirhinal-lesioned monkeys are likewise unimpaired on a task variation of VPC for spatial location, but they show a deficit in an object-in-place variation that correlates with their impairment on the standard
VPC (Bachevalier & Nemanic 2008), suggesting that perirhinal participation is more dependent on the nonspatial component of the object-in-place association.
Perirhinal ablation also impairs an object-in-context variation of VPC where an object is paired with a different multicolored background than was present during encoding, but not when the background is consistent between sampling and selection (Bachevalier et al. 2015). Although the perirhinal cortex appears to have a limited role in spatial memory processing, it is evidently more involved in contextual memory processing.
Evidence from intracerebral pharmacological manipulation in monkeys.
In keeping with lesion findings, bilateral microinfusion of scopolamine (SCOP: 3µl,
1-10mM), a broad-spectrum muscarinic cholinergic receptor antagonist, into the perirhinal cortex of a rhesus macaque impairs visual recognition memory as evaluated by DNMS performance (Tang et al. 1997). This deficit can be reproduced with microinfusion of the muscarinic M1-selective antagonist pirenzepine (3.5µl, 1-10mM), but not of M2-selective methoctramine (3.5µl, 0.5-
1.0mM), in the perirhinal cortex (Wu et al. 2012). Additionally, DNMS is impaired following bilateral glutamate blockade in the medial perirhinal cortex (area 35 and medial area 36), but not in the lateral perirhinal cortex (lateral area 36), using focal
33 microinfusion of KYNA (2-3 injections of 2µl, 600nmol), NMDA receptor antagonist
AP-7 (2-3 injections of 2µl, 100nmol), or AMPA receptor antagonist NBQX (2-3 injections of 1µl, 20nmol) (Malkova et al. 2015).
Evidence from electrophysiological recording. Many units in the primate perirhinal cortex are selective for complex visual stimuli (Riches et al. 1991; Miller et al. 1993; Nakamura et al. 1994; Nakamura & Kubota 1996). Unlike in the hippocampus, perirhinal populations are generally more responsive to the first presentation of a visual stimulus than to subsequent presentations in recognition memory tasks like the delayed matching-to-sample (DMS) and DNMS tasks
(Riches et al. 1991; Fahy et al. 1993), and some neurons specifically signal for the recency of a stimulus presentation (Fahy et al. 1993; Xiang & Brown 1998).
However, not all perirhinal cells have a novelty preference; some are responsive to long-term familiarity of well-established visual stimuli instead (Hölscher et al.
2003; Rolls et al. 2005). Taken together, these findings support the hypothesis that the perirhinal cortex plays a critical role for familiarity detection in recognition memory, but not for episodic recollection (Aggleton & Brown 1999, 2006; Brown &
Aggleton 2001; Yonelinas 2001; Eichenbaum et al. 2007; Squire et al. 2007).
Similar to hippocampal neurons, perirhinal cells show evidence of associative learning: many stimulus-selective units update their selectivity as new visual associations are established, and they become responsive to paired associates
(Miyashita 1988; Sakai & Miyashita 1991; Erickson & Desimone 1999; Messinger et al. 2001; Naya et al. 2003; Yanike et al. 2009). Single units can encode object-
34 place associations (Chen & Naya 2019) and temporal proximity to events (Liu &
Richmond 2000).
Evidence from neuroimaging. In humans, perirhinal activation corresponds to representation of objects, conjunctions of complex object features, stimulus associations with semantic information, and visual stimulus-stimulus associations
(Pihlajamäki et al. 2003; Elfgren et al. 2006; Clark & Tyler 2014; Erez et al. 2016).
It is active during both object and spatial encoding tasks, but the left hemisphere is preferential for object information and the right hemisphere is preferential for spatial information (Bellgowan et al. 2009). Although the perirhinal cortex has a preference for novel, or contextually novel, visual stimuli (Pihlajamäki et al. 2004;
Danckert et al. 2007), it does not encode an object’s temporal position within a sequence (Hsieh et al. 2014).
1.1.7 Parahippocampal Function
Evidence from human lesion cases. In general, patients with more parahippocampal damage have more severe cases of retrograde amnesia
(Rempel-Clower et al. 1996). Patients with lesions including the parahippocampal cortex have impaired object-place association memory in a nonnavigational paradigm as well as impaired location memory in the IST, a navigational and non- associative paradigm, across long delays (Bohbot et al. 1998, 2000, 2002; Kolarik et al. 2016). In contrast, patients with lesions restricted to the hippocampus perform no differently from controls. Even patient H.M., whose lesioned areas in
35 the MTL were expansive, was able to identify the hidden location of the sensor despite lacking any explicit memory of being in the room before (Bohbot & Corkin
2007); this surprising finding, along with his other intact spatial memory functions
(reviewed by Corkin 2002), has been attributed to the sparing of most of his parahippocampal tissue. In parahippocampal-lesioned patients, navigational exploration strategies are normal despite the memory deficit (Bohbot et al. 1998,
2000, 2002), suggesting intact spatial reasoning. Additional evidence from lesion cases indicates that the parahippocampal cortex critically supports spatial discrimination and spatial configuration learning, but not object memory (Bohbot et al. 2000, 2015).
Beyond the visuospatial realm, parahippocampal-lesioned patients who can identify whether consonant music sounds happy or sad fail to identify musical dissonance, judging it to be more pleasant than control subjects, who find it aversive (Gosselin et al. 2006).
Evidence from lesion experiments in monkeys. Unlike the perirhinal cortex, lesions in the primate parahippocampal cortex do not contribute to DNMS deficits
(Meunier et al. 1996; Nemanic et al. 2004). However, parahippocampal damage does impair recognition memory in the standard VPC task (Nemanic et al. 2004), which relies less on familiarity and more on active recollection. Parahippocampal damage also impairs memory in spatial location, object-in-place, and object-in- context variations of the VPC task (Bachevalier & Nemanic 2008; Bachevalier et al. 2015) and in object-place memory for DNML (Alvarado & Bachevalier 2005a).
36 Similarly, one-trial memory for locations and for object-place associations are impaired by parahippocampal lesions (Malkova & Mishkin 2003). The parahippocampal cortex is also critical for relational learning and reasoning in the transverse patterning task (Alvarado & Bachevalier 2005a).
Evidence from intracerebral pharmacological manipulation in monkeys.
There are no publications to date on transient pharmacological manipulations in the monkey parahippocampal cortex.
Evidence from electrophysiological recording. The primate parahippocampal cortex has both place cells (Matsumura et al. 1999; Furuya et al.
2014) and spatial view cells (Rolls 1999; Rolls et al. 1997) as found in the hippocampus. It has units that respond selectively to spatial cues such as landmarks, stimulus orientation, stimulus motion, eye movement, and eye position
(Ringo et al. 1994; Rolls & O’Mara 1995; Sato & Nakamura 2003) in addition to cells selective for nonspatial cues such as images, geographical shapes, colors, task types, and external events (Vidyasagar et al. 1991; Salzmann et al. 1993;
Matsumura et al. 1999; Sato & Nakamura 2003). Compared to the perirhinal cortex, parahippocampal cells are generally less involved in processing complex images (Riches et al. 1991; Sato & Nakamura 2003), and object-place associations are not signaled by single units (Chen & Naya 2019).
Evidence from neuroimaging. Human fMRI studies have shown that the parahippocampal cortex is preferentially active during retrieval of spatial information about complex virtual environments, episodic and semantic memories,
37 and imagined events compared to retrieval of object, person, or nonspatial contextual information (Burgess et al. 2001; Hayes et al. 2004; Hoscheidt et al.
2010; Robin et al. 2018). The parahippocampal cortex, as well as the cortical areas that project to it, is also involved in both egocentric (Weniger et al. 2010) and allocentric (Aguirre et al. 1996; Maguire et al. 1996) navigational learning. A specific subregion of the human parahippocampal cortex has been identified as the parahippocampal place area (PPA: Epstein & Kanwisher 1998; Epstein et al.
1999), which is particularly active in response to spatially defined scenes. In addition to this preference for scenes over objects (Köhler et al. 2002), the PPA also differentially activates for novel scenes over repeated scenes (Köhler et al.
2002; Howard et al. 2011) and for repeated scenes in novel contexts or from novel viewpoints (Turk-Browne et al. 2012; Kim & Maguire 2018; but see Howard et al.
2011).
Parahippocampal cortex activity codes spatial and nonspatial association memory, including nonspatial stimuli configuration (Düzel et al. 2003), temporal context (e.g. sequence order) of objects (Hsieh et al. 2014), and associated context for words (Diana 2017). To further emphasize its role in contextual processing, the parahippocampal cortex has also demonstrated preferential activation while viewing faces of famous people, with whom the subjects had pre- established, rich contextual associations, compared to unfamiliar (i.e. contextless) faces (Bar et al. 2008a). It also has a stronger response to images of contextualized objects than to objects alone (Aminoff et al. 2007; Crafa et al. 2017)
38 and to objects or scenes with strong contextual associations over similar but less associated stimuli (Bar et al. 2008b; Mullally & Maguire 2011; Li et al. 2016a). This suggests that the parahippocampal cortex is not specialized for scene processing in particular, but instead for contextual processing; its apparent preference for scenes is a reflection of their intrinsically richer context compared to other common stimuli. Likewise, space and time are, most often, encoded as contextual elements of events rather than as isolated modalities, so the parahippocampal cortex is also inclined toward signaling them.
39 1.2 METHODS
1.2.1 Overview and Experimental Design
In this study, six rhesus macaques were trained on the nonnavigational spatial memory task, the Hamilton Search Task (adapted from Hamilton 1911), with three variations: spatial with 30-second delays, spatial with 1-second delays, and color- cued with 30-second delays. Upon reaching criterion, animals were implanted with a cranial microinfusion platform and infused with kynurenic acid (KYNA; 1.5µl,
100mM) or sterile physiological saline (1.5µl) at MRI-determined coordinates in the hippocampus and parahippocampal cortex. These infusions included unilateral, bilateral, and “crossed” (different structures in opposite hemispheres) treatments in the style of the crossed-lesion disconnection technique (Parker & Gaffan 1998;
Murray et al. 1998). Treatment order was pseudo-randomized for each animal, and the task administrator was blinded to the experimental condition.
Approximately ten minutes after the end of an infusion, animals were tested on two back-to-back runs separated by a 3-5 minute delay. Most sessions included one spatial task with long delays and one of the control variations (spatial with short delays or color-cued), with the within-session task order counterbalanced across sessions. Total session duration lasted between 15 and 30 minutes and fell within the acting duration of KYNA, which is approximately 30 minutes post-microinfusion
(Forcelli et al. 2014). Bilateral inactivation of the hippocampus has been established to impair Hamilton Search Task performance on the spatial variation
40 with long delays, but not the spatial with short delays or color-cued variations
(Forcelli et al. 2014); the same selective impairment was here hypothesized to result from bilateral parahippocampal inactivation and crossed-inactivation of the hippocampus and parahippocampal cortex in opposite hemispheres. However, unilateral inactivation of either region was not expected to impair performance on any task variation. Behavioral outcomes following treatment were quantified and analyzed using a multivariate, within-subject repeated measures design.
1.2.2 Animals
Subjects. Six male rhesus macaques (Macaca mulatta) were used in this study: YO, LO, SL, RA, OD, and AN. Five were between 4 and 6 years old at the time of testing, and one (AN) was 16 years old. Animals were pair-housed (with the exception of AN, who was not compatible with any other animals in the colony) and had visual access to other conspecifics in a room with a 12h/12h light-dark cycle. Water was available ad libitum in the home cage and meals (LabDiet #5049) were provided twice daily, with the first full meal given after cognitive testing. Their diet was supplemented with fresh fruits and vegetables daily. This study was conducted under a protocol approved by the Georgetown University Institutional
Animal Care and Use Committee (IACUC) and in compliance with the standards set for primate research by the Guide for the Care and Use of Laboratory Animals
(“the Guide”) and by the Association for Assessment and Accreditation of
Laboratory Animal Care International (AAALAC).
41 Subjects YO, LO, and SL received bilateral microinfusions in the parahippocampal cortex. Subjects RA, OD, and AN received unilateral and bilateral microinfusions in the hippocampus, unilateral and bilateral microinfusions in the parahippocampal cortex, and crossed-infusions with simultaneous hippocampal and parahippocampal microinfusions in opposite hemispheres.
Animal history. Animals used in this study were previously or concurrently involved in behavioral research using object-based and social tasks. Subjects YO,
LO, SL, and RA received microinfusions in the substantia nigra pars reticulata in a study on prepulse inhibition (Aguilar et al. 2018); subjects LO, SL, RA, and OD received microinfusions in the amygdala and superior colliculus for studies on social dominance and unconditioned fear responses (unpublished); subject AN received microinfusions in the mediodorsal thalamus as well as systemic scopolamine and mecamylamine treatments in reinforcer devaluation studies
(Waguespack et al. 2018; Wicker et al. 2018); subject OD received infusions in the nucleus accumbens for a study on prepulse inhibition (unpublished); and subject
RA received microinfusions in the piriform cortex and thalamus for a study investigating the circuitry underlying seizure onset and propagation (unpublished).
In vivo and postmortem MRI scans and subsequent histological evaluations showed no substantial damage to these brain areas, and it is thus unlikely that these experiments affected the animals’ behavior in the present study.
42 1.2.3 Behavioral Testing
Apparatus and materials. Animals performed cognitive testing in the
Wisconsin General Testing Apparatus (WGTA: Harlow & Bromer 1938), which allows observation of the session through a one-way screen. The monkey is free to move within the compartment, although the entire testing arena is reachable from a fixed location. The animal compartment is separated from the testing arena by a sliding guillotine door, which enforces delay periods between trials and runs.
For the Hamilton Search Task, the testing arena contained eight black plastic boxes (87x63x32mm) in a row, each with a spring-loaded metal lid. For the spatial version of the task, the lids were gray, while for the color-cued task variation each lid was painted a unique color. These apparati were custom-made by Elmeco
Engineering (Gaithersburg, MD).
Terminology. Here, a “session” refers to the full period of cognitive testing conducted without removing the monkey from the WGTA. A session may consist of multiple back-to-back “runs,” here defined as a single performance of the
Hamilton Search Task to completion. A typical session included two runs, separated by 3-5 minutes with the guillotine door closed between the animal and the testing arena. “Trials” here refer to individual box openings within a run.
Hamilton Search Task. The Hamilton Search Task is a test of nonnavigational spatial memory. It is self-ordered, meaning subjects are required to track self- generated choices among a set of interdependent options. At the beginning of a run, each of the eight boxes was baited with the same food reinforcer (e.g. a small
43 piece of fruit or candy). The monkeys selected one box to open at a time and retrieved the food reinforcer inside, with trials separated by the guillotine door closing for 1 or 30 seconds. However, the reinforcers were not replaced throughout the run, so if the monkey selected a box that had already been opened, they would find it empty. The goal of the task is to open each of the eight boxes to collect all the food reinforcers in as few trials as possible, with a perfect performance consisting of eight trials until completion. The monkeys were given additional food rewards at the end of each run, with higher-value rewards given for better performances.
Training. In the first stage of training, monkeys were habituated to the task setup in 10-minute sessions during which they were free to collect food reinforcers set on top of the boxes. As they became comfortable reaching outside of the animal compartment to retrieve reinforcers from the testing arena and accustomed to the occasional closure of the sliding guillotine door, they were moved into the next stage of training, where food reinforcers were used to prop open the box lids.
This allowed the reinforcers to remain fully visible to the monkeys, but it required them to interact with the boxes directly to retrieve them. In this stage, the sliding door was also closed after every retrieval. Next, the food reinforcers were moved inside the boxes, so the monkeys were required to lift open the lids to locate them.
During this training, they learned through trial and error that food reinforcers would not be replaced in previously opened boxes and adapted their search strategies accordingly. They also learned that if they attempted to “cheat” by opening multiple
44 boxes at once, the door would close before they could collect any reinforcers. The monkeys only graduated to experimental testing after they reached criterion, defined here as completing the task in 15 trials or fewer without attempting to cheat across five consecutive sessions.
Maintenance training. Identical baseline sessions were dispersed among experimental sessions to ensure training maintenance. If an animal’s baseline performance ceased to reach criterion, their experiments were paused in favor of daily baseline training sessions until their performance recovered. Thus, each monkey was ensured to demonstrate a similar proficiency on the Hamilton Search
Task from the beginning to the end of the experiments.
Measures of performance. Performance on the Hamilton Search Task was assessed by three primary metrics: 1) The number of trials until the task was completed, 2) the number of correct box openings within the first eight trials, and
3) the repetition index, a measure of the frequency and severity of box opening errors. Repetition index was calculated as follows: the sum of the inverse of the number of trials that had elapsed between successive openings of a box, multiplied by ten.
Additional training. Prior to their Hamilton Search Task training, all monkeys were trained to voluntarily enter portable, custom carrier boxes (Allentown, Inc.;
Allentown, NJ) for transfer between the home cage and the WGTA. Alongside their task training, they were also trained to present their collars (Primate Products,
Inc.; Immokalee, FL) at the front of the home cage for pole-transfer to a wheeled
45 primate chair (Crist Instrument Company), which would allow free access to their heads for microinfusion but minimally restricted body movement.
1.2.4 Microinfusion Platform Implantation
Implantation. Each animal was implanted with a cranial infusion chamber custom-made from polyoxymethylene (Helm Tech Machining, Inc.; Strongsville,
OH) or INOVA-1800 filament (printed in-house with a Lulzbot 6 3D printer) in a procedure performed under complete aseptic technique. Anesthesia was initiated with ketamine (10mg/kg i.m.), then the monkey was catheterized, intubated, and maintained with isoflurane gas (1-2%). Heart rate, respiration rate, blood pressure, expired carbon dioxide, and body temperature were monitored for the duration of the surgery. The animal’s head was stabilized and centered in a stereotaxic apparatus and a sterile field was established. An 8-10cm incision was made along the midline, and the skull around the cranial area for the implant was exposed.
The position of the implant was determined using the stereotaxic apparatus to center it around the midline, and four craniotomies were drilled around it for H-bolt anchors (also 3D printed in INOVA-1800 filament). The implant and H-bolts were secured with Palacos R bone cement (Zimmer Biomet; Warsaw IN) to create a watertight chamber affixed to the skull. This chamber was sealed with a removable lid that screwed into the four corners. All monkeys received a postoperative regimen of analgesics and antibiotics determined in consultation with the overseeing veterinarian. Animals were observed daily by research staff and
46 veterinary staff for signs of infection, pain, or distress and treated according to the
Georgetown University IACUC guidelines.
Implant maintenance. Three to five times per week, the implant chamber was cleaned using aseptic technique with the monkey sitting in the primate chair. After removing the lid, the chamber was rinsed 3x with chlorohexidine or iodine solutions
(5% in saline); 3x with physiological saline; 3x with Dakin’s solution (10% bleach in saline); and another 3x with physiological saline. Then, a nonstick sterile
CURAD pad (Medline Industries, Inc.; Northfield, IL) was inserted in the chamber with 5-7 drops of an antibiotic ophthalmic solution, gentamicin (0.3%) or a neomycin/polymixin/gramicidin suspension, to maintain tissue moisture and prevent infection. A freshly sterilized lid resealed the chamber.
1.2.5 Intracerebral Drug Infusions
Coordinate determination with MRI. A minimum of one week after the surgery, animals were sedated with ketamine (10mg/kg i.m.) and xylazine (5mg/kg i.m.) for a postoperative T1-weighted MRI. An intravenous catheter was placed for emergency drug delivery, and the cranial implant chamber was cleaned as described above and fitted with a platform with a 14x16 grid of channels 2mm apart.
With the monkey’s head stabilized in an MRI-compatible stereotaxic apparatus, tungsten microelectrodes (FHC; Bowdoinham, ME) were inserted through the grid and hand-drilled channels in the skull with the tips positioned an estimated 2-10mm dorsal to the approximate brain regions of interest. The final coordinates used for
47 microinfusions were calculated based on the position of the electrodes in the scan
(Fig 1.1A). Pulse rate was monitored continuously throughout the scanning process, and the animals were wrapped in blankets to maintain healthy body temperature.
Microinfusion drugs. KYNA, a broad-spectrum glutamate receptor antagonist, was injected unilaterally or bilaterally into the parahippocampal cortex or the hippocampus to inactivate the brain region. Each infusion into the parahippocampal cortex consisted of a single 1.5µl injection of 100mM KYNA, while the larger hippocampus received two 1.5µl injections in sites 2-4.5mm apart; this dose was sufficient to produce hippocampal inactivation resulting in impaired
Hamilton Search Task performance in Forcelli et al. (2014). Sterile physiological saline was injected at the same volume as a control treatment.
Drug preparation. KYNA was dissolved in sodium hydroxide (1N) and neutralized with hydrochloric acid (10N) to pH 6.5-7.5, with sterile water constituting the volume remaining to reach a final concentration of 100mM. The solution underwent suction filtration and was stored in 100µl aliquots at -20˚
Celsius. For control experiments, sterile physiological saline was likewise filtered and stored in 100µl aliquots at -20˚C. Drugs were thawed to room temperature immediately prior to microinfusion.
Microinfusion. Microinfusions were performed in the awake, behaving animal using minimal restraint, i.e. the primate chair and a custom-fit thermoplastic mask
(CIVCO Radiotherapy; Orange City, IA). The lid of the microinfusion implant
48 chamber was removed and the chamber cleaned as previously described. The grid platform was secured in the chamber, and using aseptic technique, three- piece telescoping cannula guides were stabilized at the predetermined coordinates and lowered 27ga stainless steel injection cannulae into the target regions in the brain. Infusions were delivered at 0.2µl/min with a pump-driven 10µl Hamilton syringe connected to the cannulae via polyethylene tubing. Bilateral injections were delivered simultaneously, and the double hippocampal injections were performed back-to-back. To prevent reflux of the drug up the cannula tracts and into nearby brain regions, the cannulae were not immediately removed from the target site after the injection concluded; a 2-minute diffusion time was allowed for hippocampal injections and a 4-minute diffusion time for parahippocampal injections, as access to the parahippocampal cortex necessitated cannula passage through the hippocampus. The implant chamber was resealed after the cannulae, guides, and grid platform were removed. Behavioral testing began approximately ten minutes after the infusion concluded.
Treatment order was pseudo-randomized, and a minimum of 48 hours elapsed between same-site injections. Individual animals received a maximum of 34 microninfusions per brain region across all experiments, but no more than 12 at a single target site. Multiple target sites were selected along the rostrocaudal axis of both the parahippocampal cortex and the hippocampus.
49 1.2.6 Microinfusion Site Verification
Perfusion. Animals were anesthetized with ketamine (10mg/kg i.m.), catheterized intravenously, and given an overdose of barbiturate (100mg/kg sodium pentobarbital i.v.). Each animal was perfused through the heart first with physiological saline and then with aldehyde fixatives. The brain was removed from the skull and fixed in 4% paraformaldehyde for 12-24 hours before scanning.
Postmortem MRI. Brains were gently wrapped in saline-soaked towels and plastic for a high-resolution postmortem MRI (7-Tesla Bruker Biospin) scan (Fig
1.1B).
Cryoprotection. After the scan, the brain was placed in solutions of increasing glycerol concentration (10% glycerol in 0.1M phosphate buffer, followed by 20% in phosphate buffer) for 48 hours each on an orbital shaker. Then the brain was blocked, flash-frozen in isopentane to -80˚C, and stored in a -80˚ freezer until slicing.
Histology. Brains were sectioned into 40µm slices using a freezing-stage 860 sliding microtome (American Optical; Buffalo, NY). Sections were then mounted, defatted, stained with thionin, and coverslipped to visualize cannula tracks (Fig
1.1C-D).
1.2.7 Data Analysis
Within-subject averages for trials to complete, number of correct trials within the first eight, repetition index, and box opening latency were compared between
50 treatment conditions for each unilateral, bilateral, or crossed-site microinfusion target using a mixed-effects analysis with a post hoc Sidak’s correction for multiple comparisons in GraphPad Prism 8. Unilateral KYNA microinfusion was compared to bilateral saline microinfusion for each brain region. The three measures of memory performance were also compared to chance levels, as calculated by
Monte Carlo simulations (Forcelli et al. 2014), using one-sample, one-tailed t-tests.
Additionally, saline infusions across all target regions were aggregated to identify any underlying differences in performance among subjects or task types using a mixed-effects analysis with a post hoc Holm-Sidak test to correct for multiple comparisons. Significance was considered at an alpha-level of 0.05. To identify outlier runs for each monkey, performance (measured by trials to complete) on each task type was collapsed across treatments for a ROUT (robust regression and outlier removal) test in GraphPad Prism 8 with the false discovery rate conservatively set at Q=0.1%.
51 1.3 RESULTS
1.3.1 Microinfusion Site Verification
Across subjects, we intended to include microinfusion sites along the full rostral-caudel length of the parahippocampal cortex and hippocampus. Due to the larger volume of the hippocampus compared to the parahippocampal cortex, each hippocampal microinfusion comprised two back-to-back 1.5µl injections, consistent with our previous study (Forcelli et al., 2014), while each parahippocampal microinfusion comprised a single 1.5µl injection. Histology and postmortem MRI verified that microinfusions were correctly placed within the parahippocampal cortex and/or hippocampus in all six subjects and that minimal tissue damage was inflicted (Fig 1.1).
52
53 F
Figure 1.1: Microinfusion site targeting and verification. A) An MRI scan of subject LO in vivo with contrast generated by tungsten electrodes placed above the target sites, used to calculate site coordinates for microinfusion. B) Postmortem MRI scans for subjects AN and RA showing cannula tracks where microinfusions were placed in the parahippocampal cortex and hippocampus. These sites are mapped in E-F. C) A thionin-stained section showing a cannula track into the hippocampal microinfusion site of subject OD. D) A thionin-stained section showing a cannula track and small lesion at the parahippocampal microinfusion site in subject SL. E) Based on the postmortem MRI scans and histology, the parahippocampal and hippocampal microinfusion sites for each monkey were mapped onto coronal section schematics of the brain. The parahippocampal cortex is shaded in gray in the right hemisphere, with boundaries based on Saleem et al. (2007). Predominantly, subjects YO, LO, and SL each have one site in the parahippocampal cortex per hemisphere; subjects OD and AN each have one parahippocampal and two hippocampal sites per hemisphere; and subject RA has two parahippocampal and two hippocampal sites per hemisphere, with the anterior and posterior pairs of parahippocampal sites used on separate experimental sessions. F)
The parahippocampal sites for each monkey are also mapped onto a schematic of the ventral surface of the brain with markers scaled to indicate the approximate drug spread for each microinfusion. In this schematic, only the anterior parahippocampal sites are shown for subject RA, as the posterior sites for this animal largely overlapped with those indicated for subjects YO, LO, and SL.
54 1.3.2 Number of Task Runs Per Animal and Condition
Several individual tests or conditions were omitted from this study. First, subject LO displayed consistently poor performance on the 30s spatial task when it followed a color-cued task within a session at baseline (no infusion) or following a sham infusion. Therefore, that specific task order combination was eliminated from LO’s otherwise randomized testing schedule. No other animals demonstrated a within-session order effect. Additionally, several outliers were removed from the dataset: one of each task type for subject OD (with two of those runs from the same session), one color-cued run for RA, and two 1s spatial runs for AN.
Of the tests that were included in the final data analysis, Table 1.1 describes the number of runs for each animal x treatment x target x task type. In every condition, 2-8 runs were averaged for multi-subject analyses, with the exception of one condition for LO where there was only a single run due to premature implant loss. Furthermore, the bilateral hippocampal treatment analysis (see Chapter
1.34) included data from the four animals published in Forcelli et al. (2014), where all tasks following saline microinfusion were presented as a single, combined control group. Here, those data are separated by task type.
55 Table 1.1: Number of Hamilton Search Task runs per experimental condition.
Six animals performed on three task variations (30s spatial, 1s spatial, and 30s color-cued) after microinfusion of KYNA or saline bilaterally in the parahippocampal cortex; bilaterally in the hippocampus (these data analyses also included the four animals published in Forcelli et al. 2014; represented symbolically by , , , ); contralaterally in the hippocampus and parahippocampal cortex; or unilaterally in the hippocampus or parahippocampal cortex.
1.3.3 Bilateral Inactivation of the Parahippocampal Cortex
Trials to complete. There was no significant main effect of task type (30s spatial, 1s spatial, or color-cued) on Hamilton Search Task performance as measured by the number of trials required to complete the task (F2,10=3.232, p=0.0827), but there was a significant main effect of bilateral drug treatment (KYNA or saline) in the parahippocampal cortex (F1,5=11.07, p=0.0208) and a significant interaction between treatment and task type (F2,10=11.37, p=0.0027) (Fig 1.2A).
The interaction effect was driven by a significant impairment in the KYNA condition
56 as compared to the saline condition in the 30s spatial task (p=0.0002, Sidak corrected). The KYNA and saline conditions did not differ for the 1s spatial task
(p=0.3232, Sidak corrected) or the color-cued task (p=0.5126, Sidak corrected).
The selective impairment in the 30s spatial task after KYNA microinfusion was further analyzed in comparison to chance performance, which requires an average of 21 trials to complete the Hamilton Search Task as determined by Monte Carlo simulation (Forcelli et al. 2014), and a one-tailed t-test revealed that performance in this condition did not differ from chance (t=0.7663, df=5, p=0.4781).
Repetition index. The repetition index is a calculation describing the frequency and severity of box-opening errors. Analysis of this measure reveled a significant main effect of treatment (F1,5=11.28, p=0.0201) and a significant interaction between treatment and task type (F2,10=6.146, p=0.0182), although there was no main effect of task type (F2,10=3.860, p=0.0573) (Fig 1.2B). Repetition index was selectively impaired in the 30s spatial task after bilateral parahippocampal inactivation (p=0.0018, Sidak corrected), with no difference between treatment conditions on the 1s spatial task (p=0.1419, Sidak corrected) or the color-cued task
(p=0.9007, Sidak corrected). Chance level performance for repetition index is 51.4, and performance on the 30s spatial task after bilateral inactivation of the parahippocampal cortex did not differ significantly from this number (t=1.664, df=5, p=0.1571).
Correct trials in the first eight. There was a significant treatment by task type interaction on performance across the first eight trials (F2,10=10.69, p=0.0033), but
57 no main effect of treatment (F1,5=0.9770, p=0.3683) or task type (F2,10=0.04666, p=0.9546) (Fig 1.2C). Bilateral inactivation of the parahippocampal cortex, as compared to saline treatment, significantly impaired performance across the first eight trials in the 30s spatial task (p=0.0185, Sidak corrected); this treatment also significantly facilitated initial performance on the color-cued task (p=0.0219, Sidak corrected). Performance across the first eight trials of the 1s spatial task was not affected by treatment condition (p=0.3274, Sidak corrected). Chance level performance is 5.3 correct box openings in the first eight trials. Performance on the 30s spatial task after bilateral parahippocampal inactivation did not differ from chance (t=1.772, df=5, p=0.1365), but color-cued performance after bilateral parahippocampal inactivation was significantly higher (t=6.546, df=5, p=0.0012).
Latency. As a control for nonspecific drug effects on behavior, we also averaged the latency to box opening across the trials of each Hamilton Search
Task run and compared these averages among experimental conditions. There was no significant effect of bilateral parahippocampal treatment (F1,5=1.911, p=0.2254), task type (F2,10=0.07850, p=0.9251), or treatment by task type interaction (F2,10=1.985, p=0.1880) on box opening latency (Fig 1.2D).
58
Figure 1.2: Hamilton Search Task performance after bilateral inactivation of the parahippocampal cortex. Animals (n=6) performed the 30s spatial, 1s spatial, and 30s color- cued variations of the task after bilateral microinfusion of KYNA or saline in the parahippocampal cortex. A) The total number of trials required for the monkey to locate all eight reinforcer locations, such that a perfect score would be 8 trials and random performance would average to 21 trials, represented by the dashed bar. B) The repetition index, a measure of the frequency and severity of box opening errors, where chance performance of 51.4 is represented by the dashed bar. C)
The number of correct box selections within the first eight trials, a measure of early task performance with a chance level caluclated at 5.3 trials, represented by the dashed bar. D) The average latency to box opening across trials. For all graphs, each symbol represents the average of an individual monkey’s performance under those conditions, error bars represent standard error to the mean (SEM), and *p<0.05, **p<0.01, and ***p<0.001.
59 1.3.4 Bilateral Inactivation of the Hippocampus
For these analyses, data from Forcelli et al. (2014) were included from four additional subjects. In the original publication, all tests after saline infusion were aggregated into a single control group, but here they are separated out by task type. Thus, seven animals in total were included for this part of the experiment.
Trials to complete. There was a near-significant interaction between drug treatment and task type on the number of trials required to complete the task
(F2,6=5.013, p=0.0525), but no main effect of treatment (F1,6=4.199, p=0.0863) or task type (F2,12=1.144, p=0.3509) (Fig 1.3A). Treatment condition did not affect performance on the 1s spatial task (p=0.4704, Sidak corrected) or the color-cued task (p=0.9874, Sidak corrected), but the 30s spatial task was selectively and significantly impaired by bilateral hippocampal inactivation with KYNA compared to saline microinfusion (p=0.0122, Sidak corrected) such that performance did not differ from chance (t=0.3854, df=6, p=0.7132).
Repetition index. There was no main effect of treatment (F1,6=3.300, p=0.1192) or task type (F2,12=1.868, p=0.1966) on repetition index, nor a significant interaction between treatment and task type (F2,6=4.518, p=0.0635). However, performance on the 30s spatial task following bilateral hippocampal inactivation was significantly impaired (p=0.0215, Sidak corrected) and did not differ from chance performance (t=0.3928, df=6, p=0.7080) (Fig 1.3B). Treatment condition did not affect the 1s spatial task (p=0.5210, Sidak corrected) or color-cued task
(p=0.9390, Sidak corrected).
60 Correct trials in the first eight. There were no main effects of treatment
(F1,6=0.03110, p=0.8658) or task type (F2,12=0.8415, p=0.4550) on the number of correct trials within the first eight following hippocampal microinfusion, nor a significant interaction between treatment and task type (F2,6=1.617, p=0.2743) (Fig
1.3C). There were no differences in performance with KYNA or saline microinfusion on the 30s spatial (p=0.4560, Sidak corrected), 1s spatial (p=0.8483,
Sidak corrected), or color-cued (p=0.9951, Sidak corrected) tasks.
Latency. There was no significant effect of treatment (F1,6=0.0006043, p=0.9812), task type (F2,12=0.1150, p=0.8923), or treatment by task type interaction (F2,5=0.3605, p=0.7141) on box opening latency (Fig 1.3D).
61 Figure 1.3: Hamilton Search Task performance after bilateral inactivation of the hippocampus. Animals (n=7, including 4 published in Forcelli et al. 2014) performed the 30s spatial, 1s spatial, and 30s color-cued variations of the task after bilateral microinfusion of KYNA or saline in the hippocampus. A) The total number of trials required for the monkey to locate all eight reinforcer locations, such that a perfect score would be 8 trials and random performance would average to 21 trials, represented by the dashed bar. B) The repetition index, a measure of the frequency and severity of box opening errors, where chance performance of 51.4 is represented by the dashed bar. C) The number of correct box selections within the first eight trials, a measure of early task performance with a chance level caluclated at 5.3 trials, represented by the dashed bar. D) The average latency to box opening across trials. For all graphs, each symbol represents the average of an individual monkey’s performance under those conditions, error bars represent
SEM, and *p<0.05.
1.3.5 Crossed-Inactivation of the Parahippocampal Cortex and Hippocampus
Trials to complete. There was no significant main effect of drug treatment
(F1,2=7.754, p=0.1084) or task type (F2,4=2.410, p=0.2057) on performance following crossed-infusion, nor was there a significant interaction between treatment and task type (F2,4=2.885, p=0.1676) (Fig 1.4A). However, the number of trials to complete the 30s spatial task was significantly and selectively increased by crossed-inactivation of the parahippocampal cortex and hippocampus
(p=0.0373, Sidak corrected), such that performance in this condition did not differ from chance (t=0.5040, df=2, p=0.6643). Performance on the 1s spatial task
(p=0.9923, Sidak corrected) and color-cued task (p=0.7072, Sidak corrected) were not affected by crossed-inactivation compared to crossed-infusion of saline.
62 Repetition index. Although there was no main effect of treatment (F1,2=7.529, p=0.1111), task type (F2,4=5.534, p=0.0705), or treatment by task type interaction
(F2,4=4.116, p=0.1069), the repetition index was significantly different between
KYNA and saline crossed-infusions selectively on the 30s spatial task (p=0.0361,
Sidak corrected) (Fig 1.4B). Crossed-inactivation impaired performance on the 30s spatial task such that the repetition index did not differ from chance (t=0.4455, df=2, p=0.6996). There was no difference in performance between crossed-infusion of
KYNA or saline for the 1s spatial task (p=0.9489, Sidak corrected) or the color- cued task (p=0.3559, Sidak corrected).
Correct trials in the first eight. There was no significant effect of crossed- infusion treatment (F1,2=1.001, p=0.4225) or task type (F2,4=3.394, p=0.1375) on performance across the first eight trials, and there was no significant interaction between treatment and task type (F2,4=2.282, p=0.2182) (Fig 1.4C). There were no differences between crossed-infusion of KYNA and saline for the 30s spatial
(p=0.9608, Sidak corrected), 1s spatial (p=0.1700, Sidak corrected), or color-cued
(p=0.9993, Sidak corrected) tasks.
Latency. There was no significant main effect of treatment (F1,2=0.1203, p=0.7618) or task type (F2,4=0.04860, p=0.9531) on box opening latency (Fig 1.4D).
While there was a significant interaction between treatment and task type
(F2,4=14.95, p=0.0139), post hoc analysis revealed no significant pairwise comparisons in the 30s spatial (p=0.2533, Sidak corrected), 1s spatial (p=0.3123,
Sidak corrected), or color-cued (p=0.8476, Sidak corrected) tasks.
63
Figure 1.4: Hamilton Search Task performance after crossed-inactivation of the contralateral hippocampus and parahippocampal cortex. Animals (n=3) performed the 30s spatial, 1s spatial, and 30s color-cued variations of the task after simultaneous contralateral microinfusion of KYNA or saline in the hippocampus and parahippocampal cortex. A) The total number of trials required for the monkey to locate all eight reinforcer locations, such that a perfect score would be 8 trials and random performance would average to 21 trials, represented by the dashed bar. B) The repetition index, a measure of the frequency and severity of box opening errors, where chance performance of 51.4 is represented by the dashed bar. C) The number of correct box selections within the first eight trials, a measure of early task performance with a chance level caluclated at 5.3 trials, represented by the dashed bar. D) The average latency to box opening across trials. For all graphs, each symbol represents the average of an individual monkey’s performance under those conditions, error bars represent SEM, and *p<0.05.
64 1.3.6 Unilateral Inactivations
Trials to complete. In comparing unilateral microinfusion of KYNA to bilateral microinfusion of saline in the parahippocampal cortex (Fig 1.5A) or hippocampus
(Fig 1.6A), there were no significant main effects of treatment condition
(parahippocampal cortex: F1,2=6.978, p=0.1184, and hippocampus: F1,2=4.581, p=0.1657), task type (parahippocampal cortex: F2,4=0.7038, p=0.5472, and hippocampus: F2,4=0.06947, p=0.9340), or treatment by task type interactions
(parahippocampal cortex: F2,4=1.917, p=0.2607, and hippocampus: F2,4=3.815, p=0.1183) on the number of trials required to complete the Hamilton Search Task.
Unilateral inactivation did not significantly affect performance on the 30s spatial task (parahippocampal cortex: p=0.8550, and hippocampus: p=0.6588, Sidak corrected), 1s spatial task (parahippocampal cortex: p=0.5042, and hippocampus: p=0.8419, Sidak corrected), or color-cued task (parahippocampal cortex: p=0.4133, and hippocampus: p=0.1507, Sidak corrected).
Repetition index. There was no significant main effect of treatment
(parahippocampal cortex: F1,2=1.561 p=0.3379, and hippocampus: F1,2=0.08822, p=0.7945), task type (parahippocampal cortex: F2,4=0.1573, p=0.8595, and hippocampus: F2,4=0.1953, p=0.8300), or treatment by task type interaction
(parahippocampal cortex: F2,4=1.739, p=0.2861, and hippocampus: F2,4=6.087, p=0.0612) on repetition index. Unilateral inactivation of the parahippocampal cortex (Fig 1.5B) or hippocampus (Fig 1.6B) did not significantly affect performance on the 30s spatial task (parahippocampal cortex: p=0.9980, and
65 hippocampus: p=0.3284, Sidak corrected), 1s spatial task (parahippocampal cortex: p=0.2407, and hippocampus: p=0.9698, Sidak corrected), or color-cued task (parahippocampal cortex: p=0.3246, and hippocampus: p=0.1287, Sidak corrected).
Correct trials in the first eight. There was no significant effect of treatment
(parahippocampal cortex: F1,2=0.4808, p=0.5598, and hippocampus: F1,2=3.524, p=0.2013) or task type (parahippocampal cortex: F2,4=0.7880, p=0.5146, and hippocampus: F2,4=0.1273, p=0.8839) on initial task performance, nor was there a significant interaction between treatment and task type (parahippocampal cortex:
F2,4=0.4963, p=0.6419, and hippocampus: F2,4=0.5288, p=0.6255) (Figs 1.35C,
1.36C). Unilateral inactivation did not significantly affect performance on the 30s spatial task (parahippocampal cortex: p=0.9464, and hippocampus: p>0.9999,
Sidak corrected), 1s spatial task (parahippocampal cortex: p=0.9855, and hippocampus: p=0.9742, Sidak corrected), or color-cued task (parahippocampal cortex: p=0.8208, and hippocampus: p=0.5240, Sidak corrected).
Latency. There was no effect of treatment (parahippocampal cortex:
F1,2=0.01205, p=0.9226, and hippocampus: F1,2=2013, p=0.0901) or task type
(parahippocampal cortex: F2,4=0.03414, p=0.9667, and hippocampus: F2,4=0.1797, p=0.8419) on box opening latency (Figs 1.35D, 1.36D). There was also no significant main interaction between treatment and task type (parahippocampal cortex: F2,4=3.206, p=0.1476, and hippocampus: F2,4=4.661, p=0.0901).
66
Figure 1.5: Hamilton Search Task performance after unilateral inactivation of the parahippocampal cortex. Animals (n=3) performed the 30s spatial, 1s spatial, and 30s color- cued variations of the task after unilateral microinfusion of KYNA or bilateral microinfusion of saline in the parahippocampal cortex. A) The total number of trials required for the monkey to locate all eight reinforcer locations, such that a perfect score would be 8 trials and random performance would average to 21 trials, represented by the dashed bar. B) The repetition index, a measure of the frequency and severity of box opening errors, where chance performance of 51.4 is represented by the dashed bar. C) The number of correct box selections within the first eight trials, a measure of early task performance with a chance level caluclated at 5.3 trials, represented by the dashed bar. D) The average latency to box opening across trials. For all graphs, each symbol represents the average of an individual monkey’s performance under those conditions, error bars represent
SEM, and significance was set at p<0.05.
67
Figure 1.6: Hamilton Search Task performance after unilateral inactivation of the hippocampus. Animals (n=3) performed the 30s spatial, 1s spatial, and 30s color-cued variations of the task after unilateral microinfusion of KYNA or bilateral microinfusion of saline in the hippocampus. A) The total number of trials required for the monkey to locate all eight reinforcer locations, such that a perfect score would be 8 trials and random performance would average to
21 trials, represented by the dashed bar. B) The repetition index, a measure of the frequency and severity of box opening errors, where chance performance of 51.4 is represented by the dashed bar. C) The number of correct box selections within the first eight trials, a measure of early task performance with a chance level caluclated at 5.3 trials, represented by the dashed bar. D) The average latency to box opening across trials. For all graphs, each symbol represents the average of an individual monkey’s performance under those conditions, error bars represent SEM, and significance was set at p<0.05.
68 1.3.7 Group Observations
Subject differences. Across all control (saline microinfusion) sessions, there were no significant differences in performance between task types
(F1.715,8.577=0.7588, p=0.4779). There were also no significant differences in performance across subjects (F1.715,4.117=1.722, p=0.2810). There was a significant difference in average box opening latency between subjects SL and OD
(p=0.0349), but not between any other animals.
Lateralization. There were no significant differences among left and right unilateral parahippocampal or hippocampal inactivations (F1.752,3.503=2.825, p=0.1851), nor was there a significant brain region by task type interaction
(F1.099,1.099=2.895, p=0.3282). Likewise, performance after crossed-inactivation of the left parahippocampal cortex with right hippocampus did not differ from performance after crossed-inactivation of the right parahippocampal cortex with left hippocampus (F1,2=5.012, p=0.1546), and there was no interaction between crossed-infusion configuration and task type (F2,3=1.279, p=0.3966).
69 1.4 DISCUSSION
These experiments have demonstrated that bilateral pharmacological inactivation of the hippocampus or parahippocampal cortex in macaques impairs long-term, nonnavigational spatial memory on the Hamilton Search Task. Long- term spatial memory is also selectively impaired by simultaneous, contralateral inactivation of the hippocampus and parahippocampal cortex, but not by unilateral inactivation of either structure. In all cases, deficits were selective for the spatial- only condition with 30s delays; animals were unimpaired when tested at 1s delays or when color cues were introduced to enable the additional option of a nonspatial strategy. Altogether, these data indicate that the hippocampal-parahippocampal cortex pathway, specifically, is critical for long-term spatial memory. This does not preclude additional, independent contributions from either brain region toward spatial processing, but as long as hippocampal-parahippocampal projections are intact in one hemisphere, they can compensate for unilateral inactivation of either region in the opposite hemisphere.
These findings are consistent with human imaging studies that have identified hippocampal and parahippocampal involvement in long-term, nonnavigational spatial memory (hippocampus, Hayes et al. 2004; Mayes et al. 2004b; Parslow et al. 2004; Hoscheidt et al. 2010; Kyle et al. 2015; Nielson et al. 2015; and parahippocampal cortex, Burgess et al. 2001; Hayes et al. 2004; Hoscheidt et al.
2010; Robin et al. 2018) and with human studies that have demonstrated
70 nonnavigational spatial memory deficits in patients with focal hippocampal
(Parslow et al. 2005; Bartsch et al. 2010) or parahippocampal (Bohbot et al. 1998,
2000, 2002, 2015; Kolarik et al. 2016) lesions. Unlike humans, however, there is no evidence in macaques for lateralization of nonnavigational spatial memory processing, which in humans is more specialized in the right MTL (Bohbot et al.
1998; Hayes et al. 2004). Moreover, the lack of impairment on the 1s spatial task across all treatment conditions is also consistent with human and animal research indicating that the MTL is not critically involved working memory. Rather, imaging studies in humans (Postle et al. 2000; Curtis 2006; Ricciardi et al. 2006; Ikkai &
Curtis 2011; Raabe et al. 2013) and electrophysiology in monkeys (Funahashi et al. 1989, 1990; Mochizuki & Funahashi 2016) have both identified significant roles for the prefrontal cortex and posterior parietal cortex in spatial working memory
(but see Lee & Rudebeck 2010).
Rhesus macaques have trichromatic color vision similar to humans other than a slightly higher sensitivity to short (<520nm, e.g. cyan, blue, violet) wavelengths, a slightly lower sensitivity to long (>600nm, e.g. red) wavelengths, and overall higher chromatic—but lower luminance—contrast sensitivity (Crawford 1977;
Jacobs & Deegan 1997; Dobkins et al. 2000). They are therefore not expected to have trouble differentiating box colors in the color-cued variation of the Hamilton
Search Task, and indeed, the animals in this study performed equally well on the spatial and color-cued task variations under control conditions. Color discrimination is not impaired by hippocampal or perirhinal lesions (Lee et al.
71 2005c; Buckley et al. 1997, 2001), so it is unsurprising that pharmacological inactivation of the hippocampus or the parahippocampal cortex (which has fewer connections with primary visual cortical areas than the perirhinal cortex; reviewed in Chapter 1.13) did not significantly impact performance on the color-cued
Hamilton Search Task. Under normal conditions, the monkeys could solve the color-cued task using a color-based strategy, a spatial strategy, or a combination of the two. Evidently, when spatial memory was impaired, they were able to successfully complete the task relying on color cues alone, even though the monkeys were trained first and more extensively on the spatial-only version of the task and conceivably could have developed a preference for the spatial strategy even when color cues were available. This training paradigm likely also explains why the monkeys did not develop a higher proficiency on the color-cued task than on the spatial-only task under baseline conditions, even though more relevant visual cues were available for them to utilize.
While the number of trials to completion and the repetition index are measures of overall performance on the Hamilton Search Task, the number of correct trials in the first eight is strictly a measure of early task performance. Early in the run, there is still a smaller memory load and less within-session interference as well as a shorter total memory span required. Unlike the other two performance measures, this was exclusively impaired on the 30s spatial task after bilateral parahippocampal inactivation, but not by any other treatment condition, including hippocampal or crossed-inactivation. This may indicate a selective, independent
72 role for the parahippocampal cortex for smaller spatial memory loads and/or spatial memory across shorter durations, which is consistent with the associative hierarchy hypothesis for MTL processing (Lavenex & Amaral 2000) as well as a related hypothesis proposed by Burke (2018), describing the MTL as a dual network for fine and coarse information processing. According to both theories, the parahippocampal cortex is more involved than the hippocampus in processing the spatial and contextual details of an object, place, or event in conjunction with neocortical fields. This information is integrated and iteratively processed throughout the rest of the MTL, and the hippocampus binds together complex associative memories with a broader focus on informational conjunctions.
Interestingly, bilateral parahippocampal inactivation also significantly improved performance on the color-cued task compared to saline sessions. It is possible that with reduced spatial discrimination ability, monkeys were able to locate boxes based on color cues without interference from the spatial element; their training and testing history on the Hamilton Search Task primed them to track spatial information, and this may actually have been a distraction to them on color-cued runs at baseline. Alternatively, parahippocampal inactivation may have facilitated color-cued performance more directly, in the same way hippocampal lesions have been demonstrated to facilitate memory for spatial configurations in the transverse patterning task (Saksida et al. 2007), likely via disinhibition of a closely connected cortical structure.
73 The hippocampus has a well-established selectivity for allocentric (i.e. guided by external cues) over egocentric (i.e. guided by self-generated cues) spatial processing in humans (Bohbot et al. 1998; Spiers et al. 2001a, 2001b) and in monkeys (Hampton et al. 2004, Lavenex et al. 2006). On the other hand, the parahippocampal cortex seems to be involved in both processes (Bohbot et al.
1998; Weniger & Irle 2006; Weniger et al. 2010), although its role in egocentric spatial memory is still under-studied. Navigation is generally considered a primarily allocentric function, while nonnavigational spatial tasks are typically more egocentric; this is the basis of one leading theory for why many nonnavigational spatial memory tasks are hippocampus-independent (Malkova et al. 2003;
Bachevalier & Nemanic 2008). It is unclear whether monkeys use primarily allocentric or egocentric strategies in the Hamilton Search Task, as it could potentially be solved using either or both. However, this offers a possible alternative explanation for the contrast between early task performance
(dependent on the parahippocampal cortex but not the hippocampus) and overall task performance (dependent on input from both regions) beyond the differences in cumulative memory load or total task time. Monkeys may begin the run using a parahippocampal-dependent egocentric strategy, a hypothesis supported by their tendency to begin with central stimulus positions (i.e. closest to the animal) and move outward (Forcelli et al. 2014; Basile & Hampton 2018), and later switch to a more allocentric strategy requiring both the hippocampus and parahippocampal cortex.
74 In humans, the parahippocampal cortex can support memory for spatial configurations within a scene from a single viewpoint without hippocampal contribution (Bohbot et al. 1998, 2015; Brewer et al. 1998; Epstein & Kanwisher
1998). However, when a complex cognitive map of the spatial relationships between objects and environmental landmarks is required, both the parahippocampal cortex and the hippocampus are necessary (Bohbot et al. 2004;
Iaria et al. 2003; Konishi et al. 2013). Perhaps during early task performance, monkeys process the array of boxes inside the testing arena as a single scene, but when the memory load for individual, previously-visited locations increases later in the run and adds new “dimensions” of information not viewed simultaneously, hippocampal cognitive mapping is introduced to track the self- ordered selections.
Basile and Hampton (2019) reported that hippocampectomized monkeys performed equally as well and used similar strategies to intact monkeys on a variation of the Hamilton Search Task, a result that is in apparent contrast with both the current study and the prior study from our lab (Forcelli et al. 2014). This lesion study utilized a touchscreen for the task; the spatial stimuli were an array of white squares randomly assigned within 24 possible positions, and the spatial configuration for each run was different. The monkeys were trained on the task postoperatively using only 1s intertrial intervals, with the number of spatial stimuli increasing between sessions to a maximum of six positions. While Basile and
Hampton suggest that their task was equivalent to the Hamilton Search Task, they
75 used varying spatial memory loads more akin to the spatial variation of the delayed recognition span task (Beason-Held et al. 1999) than to the fixed eight positions of the Hamilton Search Task. At first glance it may seem intriguing that hippocampal lesions did not impair task learning or memory load, but this finding is actually consistent with other spatial tasks (Murray et al. 1998; Beason-Held et al. 1999), including findings for 1s spatial runs after hippocampal inactivation in Forcelli et al.
(2014) and in the present study. Moreover, in the lesion study, each monkey was tested on only two sessions using eight spatial locations: one of these sessions had 1s delays, and the other had 30s delays. By contrast, animals in the present study worked with the same eight-box array hundreds of times, likely leading to between-session interference effects and recency effects (Brophy et al. 2009). Put another way, these animals not only had to remember what box they visited on the preceding trial, but also ignore the fact that they had visited the same box many times in prior sessions. This temporal context may also lead to heightened hippocampal dependence (Agster et al. 2002; Kumaran and Maguire 2006; Brown et al. 2010; Brown and Stern 2014). Finally, the animals used by Basile and
Hampton were trained post-lesioning, whereas the monkeys in the present study were trained prior to inactivation, leading to at least two additional explanations for the supposed differences. First, there is a well-established difference between the pre- and post-training lesion effects on MTL-dependent tasks (Malamut et al. 1984;
Gaffan 1994; Mitchell et al. 2008; Froudist-Walsh et al. 2018). Second, while lesions allow for neuroplasticity, recovery of function, and development of alternate
76 behavioral strategies, transient pharmacological inactivations do not. In sum, the critiques leveled by Basile and Hampton fail to take into account the myriad differences in experimental design and task methodology, any one of which could explain why their monkeys were not impaired on the single session each had with
30s intertrial delays. Per the recommendation of Vaidya et al. (2019), the most productive strategy for the field of neuroscience as a whole is to triangulate information gleaned from focal manipulations, correlative research (e.g. electrophysiological recording or brain imaging), and lesions, not to dismiss contradictory evidence outright.
Microinfusion of gadolinium, a contrast agent, immediately prior to an MRI scan reliably demonstrates that a 1-2µl injection has a diffusion diameter of approximately 3-4mm with minimal reflux back up the cannula tract (Asthagiri et al.
2011; Dybdal et al. 2012; DesJardin et al. 2013; Forcelli et al. 2014; Yang et al.
2014; Malkova et al. 2015). Although the 1.5µl injection volume would not have covered the entire hippocampus or parahippocampal cortex in the present study, drug spread into neighboring brain regions was thus highly unlikely. Several primate studies have even indicated that smaller hippocampal lesions produce more profound deficits than larger lesions (Murray & Mishkin 1998; Baxter &
Murray 2001b), so it is possible a small microinfusion area is similarly impactful.
Reversible focal manipulations (e.g. cold lesions, Voytko 1986; Horel et al. 1987) often produce greater behavioral changes than permanent damage because they do not allow time for neuroplastic adaptation or behavioral compensation.
77 Microinfusion also has the advantage of allowing each monkey to serve as its own control throughout a variety of transient experimental conditions. In terms of methodology, it is also important to note that all animals were trained on the
Hamilton Search Task to criterion before introducing any pharmacological interventions, as hippocampal lesions do impair spatial task learning in some paradigms (Murray et al. 1998; Beason-Held et al. 1999).
Proceeding from the present findings, one obvious future research direction would be to investigate the role of the entorhinal cortex in the Hamilton Search
Task with a series of 1) pharmacological entorhinal inactivations and 2) crossed- inactivations with the entorhinal cortex and the hippocampus or parahippocampal cortex. Although there are direct projections between the hippocampus and parahippocampal cortex (Rosene & Van Hoesen 1977; Suzuki & Amaral 1990;
Witter & Amaral 1991), the majority of their communication is via the entorhinal cortex along the perforant path (Van Hoesen et al. 1972; Kosel et al. 1982; Insausti et al. 1987; Witter et al. 1989; Suzuki & Amaral 1990, 1994a, 1994b; Witter &
Amaral 1991). It is likely that the entorhinal cortex is also a critical part of the MTL pathway supporting long-term, nonnavigational spatial memory in the Hamilton
Search Task.
The present research would also benefit from a follow-up study examining sex differences on the Hamilton Search Task and its modulation by the MTL. In humans, males demonstrate a longer spatial memory span in nonnavigational spatial memory tasks like the Corsi block-tapping test (Shah et al. 2013), but there
78 are no apparent sex differences in spatial organizational skills or application of egocentric and allocentric strategies for nonnavigational spatial search tasks.
Additionally, acute stress impairs spatial stimulus-response learning in men but not in women, while the opposite is true for spatial memory in virtual navigation
(Guenzel et al. 2014a).
Altogether, this study has established a critical role for the parahippocampal cortex and for the parahippocampal-hippocampal pathway in long-term, nonnavigational spatial memory as evaluated by the Hamilton Search Task.
Together, these results support the associative hierarchy model of memory processing in the MTL and underscore the importance of considering the clinical relevance of the parahippocampal cortex in neuropsychiatric disorders featuring spatial memory impairment, such as Alzheimer’s disease and schizophrenia.
79 Chapter II: Hippocampal Cholinergic Transmission in Nonnavigational Spatial Memory
2.1 INTRODUCTION
2.1.1 Introduction to the Hippocampal Cholinergic System
The diagonal band of Broca is a large fiber tract connecting the septum, olfactory tubercle, olfactory bulb, and hippocampus across hemispheres through the fornix (Liu et al. 2018). A bundle of reciprocal septohippocampal projections in this band makes up the largest cholinergic input to the hippocampus. Loss of cholinergic neurons in that system, as well as cholinergic loss within the MTL itself, are associated with age-related cognitive impairment and neurodegenerative disorders such as Alzheimer’s disease (AD) and Lewy body dementia. Memory loss associated with AD in particular is profound and manifests early on in disease progression, and decades of research in humans and animal models have largely upheld the “cholinergic hypothesis” (reviewed in Terry & Buccafusco 2003;
Contestabile 2011) attributing its development primarily to the degradation of cholinergic innervation. Cholinesterase inhibitors, which prevent the breakdown of acetylcholine and thereby increase its duration of action, are a long-established first-line therapy for AD to reduce the rate of cognitive decline (Farlow & Cummings
2007). However, the specific role of hippocampal cholinergic transmission in mediating memory is still not fully understood.
80 2.1.2 Nicotinic Receptor Structures and Distribution
Nicotinic receptor structure. There are two major categories of cholinergic receptors: nicotinic and muscarinic. Nicotinic acetylcholine receptors (nAChRs) are pentameric ligand-gated ion channels for Na+, K+, and/or Ca2+. Although they exist in a remarkable diversity of subtypes, nAChRs all share the same basic structure: five subunits arranged symmetrically around a central aqueous ion pore perpendicular to the cell membrane. Each subunit has 458-627 amino acid residues and consists of an extracellular N-terminal domain involved in orthosteric or allosteric ligand binding as well as four transmembrane α-helices, connected by alternating intra- and extracellular loops and ending with an extracellular carboxy terminal (Jensen et al. 2005; Shorey-Kendrick et al. 2015). The second helix from the N-terminal domain in each subunit is positioned inwardly to make up the ion pore and, together, these gate and guide ion passage (Miyazawa et al. 2003). The other three helices from each subunit make up the surrounding scaffolding.
The pharmacological properties of nAChRs depend on their subunits.
Neuronal nAChRs have twelve possible subunits, α2-10 and ß2-4. Combinations of α2-6 and ß2-4 make up high-affinity heteropentameric channels, most commonly in an αßαßß arrangement (Nelson et al. 2003). Subunits α5 and ß3 do not contribute to ligand binding and α6 is only functional in combination with ß4
(Stauderman et al. 1998; Chavez-Noriega et al. 1997, 2000; Kuryatov et al. 2000;
Dowell et al. 2003), but α2-4 form orthosteric binding sites where they interface with ß2 and ß4. Subunits α7-10 form low-affinity homopentameric channels with
81 greater Ca2+ permeability and faster desensitization (Jensen et al. 2005; Shorey-
Kendrick et al. 2015), although α9-10 are not found in the central nervous system and α8 is specific to chickens. In the human central nervous system, subunits α4,
ß2, and α7 are predominant, and thus, so are heteropentameric α4ß2* (* indicating a potential additional subunit type) and homopentameric α7 receptor types
(Paterson & Nordberg 2000). All three of these subunits are highly expressed in the hippocampus across species (Sher et al. 2004; Wada et al. 1989; Picciotto et al. 2001; Clarke et al. 1985). In the rhesus macaque, α2 is distributed as widely as α4, so there may also be a greater population of α2ß2* receptors in the primate brain than in the rat (Han et al. 2000). On average, old world monkeys share
97.5% homology with the protein structures of human nAChRs, while humans and mice share only 88.9% homology (Shorey-Kendrick et al. 2015).
Nicotinic α4ß2* receptors. In rhesus macaques, receptor autoradiography has revealed high α4ß2* receptor expression throughout the brain. In the MTL, the signal is strongest in the presubiculum and subiculum, with sparser distribution through the dentate gyrus and CA fields of the hippocampus (Han et al. 2003).
These match findings in the human brain (Perry et al. 1992; Rubboli et al. 1994).
More than any other nAChRs, α4ß2* are significantly upregulated in the brains of smokers (Benwell et al. 1988), and it is also the subtype earliest affected by aging and neurodegeneration (Perry et al. 2000). In the hippocampus and other regions,
α4ß2* receptors are mostly found on the soma and dendrites of interneurons,
82 where they directly and indirectly modulate neurotransmission (Jensen et al. 2005;
Albuquerque et al. 2009).
Nicotinic α7 receptors. Compared to humans, the rhesus monkey α7 subunit has the highest protein structure homology at 99.2%, differing between species by only four amino acids even though its genomic structure is the most divergent
(Papke et al. 2005; Shorey-Kendrick et al. 2015). Rhesus α7 receptors are comparably inhibited by selective (e.g. methyllycaconitine) and nonselective (e.g. mecamylamine) antagonists, but endogenous (i.e. acetylcholine, choline) and exogenous (e.g. cytosine) agonists have significantly higher potencies than with the human receptor. The rhesus α7 is highly expressed in the hippocampus, especially in CA1 and the dentate gyrus, along with the thalamus, frontal cortex, and striatum (Han et al. 2000, 2003; Bois et al. 2015; Hillmer et al. 2017) and has a particular role in regulating glutamatergic transmission throughout the brain
(Wonnacott 1997; Sher et al. 2004). In the hippocampus, there is also a large population of somatodendritic α7 receptors located on GABAergic interneurons
(Hajos et al. 2005). Surprisingly, α7 knockout mice show no behavioral abnormalities despite lacking fast hippocampal nAChR currents (Orr-Urtreger et al.
1997).
2.1.3 Muscarinic Receptor Structures and Distribution
Muscarinic acetylcholine receptors (mAChRs) are metabotropic, class I heptahelical G protein-coupled receptors, and they are highly homologous
83 between species (Hulme et al. 1990). Although the agonist-binding sites are largely conserved between mAChR subtypes, they have differential allosteric binding sites which allow selective modulator binding (Birdsall & Lazareno 2005).
When activated, the receptors couple with heterotrimeric guanine nucleotide- binding proteins (G proteins) to activate a wide variety of signaling cascades and thereby modulate neuronal excitability and ion channel activity. Of the five muscarinic receptor subtypes that have been characterized, M1, M3, and M5 are excitatory Gαq/11-coupled receptors while M2 and M4 are inhibitory Gαi/o-coupled receptors. M1-5 are all expressed pre- and postsynaptically in the hippocampus
(Levey 1996).
M1 receptors. In the human and monkey, M1 receptors densely populate forebrain structures including the hippocampus, amygdala, striatum, and cerebral cortex (Cortés et al. 1986, 1987; Flynn & Mash 1993; Oki et al. 2005). Within the
MTL, M1 receptors are located primarily on pyramidal cells in the dentate gyrus,
CA fields, subiculum, presubiculum, and parahippocampal cortex (Mash et al.
1988; Shiozaki et al. 2001; Scarr et al. 2016). M1 knockout mice show reduced long-term potentiation and impaired memory in tasks requiring corticohippocampal communication for performance or consolidation, but normal or enhanced memory on some memory tasks like DMS (Anagnostaras et al. 2003).
M2 receptors. In the human and monkey, M2 receptors are prevalent in primary sensory cortical areas and nuclei in the thalamus, hypothalamus, and brainstem
(Cortés et al. 1986; Flynn & Mash 1993). In Alzheimer’s disease, postsynaptic M2
84 receptor populations decline while postsynaptic M1 are relatively preserved, and this imbalance is one of the key features of the cholinergic hypothesis (Terry &
Buccafusco 2003). In the MTL, the highest density is found in the CA2 field, subiculum, parasubiculum, and perirhinal and entorhinal cortices (Mash et al.
1988).
M3 receptors. Rhesus monkey M3 receptors in the central nervous system largely follow M1 distribution patterns but at lower overall levels, with its highest density in the orbitofrontal gyrus and temporal lobe (Flynn & Mash 1993; Felder et al. 2000).
M4 receptors. M4 receptors in the brain are primarily expressed in dopaminergic neurons in the corpus striatum (Felder et al. 2000; Oki et al. 2005).
In the spinal cord, M4 receptors also mediate nociception (Duttaroy et al. 2002).
M5 receptors. M5 receptors are largely understudied. In the rat brain, they are the exclusive or predominant muscarinic receptor expressed in dopaminergic neurons in the substantia nigra pars compacta and the ventral tegmental area, thereby facilitating dopamine release in the striatum and nucleus accumbens, respectively (Vilaro et al. 1990; Eglen & Nahorski 2000).
2.1.4 Cholinergic Contributions to Memory Function
Nicotine administration improves attention and short-term memory in monkeys
(Elrod et al. 1988; Buccafusco & Jackson 1991; Prendergast et al. 1998) and in humans with or without cognitive impairment (White & Levin 1999; Newhouse &
85 Kelton 2000; Min et al. 2001; Sahakian et al. 1989). Similar effects are found after administration of α4ß2- and α7-selective agonists in primate tasks such as DMS
(Buccafusco et al. 2007). Nicotine also exerts a neuroprotective influence against neurodegenerative diseases, such as Alzheimer’s and Parkinson’s, which are largely characterized by profound memory deficits (Picciotto & Zoli 2008; Quik et al. 2008). Donepezil, a positive allosteric modulator of α7 receptors, also improves spatial memory in aging, cognitively impaired monkeys (Callahan et al. 2013).
In macaques, nonnavigational spatial memory in the Hamilton Search Task and in other self-ordered spatial search tasks is impaired by systemic administration of scopolamine (SCOP), a nonselective muscarinic cholinergic receptor antagonist
(Levin & Bowman 1986; Taffe et al. 1999). As M1 is the most expressed muscarinic receptor in the MTL and has been linked most closely to learning and memory, research on the therapeutic potential of targeting specific muscarinic subtypes in neuropsychiatric disease states has focused primarily on regulating M1 function.
SCOP also impairs performance on a hippocampus-dependent passive avoidance task across species, while M1-selective positive allosteric modulators (PAMs) reverse the memory deficit in both rodents (Budzik et al. 2010) and humans
(Nathan et al. 2012). In macaques, age-related decline in hippocampal M1 receptor binding is associated with deficits in spatial learning and memory (Haley et al. 2011). Selective, systemic M2 antagonism also improves working memory in squirrel monkeys on a delayed visual discrimination task (Carey et al. 2001).
86 2.2 METHODS
2.2.1 Overview and Experimental Design
In this study, three rhesus macaques were trained on the Hamilton Search Task with three variations: spatial with 30s delays, spatial with 1s delays, and color-cued with 30s delays. Upon reaching criterion, animals were implanted with a cranial microinfusion platform and infused with scopolamine (SCOP; 1.5µl, 10mM), mecamylamine (MECA; 1.5µl, 10mM), or sterile physiological saline (1.5µl) at MRI- determined coordinates in the hippocampus. Treatment order was pseudo- randomized for each animal, and the task administrator was blinded to the experimental condition. Approximately ten minutes after the end of an infusion, animals were tested on two back-to-back runs separated by a 3-5 minute delay.
Most sessions included one spatial task with long delays and one of the control variations (spatial with short delays or color-cued), with the within-session task order counterbalanced across sessions. Total session duration lasted between 15 and 30 minutes. Hamilton Search Task performance on the spatial variation with long delays, but not spatial with short delays or color-cued variations, was hypothesized to be impaired following bilateral hippocampal infusion of either
SCOP or MECA. Behavioral outcomes following treatment were quantified and analyzed using a multivariate, within-subject repeated measures design.
All methods for this experiment are as described in Chapter 1.2, with exceptions as follows.
87 2.2.2 Animals
Subjects. Three male rhesus macaques (Macaca mulatta) were used in this study: OD, AN, and RE. At the time of testing, OD and RE were 5-6 years old and
AN was 16 years old. Animals were housed and maintained as described in
Chapter 1.22.
Animal history. Animals used in this study were previously or concurrently involved in behavioral research using object-based and social tasks. OD and AN participated in the experiments described in Chapter 1.2, and their prior history is described in 1.22. Additionally, RE received microinfusions in the amygdala and superior colliculus for studies on social dominance and unconditioned fear responses (unpublished).
2.2.3 Microinfusion Drugs
Microinfusion drugs. Scopolamine (SCOP), a mAChR antagonist, and mecamylamine (MECA), a noncompetitive nAChR antagonist, were injected into the hippocampus to block either population of cholinergic receptors. Each infusion consisted of two 1.5µl injections of SCOP (10mM), MECA (10mM), or sterile physiological saline in the hippocampus at sites 2-4.5mm apart. The SCOP dose was selected based on the highest effective dose (3µl, 10mM) used for microinfusion in the perirhinal cortex of rhesus macaques in Tang et al. (1997) to produce impaired DNMS performance. Intracerebral microinfusion of MECA has not previously been published in monkeys, so the dose was selected based on
88 intrahippocampal doses found to produce memory impairments in rodents (Kim &
Levin 1996; Barros et al. 2004).
Drug preparation. Scopolamine hydrobromide and mecamylamine hydrochloride (Sigma-Aldrich, Inc.; St. Louis, MO) were each dissolved in sterile saline to a concentration of 10mM. The solution was suction-filtered and stored in
100µl aliquots at -20˚C. For control experiments, sterile physiological saline was likewise filtered and stored. Drugs were thawed to room temperature immediately prior to microinfusion.
89 2.3 RESULTS
2.3.1 Microinfusion Site Verification
Histology and postmortem MRI scans verified that the microinfusions were placed within the intended region of the hippocampus for all three animals (refer to Fig 1.1 for subjects OD and AN) and that no significant tissue damage was inflicted in the hippocampus.
2.3.2 Number of Task Runs Per Animal and Condition
Partway through the study, a testing order effect was detected in RE’s performance data. Analysis revealed that he was significantly impaired on the second run of any session, regardless of task type or treatment condition (t=3.335, df=11, p=0.0067; paired t-test). Thus, the second run from each of his sessions was removed from the final analysis, and for the remainder of the study, RE was only tested on a single run after each infusion. The other subjects had no order effect, and no other runs were identified as outliers or removed from the dataset for any animals.
Table 2.1 describes the number of runs for each animal x treatment x task type, with a total 2-6 runs per condition that were averaged for analysis across subjects.
90 Table 2.1: Number of Hamilton Search Task runs per experimental condition.
Three animals performed on three task variations (30s spatial, 1s spatial, 30s color-cued) after bilateral hippocampal microinfusion of saline, SCOP, or MECA. Symbols associated with specific subjects are consistent throughout the graphs in this chapter.
2.3.3 Bilateral Cholinergic Antagonism in the Hippocampus
Trials to complete. There was no significant main effect of treatment
(F1.371,2.743=0.07796, p=0.8667), task type (F1.337,2.675=0.1149, p=0.8240), or treatment by task type interaction (F1.933,3.867=0.1850, p=0.8318) on the number of trials required to complete the task. Performance on the 30 sec spatial task was significantly better than chance, calculated to average at 21 trials, after bilateral microinfusion of SCOP (t=5.698, df=2, p=0.0294) or MECA (t=4.328, df=2, p=0.0495).
Repetition index. There was no significant main effect of treatment
(F1.055,2.110=0.8368, p=0.4600), task type (F1.732,3.465=0.6825, p=0.5429), or treatment by task type interaction (F1.500,3.000=0.09288, p=0.8668) on repetition index. Performance on the 30 sec spatial task was significantly better than chance, calculated to an average repetition index of 51.4, after bilateral microinfusion of
SCOP (t=11.09, df=2, p=0.0080) or MECA (t=5.819, df=2, p=0.0283).
91 Correct trials in the first eight. There was no significant main effect of treatment (F1.168,2.335=0.2500, p=0.6939), task type (F1.125,2.250=0.9307, p=0.4416), or treatment by task type interaction (F1.123,2.247=0.7736, p=0.4797) on the number of correct box selections within the first eight trials of the run. However, performance on the 30 sec spatial task was not different from chance, calculated to average at 5.3 trials, after bilateral microinfusion of SCOP (t=6928, df=2, p=0.5601) or MECA (t=8109, df=2, p=0.5026).
Latency. There was no significant main effect of treatment (F1.584,3.168=1.971, p=0.2700), task type (F1.089,2.178=0.1108, p=0.7880), or treatment by task type interaction (F1.119,2.239=0.3899, p=0.6122) on the average box opening latency.
92 Figure 2.1: Hamilton Search Task performance after cholinergic receptor blockade in the hippocampus. Animals (n=3) performed the 30s spatial, 1s spatial, and 30s color-cued variations of the task after bilateral microinfusion of SCOP, MECA, or saline in the hippocampus. A) The total number of trials required for the monkey to locate all eight reinforcer locations, such that a perfect score would be 8 trials and random performance would average to 21 trials, represented by the dashed bar. B) The repetition index, a measure of the frequency and severity of box opening errors, where chance performance of 51.4 is represented by the dashed bar. C) The number of correct box selections within the first eight trials, a measure of early task performance with a chance level caluclated at 5.3 trials, represented by the dashed bar. D) The average latency to box opening across trials. For all graphs, each symbol represents the average of an individual monkey’s performance under those conditions, error bars represent SEM, and significance was set at p<0.05.
2.3.4 Group Observations
Across subjects, the only significant difference in performance after saline microinfusion was between RE and OD on the 30s spatial task (p=0.0422).
Otherwise, the three animals demonstrated equivalent ability to complete the tasks under control conditions. However, there was a significant main effect of subject identity on average latency to box openings (F2,59=35.06, p<0.0001). Post hoc analysis revealed that OD had significantly longer latencies than RE and AN on the 30s spatial, 1s spatial, and color-cued tasks (OD vs RE, p=0.0003, p=0.0005, and p=0.0012, respectively; OD vs AN, p<0.0001, p=0.208, and p=0.0002, respectively).
93 2.4 DISCUSSION
Bilateral focal microinfusion of SCOP and MECA into the hippocampus had no effect on nonnavigational spatial memory in the Hamilton Search Task with long or short intertrial delays, nor did either impair performance on the color-cued task variation. Across all three task types, drug treatments did not differ from saline control infusions. Two of the subjects (OD and AN) also participated in the experiments described in Chapter 1, for which they each received bilateral hippocampal injections of KYNA in the same sites as were targeted here. The
KYNA injections impaired performance on the Hamilton Search Task relative to saline treatment, and they serve as a positive control for microinfusion localization in these animals. However, the treatments themselves also require positive controls. One potential validation measure would be to attempt to replicate Tang et al. (1997) and, using the same methods as here, ascertain whether this particular SCOP preparation and dose infused into the perirhinal cortex can reproduce the visual recognition memory deficits. Unfortunately, there are no previously published studies in which MECA was microinfused in the primate brain, so establishing a positive control may involve some guesswork.
In light of the strong correlation between memory impairment and cholinergic loss in the MTL during the early stages of Alzheimer’s disease as well as the efficacy of cholinesterase inhibitors in managing cognitive symptoms, it was surprising that this experiment failed to identify any critical reliance of spatial
94 memory on hippocampal cholinergic receptor populations. These results are in stark contrast to findings in rodent models, which have reported impairments in spatial behavior, learning, and memory following hippocampal cholinergic deafferentation (Jonasson et al. 2004; Martin & Wallace 2007) or intrahippocampal muscarinic blockade (Carli et al. 1997). In the reverse, systemic M1 agonism and intrahippocampal nicotine infusion each improve spatial memory in rodents
(Sharifzadeh et al. 2005; Vanover et al. 2008; Melichercik et al. 2012). In monkeys, systemic muscarinic blockade even impairs Hamilton Search Task performance
(Levin & Bowman 1986), although it is possible the deficits were due to anticholinergic action in other brain regions, such as the parahippocampal cortex.
However, without establishing a dose-response curve in the hippocampus, it is of course impossible to rule out the possibility that higher concentrations or more widespread infusions in the hippocampus may have had an effect where these did not. This seems unlikely given that 1µM SCOP entirely blocks spontaneous synaptic muscarinic activity in vitro (Mamaligas & Ford 2016), and 10µM MECA is likewise sufficient for total inhibition of all but α7 nicotinic receptors in vitro
(Wonnacott & Barik 2007).
On the other hand, these findings are consistent with a large body of work suggesting that hippocampal acetylcholine facilitates memory processes but does not play a critical role. Depletion of cholinergic input to the hippocampus does not impair spatial learning and memory in rodents (Berger-Sweeney et al. 1994; Baxter et al. 1995, 1996; Baxter and Gallagher 1996; Dornan et al. 1996; McMahan et al.
95 1997; Pang and Nocera 1999; Bizon et al. 2003; Kirby and Rawlins 2003; Frick et al. 2004), and memory deficits resulting from systemic cholinergic antagonism may be attributed to extrahippocampal brain areas. However, it also remains a possibility that long-term, nonnavigational memory in the Hamilton Search Task depends specifically on nicotinic α7-mediated neurotransmission in the hippocampus. The α7 receptor subtype is highly expressed in the macaque hippocampus (Han et al. 2000, 2003; Bois et al. 2015; Hillmer et al. 2017), and it may not be sufficiently inhibited at the drug concentrations used. Furthermore, the
KYNA concentration (100 mM) used to demonstrate that hippocampal inactivation impairs long-term, nonnavigational spatial memory by Forcelli et al. (2014) likely has significant inhibitory activity at α7 receptors as well as glutamate receptors, and these previous findings could be due in part to loss of α7 function in the hippocampus.
96 Chapter III: Reinforcer Devaluation and Habit Formation by Extended Training
3.1 INTRODUCTION
3.1.1 Introduction to Goal-Directed Action and Habit
Beyond innate reflexes, actions are driven by two general mechanisms: they may be intentional and outcome-oriented, or they may be the automated result of an established habit. Goal-directed actions are deliberate, conscious, and actively modulated according to updated outcome-related information. Habit, on the other hand, is typically characterized by inflexibility and insensitivity to updated value information, and the process is unconscious, automatic, and slow or incremental
(Seger & Spieling 2011). However, these two mechanisms are not entirely discrete, and repetition of some goal-directed actions may gradually transform them into habit. In the healthy brain, the goal-directed system and habit-based system are parallel, complementary, bidirectional processes (Lipton et al. 2019). The hierarchical decision-making theory (Dezfouli et al. 2014) describes sequence learning in particular as a cooperative conjunction of the goal-directed and habit- based systems: in short, a goal-directed choice initiates an automatic sequence of actions triggered by contextual cues. This sequence is habit-bound and difficult to disrupt. Action “chunking” of this sort is the basis for implicit procedural memory, and disorder or imbalance in the decision-making hierarchy in either direction is a common feature in neuropsychiatric disorders (de Wit et al. 2011; Gillan et al.
97 2011; Keramati & Gutkin 2013; Sjoerds et al. 2013; Morris et al. 2015; Delorme et al. 2016). Behavioral paradigms prompting the transition between goal-directed behavior and habit-based responding have been successfully developed in rodent models, but their full translational value for human behavior and pathology is yet unclear (reviewed in Corbit & Janak 2016).
3.1.2 Outcome Devaluation and Habit Formation in Rodents
Adams and Dickinson (1981) developed the canonical outcome devaluation test for assessing goal-directed behavior. In this paradigm and its offshoots, rodents are trained to associate an action (i.e. a lever press) with a reward (e.g. liquid sucrose). The reward is then devalued, either by satiation with the reward
(Balleine & Dickinson 1998), adding an aversive taste to the reward (Adams &
Dickinson 1981), or adding lithium chloride to induce nausea (Paredes-Olay &
Lôpez 2002). In the testing phase, animals under normal conditions quickly stop repeating the reward-associated action, thus demonstrating the ability to reevaluate how their performance might support or contradict their current wants and needs (Heyes & Dickinson 1990). While moderate training produces this purposeful, goal-directed action in the testing phase, however, extensive training may result in an instrumental habit formation that can override the animal’s ability to update reward value information. If the animal responds out of habit rather than goal-directed decision-making, it will persist in the trained action even after the
98 reward is devalued (Adams 1982; Dickinson et al. 1995; reviewed in Balleine &
O’Doherty 2010).
These and other findings have contributed to the dual-system theory, which describes the shift from a goal-directed system into a dissociable habit system (de
Wit & Dickinson 2009; Balleine & O’Doherty 2010). Decision-making with the goal- directed system relies on circuits in the hippocampus, medial prefrontal cortex, amygdala, and caudate nucleus (Balleine & Dickinson 1998; Corbit & Balleine
2003; Smith & Graybiel 2016). In contrast, the habit system depends primarily on the dorsolateral striatum (Yin et al. 2004, 2005; Yin & Knowlton 2006; Balleine &
O’Doherty 2010; Quinn et al. 2013).
3.1.3 Outcome Devaluation in Monkeys
The reinforcer devaluation task has also been adapted for monkeys. In the standard paradigm, monkeys are trained on concurrent visual discriminations for pairs of physical objects or touchscreen images. Each pair includes one object consistently baited with one of two fixed food reinforcers (e.g. peanut or M&M), while the other object is an unbaited foil. Monkeys develop specific stimulus- reward associations through repeated trial and error. For the devaluation testing phase, monkeys are sated on one of the food reinforcers immediately prior to making a series of selections between stimuli pairs where one is associated with the devalued reinforcer and the other is associated with the nondevalued reinforcer.
99 Under normal conditions, monkeys will shift their selection ratios to reflect the updated value information.
Successful reinforcer devaluation is dependent on a bilaterally intact amygdala
(Malkova et al. 1997; Thornton et al. 1998; Izquierdo & Murray 2004, 2007;
Wellman et al. 2005; Machado & Bachevalier 2007; Kazama & Bachevalier 2013), orbitofrontal cortex (Izquierdo & Murray 2004; Izquierdo et al. 2004; Machado &
Bachevalier 2007; West et al. 2011; Rudebeck et al. 2013; Kazama et al. 2014;
Murray et al. 2015), and mediodorsal thalamus (Mitchell et al. 2007; Wicker et al.
2018; Chakraborty et al. 2019), but not on the hippocampus (Chudasama et al.
2008), perirhinal cortex (Thornton et al. 1998; Chudasama et al. 2008), ventrolateral prefrontal cortex (Baxter et al. 2009; Croxson et al. 2011), or anterior cingulate cortex (Chudasama et al. 2013). Selective, transient pharmacological manipulation of focal areas has further revealed that the amygdala and
Brodmann’s area 13 of the orbitofrontal cortex are critical during the value-updating phase and area 11 of the orbitofrontal cortex is critical during the goal selection phase, but not vice versa (Wellman et al. 2005; Murray et al. 2015). The mediodorsal thalamus is critically involved in both phases (Wicker et al. 2018).
Orbitofrontal cortex volume, as associated with age, is predictive of devaluation performance (Burke et al. 2014). Disconnection studies in monkeys have also identified critical ipsilateral and contralateral projections among the amygdala, orbitofrontal cortex, and mediodorsal thalamus (Baxter et al. 2000; Izquierdo &
Murray 2004; Izquierdo et al. 2010). Systemic cholinergic antagonism with
100 scopolamine or mecamylamine does not impact reward devaluation (Waguespack et al. 2018).
3.1.4 Outcome Devaluation and Habit Formation in Humans
The outcome devaluation paradigm has been adapted for human research in several ways. First, many studies use selective satiety of the food (or other sensory) reinforcer (Valentin et al. 2007; Tricomi et al. 2009; Hogarth & Chase
2011; Watson et al. 2014). Second, many studies utilize secondary reinforcers, typically currency, which is devalued by instruction (de Wit et al. 2007, 2009; Gillan et al. 2011). A third category employs aversive reinforcers, such as electric shocks, and devaluation is effected by removing the electrodes delivering shocks associated with one set of stimuli (Gillan et al. 2014, 2015). Fourth, in a “slips-of- action” paradigm, participants learn which responses (e.g. left or right key presses) earn points in association with specific visual stimuli; some outcomes are then devalued by nullifying their point value, and participants are instructed to suppress those trained responses while continuing to perform still-valued responses (Gillan et al. 2011; de Wit et al. 2012). One study even achieved devaluation by delivering health warnings against consumption of the rewards (chocolate and tobacco) prior to testing (Hogarth & Chase 2011). Confirming research in rodent models, human imaging studies using these outcome devaluation tasks have identified the orbitofrontal cortex and ventromedial prefrontal cortex as critical contributors to
101 goal-directed decision-making (Gottfried et al. 2003; Valentin et al. 2007; de Wit et al. 2009).
Across all these devaluation methods, habit is assessed in one of two ways: the “traditional” habit response represents a failure to prevent a negative action
(Adams 1982), while another incarnation of habit is to persist in choosing a devalued reinforcer when given the choice between that and a different, nondevalued reinforcer (Schwabe & Wolf 2009). Consistent with rodent studies, human imaging indicates that habit override is reliant on the striatum (Tricomi et al. 2009; Wunderlich et al. 2012; Delorme et al. 2016). However, with healthy participants and no experimental interventions, only one study to date has been able to establish a habit response after extensive training in any variation of the outcome devaluation task (Tricomi et al. 2009), and many attempts to replicate the effect—including two attempts to replicate that study exactly—have failed (de Wit et al. 2018). This is a startling contrast to findings in rodent models.
3.1.5 Goal-Directed vs Habit Behavior in Neuropathology
Beyond the outcome devaluation paradigm, complementary function of the goal-directed system and habit system is a central component of mental health.
Habits associated with self-related goals and values contribute to cognitive self- integration, self-esteem, and an orientation toward self-actualization (Verplanken
& Sui 2019). On the other hand, habitual repetitive thinking about one’s problems, worries, and negative feelings cued by goal-independent contexts is a significant
102 component in the onset and maintenance of depression (Watkins & Nolen-
Hoeksema 2014). In fact, maladaptive habit formation is a hallmark of a number of different neuropsychiatric conditions, particularly in disorders involving pathological compulsion. For example, Tourette’s syndrome (Delorme et al. 2016) and obsessive-compulsive disorder (OCD: Gillan et al. 2011, 2014, 2015) both feature a rapid shift away from goal-directed control in scenarios like devaluation.
OCD, in fact, is more or less defined by a maladaptive habit-based corticostriatal system hijacking behavior against the patient’s will and reason (Graybiel & Rauch
2000; Gillan & Robbins 2014) rather than a disorder characterized by goal-directed system deficits.
Behavioral and chemical addictions also feature profound habit system abnormalities. The relationship between goal-directed action and addiction is (at minimum) three-fold. First, addictive substances bias decision-making in favor of drug-seeking as a primary goal (Madden et al. 1997; Kirby et al. 1999; Camchong et al. 2011; Hogarth & Chase 2011; Bickel et al. 2012; Lucantonio et al. 2014).
Second, the goal-directed system mediated by the prefrontal cortex is suppressed
(Baler & Volkow 2006; Goldstein & Volkow 2011; Morein-Zamir et al. 2013; McKim et al. 2016). Third, exposure to addictive substances promotes rapid habit formation in the striatal-mediated system, even for habits unrelated to drug use
(Everett & Robbins 2005; Monterosso et al. 2005; Baler & Volkow 2006; Noël et al.
2007; Ersche et al. 2012; Sjoerds et al. 2013; Morein-Zamir & Robbins 2015). The cumulative imbalance between the goal-directed system and the habit system
103 alone is as impactful as the separate contributions of their independent dysfunction
(Sebold et al. 2014). In rodent models, even moderate doses and limited exposure to addictive substances including alcohol, nicotine, and cocaine rapidly induce habit formation (Dickinson et al. 2002; Miles et al. 2003; Ciccocioppo et al. 2004;
Corbit et al. 2012; Mangieri et al. 2012; Gourley et al. 2013; Clemens et al. 2014;
Lopez et al. 2014; Schmitzer-Torbert et al. 2015; Loughlin et al. 2017). This is associated with changes in corticostriatal pathways, particularly in the dorsal striatum (Belin et al. 2009; Barker et al. 2015; Corbit & Janak 2016; Gremel &
Lovinger 2016; Ron & Barak 2016).
The involuntary persistence of habitual behavior associated with negative consequences that characterizes drug dependency highlights it as a candidate for translational modeling in an outcome devaluation paradigm. Patients with substance dependencies even fail to modify their behavior to avoid negative consequences in the slips-of-action devaluation paradigm (Ersche et al. 2016).
The extensive training, or “overtraining,” paradigm in rodents, which leads to robust habit formation and resulting devaluation failure, may have translational value as a model of the habit component in addiction, but it is unclear whether it would have more than face validity. The overtraining effect has not been reproducibly demonstrated in healthy, neurotypical human subjects (de Wit et al. 2018), and there have been no attempts to replicate it in nonhuman primates until now.
104 3.2 METHODS
3.2.1 Overview and Experimental Design
Across three task cycles, four rhesus macaques were trained on 40 object- reward associations, which were divided into two main groups: low-repetition (LR) objects that were presented 1-3 times per week, and high-repetition (HR) objects that were presented multiple times per day and became “overtrained” over the course of the total 150 testing days. At the conclusion of each of these training cycles, the monkeys were tested for evidence of reward value updating in a reinforcer devaluation task. In the standard version of this task, monkeys typically shift their preference away from a food reinforcer that has been satiated immediately prior to testing and toward a contrasting (e.g. salty versus sweet) food reinforcer, demonstrating that the satiated reinforcer has been transiently and contextually devalued (Malkova et al. 1997; Thornton et al. 1998; Baxter et al. 2000,
2009; Izquierdo et al. 2004; Izquierdo & Murray 2004, 2007, 2010; Wellman et al.
2005; Machado & Bachevalier 2007; Mitchell et al. 2007; Chudasama et al. 2008,
2013; Croxson et al. 2011; West et al. 2011; Kazama & Bachevalier 2013;
Rudebeck et al. 2013; Burke et al. 2014; Kazama et al. 2014; Murray et al. 2015;
Waguespack et al. 2018; Wicker et al. 2018). In the present study, an additional variable was introduced: the effect of object training history. Monkeys were hypothesized to develop a habit response to the overtrained HR objects compared to the LR objects, which had a quantitative training history comparable to most
105 published studies on this primate behavioral paradigm (Table 3.1). As a result, the monkeys were expected to lose the reinforcer devaluation effect with overtraining, as they would no longer be making selections based on the anticipated reward outcome (goal-oriented responding) and instead act according to cue-response associations (habitual responding). Behavioral results were quantified and analyzed using a multivariate, within-subject repeated measures design.
3.2.2 Animals
Subjects. Four male rhesus macaques were used in this study: OD, RA, SA, and FR. At the time of testing, the animals were ages 4-7 and weighed between
6.2 and 10.7 kg. Animals were housed and maintained as described in Chapter
1.22.
Animal history. Three of the animals (OD, RA, and SA) had some previous training on the Hamilton Search Task (described in Chapter 1.23) in the WGTA.
None received any experimental treatments or surgical interventions prior to or during this study.
106 Table 3.1: Review of concurrent visual discrimination training in reinforcer devaluation tasks in monkeys.
Publication Animals Type Pairs Criterion Ave # Exp Exp Range Malkova et al. 1997 7 rhesus M 60 90% correct 14.3 NR macaques across 5 sessions Thornton et al. 1998 17 rhesus M 60 90% correct 9.5 6-13 macaques across 3 sessions Baxter et al. 2000 8 rhesus M 60 90% correct NR NR macaques across 5 sessions Izquierdo & Murray 2004 12 rhesus M 60 90% correct S1: 17.3, S1: 13-24, macaques across 5 sessions S2: 26.7 S2: 21-29 Izquierdo et al. 2004 10 rhesus M 60 90% correct S1: 15.5, NR macaques across 5 sessions S2: 24.6, NA: 21.7 Wellman et al. 2005 6 pigtail M 60 90% correct 41 31-49 macaques across 5 sessions Izquierdo & Murray 2007 9 rhesus M 60 90% correct NR NR macaques across 5 sessions Machado & Bachevalier 36 rhesus M 60 90% correct NR NR 2007 macaques across 5 sessions Mitchell et al. 2007 9 rhesus T 60 90% correct 16.4 NR macaques across 5 sessions Chudasama et al. 2008 17 rhesus M 60 90% correct 16.9 9-35 macaques across 5 sessions Baxter et al. 2009 8 rhesus T 60 90% correct 22.3 16-32 macaques across 5 sessions Izquierdo & Murray 2010 11 rhesus M 60 90% correct S1: 17.2, S1: 8-41, macaques across 5 sessions S2: 27.3 S2: 16-53 Croxson et al. 2011 8 rhesus T 60 90% correct NR NR macaques across 5 sessions West et al. 2011 2 rhesus, M 60 90% correct 31 25-39 2 pigtail across 5 sessions macaques Chudasama et al. 2013 8 rhesus M 60 90% correct 14.8 NR macaques across 5 sessions Rudebeck et al. 2013 19 rhesus M 60 90% correct S1: 17, S1: 10-37, macaques across 5 sessions S2: NR S2: NR Kazama & Bachevalier 10 rhesus M 60 90% correct NR NR 2013 macaques across 5 sessions Burke et al. 2014 16 bonnet M 40 90% correct NR NR macaques across 5 sessions Kazama et al. 2014 9 rhesus M 60 90% correct S1: 21.8, NR macaques across 5 sessions S2: 35.4 Browning et al. 2015 5 rhesus T 60 90% correct 19.2 NR macaques across 5 sessions Murray et al. 2015 5 rhesus T 60 90% correct NR NR macaques across 5 sessions Waguespack et al. 2018 4 rhesus M 40 90% correct NR NR macaques across 5 sessions Wicker et al. 2018 4 rhesus M 40 90% correct NR NR macaques across 5 sessions Chakraborty et al. 2019 4 rhesus T 60 90% correct 15.7* 15-16* macaques across 5 sessions *excluded one outlier requiring 66 sessions to reach criterion
107 This is a comprehensive review of publications to date reporting on the standard manual (M) or touchscreen-based (T) reinforcer devaluation task in monkeys, indicating how many exposures
(Exp) monkeys had to concurrent visual discrimination pairs prior to devaluation testing. Note that most studies report the number of trials performed before reaching criterion, while the numbers indicated here include trials in the criterion run. In some studies, the same set of discriminations were retrained after the first round of devaluation testing and retested again (first stage, S1; second stage, S2) or were trained and tested on a new set of stimuli (new acquisition, NA) after the first round of devaluation testing. In these cases, the cumulative number of exposures to each set is indicated, including discrimination trials performed in baseline sessions between devaluation testing days. Some studies did not report the number of sessions or trials performed before devaluation testing (not reported, NR).
3.2.3 Behavioral Testing
Apparatus and materials. Concurrent visual discrimination training and reinforcer devaluation were performed in the WGTA (described in Chapter 1 methods). In the testing arena was a tray with two wells, 25mm in diameter and
5mm deep, 18cm apart along the midline and equidistant to the animal compartment. The stimuli used for the task included 80 unique objects different in shape, size, color, and material (wood, metal, cardboard, plastic, and canvas), and an additional 20 unique objects were used for pre-training. Food reinforcers used to bait objects were one-half roasted peanuts (Planters; Glenview, IL) or one-third fruit snacks (General Mills, Inc.; Minneapolis, MN), and the animals did not receive these in any other context.
108 Terminology. Here, a “session” refers to the full period of training or testing conducted without removing the monkey from the WGTA. A “trial” consists of a single presentation of two objects, and all trials were separated by closing the sliding guillotine door between the animal compartment and the testing arena. The behavioral testing was divided into three chronological “cycles,” each consisting of
34 days of concurrent visual discrimination training followed by four four-day weeks for reward devaluation testing.
Pre-training. First, animals were trained to voluntarily enter portable, custom carrier boxes (Allentown, Inc.; Allentown, NJ) for transfer between the home cage and the WGTA. Leading up to the experimental period, animals were habituated to the WGTA and experimental setup in once-daily pretraining sessions. In the first stage of pretraining, monkeys were presented with food reinforcers uncovered in the testing tray wells. Each time they retrieved a reinforcer, the guillotine door was closed between the animal compartment and the testing arena for approximately 5 seconds and the food reward replaced. These sessions were ended after 5 minutes or after the monkey retrieved 15 treats. Once the monkey comfortably and consistently picked up the food rewards from the testing arena (2-
5 days), they graduated to the second stage of pretraining in which the food rewards were hidden beneath objects. Two objects were presented at a time from a set of 20 unique objects (independent from the object set used during experimental testing), paired randomly and all covering food reinforcers in the tray wells. Each time the monkey displaced an object and retrieved the reinforcer
109 underneath, the guillotine door was closed briefly to replace the reinforcer and swap out the objects for a new pair. If the monkey did not displace any objects within two minutes, the door was closed and the objects replaced with two new ones. These sessions ended after 15 minutes or after the monkey displaced ten objects. Once they learned to displace objects and look for food reinforcers consistently (3-7 days), their pretraining was concluded.
Cycle I concurrent visual discrimination training. Across 34 daily sessions
(five days per week), monkeys were presented with the 20 HR objects (ten peanut- associated, ten fruit snack-associated) paired with their assigned foil (unbaited) objects three times per day (102 total exposures) and the 20 LR objects (ten peanut-associated, ten fruit snack-associated) with their foils once every other day
(17 total exposures), all in pseudo-random order. For each of the 70 trials per session, the monkey was able to displace one object and, if they correctly chose a baited object, retrieve the food reinforcer beneath. Across sessions, they learned through trial and error which objects were baited and, incidentally, the specific object-reward associations.
Cycle II concurrent visual discrimination training. Across 34 daily sessions
(five days per week) with 80-90 trials each, the HR objects with their foils were presented to the monkey four times per day (136 total exposures) and the LR objects with their foils once per week (7 total exposures) in pseudo-random order.
Cycle III concurrent visual discrimination training. Across 34 daily sessions (five days per week) of 80-90 trials each, HR objects were each
110 presented eight times in a row every other day (136 total exposures) rather than in pseudo-randomized order; as in Cycle II, LR objects were presented once per week (7 total exposures). Unlike Cycles I and II, the baited objects were no longer assigned to specific foils and instead rotated through all 40 foils, such that pairings were only repeated once every 40 exposures of any given baited object.
Reward devaluation, Cycles I-III. After each discrimination training cycle, monkeys underwent four four-day weeks of devaluation testing. For each of these weeks, Days 1 and 3 were baseline sessions resembling a standard object- association training session: All HR objects were presented three times per baseline day and all LR objects were presented once per baseline day. For Cycles
I and II, baited objects were presented with their assigned foils in pseudo-random order, but for Cycle III, baited objects were presented with a different foil each time and HR objects were repeated consecutively.
Day 2 of each testing week was a probe session. Instead of pairing baited and unbaited objects, monkeys were presented with 20 trials in which they were given a choice between one peanut-associated object and one fruit snack-associated object. Pairs were counterbalanced to include HR/HR, HR/LR, and LR/LR object combinations for all possible peanut/fruit snack pairings.
On Day 4 of each testing week, the monkeys were fasted for 18-24 hours before they were provided with either 150 grams of peanuts or 200 grams of fruit snacks in their home cage. They were allowed to consume as much of the portion as they liked for 30 minutes, after which time the leftover portion was removed from the
111 cage. They were given 15 minutes afterward to finish any food stored in their cheek pouches before being transferred to the WGTA for testing. The subsequent session was set up like a probe session, in which monkeys made 20 selections between peanut-associated and fruit snack-associated objects among all possible
HR/HR, HR/LR, and LR/LR combinations, counterbalanced. Weeks with peanut satiation or fruit snack satiation were counterbalanced within and across the three cycles of testing.
Experimental measures. The proportion of selections made for peanut- associated versus fruit snack-associated objects during probe sessions was considered indicative of each monkey’s preferred reinforcer. To assess the effect of selective satiation, the devaluation index was calculated as previously described
(West et al. 2012): (Devalued - Nondevalued)/(Devalued + Nondevalued). A devaluation index of -1 would indicate that only objects associated with the nondevalued food reinforcer were selected (i.e. total devaluation effect); a score of 1 would indicate that only objects associated with the devalued food reinforcer were selected; and a score of 0 would indicate equal numbers of devalued and nondevalued rewards were chosen.
Because the devaluation index does not take into account the monkey’s baseline reward preference or performance in probe sessions, the difference score
(Malkova et al. 1997) was calculated as a second measure of performance. The difference score was the sum of the differences between the total number of objects associated with a reinforcer when it was devalued across two satiation
112 sessions and the total number of objects associated with that same reinforcer across the four probe sessions, but including only the trials in which the specific combination of reinforcer and training history matched. For example, the difference score for devalued HR objects selected over nondevalued LR objects would only count the peanut-associated HR objects paired with fruit snack- associated LR objects after peanut satiation and the fruit snack-associated HR objects paired with peanut-associated LR objects after fruit snack satiation, with the differences calculated from among those same pairing populations across baseline sessions. A positive difference score indicates that the monkey shifted selections away from the devalued reinforcers compared to baseline sessions.
3.2.4 Data Analysis
At each timepoint (i.e. at the end of Cycles I-III), the data from four weeks of devaluation testing, two with peanut satiation and two with fruit snack satiation, were aggregated to calculate the devaluation index and difference score for each combination of object types: devalued HR with nondevalued HR (HH), devalued
HR with nondevalued LR (HL), devalued LR with nondevalued HR (LH), and devalued LR with nondevalued LR (LL). Each of these combinations was represented in a total of 20 baseline trials and 20 post-satiation trials across each testing cycle. In GraphPad Prism 8, the effects of object combination and timepoint were assessed using two-way analysis of variance (ANOVA) with a post hoc
Tukey’s test for multiple comparisons. Object selections across probe sessions
113 were evaluated likewise, and reinforcer preference was determined using a paired, two-tailed t-test to analyze the selection of fruit snack-associated and peanut- associated objects throughout probe trials. Concurrent visual discrimination learning was compared between the HR and LR training schedules using a paired, two-tailed t-test. Additionally, devaluation index for HH and LL pairs across the three testing cycles was used for a simple linear regression analysis. Significance for all tests was considered at an alpha-level of 0.05.
114 3.3 RESULTS
3.3.1 Concurrent Visual Discrimination Training
In standard reinforcer devaluation studies in monkeys, concurrent visual discrimination training continues until the animal performs to criterion, almost always defined as making 90% correct selections of the rewarded stimulus across five consecutive sessions (see Table 3.1), which is equivalent to five presentations of every stimulus pair. Here, monkeys were required to continue object association training for a fixed number of sessions regardless of performance, but learning rate was compared between the HR and LR training schedules. In keeping with the literature, criterion performance with HR or LR objects independently was defined as making 90% correct selections across five consecutive rounds of each object set (i.e. 90 or more correct trials out of 100). All four monkeys reached criterion for the HR objects within the first training cycle (average±SEM: 540±50.2 trials with
27±2.5 presentations of each object, including the criterion run) and remained at or above criterion through the second and third cycles, but only one monkey (OD) reached criterion for LR objects prior to the first round of devaluation testing.
Subject SA did not reach criterion for the LR objects at all, never performing above
87% correct across any 100 consecutive trials; however, the amount of training before he reached his personal maximum performance (460 trials with 23 presentations) was equivalent to the amount of training the other monkeys needed to reach criterion. On average, the other three monkeys acquired the LR object
115 discriminations with fewer total trials than required for HR object discriminations
(446.7±80.6 trials with 22.3±4.0 presentations per object) despite the reduced presentation frequency and absence of within-session repetition. This difference from the rate of HR learning was not statistically significant (t=1.394, df=3, p=0.2576), however. The number of cumulative presentations of each object type prior to devaluation testing in each cycle is shown in Table 3.2.
Table 3.2: Number of LR and HR exposures for each testing cycle.
After 34 days of concurrent visual discrimination training in each cycle, these are the cumulative numbers of exposures to individual LR or HR objects prior to the devaluation testing weeks. The totals indicated for Cycles II and III include the additional discrimination trials from the baseline sessions of prior devaluation testing weeks.
3.3.2 Reinforcer Devaluation
Performance during probe sessions. In probe sessions (with no selective satiation) through the three testing cycles, monkeys demonstrated a robust and significant preference for fruit snacks over peanuts (t=6.763, df=2, p=0.0212) across all combinations of HR and LR objects, selecting on average 81.6±7.8% fruit snacks in Cycle I, 70.3±9.1% in Cycle II, and 71.3±7.8% in Cycle III (Fig 3.1).
There was a significant main effect of object combination, as defined by relative training schedules and associated reinforcers, on the ratio of fruit snack-associated
116 objects selected over peanut-associated objects (F1.163,3.489=10.12, p=0.0394).
There was no significant effect of training cycle (F1.027,3.082=1.654, p=0.2882) or object combination by cycle interaction (F1.901,5.704=0.8094, p=0.4845). Post hoc analysis revealed a single significant pairwise comparison in the second training cycle, such that significantly more HR fruit snack-associated objects were selected when paired with HR peanut-associated objects than LR fruit snack-associated objects paired with HR peanut-associated objects (p=0.0467; Tukey-corrected).
Figure 3.1: Reinforcer preference across probe sessions. Selections made between fruit snack- and peanut-associated objects during probe sessions, when there was no prior selective satiation. A) Each bar indicates the percentage of fruit snack- or peanut-associated objects selected across the four probe sessions of each testing cycle. These include trials with all combinations of HR and LR fruit snack- and peanut-associated objects. B) The percentage of fruit snack-associated objects selected over peanut-associated objects in each testing cycle separated out by trials with different fruit snack-associated/peanut-associated object pairings: HR/HR, HR/LR,
LR/HR, and LR/LR. Error bars represent SEM and significance was determined at *p<0.05.
117 Relative training effect on devaluation index. There was a significant main effect of object combination (F1.427,4.282=11.58, p=0.0213) on devaluation index, but no main effect of testing cycle (F1.175,3.524=0.2399, p=0.6897) or object combination by testing cycle interaction (F1.261,3.782=0.6537, p=0.5026). Correction for multiple comparisons revealed no significant differences between specific object combinations at any timepoint (Fig 3.2A).
In a separate analysis of the devaluation effect following only satiation with the preferred reinforcer (fruit snacks, for all cases here; Fig 3.2B), there remained a main effect of object combination (F3,6=16.09, p=0.0006), but not of testing cycle
(F2,6=0.4573, p=0.6534) or combination by cycle interaction (F6,18=0.3929, p=0.8740). The only significant pairwise comparisons identified were identified between HL and LH object combinations in the first (p=0.0348; Tukey-corrected) and third (p=0.0027; Tukey-corrected) testing cycles—with a near-significant difference in the second cycle as well (p=0.0520; Tukey-corrected)—and between
LH and LL object combinations in the third testing cycle (p=0.0064; Tukey- corrected). In contrast, an analysis of the devaluation effect following satiation with the non-preferred reinforcer (peanuts; Fig 3.2C) revealed no significant effects of object combination (F1.535,4.605=1.481, p=0.3065), testing cycle (F1.019,3.057=0.8686, p=0.4214), or combination by cycle interaction (F1.086,3.258=0.7129, p=0.4683).
118
Figure 3.2: Devaluation index as a function of training cycle and relative object history. The devaluation index, a measure of outcome devaluation after selective satiety, across the three training cycles and evaluated according to relative object training history. A) Overall devaluation effect across all test sessions. B) Devaluation across test sessions after satiation with the preferred food reinforcer, fruit snacks. C) Devaluation across test sessions after satiation with the nonpreferred food reinforcer, peanuts. For all graphs, error bars represent SEM and significance was determined at *p<0.05, **p<0.01.
Effects of raw presentation number on devaluation index. In a linear regression analysis of the devaluation effect with HH and LL pairs as a function of
119 the total number of prior exposures to each object type (Fig 3.3), there was no significant correlation between devaluation and training history (F1,22=0.2613, p=0.6143, R2=0.01174).
Devaluation Index by Number of Exposures (LL and HH) 1.0 LL HH 0.5
# Object Exposures 0.0 100 200 300 400
Devaluation Index -0.5
-1.0
Figure 3.3: Devaluation index as a function of cumulative training. Based only on LL and HH combinations from the three testing cycles, the dashed line represents the linear regression analysis for devaluation index as a function of number of training exposures. With significance set at p<0.05, no significant linear relationship was found. Error bars represent SEM.
Difference scores. The difference scores revealed no significant main effects of object combination (F1.166,3.497=2.391, p=0.2111), testing cycle
(F1.040,3.120=0.2588, p=0.6535), or combination by cycle interaction
(F1.459,4.377=0.6093, p=0.5360) (Fig 3.4A). In a separate analysis of the devaluation effect only after satiation with the preferred reinforcer (fruit snacks), there was a significant main effect of object combination (F1.650,4.951=8.528, p=0.0270), but no main effect of testing cycle (F1.161,3.484=0.01519, p=0.9331) or combination by cycle
120 interaction (F1.493,4.478=0.8535, p=0.4518). However, post hoc analysis revealed no significant pairwise comparisons (Fig 3.4B). After satiation with the non- preferred reinforcer (peanuts), there was no main effect of object combination
(F1.593,4.779=3.285, p=0.1293) or testing cycle (F1.111,3.334=2.130, p=0.2358) nor a combination by cycle interaction (F1.582,4.745=1.094, p=0.3876) (Fig 3.4C).
Figure 3.4: Difference score as a function of training cycle and relative object history. The difference scores, measuring how object selections shifted between baseline and satiation conditions, across the three training cycles and evaluated according to relative object training
121 history. A) Overall difference scores across all test sessions. B) Difference scores across test sessions after satiation with the preferred food reinforcer, fruit snacks. C) Difference scores across test sessions after satiation with the nonpreferred food reinforcer, peanuts. For all graphs, error bars represent SEM and significance was determined at p<0.05.
3.3.3 Group Observations
On average, probe and satiation sessions were 8.45 minutes long and were not significantly different from each other in duration (t=0.5330, df=11, p=0.6047).
During satiation, animals consumed significantly more fruit snacks on average than peanuts (average±SEM: 116.2±18.3g and 69.1±10.8g, respectively; t=6.172, df=2, p=0.0253).
122 3.4 DISCUSSION
The number of total exposures to the LR objects during concurrent visual discrimination training across all three experimental cycles (17, 32, and 47 cumulative exposures prior to devaluation testing at each stage, respectively) was comparable to the standard training for devaluation tasks in the monkey literature
(see Table 3.1). The HR training schedule, on the other hand, was designed to emulate the overtraining paradigm primarily investigated in rodent models (Adam
1982; Dickinson et al. 1995; Balleine & O’Doherty 2010). Monkeys were trained on the HR discriminations for nine months with 430 trials per object before the final round of devaluation testing and a final total of 454 trials per object at the conclusion of the study. This surpassed the number of trials required for rats to shift into instrumental habit responding; 360 positively reinforced lever presses across 12 sessions are sufficient to significantly reduce outcome devaluation
(Dickinson et al. 1995). In the present study, however, there were no significant differences in devaluation index between HH and LL pairings at any timepoint, and in fact, the devaluation index for LL pairings in the first cycle (-0.35) after 17 exposures was almost identical to the devaluation index for HH pairings in the last cycle (-0.40) after 430 exposures despite the extreme difference in training history.
Regression analysis of devaluation index as a function of the number of total object exposures also revealed no effect of extended training. The hypothesized formation of a habit response would have been supported by a shift in the
123 devaluation index toward 0, but instead, the devaluation effect (as evidenced by a negative devaluation index) was robust regardless of training history. This was startling, but not inconsistent with a growing body of literature in humans reporting failures to replicate the loss of outcome devaluation as a result of overtraining as has been established in rodent models (de Wit et al. 2018). In one example (de
Wit et al. 2013), subjects spent six days learning moderately-trained discriminations (36 total trials for each) and extensively-trained discriminations
(144 total trials for each), but like the monkeys reported here, their performance after devaluation was not different between the two stimulus groups. These data strengthen the case for fundamental species differences in relation to this behavioral paradigm.
Although the absolute amount of training did not impact outcome devaluation, there were some significant effects of training history differentials when animals made selections between HR and LR objects both under baseline conditions and after selective satiation. In probe sessions in the second cycle, monkeys on average selected a significantly higher proportion of HR objects associated with the preferred reinforcer (fruit snacks) than LR fruit snack-associated objects when either set was paired with HR peanut-associated objects. In contrast, HR and LR peanut-associated objects were equally likely to be selected when paired with HR fruit snack-associated objects. Thus, extended training with peanut-associated objects can override the monkey’s preference for fruit snacks when there is a training differential, suggesting that there is some form of habit-based selection
124 response to HR objects over LR objects. With objects of equal training history or when the training differential and reinforcer preference are aligned in the same direction (i.e. the preferred reinforcer is associated with more highly trained objects), however, selections seem to be made based on preference alone.
After selective satiation with the preferred reinforcer (fruit snacks), monkeys also demonstrated significant differences in outcome devaluation between HL and
LH object pairs in the first and third cycle as well as between LH and LL pairs in the third cycle. In summary, the devaluation effect was reduced or even absent
(i.e. the devaluation index was at or above 0) when devalued fruit snack- associated objects were paired with LR peanut-associated objects. There were no such differences after peanut satiation, which produced a robust and consistent devaluation effect regardless of object combination. This discrepancy may be indicative of perceptual prioritization for stimuli associated with the higher-value reward, which is essentially an attentional habit system involving the visual cortex that is active even during goal-directed behavior (Anderson 2016; Luque et al.
2017). In practice, this would mean that a greater anticipation for fruit snacks over peanuts during training would translate to a heightened attentional state in response to fruit snack-associated objects compared to peanut-associated objects, resulting in an automatic perceptual prioritization for familiar fruit snack-associated objects even after satiation that interferes with outcome devaluation. However, it seems that extensive training with the objects associated with the nonpreferred reinforcer (peanuts) may override this attentional prioritization sufficiently for the
125 animal to make selections based on satiety condition instead, regardless of the relative training history for the paired fruit snack-associated object. In contrast, with selective peanut satiation, the animal’s food preference (and therefore attentional prioritization) and satiation state are aligned in the same direction, producing strong and uniform outcome devaluation across all object combinations and training cycles. Rather than finding goal-directed behavior replaced by instrumental habit-responding, these results taken together support the idea that overtraining (100 or more object exposures, in this paradigm) actually improves goal-directed decision-making when it is conditionally inhibited by attentional habit.
It is important to note that novel object pairings presented in probe and satiation sessions, compared to the consistently paired objects presented for concurrent discrimination training in Cycles I and II, may have disturbed any instrumental habit effect that developed during training. The Cycle III training schedule was designed to control for this possibility by shuffling the foil objects among the reinforced objects, so that different (albeit not novel after each reinforced object had cycled through all 40 foils for the first time) pairings were presented each session. This way, the unpredictability of pairings during satiation sessions was not a divergence from the trained paradigm. This design also confirmed that monkeys were selecting objects based on singular object-reward associations rather than responding to associations with specific object pairings, as their accuracy was not reduced in Cycle III discrimination training compared to Cycle II.
126 However, it is still possible that the difference between the trial setup for concurrent visual discrimination training (i.e. choices between reinforced and foil objects) and the trial setup for probe and satiation days (i.e. choices between two reinforced objects) may have been sufficient to break the conditions for any habit- based responding that could have developed during training. A potential follow-up study could observe the effects of overtraining within probe-like sessions after initial acquisition of the discriminations, so the monkeys would be making the same types of decisions during training as they do after satiation. However, this would inevitably lead to a differential in reward history and exaggerated attentional prioritization between the objects associated with preferred versus nonpreferred reinforcers; in the most extreme case, this could result in an animal exclusively selecting objects associated with the preferred reinforcer during overtraining, so a lack of devaluation effect could just as easily represent low familiarity with the object-reward associations for the nonpreferred reinforcer as it could a habit formation. Even if two equally valued reinforcers were selected based on prior preference testing, monkeys often demonstrate shifting preferences during long- term behavioral studies (Izquierdo & Murray 2007), so there is no guarantee the equal valuation would remain stable throughout training.
127 Chapter IV: General Discussion
4.1 DISCUSSION
4.1.1 Overlapping Circuitry for Spatial Memory and Reward Devaluation
The Hamilton Search Task and the reinforcer devaluation paradigm in monkeys are tasks dependent on instrumental rule learning, positive motivational states, and declarative memory. Rule learning involves generalization from prior experiences, interpretation of those generalizations to the present context, and updating the parameters based on performance (Hasselmo & Stern 2018). This process is dependent on the prefrontal cortex and other cortical regions (Miller & Cohen 2001;
Wallis et al. 2001), although its specific neural circuits are not well understood. In contrast, behavioral motivation primarily involves the nucleus accumbens and midbrain dopaminergic signaling, such as in the substantia nigra pars compacta
(Bissonette & Roesch 2016). Intracerebral microinfusion and other techniques for site-specific manipulation are critical to evaluate structure-function relationships for brain regions involved in complex behavioral tasks such as the ones used here, as any systemic manipulations would be confounded by potential alterations along any of these critical axes.
Beyond the task constructs, nonnavigational spatial memory and reinforcer devaluation are processes guided by different dominant brain circuits, but with notably overlapping input from supporting neural pathways. The Hamilton Search
128 Task, for example, is a voluntary, reward-oriented activity requiring input from the goal-directed decision-making system mediated by the prefrontal cortex. Likewise, anticipatory activity and goal-directed decision-making are hippocampus- dependent in rodent models of reinforcer devaluation (Balleine & Dickinson 1998;
Corbit & Balleine 2003; Burton et al. 2009; Smith & Graybiel 2016), and although monkeys with hippocampal or other MTL lesions are not impaired in the standard reinforcer devaluation task (Thornton et al. 1998; Chudasama et al. 2008), the hippocampus is involved in supplying specific contextual information for goal- directed navigation and discriminations with complex or ambiguous sensory, spatial, or temporal dimensions (Gruber & McDonald 2012). Human imaging studies have shown that the hippocampus is preferentially active during deliberation for value-based decisions compared to perceptual decisions, and patients with hippocampal damage make value-based choices more randomly and with longer deliberation times (Bakkour et al. 2019). Cue and reward signals are also dependent on the entorhinal cortex in monkeys (Sugase-Miyamoto &
Richmond 2007).
4.1.2 Lateralization of Function
In humans, there are many examples of hemisphere-specific functional specializations. Spatial novelty, including object-background scene novelty (Goh et al. 2004), novel spatial relationships (Köhler et al. 2005), and navigational wayfinding (Hartley et al. 2003), is preferentially processed in the right
129 hippocampus. Likewise, the right parahippocampal gyrus is specialized for processing spatial information for locations and configurations (Bohbot et al. 1998,
2000, 2002, 2015). Affective processing, including outcome devaluation, is also more strongly associated with the right hemisphere (Morris et al. 1998; Whalen et al. 1998). However, there is little evidence of lateralization in monkeys. In
Experiment 1, unilateral inactivation of the hippocampus or parahippocampal cortex was insufficient to impair nonnavigational spatial memory regardless of whether the microinfusion was in the left or right hemisphere. Crossed-inactivation of the hippocampus and parahippocampal cortex also showed no differences in the degree of impairment based on left/right combination. Although Experiment 3 did not assess specific brain regions involved in concurrent visual discrimination learning or in devaluation, previous research has indicated that the devaluation effect is equally impaired by combined unilateral lesions of the amygdala and orbital prefrontal cortex in either hemisphere (Izquierdo & Murray 2004) rather than demonstrating a right-side preference as in humans. There is, however, evidence that high-anxiety temperaments in monkeys are associated with higher baseline activity in the right prefrontal cortex (Kalin et al. 1998).
4.1.3 Stress Effects on Cognitive Function
While goal-directed action more precisely supports one’s wants and needs, it is also more effortful than habit-based behavior. From an evolutionary perspective, it makes sense that during times of stress, the brain is more likely to depend on
130 rapid, automatic habit functions and minimize reliance on active memory retrieval.
Acute stress and administration of exogenous glucocorticoids impair outcome devaluation and promote rapid habit formation (Schwabe & Wolf 2009, 2010;
Guenzel et al. 2014b). However, early life stress and even prenatal exposure to maternal stress can also bias a person toward overreliance on outcome-insensitive habits correlated with addiction and obesity (Schwabe et al. 2012a; Patterson et al. 2013). Involuntary, compulsive drug use as a maladaptive habit process is further amplified by prolonged or chronic stress, and acute stress triggering a rapid shift into habit-based action can induce relapse (Schwabe et al. 2011). Prenatal, acute, and chronic stress also impair hippocampus-dependent spatial memory in a sex-dependent manner (Schwabe et al. 2008, 2012a; Guenzel et al. 2014a).
However, while acute stress immediately before spatial stimulus-response learning is disruptive, acute stress or glucocorticoid administration immediately after learning or prior to retrieval improves striatum-dependent, spatial stimulus- response memory consolidation and retention (Schwabe et al. 2008, 2012b;
Guenzel et al. 2014b). Taken altogether, it seems likely that in the present experiments, some of the between-session variation in performance on the
Hamilton Search Task for Experiments 1 and 2 as well as the concurrent visual discrimination training and reinforcer devaluation for Experiment 3 was influenced by acute stressful events in the monkeys’ lives outside of training, such as negative social interactions with their cage-mates.
131 4.1.4 Relevance to Schizophrenia
Schizophrenia is a neuropsychiatric disorder characterized by positive symptoms, including hallucination and delusional or disordered thinking; negative symptoms, including reduced verbal and emotional expression; and cognitive symptoms, including general executive dysfunction and deficits in attention and memory. Among its cognitive deficits, seven fundamental dimensions of impairment have been described: speed of processing, attention/vigilance, working memory, verbal learning and memory, visual learning and memory, reasoning and problem solving, and verbal comprehension (Nuechterlein et al.
2004). Deficits in spatial memory (Wilkins et al. 2013) and outcome devaluation
(Morris et al. 2015) are often featured. Although the cause of schizophrenia is unknown, the predominant serotonin hypothesis posits that chronic serotonergic overstimulation in the cerebral cortex disrupts glutamate signaling and ultimately results in global synaptic atrophy (Eggers 2013a). It has also been hypothesized that serotonergic overdrive begins in the dorsal raphe nucleus and projects through the entorhinal cortex to the hippocampus, where glutamate signaling is upregulated to excitotoxic levels, initiating the first stage of schizophrenia-related atrophy in CA1 (Schobel et al. 2013; Eggers 2013b).
Patients with schizophrenia, and often their schizophrenia-free relatives, have episodic memory deficits including spatial memory deficits unrelated to general intelligence (Aleman et al. 1999; Lepage et al. 2007; Leavitt & Goldberg 2009;
Kopald et al. 2012; Wilkins et al. 2013; Li et al. 2016b). Imaging patients with
132 schizophrenia as well as their healthy siblings during memory encoding reveals low activation of the hippocampus and parahippocampal cortex compared to unrelated healthy controls (Di Giorgio et al. 2012). These indicate a genetic risk for schizophrenia related to abnormal MTL processing in addition to the changes that occur within the hippocampus during disease progression.
NMDA-related glutamate transmission in the hippocampus in particular is reduced in patients with schizophrenia (Beneyto et al. 2007). Interestingly, endogenous levels of KYNA in the brain are higher in patients with schizophrenia than in the general population (Plitman et al. 2017), which likely contributes to overall hypofunction. Muscarinic receptor binding with SCOP is also significantly reduced across the entire central nervous system, with a particular reduction in M1 availability in the dentate gyrus (Hopper et al. 2019). Because the receptor expression itself is reduced, subtype-selective PAMs do not effectively rescue memory impairment in all cases because the target receptors simply are not available to be modulated. Reduced hippocampal M1 expression is associated with impaired delayed recognition of verbal memory (Bakker et al. 2018), but there are no abnormalities in the hippocampal M2 population (Scarr et al. 2018). Some patients also have elevated serum levels of autoantibodies targeting the M1 receptor (Jones et al. 2014). Although M4 receptors have a lower density in the hippocampus at baseline, their expression is markedly reduced in schizophrenia.
In the healthy brain, M1 and M4 work together to modulate the excitability and connectivity of hippocampal pyramidal neurons (Dasari & Gulledge 2011), so this
133 is a compounded loss in schizophrenia with devastating consequences to hippocampal function. Additionally, two single nucleotide polymorphisms in the M4 receptor gene are associated with increased risk of schizophrenia (Scarr et al.
2013).
While reinforcer devaluation is generally impaired in patients with schizophrenia, they also demonstrate a stronger than average devaluation effect for rewards that require physical effort to obtain (Hartmann et al. 2015), accompanied by striatal hypofunction (Kirschner et al. 2016). Apathy, defined as a negative motivational state, is one of the primary symptoms of schizophrenia, and this is an example of how the reward valuation process informs decision- making in a multidimensional way.
4.1.5 Relevance Alzheimer’s Disease
Alzheimer’s disease (AD) is a neurodegenerational disorder characterized by progressive memory loss, spatial disorientation, and eventually, profound and global cognitive dysfunction. While the defining pathophysiological features of AD are amyloid-ß (Aß) plaques and tau tangles in the brain, cholinergic neuron loss in the MTL and the diagonal band of Broca are among the earliest changes in the development of AD. AD can be distinguished from normal aging-related cognitive decline based on whether deficits include impaired allocentric spatial memory, which is evaluated in diagnostic tests such as the 4 Mountains Test (Wood et al.
2016). Expression of nicotinic α3 and α4 subunits is not different between the
134 temporal lobe of AD patients and normal controls, but α7 is upregulated in the hippocampus in AD even while α7 receptor binding is decreased (Hellström-
Lindahl et al. 1999). Exposure to Aß plaques impairs nAChR function in vitro
(Wevers et al. 2000) and in particular prevents α7 subunits from forming functional complexes (Wang et al. 2000). Muscarinic M4 binding in the dentate gyrus and
CA4 field of the hippocampus is also significantly reduced in AD (Mulugeta et al.
2003), as is M1 receptor expression (Shiozaki et al. 2001), but M2 are normal.
In addition to memory-related impairments, apathy, in conjunction with or discrete from AD-related depression, is a common feature in AD that impairs goal- related decision-making as well as reward valuation processing (Landes et al.
2005; Starkstein et al. 2005; Perry & Kramer 2015). Furthermore, Aß pathology in a mouse model severely impairs outcome devaluation (Lelos et al. 2011).
4.1.6 Conclusions
Experiments 1 and 2 have demonstrated that glutamatergic but not cholinergic transmission in the hippocampus is critical for nonnavigational spatial memory in one of the few hippocampus-dependent spatial tasks in use for nonhuman primates. Glutamatergic transmission in the parahippocampal cortex is also critical, as are intact projections between the hippocampus and parahippocampal cortex.
Unlike in humans, there is no evidence for a right hemispheric specialization for spatial memory in macaques. The dissociable roles of the hippocampus and parahippocampal cortex specifically during early task performance support an
135 associative hierarchy model of MTL function that attributes the parahippocampal cortex a role in processing spatial information instead of merely relaying cortical information to the hippocampus.
Experiment 3 has demonstrated that, unlike the rodent model but consistent with findings in human versions of the task, overtraining with concurrent visual discriminations does not result in a habit-based response pattern that impairs outcome devaluation in macaques. This validates findings across the monkey literature where animals were trained for extended or repeated periods of time, but it fails to establish a primate model for the shift in habit reliance that contributes to addiction and other neuropsychiatric disorders. However, it does provide evidence that reward value-dependent attentional habits can be overruled by extended training.
Behavioral research in monkeys offers a critical intermediary when rodent models fail to translate to human behavior or when the cognitive or behavioral process in question cannot be easily or accurately rendered. It also provides more definitive information about structure-function relationships than can be gleaned from human research, where case studies are riddled with confounding variables and other methods for evaluating specific brain structures primarily detect correlations. Primate models are invaluable for investigating the neural substrates of normal or disordered behavior and cognition, and techniques for monkey research only continue to improve in specificity.
136 References
Adams CD (1982). Variations in the sensitivity of instrumental responding to reinforcer devaluation.
Q J Exp Psychol 34:77-98.
Adams CD, Dickinson A (1981). Instrumental responding following reinforcer devaluation. Q J Exp
Psychol 33:109-121.
Aggleton JP, Brown MW (1999). Episodic memory, amnesia, and the hippocampal-anterior
thalamic axis. Behav Brain Sci 22(3):425-444.
Aggleton JP, Brown MW (2006). Interleaving brain systems for episodic and recognition memory.
Trends Cogn Sci 10(10):455-463.
Aggleton JP, Desimone R, Mishkin M (1986). The origin, course, and termination of the
hippocampothalamic projections in the macaque. J Comp Neurol 243(3):409-421.
Aggleton JP, Wright NF, Rosene DL, Saunders RC (2015). Complementary patters of direct
amygdala and hippocampal projections to the macaque prefrontal cortex. Cereb Cortex
25(11):4351-4373.
Aggleton JP, Wright NF, Vann SD, Saunders RC (2012). Medial temporal lobe projections to the
retrosplenial cortex of the macaque monkey. Hippocampus 22(9):1883-1900.
Agster KL, Fortin NJ, Eichenbaum H (2002). The hippocampus and disambiguation of overlapping
sequences. J Neurosci 22(13):5760-5768.
Aguilar BL, Forcelli PA, Malkova L (2018). Inhibition of the substantia nigra pars reticulata produces
divergent effects on sensorimotor gating in rats and monkeys. Sci Rep 8:9369.
Aguirre GK, Detre JA, Alsop DC, D’Esposito M (1996). The parahippocampus subserves
topographical learning in man. Cereb Cortex 6:823-829.
Albuquerque EX, Pereira EFR, Alkondon M, Rogers SW (2009). Mammalian nicotinic acetylcholine
receptors: from structure to function. Physiol Rev 89(1):73-120.
Aleman A, Hijman R, de Haan EH, Kahn RS (1999). Memory impairment in schizophrenia: a meta-
analysis. Am J Psychiat 156:1358-1366.
137 Alvarado MC, Bachevalier J (2005a). Comparison of the effects of damage to the perirhinal and
parahippocampal cortex on transverse patterning and location memory in rhesus macaques. J
Neurosci 25:1599-1609.
Alvarado MC, Bachevalier J (2005b). Selective neurotoxic damage to the hippocampal formation
impairs performance of the transverse patterning and location memory tasks in rhesus
macaques. Hippocampus 15:118-131.
Alvarez P, Zola-Morgan S, Squire LR (1995). Damage limited to the hippocampal region produces
long-lasting memory impairment in monkeys. J Neurosci 15:3796-3807.
Amaral DG, Cowan WM (1980). Subcortical afferents to the hippocampal formation in the monkey.
J Comp Neurol 189:573-591.
Amaral DG, Insausti R, Cowan WM (1983). Evidence for a direct projection from the superior
temporal gyrus to the entorhinal cortex in the monkey. Brain Res 275(2):263-277.
Amaral DG, Insausti R, Cowan WN (1987). The entorhinal cortex of the monkey. I.
Cytoarchitectonic organization. J Comp Neurol 264:326-355.
Aminoff E, Gronau N, Bar M (2007). The parahippocampal cortex mediates spatial and nonspatial
associations. Cereb Cortex 17:1493-1503.
Anagnostaras SG, Murphy GG, Hamilton SE, Mitchell SL, Rahnama NP, Nathanson NM, Silva AJ
(2003). Selective cognitive dysfunction in acetylcholine M1 muscarinic receptor mutant mice.
Nat Neurosci 6(1):51-58.
Anderson BA (2016). The attention habit: how reward learning shapes attentional selection. Ann N
Y Acad Sci 1369(1):24-39.
Anderson MI, Jeffery KJ (2003). Heterogenous modulation of place cell firing by changes in context.
J Neurosci 23(26):8827-8835.
Anderson RA, Asanuma C, Essick G, Siegel RM (1990). Corticocortical connections of anatomically
and physiologically defined subdivisions within the inferior parietal lobule. J Comp Neurol
196:65-113.
138 Angeli SJ, Murray EA, Mishkin M (1993). Hippocampectomized monkeys can remember one place
but not two. Neuropsychologia 31:1021-1030.
Aronov D, Nevers R, Tank DW (2017). Mapping of a non-spatial dimension by the hippocampal-
entorhinal circuit. Nature 543:719-722.
Asthagiri AR, Walbridge S, Heiss JD, Lonser RR (2011). Effect of concentration on the accuracy of
convective imaging distribution of a gadolinium-based surrogate tracer. J Neurosurg
115(3):467-473.
Augustinack JC, van der Kouwe AJ, Salat DH, Benner T, Stevens AA, Anesse J, Fischl B, Frosch
MP, Corkin S (2014). HM’s contributions to neuroscience: a review and autopsy studies.
Hippocampus 24:1267-1286.
Bachevalier J, Brickson M, Hagger C (1993). Limbic-dependent recognition memory in monkeys
develops early in infancy. NeuroReport 4:77-80.
Bachevalier J, Nemanic S (2008). Memory for spatial location and object-place associations are
differently processed by the hippocampal formation, parahippocampal areas TH/TF and
perirhinal cortex. Hippocampus 18:65-80.
Bachevalier J, Nemanic S, Alvarado MC (2015). The influence of context on recognition memory
in monkeys: Effects of hippocampal, parahippocampal, and perirhinal lesions. Behav Brain Res
285:89-98.
Bakker G, Vingerhoets C, Boucherie D, Caan M, Bloemen O, Eersels J, Booij J, van Amelsvoort T
(2018). Relationships between muscarinic M1 receptor binding and cognition in medication-
free subjects with psychosis. Neuroimage Clin 18:713-719.
Bakkour A, Palombo DJ, Zylberberg A, Kang YHR, Reid A, Verfaellie M, Shadlen MN, Shohamy D
(2019). The hippocampus supports deliberation during value-based decisions. eLife 8:e46080.
Baler RD, Volkow ND (2006). Drug addiction: the neurobiology of disrupted self-control. Trends
Mol Med 12:559-566.
Balleine BW, Dickinson A (1998). Goal-directed instrumental action: contingency and incentive
learning and their cortical substrates. Neuropharmacol 37(4-5):407-419.
139 Balleine BW, O’Doherty JP (2010). Human and rodent homologies in action control: corticostriatal
determinants of goal-directed and habitual action. Neuropsychopharmacol 35:48-69.
Bao X, Gjorgieva E, Shanahan LK, Howard JD, Kahnt T, Gottfried JA (2019). Grid-like
representations support olfactory navigation of a two-dimensional odor space. Neuron
102(5):1066-1075.
Bar M, Aminoff E, Ishai A (2008a). Famous faces activate contextual associations in the
parahippocampal cortex. Cereb Cortex 18:1233-1238.
Bar M, Aminoff E, Schacter DL (2008b). Scenes unseen: The parahippocampal cortex intrinsically
subserves contextual associations, not scenes or places per se. J Neurosci 28(34):8539-8544.
Barense MD, Bussey TJ, Lee AC, Rogers TT, Davies RR, Saksida LM, Murray EA, Graham KS
(2005). Functional specialization in the human medial temporal lobe. J Neurosci 25(44):10239-
10246.
Barker JM, Corbit LH, Robinson DL, Gremel CM, Gonzales RA, Chandler LJ (2015). Corticostriatal
circuitry and habitual ethanol seeking. Alcohol 49:817-824.
Barros DM, Ramirez MR, dos Reis EA, Izquierdo I (2004). Participation of hippocampal nicotinic
receptors in acquisition, consolidation and retrieval of memory for one trial inhibitory avoidance
in rats. Neuroscience 126(3):651-656.
Bartsch T, Schönfeld R, Müller FJ, Alfke K, Leplow B, Aldenhoff J, Deuschl G, Koch JM (2010).
Focal lesions of human hippocampal CA1 neurons in transient global amnesia impair place
memory. Science 328(5984):1412-1415.
Basile BM, Hampton RR (2018). Nonnavigational spatial memory performance is unaffected by
hippocampal damage in monkeys. Hippocampus 29:93-101.
Baxter MG, Bucci DJ, Gorman LK, Wiley RG, Gallagher M (1995). Selective immunotoxic lesions
of basal forebrain cholinergic cells: effects on learning and memory in rats. Behav Neurosci
109(4):714-722.
Baxter MG, Bucci DJ, Sobel TJ, Williams MJ, Gorman LK, Gallagher M (1996). Intact spatial
learning following lesions of basal forebrain cholinergic neurons. Neuroreport 7(8):1417-1420.
140 Baxter MG, Gaffan D, Kyriazis DA, Mitchell AS (2009). Ventrolateral prefrontal cortex is required
for performance of a strategy implementation task but not reinforcer devaluation effect in
monkeys. Eur J Neurosci 29:2049-2059.
Baxter MG, Gallagher M (1996). Intact spatial learning in both young and aged rats following
selective removal of hippocampal cholinergic input. Behav Neurosci 110(3):460-467.
Baxter MG, Murray EA (2001a). Impairments in visual discrimination learning and recognition
memory produced by neurotoxic lesions of rhinal cortex in rhesus monkeys. Eur J Neurosci
13:1228-1238.
Baxter MG, Murray EA (2001b). Opposite relationship of hippocampal and rhinal cortex damage to
delayed nonmatching-to-sample deficits in monkeys. Hippocampus 11:61-71.
Baxter MG, Parker A, Lindner CCC, Izquierdo AD, Murray EA (2000). Control of response selection
by reinforcer value requires interaction of amygdala and orbital prefrontal cortex. J Neurosci
20(11):4311-4319.
Bayley PJ, Gold JJ, Hopkins RO, Squire LR (2005). The neuroanatomy of remote memory. Neuron
46:799-810.
Beason-Held LL, Rosene DL, Killiany RJ, Moss MB (1999). Hippocampal formation lesions produce
memory impairment in the rhesus monkey. Hippocampus 9:562-574.
Belcher AM, Harrington RA, Malkova L, Mishkin M (2006). Effects of hippocampal lesions on the
monkey’s ability to learn large sets of object-place associations. Hippocampus 16(4):361-367.
Belin D, Jonkman S, Dickinson A, Robbins TW, Everitt BJ (2009). Parallel and interactive learning
processes within the basal ganglia: relevance for the understanding of addiction. Behav Brain
Res 199:89-102.
Bellgowan PSF, Buffalo EA, Bodurka J, Martin A (2009). Lateralized spatial and object memory
encoding in entorhinal and perirhinal cortices. Learn Mem 16:433-438.
Bellmund JLS, Deuker L, Doeller CF (2019). Mapping sequence structure in the human lateral
entorhinal cortex. eLife 8:e45333.
141 Bellmund JLS, Deuker L, Navarro Schröder T, Doeller CF (2016). Grid-like representations in
mental simulation. eLife 5:e17089.
Bellmund JLS, Gärdenfors P, Moser EI, Doeller CF (2018). Navigating cognition: spatial codes for
human thinking. Science 362(6415):eaat6766.
Beneyto M, Kristiansen LV, Oni-Orisan A, McCullumsmith RE, Meador-Woodruff JH (2007).
Abnormal glutamate receptor expression in the medial temporal lobe in schizophrenia and
mood disorders. Neuropsychopharmocol 32:1888-192.
Benwell ME, Balfour DJ, Anderson JM (1988). Evidence that tobacco smoking increases the
density of (-)-[3H]nicotine binding sites in human brain. J Neurochem 50(4):12431247.
Berger-Sweeney J, Heckers S, Mesulam MM, Wiley RG, Lappi DA, Sharma M (1994). Differential
effects on spatial navigation of immunotoxin-induced cholinergic lesions of the medial septal
area and nucleus basalis magnocellularis. J Neurosci 14(7):4507-4519.
Best PJ, Thompson LT (1989). Persistence, reticence, and opportunism of place-field activity in
hippocampal neurons. Psychobiol 17(3):236-246.
Bickel WK, Jarmolowicz DP, Mueller ET, Koffarnus MN, Gatchalian KM (2012). Excessive
discounting of delayed reinforcers as a trans-disease process contributing to addiction and
other disease-related vulnerabilities: Emerging evidence. Pharmacol Ther 134:287-297.
Birdsall NJM, Lazareno S (2005). Allosterism at muscarinic receptors: ligands and mechanisms.
Mini Rev Med Chem 5:523-543.
Bissonette GB, Roesch MR (2016). Neurophysiology of reward-guided behavior: correlates related
to predictions, value, motivation, errors, attention, and action. Curr Top Behav Neurosci
27:199-230.
Bizon JL, Han JS, Hudon C, Gallagher M (2003). Effects of hippocampal cholinergic deafferentation
on learning strategy selection in a visible platform version of the water maze. Hippocampus
13(6):676-684.
142 Blatt GJ, Pandya DN, Rosene DL (2003). Parcellation of cortical afferents to three distinct sectors
in the parahippocampal gyrus of the rhesus monkey: an anatomical and neurophysiological
study. J Comp Neurol 466(2):161-179.
Bohbot VD, Allen JJB, Dagher A, Dumoulin SO, Evans AC, Petrides M, Kalina M, Stepankova K,
Nadel L (2015). Role of the parahippocampal cortex in memory for the configuration but not
the identity of objects: converging evidence from patients with selective thermal lesions and
fMRI. Front Hum Neurosci 9:431.
Bohbot VD, Allen JJB, Nadel L (2000). Memory deficits characterized by patterns of lesions to the
hippocampus and parahippocampal cortex. Ann N Y Acad Sci 911:355-368.
Bohbot VD, Corkin S (2007). Posterior parahippocampal place learning in H.M. Hippocampus
17(9):863-872.
Bohbot VD, Iaria G, Petrides M (2004). Hippocampal function and spatial memory: evidence from
functional neuroimaging in healthy participants and performance of patients with medial
temporal lobe resections. Neuropsychol 18(3):418-425.
Bohbot VD, Jech R, Růzicka E, Nadel L, Kalina K, Stepánková K, Bures J (2002). Rat spatial
memory tasks adapted for humans: characterization in subjects with intact brain and subjects
with medial temporal lobe lesions. Physiol Res 51(Suppl 1):S49-S64.
Bohbot VD, Kalina M, Stepankova K, Spackkova N, Petrides M, Nadel L (1998). Spatial memory
deficits in patients with lesions to the right hippocampus and to the right parahippocampal
cortex. Neuropsychologia 36(11):1217-1238.
Bois F, Gallezot JD, Zheng MQ, Lin SF, Esterlis I, Cosgrove KP, Carson RE, Huang Y (2015).
Evaluation of [(18)F]-(-)-norchlorofluorohomeopibatidine ([(18F]-(-)-NCFHEB) as a PET
radioligand to image the nicotinic acetylcholine receptors in non-human primates. Nucl Med
Biol 42(6):570-577.
Bostock E, Muller RU, Kubie JL (1991). Experience-dependent modifications of hippocampal place
cell firing. Hippocampus 1(2):193-205.
143 Bowles B, Crupi C, Mirsattari SM, Pigott SE, Parrent AG, Pruessner JC, Yonelinas AP, Köhler S
(2007). Impaired familiarity with preserved recollection after anterior temporal-lobe resection
that spares the hippocampus. Proc Natl Acad Sci USA 104(41):16382-16387.
Bowles B, Duke D, Rosenbaum RS, McRae K, Köhler S (2016). Impaired assessment of cumulative
lifetime familiarity for object concepts after left anterior temporal-lobe resection that includes
perirhinal cortex but spares the hippocampus. Neuropsychologia 90:170-179.
Brandt KR, Conway MA, James A, von Oertzen TJ (2018). Déjà vu and the entorhinal cortex:
dissociating recollective from familiarity disruptions in a single case patient. Memory 7:1-10.
Brandt KR, Eysenck MW, Nielsen MK, von Oertzen TJ (2016). Selective lesion to the entorhinal
cortex leads to an impairment in familiarity but not recollection. Brain Cogn 104:82-92.
Brasted PJ, Bussey TJ, Murray EA, Wise SP (2005). Conditional motor learning in the nonspatial
domain: effects of errorless learning and the contribution of the fornix to one-trial learning.
Behav Neurosci 119:662-676.
Bretas RV, Matsumoto J, Nishimaru H, Takamura Y, Hori E, Ono T, Nishijo H (2019). Neural
representation of overlapping path segments and reward acquisitions in the monkey
hippocampus. Front Syst Neurosci 13:48.
Brewer JB, Zhao Z, Desmond JE, Glover GH, Gabrieli JD (1998). Making memories: brain activity
that predicts how well visual experience will be remembered. Science 281(5380):1185-1187.
Brodmann K (1909). Vergleichende Lokalisationslehre der Großhirnrinde in ihren Prinzipien
dargestellt auf Grund des Zellenbaues. Leipzig: Johann Ambrosius Barth.
Brophy LM, Jackson M, Crowe SF (2009). Interference effects on commonly used memory tasks.
Arch Clin Neuropsychol 24(1):105-112.
Brown MW, Aggleton JP (2001). Recognition memory: what are the roles of the perirhinal cortex
and hippocampus? Nat Rev Neurosci 2:51-61.
Brown MW, Wilson FAW, Riches IP (1987). Neuronal evidence that inferomedial temporal cortex
is more important than hippocampus in certain processes underlying recognition memory.
Brain Res 409:158-162.
144 Brown TI, Ross RS, Keller JB, Hasselmo ME, Stern CE (2010). Which way was I going? Contextual
retrieval supports the disambiguation of well learned overlapping navigational routes. J
Neurosci 30(21):7414-7422.
Brown TI, Stern CE (2014). Contributions of medial temporal lobe and striatal memory systems to
learning and retrieving overlapping spatial memories. Cereb Cortex 24(7):1906-1922.
Browning PGF, Chakraborty S, Mitchell AS (2015). Evidence for mediodorsal thalamus and
prefrontal cortex interactions during cognition in macaques. Cereb Cortex 25:4519-4534.
Buccafusco JJ, Jackson WJ (1991). Beneficial effects of nicotine administered prior to a delayed
matching-to-sample task in young and aged monkeys. Neurobiol Aging 12(3):233-238.
Buccafusco JJ, Terry AV Jr, Decker MW, Gopalakrishnan M (2007). Profile of nicotinic
acetylcholine receptor agonists ABT-594 and A-582941, with differential subtype selectivity, on
delayed matching accuracy by young monkeys. Biochem Pharmacol 74(8):1202-1211.
Buckley MJ (2005). The role of the perirhinal cortex and hippocampus in learning, memory, and
perception. Q J Exp Psychol 58B:246268.
Buckley MJ, Booth MC, Rolls ET, Gaffan D (2001). Selective perceptual impairments after
perirhinal cortex ablation. J Neurosci 21:9824-9836.
Buckley MJ, Charles DP, Browning PGF, Gaffan D (2004). Learning and retrieval of concurrently
presented spatial discrimination tasks: Role of the fornix. Behav Neurosci 118:138-149.
Buckley MJ, Gaffan D (1998a). Perirhinal cortex ablation impairs configural learning and paired-
associate learning equally. Neuropsychologia 36:535-546.
Buckley MJ, Gaffan D (1998b). Perirhinal cortex ablation impairs visual object identification. J
Neurosci 18(6):2268-2275.
Buckley MJ, Gaffan D, Murray EA (1997). Functional double dissociation between two inferior
temporal cortical areas: Perirhinal cortex versus middle temporal gyrus. J Neurophysiol 77:587-
598.
Buckmaster CA, Eichenbaum H, Amaral DG, Suzuki WA, Rapp PR (2004). Entorhinal cortex
lesions disrupt the relational organization of memory in monkeys. J Neurosci 24(44):9811-9825.
145 Budzik B, Garzya V, Shi D, Walker G, Woolley-Roberts M, Pardoe J, Lucas A, Tehan B, Rivera RA,
Langmead CJ, Watson J, Wu Sining, Forbes IT, Jin J (2010). Novel N-substituted
benzimidazolones as potent, selective, CNS-penetrant, and orally active M1 mAChR agonists.
ASC Med Chem Lett 1(6):244-248.
Buffalo EA, Ramus SJ, Squire LR, Zola SM (2000). Perception and recognition memory in monkeys
following lesions of area TE and perirhinal cortex. Learn Mem 7:375-382.
Buffalo EA, Reber PJ, Squire LR (1998). The human perirhinal cortex and recognition memory.
Hippocampus 8:330-339.
Burgess N, Maguire EA, Spiers HJ, O’Keefe J (2001). A temporoparietal and prefrontal network for
retrieving the spatial context of lifelike events. NeuroImage 14:439-453.
Burgess N, Reece M, O’Keefe J (1994). A model of hippocampal function. Neural Netw 7:1065-
1081.
Burke SN, Gaynor LS, Barnes CA, Bauer RM, Bizon JL, Roberson ED, Ryan L (2018). Shared
functions of perirhinal and parahippocampal cortices: implications for cognitive aging. Cell
Press Rev 41(6):349-359.
Burke SN, Thome A, Plange K, Engle JR, Trouard TP, Gothard KM, Barnes CA (2014).
Orbitofrontal cortex volume in area 11/13 predicts reward devaluation, but not reversal learning
performance, in young and aged monkeys. J Neurosci 34(30):9905-9916.
Burton BG, Hok V, Save E, Poucet B (2009). Lesions of the ventral and intermediate hippocampus
abolishes anticipatory activity in the medial prefrontal cortex of the rat. Behav Brain Res
199(2):222-234.
Bussey TJ, Saksida LM (2002). The organization of visual object representations: a connectionist
model of effects of lesions in perirhinal cortex. Eur J Neurosci 15:355-364.
Bussey TJ, Saksida LM, Murray EA (2003). Impairments in visual discrimination after perirhinal
cortex lesions: testing ‘declarative’ vs. ‘perceptual-mnemonic’ views of perirhinal cortex
function. Eur J Neurosci 14:649-660.
146 Bussey TJ, Saksida LM, Murray EA (2005). The perceptual-mnemonic/feature conjunction model
of perirhinal cortex function. Q J Exp Psychol 58B:269-282.
Bussey TJ, Saksida LM, Murray EA (2006). Perirhinal cortex and feature-ambiguous
discriminations. Learn Mem 13:103-105.
Cahusac PMB, Miyashita Y, Rolls ET (1989). Responses of hippocampal formation neurons in the
monkey related to delayed spatial response and object-place memory tasks. Behav Brain Res
33:229-240.
Cahusac PM, Rolls ET, Miyashita Y, Niki H (1993). Modification of the responses of hippocampal
neurons in the monkey during the learning of a new conditional spatial response task.
Hippocampus 3(1):29-42.
Cai DJ, Aharoni D, Shuman T, Shobe J, Biane J, Song W, Wei B, Veshkini M, La-Vu M, Lou J,
Flores SE, Kim I, Sano Y, Zhou M, Baumgaertel K, Lavi A, Kamata M, Tuszynski M, Mayford
M, Golshani P, Silva AJ (2016). A shared neural ensemble links distinct contextual memories
encoded close in time. Nature 534(7605):115-118.
Callahan PM, Hutchings EJ, Kille NJ, Chapman JM, Terry AV Jr (2013). Positive allosteric
modulator of α7 nicotinic-acetylcholine receptors, PNU-120596 augments the effects of
donepezil on learning and memory in aged rodents and non-human primates. Neuropharmacol
67:201-212.
Camchong J, MacDonald AW, Nelson B, Bell C, Mueller BA, Specker S, Lim KO (2011). Frontal
hyper connectivity related to discounting and reversal learning in cocaine subjects. Biol
Psychiat 69:1117-1123.
Carey GJ, Billard W, Binch H 3rd, Cohen-Williams M, Crosby G, Grzelak M, Guzik H, Kozlowski JA,
Lowe DB, Pond AJ, Tedesco RP, Watkins RW, Coffin VL (2001). SCH 57790, a selective
muscarinic M(2) receptor antagonist, releases acetylcholine and produces cognitive
enhancement in laboratory animals. Eur J Pharmacol 431(2):189-200.
147 Carli M, Luschi R, Samanin R (1997). Dose-related impairment of spatial learning by
intrahippocampal scopolamine: antagonism by ondansetron, a 5-HT3 receptor antagonist.
Behav Brain Res 82(2):185-194.
Carmichael ST, Price JL (1995). Limbic connections of the orbital and medial prefrontal cortex in
macaque monkeys. J Comp Neurol 363(4):615-641.
Chakraborty S, Ouhaz Z, Mason S, Mitchell AS (2019). Macaque parvocellular mediodorsal
thalamus: dissociable contributions to learning and adaptive decision-making. Eur J Neurosci
49:1040-1054.
Charles DP, Browning PG, Gaffan D (2004). Entorhinal cortex contributes to object-in-place scene
memory. Eur J Neurosci 20(11):3157-3164.
Chavez-Noriega LE, Crona JH, Washburn MS, Urrutia A, Elliott KJ, Johnson EC (1997).
Pharmacological characterization of recombinant human neuronal nicotinic acetylcholine
receptors in hα2ß2, hα2ß4, hα3ß4, hα3ß4, hα4ß2, hα4ß4, and hα7 expressed in Xenopus
oocytes. J Pharmacol Exp Ther 280:346-356.
Chavez-Noriega LE, Gillespie A, Stauderman KA, Crona JH, Claeps BO, Elliott KJ, Reid RT, Rao
TS, Velicelebi G, Harpold MM, Johnson EC, Corey-Naeve J (2000). Characterization of the
recombinant human neuronal nicotinic acetylcholine receptors α3ß2 and α4ß2 stably
expressed in HEK293 cells. Neuropharmacol 39:2543-2560.
Chen H, Naya Y (2019). Forward processing of object-location association from the ventral stream
to medial temporal lobe in nonhuman primates. Cereb Cortex bhz164.
Chen X, Vieweg P, Wolbers T (2019). Computing distance information from landmarks and self-
motion cues - Differential contributions of anterior-lateral vs. posterior-medial entorhinal cortex
in humans. Neuroimage 202:116074.
Chudasama Y, Daniels TE, Gorrin DP, Rhodes SEV, Rudebeck PH, Murray EA (2013). The role of
the anterior cingulate cortex in choices based on reward value and reward contingency. Cereb
Cortex 23:2884-2898.
148 Chudasama Y, Izquierdo A, Murray EA (2004). Distinct contributions of the amygdala and
hippocampus to fear expression. Eur J Neurosci 30(12):2327-2337.
Chudasama Y, Wright K, Murray EA (2008). Hippocampal lesions in rhesus monkey disrupt
emotional responses but not reinforcer devaluation effects. Biol Psychiat 63:1084-1091.
Ciccocioppo R, Martin-Fardon R, Weiss F (2004). Stimuli associated with a single cocaine
experience elicit long-lasting cocaine-seeking. Nat Neurosci 7:495-496.
Clarke A, Tyler LK (2014). Object-specific semantic coding in human perirhinal cortex. J Neurosci
34(14):4766-4775.
Clarke PBS, Schwartz RD, Paul SM, Pert CB, Pert A (1985). Nicotinic binding in rat brain:
autoradiographic comparison of [3H]acetylcholine, [3H]nicotine and [125I]-alpha-bungarotoxin. J
Neurosci 5(5):1307-1315.
Clemens KJ, Castino MR, Cornish JL, Goodchild AK, Holmes NM (2014). Behavioral and neural
substrates of habit formation in rats intravenously self-administering nicotine.
Neuropsychopharmacol 39:2584-2593.
Crawford MLJ (1977). Central vision of man and macaque: cone and rod sensitivity. Brain Res
119(2):345-356.
Croxson PL, Kyriazis DA, Baxter MG (2011). Cholinergic modulation of a specific memory function
of prefrontal cortex. Nat Neurosci 14(12):1510-1512.
Cohen NJ, Squire LR (1980). Preserved learning and retention of pattern-analyzing skill in amnesia:
dissociation of knowing how and knowing that. Science 210:207-210.
Constantinescu AO, O’Reilly JX, Behrens TEJ (2016). Organizing conceptual knowledge in
humans with a gridlike code. Science 352:1464-1468.
Contestabile A (2011). The history of the cholinergic hypothesis. Behav Brain Res 221(2):334-340.
Corbit LH, Janak PH (2016). Habitual alcohol seeking: neural bases and possible relations to
alcohol use disorders. Alcohol Clin Exp Res 40:1380-1389.
Corbit LH, Nie H, Janak PH (2012). Habitual alcohol seeking: time course and the contributions of
subregions of the dorsal striatum. Biol Psychiat 72:389-395.
149 Corbit VL, Balleine BW (2003). The role of prelimbic cortex in instrumental conditioning. Behav
Brain Res 146:145-157.
Cordeau JP, Mahut H (1964). Some long-term effects of temporal lobe resections on auditory and
visual discrimination in monkeys. Brain 87:177-188.
Corkin S (1984). Lasting consequences of bilateral medial temporal lobectomy: Clinical course and
experimental findings in H.M. Sem Neurol 4(2):249-259.
Corkin S (2002). What’s new with the amnesiac patient H.M.? Nat Rev Neurosci 3(2):153-160.
Corkin S, Amaral DG, González RG, Johnson KA, Hyman BT (1997). H.M.’s medial temporal lobe
lesion: findings from magnetic resonance imaging. J Neurosci 17(10):3964-3979.
Correll RE, Scoville WB (1965a). Effects of medial temporal lesions on visual discrimination
performance. J Comp Physiol Psychol 60:175-181.
Correll RE, Scoville WB (1965b). Performance on delayed match following lesions of medial
temporal lobe structures. J Comp Physiol Psychol 60:360-367.
Cortés R, Probst A, Palacios JM (1987). Quantitative light microscope autoradiographic localization
of cholinergic muscarinic receptors in the human brain: forebrain. Neuroscience 20(1):65-107.
Cortés R, Probst A, Tobler HJ, Palacios JM (1986). Muscarinic cholinergic receptor subtypes in the
human brain. II. Quantitative autoradiographic studies. Brain Res 362(2):239-253.
Crafa D, Hawco C, Brodeur MB (2017). Heightened responses of the parahippocampal and
retrosplenial cortices during contextualized recognition of congruent objects. Front Behav
Neurosci 11:232.
Croxson PL, Kyriazis DA, Baxter MG (2011). Cholinergic modulation of a specific memory function
of prefrontal cortex. Nat Neurosci 14:1510-1512.
Curtis CE (2006). Prefrontal and parietal contributions to spatial working memory. Neuroscience
139(1):173-180.
Czurko A, Hirase H, Csicsvari J, Buzsaki G (1999). Sustained activation of hippocampal pyramidal
cells by ‘space clamping’ in a running wheel. Eur J Neurosci 11:344-352.
150 Danckert SL, Gati JS, Menon RS, Köhler S (2007). Perirhinal and hippocampal contributions to
visual recognition memory can be distinguished from those of occipito-temporal structures
based on conscious awareness of prior occurrence. Hippocampus 17(11):1081-1092.
Dasari S, Gulledge AT (2011). M1 and M4 receptors modulate hippocampal pyramidal neurons. J
Neurophysiol 105(2):779-792.
Delorme C, Salvador A, Valabrègue R, Roze E, Palminteri S, Vidailhet M, de Wit S, Robbins T,
Hartmann A, Worbe Y (2016). Enhanced habit formation in Gilles de la Tourette syndrome.
Brain 139(Pt 2):605-615.
DesJardin JT, Holmes AL, Forcelli PA, Cole CE, Gale JT, Wellman LL, Gale K, Malkova M (2013).
Defense-like behaviors evoked by pharmacological disinhibition of the superior colliculus in the
primate. J Neurosci 33(1):150-155.
Deuker L, Bellmund JL, Navarro Schröder T, Doeller CF (2016). An event map of memory space in
the hippocampus. eLife 5:e16534.
Devito JL (1980). Subcortical projections to the hippocampal formation in squirrel monkey (Saimiri
sciureus). Brain Res Bull 3:285-289. de Wit S, Barker RA, Dickinson AD, Cools R (2011). Habitual versus goal-directed action control in
Parkinson disease. J Cogn Neurosci 23:1218-1229. de Wit S, Corlett PR, Aitken MR, Dickinson A, Fletcher PC (2009). Differential engagement of the
ventromedial prefrontal cortex by goal-directed and habitual behavior toward food pictures in
humans. J Neurosci 29:11330-11338. de Wit S, Dickinson A (2009). Associative theories of goal-directed behavior: a case for animal-
human translational models. Psychol Res 73:463-476. de Wit S, Kindt M, Knot SL, Verhoeven AAC, Robbins TW, Gasull-Camos J, Evans M, Mirza H,
Gillan CM (2018). Shifting the balance between goals and habits: five failures in experimental
habit induction. J Exp Psychol Gen 147(7):1043-1065.
151 de Wit S, Niry D, Wariyar R, Aitken MR, Dickinson A (2007). Stimulus-outcome interactions during
instrumental discrimination learning by rats and humans. J Exp Psychol Anim Behav Process
33(1):1-11. de Wit S, Ridderinkhof KR, Fletcher PC, Dickinson A (2013). Resolution of outcome-induced
response conflict by humans after extended training. Psychol Res 77:780-793. de Wit S, Standing HR, Devito EE, Robinson OJ, Ridderinkhof KR, Robbins TW, Sahakian BJ
(2012). Reliance on habits at the expense of goal-directed control following dopamine
precursor depletion. Psychopharmacol 219:621-631.
Dezfouli A, Lingawi NW, Balleine BW (2014). Habits as action sequences: hierarchical action
control and changes in outcome value. Philos Trans R Soc Lond B Biol Sci
369(1655):20130482.
Diana RA (2017). Parahippocampal cortex processes the nonspatial context of an event. Cereb
Cortex 27:1808-1816.
Dickinson A, Balleine BW, Watt A, Gonzalez F, Boakes RA (1995). Motivational control after
extended instrumental training. Animal Learn Behav 23:197-206.
Dickinson A, Wood N, Smith JW (2002). Alcohol seeking by rats: action or habit? Q J Exp Psychol
B 55:331-348.
Di Georgio A, Gelao B, Caforio G, Romano R, Andriola I, D’Ambrosio E, Papazacharias A, Elifani
F, Lo Bianco L, Taurisano P, Fazio L, Popolizio T, Blasi G, Bertolino A (2012). Evidence that
hippocampal-parahippocampal dysfunction is related to genetic risk factor for schizophrenia.
Psychol Med 43:1661-1671.
Ding SL (2013). Comparative anatomy of the prosubiculum, subiculum, presubiculum,
postsubiculum, and parasubiculum in human, monkey, and rodent. J Comp Neurol 21(8):4145-
4162.
Dobkins KR, Thiele A, Albright TD (2000). Comparison of red-green equiluminance points in
humans and macaques: evidence for different L:M cone ratios between species. J Opt Soc Am
A Opt Image Sci Vis 17(3):545-556.
152 Doeller CF, Barry C, Burgess N (2010). Evidence for grid cells in a human memory network. Nature
463:657-661.
Doré FY, Thornton JA, White NM, Murray EA (1998). Selective hippocampal lesions yield nonspatial
memory impairments in rhesus monkeys. Hippocampus 8:323-329.
Dornan WA, McCampbell AR, Tinkler GP, Hickman LJ, Bannon AW, Decker MW, Gunther KL (1996).
Comparison of site-specific injections into the basal forebrain on water maze and radial arm
maze performance in the male rat after immunolesioning with 192 IgG saporin. Behav Brain
Res 82(1):93-101.
Dowell C, Olivera BM, Garrett JE, Staheli ST, Watkins M, Kuryatov A, Yoshikami D, Lindstrom JM,
McIntosh JM (2003). α-Conotoxin PIA is selective for α6-containing nicotinic acetylcholine
receptors. J Neurosci 23:8445-8452.
Drachman DA, Ommaya AK (1964). Memory and the hippocampal complex. Arch Neurol 10:411-
425.
Duttaroy A, Gomez J, Gan JW, Siddiqui N, Basile AS, Harman WD, Smith PI, Felder CC, Levey AI,
Weiss J (2002). Evaluation of muscarinic agonist-induced analgesia in muscarinic
acetylcholine receptor knockout mice. Mol Pharmacol 62:1084-1093.
Duvernoy HM (2005). Introduction. The Human Hippocampus, Third Edition. Berlin: Springer-
Verlag. 1.
Düzel E, Habib R, Rotte M, Guderian S, Tulving E, Heinze HJ (2003). Human hippocampal and
parahippocampal activity during visual associative recognition memory for spatial and
nonspatial stimulus configurations. J Neurosci 23(28):9439-9444.
Dybdal D, Forcelli PA, Dubach M, Oppedisano M, Holmes A, Malkova L, Gale K (2013). Topography
of dyskinesias and torticollis evoked by inhibition of substantia nigra pars reticulata. Mov
Discord 28(4):460-468.
Eacott MJ, Gaffan D, Murray EA (1994). Preserved recognition memory for small sets, and impaired
stimulus identification for large sets, following rhinal cortex ablations in monkeys. Eur J
Neurosci 6:1466-1478.
153 Eggers AE (2013a). A serotonin hypothesis of schizophrenia. Med Hypotheses 80(6):791-794.
Eggers AE (2013b). An explanation of why schizophrenia begins with excitotoxic damage to the
hippocampus. Med Hypotheses 81:1056-1058.
Eglen RM, Nahorski SR (2000). Muscarinic M5 receptors: a silent or emerging subtype? Br J
Pharmacol 130:13-21.
Eichenbaum H (2000). A cortical-hippocampal system for declarative memory. Nat Rev Neurosci
1(1):41-50.
Eichenbaum H (2014). Time cells in the hippocampus: a new dimension for mapping memories.
Nat Rev Neurosci 15(11):732-744.
Eichenbaum H (2017). On the integration of space, time, and memory. Neuron 95(5):1007-1018.
Eichenbaum H, Dudchenko P, Wood E, Shapiro M, Tanila Heikki (1999). The hippocampus,
memory, and place cells: is it spatial memory or a memory space? Neuron 23:209-226.
Eichenbaum H, Yonelinas AP, Ranganath C (2007). The medial temporal lobe and recognition
memory. Annu Rev Neurosci 30:123-152.
Eifuku S, Nishijo H, Kita T, Ono T (1995). Neuronal activity in the primate hippocampal formation
during a conditional association task based on the subject’s location. J Neurosci 15(7, Part
1):4952-4969.
Ekstrom AD, Kahana MJ, Caplan JB, Fields TA, Isham EA, Newman EL, Fried I (2003). Cellular
networks underlying human spatial navigation. Nature 425:184-188.
Elfgren C, van Westen D, Passant U, Larsson EM, Mannfolk P, Fransson P (2006). fMRI activity in
the medial temporal lobe during famous face processing. Neuroimage 30(2):609-616.
Elrod K, Buccafusco JJ, Jackson WJ (1988). Nicotine enhances delayed matching-to-sample
performance by primates. Life Sci 43(3):277-287.
Epstein R, Harris A, Stanley D, Kanwisher N (1999). The parahippocampal place area: recognition,
navigation, or encoding? Neuron 23:115-125.
Epstein R, Kanwisher N (1998). A cortical representation of the local visual environment. Nature
392(6676):598-601.
154 Erez J, Cusack R, Kendall W, Barense MD (2016). Conjunctive coding of complex features. Cereb
Cortex 26(5):2271-2282.
Erickson CA, Desimone R (1999). Responses of macaque perirhinal neurons during and after
visual stimuli association learning. J Neurosci 19:10404-10416.
Ersche KD, Gillan CM, Simon Jones P, Williams GB, Ward LHE, Luijten M, de Wit S, Sahakian BJ,
Bullmore ET, Robbins TW (2016). Carrots and sticks fail to change behavior in cocaine
addiction. Science 352(6292):1468-1471.
Ersche KD, Jones PS, Williams GB, Turton AJ, Robbins TW, Bullmore ET (2012). Abnormal brain
structure implicated in stimulant drug addiction. Science 335(6068):601-604.
Everitt BJ, Robbins TW (2005). Neural systems of reinforcement for drug addiction: from actions to
habits to compulsion. Nat Neurosci 8:1481-1489.
Ezzyat Y, Davachi L (2014). Similarity breeds proximity: pattern similarity within and across contexts
is related to later mnemonic judgments of temporal proximity. Neuron 81(5):1179-1189.
Fahy FL, Riches IP, Brown MW (1993). Neuronal activity related to visual recognition memory: long-
term memory and the encoding of recency and familiarity information in the primate anterior
and medial inferior temporal and rhinal cortex. Exp Brain Res 96:457-472.
Farlow MR, Cummings JL (2007). Effective pharmacologic management of Alzheimer’s disease.
Am J Med 120(5):388-397.
Felder CC, Bymaster FP, Ward J, Delapp N (2000). Therapeutic opportunities for muscarinic
receptors in the central nervous system. J Med Chem 43:4333-4353.
Flynn DD, Mash DC (1993). Distinct kinetic binding properties of N-[3H]-methylscopolamine afford
differential labeling and localization of M1, M2, and M3 muscarinic receptor subtypes in primate
brain. Synapse 14(4):283-296.
Forcelli PA, Palchik G, Leath T, DesJardin JT, Gale K, Malkova L (2014). Memory loss in a
nonnavigational spatial task after hippocampal inactivation in monkeys. Proc Natl Acad Sci
USA 111(11):4315-4320.
155 Frick KM, Kim JJ, Baxter MG (2004). Effects of complete immunotoxin lesions of the cholinergic
basal forebrain on fear conditioning and spatial learning. Hippocampus 14(2):244-254.
Fried I, MacDonald KA, Wilson CL (1997). Single neuron activity in human hippocampus and
amygdala during recognition of faces and objects. Neuron 18(5):753-765.
Friedman DP, Aggleton JP, Saunders RC (2002). Comparison of hippocampal, amygdala, and
perirhinal projections to the nucleus accumbens: combined anterograde and retrograde tracing
study in the macaque brain. J Comp Neurol 450(4):345-365.
Froudist-Walsh S, Browning PGF, Croxson PL, Murphy KL, Shamy JL, Veuthey TL, Wilson CRE,
Baxter MG (2018). The rhesus monkey hippocampus critically contributes to scene memory
retrieval, but not new learning. J Neurosci 38(36):7800-7808.
Funahashi S, Bruce CJ, Goldman-Rakic PS (1989). Mnemonic coding of visual space in the
monkey’s dorsolateral prefrontal cortex. J Neurophysiol 61(2):331-349.
Funahashi S, Bruce CJ, Goldman-Rakic (1990). Visuospatial coding in primate prefrontal neurons
revealed by oculomotor paradigms. J Neurophysiol 63(4):814-831.
Furuya Y, Matsumoto J, Hori E, Boas CV, Tran AH, Shimada Y, Ono T, Nishijo H (2014). Place-
related neuronal activity in the monkey parahippocampal gyrus and hippocampal formation
during virtual navigation. Hippocampus 24:113-130.
Fyhn M, Molden S, Witter MP, Moser EI, Moser MB (2004). Spatial representation in the entorhinal
cortex. Science 305(5688):1258-1264.
Gaffan D (1974). Recognition impaired and association intact in the memory of monkeys after
transection of the fornix. J Comp Physiol Psychol 86:1100-1109.
Gaffan D (1994a). Dissociated effects of perirhinal cortex ablation, fornix transection and
amygdalectomy: evidence for multiple memory systems in the primate temporal lobe. Exp Brain
Res 99:411-422.
Gaffan D (1994b). Role of the amygdala in picture discrimination learning with 24 hr intertrial
intervals. Exp Brain Res 99:411-422.
156 Gaffan D (1998). Idiothetic input into object-place configuration as the contribution to memory of
the monkey and human hippocampus: a review. Exp Brain Res 123(1-2):201-209.
Gaffan D (2002). Against memory systems. Philos Trans R Soc Lond B Biol Sci 357:1111-1121.
Gaffan D, Murray EA (1990). Amygdalar interaction with the mediodorsal nucleus of the thalamus
and the ventromedial prefrontal cortex in stimulus-reward associative learning in the monkey.
J Neurosci 10:3479-3493.
Gaffan D, Murray EA (1992). Monkeys (Macaca fascicularis) with rhinal cortex ablations succeed
in object discrimination learning despite 24-hr intertrial intervals. Behav Neurosci 106(1):30-38.
Gardiner JM, Parkin AJ (1990). Attention and recollective experience in recognition memory. Mem
Cogn 18(6):579-583.
Garvert MM, Dolan RJ, Behrens TE (2017). A map of abstract relational knowledge in the human
hippocampal-entorhinal cortex. eLife 6:e17086.
Georges-François P, Rolls ET, Robertson RG (1999). Spatial view cells in the primate
hippocampus: allocentric view not head direction or eye position or place. Cereb Cortex 9:197-
212.
Ghazanfar AA, Santos LR (2004). Primate brains in the wild: the sensory bases for social
interactions. Nat Rev Neurosci 5:603-616.
Gillan GM, Apergis-Schoute AM, Morein-Zamir S, Urcelay GP, Sule A, Fineberg NA, Sahakian BJ,
Robbins TW (2015). Functional neuroimaging of avoidance habits in OCD. Am J Psychiat
172(3):284-293.
Gillan CM, Morein-Zamir S, Urcelay GP, Sule A, Voon V, Apergis-Schoute AM, Fineberg NA,
Sahakian BJ, Robbins TW (2014). Enhanced avoidance habits in obsessive-compulsive
disorder. Biol Psychiat 75(8):631-638.
Gillan CM, Papmeyer M, Morein-Zamir S, Sahakian BJ, Fineberg NA, Robbins TW, de Wit S (2011).
Disruption in the balance between goal-directed behavior and habit learning in obsessive-
compulsive disorder. Am J Psychiat 168(7):718-726.
157 Gillan CM, Robbins TW (2014). Goal-directed learning and obsessive-compulsive disorder. Philos
Trans R Soc Lond B Biol Sci 369(1655):20130475.
Goh JO, Siong SC, Park D, Gutchess A, Hebrank A, Chee MW (2004). Cortical areas involved in
object, background, and object-background processing revealed with functional magnetic
resonance adaptation. J Neurosci 24(45):10223-10228.
Góis ZHTD, Tort ABL (2018). Characterizing speed cells in the rat hippocampus. Cell Rep
25(7):1872-1884.
Goldstein RZ, Volkow ND (2011). Dysfunction of the prefrontal cortex in addiction: neuroimaging
findings and clinical implications. Nat Rev Neurosci 12(11):652-669.
Goodale MA, Milner AD (1992). Separate visual pathways for perception and action. Trends
Neurosci 15(1):20-25.
Gosselin N, Samson S, Adolphs R, Noulhiane M, Roy M, Hasboun D, Baulac M, Peretz I (2006).
Emotional responses to unpleasant music correlates with damage to the parahippocampal
cortex. Brain 129:2585-2592.
Gothard KM, Skaggs WE, Moore KM, McNaughton BL (1996a). Binding of hippocampal CA1 neural
activity to multiple reference frames in a landmark-based navigation task. J Neurosci 16:823-
835.
Gothard KM, Skaggs WE, McNaughton BL (1996b). Dynamics of mismatch correction in the
hippocampal ensemble code for space: interaction between path integration and environmental
cues. J Neurosci 16:8027-8040.
Gottfried JA, O’Doherty J, Dolan RJ (2003). Encoding predictive reward value in human amygdala
and orbitofrontal cortex. Science 301(5636):1104-1107.
Goulet S, Doré FY, Murray EA (1998). Aspiration lesions of the amygdala disrupt the rhinal
corticothalamic projection system in rhesus monkeys. Exp Brain Res 119:131-140.
Goulet S, Murray EA (2001). Neural substrates of cross modal association memory in monkeys:
the amygdala versus the anterior rhinal cortex. Behav Neurosci 115:271-284.
158 Gourley SL, Olevska A, Gordon J, Taylor JR (2013). Cytoskeletal determinants of stimulus-
response habits. J Neurosci 33:11811-11816.
Graybiel AM, Rauch SL (2000). Toward a neurobiology of obsessive-compulsive disorder. Neuron
28(2):343-347.
Greene AJ, Gross WL, Elsinger CL, Rao SM (2006). An fMRI analysis of the human hippocampus:
Inference, context, and task awareness. J Cogn Neurosci 18(7):1156-1173.
Gremel CM, Lovinger DM (2016). Associative and sensorimotor cortico-basal ganglia circuit roles
in effects of abused drugs. Genes Brain Behav 16(1):71-85.
Gruber AJ, McDonald RJ (2012). Context, emotion, and the strategic pursuit of goals: interactions
among multiple brain systems controlling motivated behavior. Front Behav Neurosci 6:50.
Guenzel FM, Wolf OT, Schwabe L (2014a). Sex differences in stress effects on response and
spatial memory formation. Neurobiol Learn Mem 109:46-55.
Guenzel FM, Wolf OT, Schwabe L (2014b). Glucocorticoids boost stimulus-response memory
formation in humans. Psychoneuroendocrinol 45:21-30.
Hafting T, Fyhn M, Molden S, Moser SB, Moser EI (2005). Microstructure of a spatial map in the
entorhinal cortex. Neuropsychologia 48:831-853.
Hajos M, Hurst RS, Hoffmann WE, Krause M, Wall TM, Higdon NR, Groppi VE (2005). The selective
α7 nicotinic acetylcholine receptor agonist PNU-282987 enhances GABAergic synaptic activity
in brain slices and restores auditory gating deficits in anesthetized rats. J Pharmacol Exp Ther
312:1213-1222.
Hales JB, Schlesier MI, Leutgeb JK, Squire LR, Leutgeb S, Clark RE (2014). Medial entorhinal
cortex lesions only partially disrupt hippocampal place cells and hippocampus-dependent place
memory. Cell Rep 9(3):893-901.
Haley GE, Kroenke C, Schwartz D, Kohama SG, Urbanski HF, Raber J (2011). Hippocampal M1
receptor function associated with spatial learning and memory in aged female rhesus
macaques. Age (Dordr) 33(3):309-320.
Hamilton GV (1911). A study of trial and error reactions in mammals. J Anim Behav 1(1):33-66.
159 Hampson RE, Pons TP, Stanford TR, Deadwyler SA (2004). Categorization in the monkey
hippocampus: a possible mechanism for encoding information into memory. Proc Natl Acad Sci
USA 101(9):3184-3189.
Hampton RR, Hampstead BM, Murray EA (2004). Selective hippocampal damage in rhesus
monkeys impairs spatial memory in an open-field test. Hippocampus 14:808-818.
Hampton RR, Murray EA (2002). Learning of discriminations is impaired, but generalization to
altered views is intact, in monkeys (Macaca mulatta) with perirhinal cortex removal. Behav
Neurosci 116:363-377.
Han ZY, Le Novère N, Zoli M, Hill JA Jr, Champtiaux N, Changeux JP (2000). Localization of nAChR
subunit mRNAs in the brain of Macaca mulatta. Eur J Neurosci 12(10):3664-3674.
Han ZY, Zoli M, Cardona A, Bourgeois JP, Changeux JP, Le Novére N (2003). Localization of
[3H]nicotine, [3H]cytisine, [3H]epibatidine, and [125I]α-bungarotoxin binding sites in the brain of
Macaca mulatta. J Comp Neurol 461:49-60.
Hargreaves EL, Mattfeld AT, Stark CE, Suzuki WA (2012). Conserved fMRI and LFP signals during
new associative learning in the human and macaque monkey medial temporal lobe. Neuron
74:743-752.
Hargreaves EL, Rao G, Lee I, Knierim JJ (2005). Major dissociation between medial and lateral
entorhinal input to dorsal hippocampus. Science 308:1792-1794.
Harlow HF, Bromer JA (1938). A test apparatus for monkeys. Psychol Rec 2:434-436.
Hartley T, Maguire EA, Spiers HJ, Burgess N (2003). The well-worn route and the path less
traveled: distinct neural bases of route following and way finding in humans. Neuron 37(5):877-
888.
Hartmann MN, Hager OM, Reimann AV, Chumbley JR, Kirschner M, Seifritz E, Tobler PN, Kaiser
S (2015). Apathy but not diminished expression in schizophrenia is associated with discounting
of monetary rewards by physical effort. Schizophr Bull 41(2):503-512.
Hassabis D, Kumaran D, Vann SD, Maguire EA (2007). Patients with hippocampal amnesia cannot
imagine new experiences. Proc Natl Acad Sci USA 104(5):1726-1731.
160 Hasselmo ME, Stern CE (2018). A network model of behavioral performance in a rule learning task.
Phil Trans R Soc B 373:20170275.
Hayes SM, Ryan L, Schnyer DM, Nadel L (2004). An fMRI study of episodic memory: retrieval of
object, spatial, and temporal information. Behav Neurosci 118(5):885-896.
Hazama Y, Tamura R (2019). Effects of self-locomotion on the activity of place cells in the
hippocampus of a freely behaving monkey. Neurosci Lett 701:32-37.
He Q, Brown TI (2019). Environmental barriers disrupt grid-like representations in humans during
navigation. Curr Biol 29(16):2718-2722.
Hellström-Lindahl E, Mousavi M, Zhang X, Ravid R, Nordberg A (1999). Regional distribution of
nicotinic receptor subunit mRNAs in human brain: comparison between Alzheimer and normal
brain. Brain Res Mol Brain Res 66(1):94-103.
Heyes C, Dickinson A (1990). The intentionality of animal action. Mind Lang 5:87-103.
Higuchi S, Miyashita Y (1996). Formation of mnemonic neuronal responses to visual paired
associates in inferotemporal cortex is impaired by perirhinal and entorhinal cortex lesions. Proc
Natl Acad Sci 93:739-743.
Hillmer AT, Li S, Zheng MQ, Scheunemann M, Lin SF, Nabulsi N, Holden D, Pracitto R, Labore D,
Ropchan J, Teodoro R, Deuther-Conrad W, Esterlis I, Cosgrove KP, Brust P, Carson RE, Huang
Y (2017). PET imaging of α7 nicotinic acetylcholine receptors: a comparative study of
[18F]ASEM and [18F]DBT-10 in nonhuman primates, and further evaluation of [18F]ASEM in
humans. Eur J Nucl Med Mol Imaging 44(6):1042-1050.
Hogarth L, Chase HW (2011). Parallel goal-directed and habitual control of human drug-seeking:
implications for dependence vulnerability. J Exp Psychol Anim Behav Process 37(3):261-276.
Holdstock JS (2005). The role of the human medial temporal lobe in object recognition and object
discrimination. Q J Exp Psychol 58B:326-339.
Holdstock JS, Gutnikov SA, Gaffan D, Mayes AR (2000). Perceptual and mnemonic matching-to-
sample in humans: contributions of the hippocampus, perirhinal, and other medial temporal
lobe cortices. Cortex 36:301-322.
161 Holdstock JS, Mayes AR, Roberts N, Cezayirli E, Isaac CL, O’Reilly RC, Norman KA (2002). Under
what conditions is recognition spared relative to recall after selective hippocampal damage in
humans? Hippocampus 12(3):341-351.
Hölscher C, Rolls ET, Xiang J (2003). Perirhinal cortex neuronal activity related to long-term
familiarity memory in the macaque. Eur J Neurosci 18:2037-2046.
Hopper S, Pavey GM, Gogos A, Dean B (2019). Widespread changes in positive allosteric
modulation of the muscarinic M1 receptor in some participants with schizophrenia. Int J
Neuropsychopharmacol 22(10):640-650.
Horel JA, Pytko-Joiner DE, Voytko ML, Salsbury K (1987). The performance of visual tasks while
segments of the inferotemporal cortex are suppressed by cold. Behav Brain Res 23:29-42.
Hori E, Nishio Y, Kazui K, Tabuchi E, Sasaki K, Endo S, Ono T, Nishijo H (2005). Place-related
neural responses in the monkey hippocampal formation in a virtual space. Hippocampus
15(8):991-996.
Hori E, Tabuchi E, Matsumura N, Tamura R, Eifuku S, Endo S, Nishijo H, Ono T (2003).
Representation of place by monkey hippocampal neurons in real and virtual translocation.
Hippocampus 13:190-196.
Horner AJ, Bisby JA, Zotow E, Bush D, Burgess N (2016). Grid-like processing of imagined
navigation. Curr Biol 26:842-847.
Hoscheidt SM, Nadel L, Payne J, Ryan L (2010). Hippocampal activation during retrieval of spatial
context from episodic and semantic memory. Behav Brain Res 212(2):121-132.
Howard LR, Kumaran D, Ólafsdóttit HF, Spiers HJ (2011). Double dissociation between
hippocampal and parahippocampal responses to object-background context and scene novelty.
J Neurosci 31(14):5253-5261.
Hsieh LT, Fruber MJ, Jenkins LJ, Ranganath C (2014). Hippocampal activity patterns carry
information about objects in temporal context. Neuron 81(5):1165-1178.
Hulme EC, Birdsall NJM, Buckley NJ (1990). Muscarinic receptor subtypes. Ann Rev Pharmacol
Toxicol 30:633-673.
162 Iaria G, Petrides M, Dagher A, Pike B, Bohbot VD (2003). Cognitive strategies dependent on the
hippocampus and caudate nucleus in human navigation: variability and change with practice.
J Neurosci 23(13):5945-5952.
Ikkai A, Curtis CE (2011). Common neural mechanisms supporting spatial working memory,
attention and motor intention. Neuropsychologia 49(6):1428-1434.
Inhoff MC, Heusser AC, Tambini A, Martin CB, O’Neil EB, Köhler S, Meager MR, Blackmon K,
Vazquez B, Devinsky O, Davachi L (2019). Understanding perirhinal contributions to perception
and memory: Evidence through the lens of selective perirhinal damage. Neuropsychologia
124:9-18.
Insausti R, Amaral DG (2008). Entorhinal cortex of the monkey: IV. Topographical and laminar
organization of cortical afferents. J Comp Neurol 509(6):608-641.
Insausti R, Annese J, Amaral DG, Squire LR (2013). Human amnesia and the medial temporal lobe
illuminated by neuropsychological and neurohistological findings for patient E.P. Proc Natl Acad
Sci USA 110(21):E1953-1962.
Insausti R, Amaral DG, Cowan WM (1987). The entorhinal cortex of the monkey. II. Cortical
afferents. J Comp Neurol 264:356-395.
Izquierdo A, Murray EA (2004). Combined unilateral lesions of the amygdala and orbital prefrontal
cortex impair affective processing in rhesus monkeys. J Neurophysiol 91:2023-2039.
Izquierdo A, Murray EA (2007). Selective bilateral amygdala lesions in rhesus monkeys fail to
disrupt object reversal learning. J Neurosci 27(5):1054-1062.
Izquierdo A, Murray EA (2010). Functional interaction of medial mediodorsal thalamic nucleus but
not nucleus accumbens with amygdala and orbital prefrontal cortex is essential for adaptive
response selection after reinforcer devaluation. J Neurosci 30(2):661-669.
Izquierdo A, Suda RK, Murray EA (2004). Bilateral orbital prefrontal cortex lesions in rhesus
monkeys disrupt choices guided by both reward value and reward contingency. J Neurosci
24(34):7540-7548.
163 Jacobs GH, Deegan JF (1997). Spectral sensitivity of macaque monkeys measured with ERG
flicker photometry. Vis Neurosci 14(5):921-928.
Jacobs J, Kahana MJ, Ekstrom AD, Mollison MV, Fried I (2010). A sense of direction in human
entorhinal cortex. Proc Natl Acad Sci USA 107:6487-6492.
Jacobs J, Weidemann CT, Miller JF, Solway A, Burke JF, Wei XX, Suthana N, Sperling MR, Sharan
AD, Fried I, Kahana MJ (2013). Direct recordings of grid-like neuronal activity in human spatial
navigation. Nat Neurosci 16(9):1188-1190.
Jacoby LL, Dallas M (1981). On the relationship between autobiographical memory and perceptual
learning. J Exp Psychol Gen 110(3):306-340.
James A, von Oertzen TJ, Norbury R, Huppertz HJ, Brandt KR (2018). Left entorhinal cortex and
object recognition. Neuroreport 29(5):363-367.
Jarrard LE (1989). On the use of ibotenic acid to lesion selectively different components of the
hippocampal formation. J Neurosci Methods 29(3):251-259.
Jensen AA, Frølund B, Liljefors T, Krogsgaard-Larsen P (2005). Neuronal nicotinic acetylcholine
receptors: structural revelations, target identifications, and therapeutic inspirations. J Med
Chem 48(15):4705-4745.
Jonasson Z, Cahill JFX, Tobey RE, Baxter MG (2004). Sexually dimorphic effects of hippocampal
cholinergic deafferentation in rats. Eur J Neurosci 20:3041-3053.
Jones AL, Mowry BJ, McLean DE, Mantzioris BX, Pender MP, Greer JM (2014). Elevated levels of
autoantibodies targeting the M1 muscarinic acetylcholine receptor and neurofilament medium
in sera from subgroups of patients with schizophrenia. J Neuroimmunol 269(1-2):68-75.
Jones EG, Powell TPS (1970). An anatomical study of converging sensory pathways within the
cerebral cortex of the monkey. Brain 93:783-820.
Julian JB, Keinath AT, Frazzetta G, Epstein RA (2018). Human entorhinal cortex represents visual
space using a boundary-anchored grid. Nat Neurosci 21(2):191-194.
Jutras MJ, Buffalo EA (2010). Recognition memory signals in the macaque hippocampus. Proc Natl
Acad Sci USA 107(1):401-406.
164 Kalin NH, Larson C, Shelton SE, Davidson RJ (1998). Asymmetric frontal brain activity, cortisol,
and behavior associated with fearful temperament in rhesus monkeys. Behav Neurosci
112(2):286-292.
Kapur N, Brooks DJ (1999). Temporally-specific retrograde amnesia in two cases of discrete
bilateral hippocampal pathology. Hippocampus 9:247-254.
Kazama AM, Davis M, Bachevalier J (2014). Neonatal lesions of orbital frontal areas 11/13 in
monkeys alter goal-directed behavior but spare fear conditioning and safety signal learning.
Front Neurosci 8:37.
Kazama AM, Bachevalier J (2013). Effects of selective neonatal amygdala damage on concurrent
discrimination learning and reinforcer devaluation in monkeys. J Psychol Psychother Suppl 7:5.
Keramati M, Gutkin B (2013). Imbalanced decision hierarchy in addicts emerging from drug-
hijacked dopamine spiraling circuit. PLoS One 8(4):e61489.
Killian NJ, Jutras MJ, Buffalo EA (2012). A map of visual space in the primate entorhinal cortex.
Nature 491:761-764.
Killian NJ, Potter SM, Buffalo EA (2015). Saccade direction encoding in the primate entorhinal
cortex during visual exploration. Proc Natl Acad Sci USA 112(51):155743-15748.
Kim JS, Levin ED (1996). Nicotinic, muscarinic and dopaminergic actions in the ventral
hippocampus and the nucleus accumbens: effects on spatial working memory in rats. Brain
Res 725:231-240.
Kim M, Maguire EA (2018). Hippocampus, retrosplenial and parahippocampal cortices encode
multicompartment 3D space in a hierarchical manner. Cereb Cortex 28:1898-1909.
Kirby BP, Rawlins JNP (2003). The role of the septo-hippocampal cholinergic projection in T-maze
rewarded alternation. Behav Brain Res 143(1):41-48.
Kirby KN, Petry NM, Bickel WK (1999). Heroin addicts have higher discount rates for delayed
rewards than non-drug-using controls. J Exp Psychol Gen 128:78-87.
165 Kirschner M, Hager OM, Bischof M, Hartmann MN, Kluge A, Seifritz E, Tobler PN, Kaiser S (2016).
Ventral striatal hypoactivation is associated with apathy but not diminished expression in
patients with schizophrenia. J Psychiat Neurosci 41(3):152-161.
Knowlton BJ, Squire LR (1995). Remembering and knowing: Two different expressions of
declarative memory. Learn Mem Cogn 21(3):699-710.
Kolarik BS, Shahlaie K, Hassan A, Borders AA, Kaufman KC, Gurkor G, Yonelinas AP, Ekstrom AD
(2016). Impairments in precision, rather than spatial strategy, characterize performance on the
virtual water maze: a case study. Neuropsychologia 80:90-101.
Köhler S, Crane J, Milner B (2002). Differential contributions of the parahippocampal place area
and the anterior hippocampus to human memory for scenes. Hippocampus 12:718-723.
Köhler S, Danckert S, Gati JS, Menon RS (2005). Novelty responses to relational and non-relational
information in the hippocampus and the parahippocampal region: a comparison based on
event-related fMRI. Hippocampus 15(6):763-774.
Komorowski RW, Garcia CG, Wilson A, Hattori S, Howard MW, Eichenbaum H (2013). Ventral
hippocampal neurons are shaped by experience to represent behaviorally relevant contexts. J
Neurosci 33(18):8079-8087.
Komorowski RW, Manns JR, Eichenbaum H (2009). Robust conjunctive item-place coding by
hippocampal neurons parallels learning what happens where. J Neurosci 29:9918-9929.
Konishi K, Etchamendy N, Roy S, Marighetto A, Rajah N, Bohbot VD (2013). Decreased functional
magnetic resonance imaging activity in the hippocampus in favor of the caudate nucleus in
older adults tested in a virtual navigation task. Hippocampus 23(11):1005-1014.
Kopald BE, Mirra KM, Egan MF, Weinberger DR, Goldberg TE (2012). Magnitude of impact of
executive functioning and IQ on episodic memory in schizophrenia. Biol Psychiat 71:545-551.
Kosel KC, Van Hoesen GW, Rosene DL (1982). Non-hippocampal cortical projections from the
entorhinal cortex in the rat and rhesus monkey. Brain Res 244:201-213.
166 Kraus BJ, Brandon MP, Robinson RJ 2nd, Connerney MA, Hasselmo ME, Eichenbaum H (2015).
During running in place, grid cells integrate elapsed time and distance run. Neuron 88(3):578-
589.
Krayniak PF, Siegel A, Meibach RC, Fruchtman D, Scrimenti M (1979). Origin of the fornix system
in the squirrel monkey. Brain Res 160:401-411.
Kreiman G, Koch C, Fried I (2000). Category-specific visual responses of single neurons in the
human medial temporal lobe. Nat Neurosci 3(9):946-953.
Kropff E, Carmichael JE, Moser MB, Moser EI (2015). Speed cells in the medial entorhinal cortex.
Neuron 523(7561):419-424.
Ku SP, Tolias AS, Logothetis NK, Goense J (2011). fMRI of the face-processing network in the
ventral temporal lobe of awake and anesthetized macaques. Neuron 79(2):352-362.
Kumaran D, Maguire EA (2006). The dynamics of hippocampal activation during encoding of
overlapping sequences. Neuron 49(9):617-629.
Kunz L, Navarro Schröder T, Lee H, Montag C, Lachmann B, Sariyska R, Reuter M, Stirnberg R,
Stöcker T, Messing-Floeter PC, Fell J, Doeller CF, Axmacher N (2015). Reduced grid-cell-like
representations inn adults at genetic risk for Alzheimer’s disease. Science 350(6259):430-433.
Kuryatov A, Olale F, Cooper J, Choi C, Lindstrom J (2000). Human α6 AChR subtypes: subunit
composition, assembly, and pharmacological responses. Neuropharmacol 39:2570-2590.
Kyle CT, Smuda DN, Hassan AS, Ekstrom AD (2015). Roles of human hippocampal subfields in
retrieval of spatial and temporal context. Behav Brain Res 278:549-558.
Landes AM, Sperry SD, Strauss ME, Geldmacher DS (2005). Apathy in Alzheimer’s disease. J Am
Ger Soc 49(12):1700-1707.
Lavenex P, Amaral DG (2000). Hippocampal-neurocortical interaction: a hierarchy of associativity.
Hippocampus 10:420-430.
Lavenex PB, Amaral DG, Lavenex P (2006). Hippocampal lesion prevents spatial relational
learning in adult macaque monkeys. J Neurosci 26:4546-4558.
167 Lavenex P, Suzuki WA, Amaral DG (2002). Perirhinal and parahippocampal cortices of the
macaque monkey: projections to the neocortex. J Comp Neurol 447(4):394-420.
Lavenex P, Suzuki WA, Amaral DG (2004). Perirhinal and parahippocampal cortices of the
macaque monkey: Intrinsic projections and interconnections. J Comp Neurol 472(3):371-394.
Leavitt VM, Goldberg TE (2009). Episodic memory in schizophrenia. Neuropsychol Rev 19:312-
323.
Lee ACH, Berense MD, Graham KS (2005a). The contribution of the human medial temporal lobe
to perception: bridging the gap between animal and human studies. Q J Exp Psychol 58B:300-
325.
Lee ACH, Buckley MJ, Pegman SJ, Spiers H, Scahill VL, Gaffan D, Bussey TJ, Davies RR, Kapur
Narinder, Hodges JR, Graham KS (2005b). Specialization in the medial temporal lobe for
processing objects and scenes. Hippocampus 15(6):782-797.
Lee ACH, Bussey TJ, Murray EA, Saksida LM, Epstein RA, Kapur N, Hodges JR, Graham KS
(2005c). Perceptual deficits in amnesia: challenging the medial temporal lobe ‘mnemonic’ view.
Neuropsychologia 43(1):1-11.
Lee ACH, Rudebeck SR (2010). Investigating the interaction between spatial perception and
working memory in the human medial temporal lobe. J Cogn Neurosci 22(12):2823-2835.
Leichnetz GR, Astruc J (1975). Efferent connections of the orbitofrontal cortex in the marmoset
(Saguinus oedipus). Brain Res 84(2):169-180.
Leichnetz GR, Astruc J (1976). The squirrel monkey entorhinal cortex: Architecture and medial
frontal afferents. Brain Res Bull 1(4):351-358.
Lelos MJ, Thomas RS, Kidd EJ, Good MA (2011). Outcome-specific satiety reveals a deficit in
context-outcome, but not stimulus- or action-outcome, associations in aged Tg2576 mice.
Behav Neurosci 125(3):412-425.
Leonard BW, Amaral DG, Squire LR, Zola-Morgan S (1995). Transient memory impairment in
monkeys with bilateral lesions of the entorhinal cortex. J Neurosci 15:5637-5659.
168 Lepage M, Sergerie K, Pelletier M, Harvey PO (2007). Episodic memory bias and symptoms of
schizophrenia. Can J Psychiat 52:702-709.
Levey AI (1996). Muscarinic acetylcholine receptor expression in memory circuits: implications for
treatment of Alzheimer’s disease. Proc Natl Acad Sci USA 93(24):13541-13546.
Levin E, Bowman R (1986). Scopolamine effects on Hamilton search task performance in monkeys.
Pharmacol Biochem Behav 24(4):829-821.
Levy DA, Shrager Y, Squire LR (2005). Intact visual discrimination of complex and feature-
ambiguous stimuli in the absence of perirhinal cortex. Learn Mem 12(1):61-66.
Li M, Lu S, Zhong N (2016a). The parahippocampal cortex mediates contextual associative
memory: evidence from an fMRI study. Biomed Res Int 2016:9860604.
Li X, Thermenos HW, Wu Z, Momura Y, Wu K, Keshavan M, Seidman L, DeLisi LE (2016b).
Abnormal interactions of verbal- and spatial-memory networks in young people at familial high-
risk for schizophrenia. Schizophr Res 176(2-3):100-105.
Lipton DM, Gonzales BJ, Citri A (2019). Dorsal striatal circuits for habits, compulsions and
addictions. Front Syst Neurosci 13:28.
Liu AKL, Lim EJ, Ahmed I, Chang RCC, Pearce RKB, Gentleman SM (2018). Review: Revisiting
the human cholinergic nucleus of the diagonal band of Broca. Neuropathol Appl Neurobiol
44(7):647-662.
Liu Z, Richmond BJ (2000). Response differences in monkey TE and perirhinal cortex: stimulus
association related to reward schedules. J Neurophysiol 83:1677-1692.
Lopez MF, Becker HC, Chandler LJ (2014). Repeated episodes of chronic intermittent ethanol
promote insensitivity to devaluation of the reinforcing effect of ethanol. Alcohol 48:639-645.
Loughlin A, Funk D, Coen K, Lê AD (2017). Habitual nicotine-seeking in rats following limited
training. Psychopharmacol (Berl) 234(17):2619-2629.
Lucantonio F, Takahashi YK, Hoffman AF, Chang CY, Bali-Chaudhary S, Shaham Y, Lupica CR,
Schoenbaum G (2014). Orbitofrontal activation restores insight lost after cocaine use. Nat
Neurosci 17:1092-1099.
169 Ludvig N, Tang HM, Gohil BC, Botero JM (2004). Detecting location-specific neuronal firing rate
increases in the hippocampus of freely-moving monkeys. Brain Res 1014(1-2):97-109.
Luque D, Beesley T, Morris RW, Jack BN, Griffiths O, Whitford TJ, Le Pelley ME (2017). Goal-
directed and habit-like modulations of stimulus processing during reinforcement learning. J
Neurosci 37(11):3009-3017.
MacDonald CJ, Lepage KQ, Eden UT, Eichenbaum H (2011). Hippocampal “time cells” bridge the
gap in memory for discontiguous events. Neuron 71(4):737-749.
Machado CJ, Bachevalier J (2007). The effects of selective amygdala, orbital frontal cortex or
hippocampal formation lesions on reward assessment in nonhuman primates. Eur J Neurosci
25:2885-2904.
Madden GJ, Petry NM, Badger GJ, Bickel WK (1997). Impulsive and self-control choices in opioid-
dependent patients and non-drug-using control participants: drug and monetary rewards. Exp
Clin Psychopharmacol 5:256-262.
Maguire EA, Frackowiak RSJ, Frith CD (1996). Learning to find your way: a role for the human
hippocampal formation. Proc R Soc Lond B 263:1745-1750.
Mahut H, Cordeau JP (1963). Spatial reversal deficit in monkeys with amygdalo-hippocampal
ablations. Exp Neurol 7(5):426-434.
Mahut H, Moss M, Zola-Morgan S (1981). Retention deficits after combined amygdalo-hippocampal
and selective hippocampal resections in the monkey. Neuropsychologia 19(2):201-225.
Mahut H, Zola-Morgan S, Moss M (1982). Hippocampal resections impair associative learning and
recognition memory in the monkey. J Neurosci 2(9):1214-1229.
Malamut BL, Saunders RC, Mishkin M (1984). Monkeys with combined amygdalo-hippocampal
lesions succeed in object discrimination learning despite 24-hour intertrial intervals. Behav
Neurosci 98(5):759-769.
Malkova L, Bachevalier J, Mishkin M, Saunders RC (2001). Neurotoxic lesions of perirhinal cortex
impair visual recognition memory in rhesus monkeys. Neuroreport 12(9):1913-1917.
170 Malkova L, Forcelli PA, Wellman LL, Dybdal D, Dubach MF, Gale K (2015). Blockade of
glutamatergic transmission in perirhinal cortex impairs object recognition memory in macaques.
J Neurosci 35(12):5043-5050.
Malkova L, Gaffan D, Murray EA (1997). Excitotoxic lesions of the amygdala fail to produce
impairment in visual learning for auditory secondary reinforcement but interfere with reinforcer
devaluation effects in rhesus monkeys. J Neurosci 17(15):6011-6020.
Malkova L, Mishkin M (2003). One-trial memory for object-place associations after separate lesions
of hippocampus and posterior parahippocampal region in the monkey. J Neurosci 23:1956-
1965.
Mamaligas AA, Ford CP (2016). Spontaneous synaptic activation of muscarinic receptors by striata
cholinergic neuron firing. Neuron 91:574-586.
Mandler G (1980). Recognizing: The judgment of previous occurrence. Psychol Rev 87(3):252-271.
Mangieri RA, Cofresí RU, Gonzales RA (2012). Ethanol seeking by Long Evans rats is not always
a goal-directed behavior. PLoS One 7:1-13.
Mankin EA, Sparks FT, Slayyeh B, Sutherland RJ, Leutgeb S, Leutgeb JK (2012). Neuronal code
for extended time in the hippocampus. Proc Natl Acad Sci USA 109(47):19462-19467.
Manns JR, Hopkins RO, Reed JM, Kitchener EG, Squire LR (2003). Recognition memory and the
human hippocampus. Neuron 37(1):171-180.
Manns JR, Howard MW, Eichenbaum H (2007). Gradual changes in hippocampal activity support
remembering the order of events. Neuron 56(3):530-540.
Martin MM, Wallace DG (2007). Selective hippocampal cholinergic deafferentation impairs self-
movement cue use during a food hoarding task. Behav Brain Res 1(1):78-86.
Martin-Elkins CL, Horel JA (1992). Cortical afferents to behaviorally defined regions of the inferior
temporal and parahippocampal gyro as demonstrated by WGA-HRP. J Comp Neurol
321(2):177-192.
Mash DC, White WF, Mesulam MM (1988). Distribution of muscarinic receptor subtypes within
architecture subregions of the primate cerebral cortex. J Comp Neurol 278(2):265-274.
171 Matsumura N, Nishijo H, Tamura R, Eifuku S, Endo S, Ono T (1999). Spatial- and task-dependent
neuronal responses during real and virtual translocation in the monkey hippocampal formation.
J Neurosci 19:2381-2393.
Mau W, Sullivan DW, Kinski NR, Hasselmo ME, Howard MW, Eichenbaum H (2018). The same
hippocampal CA1 population simultaneously codes temporal information over multiple
timescales. Curr Biol 28(10):1499-1508.
Mayes AR, Holdstock JS, Isaac CL, Montaldi D, Grigor J, Gummer A, Carina P, Downes JJ, Tsivilis
D, Gaffan D, Gong Q, Norman KA (2004a). Associative recognition in a patient with selective
hippocampal lesions and relatively normal item recognition. Hippocampus 14(6):763-784.
Mayes AR, Montaldi D, Spencer TJ, Roberts N (2004b). Recalling spatial information as a
component of recently and remotely acquired episodic or semantic memories: an fMRI study.
Neuropsychology 18(3):426-441.
McKenzie S, Frank AJ, Kinsky NR, Porter B, Rivière PD, Eichenbaum H (2014). Hippocampal
representations of related and opposing memories develop within distinct, hierarchically
organized neural schemas. Neuron 83(1):202-215.
McKim TH, Bauer DJ, Boettiger CA (2016). Addiction history associates with the propensity to form
habits. J Cogn Neurosci 28(7):1024-1038.
McMahan RW, Sobel TJ, Baxter MG (1997). Selective immunolesions of hippocampal cholinergic
input fail to impair spatial working memory. Hippocampus 7(2):130-136.
McNaughton BL, Barnes CA, O’Keefe J (1983). The contributions of position, direction, and velocity
to single unit activity in the hippocampus of freely-moving rats. Exp Brain Res 52:41-49.
Meister MLR, Buffalo EA (2018). Neurons in primate entorhinal cortex represent gaze position in
multiple spatial reference frames. J Neurosci 38(10):2420-2441.
Melichercik A, Elliott K, Bianchi C, Ernst S, Winters B (2012). Nicotinic receptor activation in
perirhinal cortex and hippocampus enhances object memory in rats. Neuropharmacol 62(5-
6):2096-2105.
172 Messinger A, Squire LR, Zola SM, Albright TD (2001). Neuronal representation of stimulus
associations develop in the temporal lobe during learning. Proc Natl Acad Sci USA
98(1):12239-12244.
Mesulam MM, Mufson EJ (1982). Insula of the old world monkey. III: Efferent cortical output and
comments on function. J Comp Neurol 212(1):38-52.
Meunier M, Bachevalier J, Mishkin M, Murray EA (1993). Effects on visual recognition of combined
and separate ablations of the entorhinal and perirhinal cortex in rhesus monkeys. J Neurosci
13:5418-5432.
Meunier M, Barbeau E (2013). Recognition memory and the medial temporal lobe: from monkey
research to human pathology. Rev Neurol (Paris) 169(6-7):459-469.
Meunier M, Hadfield J, Bachevalier J, Murray EA (1996). Effects of rhinal cortex lesions combined
with hippocampectomy on visual recognition memory in rhesus monkeys. J Neurophysiol
75:1190-1205.
Miles FJ, Everitt BJ, Dickinson A (2003). Oral cocaine seeking by rats: action or habit? Behav
Neurosci 117(5):927-938.
Miller EK, Cohen JD (2001). An integrative theory of prefrontal cortex function. Ann Rev Neurosci
24:167-202.
Miller EK, Li L, Desimone R (1993). Activity of neurons in anterior inferior temporal cortex during a
short-term memory task. J Neurosci 7:357-362.
Min SK, Moon IW, Ko RW, Shin HS (2001). Effects of transdermal nicotine on attention and memory
in healthy elderly non-smokers. Psychopharmacol (Berl) 159(1):83-88.
Miskin M (1954). Visual discrimination performance following partial ablations of the temporal lobe:
II. Ventral surface vs hippocampus. J Comp Physiol Psychol 47:187-193.
Mishkin M (1978). Memory in monkeys severely impaired by combined but not by separate removal
of amygdala and hippocampus. Nature 273:297-298.
Mishkin M, Delacour J (1975). An analysis fo short-term visual memory in the monkey. J Exp
Psychol 1:326-334.
173 Mishkin M, Pribram KH (1954). Visual discrimination performance following partial ablations of the
temporal lobe: I. Ventral vs. lateral. J Comp Physiol Psychol 47:14-20.
Mishkin M, Ungerleider LG, Macko KA (1983). Object vision and spatial vision: two cortical
pathways. Trends Neurosci 6:414-417.
Mitchell AS, Browning PG, Baxter MG (2007). Neurotoxic lesions of the medial mediodorsal
nucleus of the thalamus disrupt reinforcer devaluation effects in rhesus monkeys. J Neurosci
27(42):11289-11295.
Mitchell AS, Browning PG, Wilson CR, Baxter MG, Gaffan D (2008). Dissociable roles for cortical
and subcortical structures in memory retrieval and acquisition. J Neurosci 28:8387-8396.
Miyashita Y (1988). Neuronal correlate of visual associative long-term memory in the primate
temporal cortex. Nature 335:817-820.
Miyashita Y, Rolls ET, Cahusac PM, Niki H, Feigenbaum JD (1989). Activity of hippocampal
formation neurons in the monkey related to a conditional spatial response task. J Neurophysiol
61(3):669-678.
Miyazawa A, Fujiyoshi Y, Unwin N (2003). Structure and gating mechanism of the acetylcholine
receptor pore. Nature 423:949-956.
Mochizuki K, Funahashi S (2016). Prefrontal spatial working memory network predicts animal’s
decision making in a free choice saccade task. J Neurophysiol 115(1):127-142.
Mohedano-Moriano A, Pro-Sistiaga P, Arroyo-Jimenez MM, Artacho-Pérula E, Insausti AM, Marcos
P, Cebada-Sánchez S, Martínez-Ruiz J, Muñoz M, Blaizot X, Martinez-Marcos A, Amaral DG,
Insausti R (2007). Topographical and laminar distribution of cortical input to the monkey
entorhinal cortex. J Anat 211(2):250-260.
Mohedano-Moriano A, Martinez-Marcos A, Pro-Sistiaga P, Blaizot X, Arroyo-Jimenez MM, Marcos
P, Ar t acho-Pérula E, Insausti R (2008). Convergence of unimodal and polymodal sensory input
to the entorhinal cortex in the fascicularis monkey. Neuroscience 151(1):255-271.
Mok RM, Love BC (2019). A non-spatial account of place and grid cells based on clustering models
of concept learning. Nat Commun 10:5685.
174 Montchal ME, Reagh ZM, Yassa MA (2019). Precise temporal memories are supported by the
lateral entorhinal cortex in humans. Nat Neurosci 22(2):284-288.
Monterosso JR, Aron AR, Cordova X, Xu J, London ED (2005). Deficits in response inhibition
associated with chronic methamphetamine abuse. Drug Alcohol Depend 79:273-277.
Morein-Zamir S, Robbins TW (2015). Fronto-striatal circuits in response to response-inhibition:
Relevance to addiction. Brain Res 1628:117-129.
Morein-Zamir S, Simon Jones P, Bullmore ET, Robbins TW, Ersche KD (2013). Prefrontal
hypoactivity associated with impaired inhibition in stimulant-dependent individuals but evidence
for hyper activation in their unaffected siblings. Neuropsychopharmacol 38:1945-1953.
Morris JS, Ohman A, Dolan RJ (1998). Conscious and unconscious emotional learning in the
human amygdala. Nature 393(6684):467-479.
Morris RGM, Frey U (1997). Hippocampal synaptic plasticity: role in spatial learning or the
automatic recording of attended experience? Philos Trans R Soc Lond B Biol Sci 352:1489-
1503.
Morris RW, Quail S, Griffiths KR, Green MJ, Balleine BW (2015). Corticostriatal control of goal-
directed action is impaired in schizophrenia. Biol Psychiat 77:187-195.
Moss M, Mahut H, Zola-Morgan S (1981). Concurrent discrimination learning of monkeys after
hippocampal, entorhinal, or fornix lesions. J Neurosci 1:227-240.
Mullally S, Maguire EA (2011). A new role for the parahippocampal cortex in representing space. J
Neurosci 31(20):7441-7449.
Muller RU, Kubie JL (1987). The effects of changes in the environment on the spatial firing of
hippocampal complex spike cells. J Neurosci 7:1951-1968.
Muller RU, Kubie JL, Ranck JB Jr. (1987). Spatial firing patterns of hippocampal complex spike
cells in a fixed environment. J Neurosci 7:1935-1950.
Mulugeta E, Karlsson E, Islam A, Kalaria R, Mangat H, Winbald B, Adem A (2003). Loss of
muscarinic M4 receptors in hippocampus of Alzheimer patients. Brain Res 960(1-2):259-262.
175 Muñoz M, Insausti R (2005). Cortical efferents of the entorhinal cortex and adjacent
parahippocampal region in the monkey (Macaca fascicularis). Eur J Neurosci 22(6):1368-1388.
Murray EA, Baxter MG, Gaffan D (1998). Monkeys with rhinal cortex damage or neurotoxic
hippocampal lesions are impaired on spatial scene learning and object reversals. Behav
Neurosci 112:1291-1303.
Murray EA, Bussey TJ (1999). Perceptual-mnemonic functions of the perirhinal cortex. Trends
Cogn Sci 3:142-151.
Murray EA, Bussey TJ, Saksida LM (2007). Visual perception and memory: a new view of medial
temporal lobe function in primates and rodents. Annu Rev Neurosci 30:99-122.
Murray EA, Gaffan D, Mishkin M (1993). Neural substrates of visual stimulus-stimulus association
in rhesus monkeys. J Neurosci 13:4549-4561.
Murray EA, Mishkin M (1984). Severe tactual as well as visual memory deficits follow combined
removal of the amygdala and hippocampus in monkeys. J Neurosci 4:2565-2580.
Murray EA, Mishkin M (1986). Visual recognition in monkeys following rhinal cortical ablations
combined wit either amygdalectomy or hippocampectomy. J Neurosci 6(7):1991-2003.
Murray EA, Mishkin M (1998). Object recognition and location memory in monkeys with excitotoxic
lesions of the amygdala and hippocampus. J Neurosci 18:6568-6582.
Murray EA, Moylan EJ, Saleem KS, Basile BM, Turchi J (2015). Specialized areas for value
updating and goal selection in the primate orbitofrontal cortex. eLife 4:e11695.
Murray EA, Wise SP (2004). What, if anything, is the medial temporal lobe, and how can the
amygdala be part of it if there is no such thing? Neurobiol Learn Mem 82:178-198.
Murray EA, Wise SP, Graham KS (2017). Representational specializations of the hippocampus in
phylogenetic perspective. Neurosci Lett 680:4-12.
Nadasdy Z, Nguyen TP, Török A, Shen JY, Briggs DE, Modur PN, Buchanan RJ (2017). Context-
dependent spatially periodic activity in the human entorhinal cortex. Proc Natl Acad Sci USA
114(17):E3516-E3525.
176 Nadel L, Moscovitch M (1997). Memory consolidation, retrograde amnesia and the hippocampal
complex. Cogn Opin Neurosci 7(2):217-227.
Nakamura K, Kubota K (1996). The primate temporal pole: its putative role in object recognition
and memory. Behav Brain Res 77:53-77.
Nakamura K, Matsumoto K, Mikami A, Kubota K (1994). Visual response properties of single
neurons in the temporal pole of behaving monkeys. J Neurophysiol 74:1206-1221.
Nathan PJ, Watson J, Lund J, Davis CH, Peters G, Dodds GM, Swirski B, Lawrence P, Bentley GD,
O’Neill BV, Robertson J, Watson S, Jones GA, Maruff P, Croft RJ, Laruelle M, Bullmore ET
(2012). The potent M1 receptor allosteric agonist GSK1034702 improves episodic memory in
humans in the nicotine abstinence model of cognitive dysfunction. Int J Neuropsychopharmacol
16(4):721-731.
Nau M, Navarro Schröder T, Bellmund JLS, Doeller CF (2018). Hexadirectional coding of visual
space in human entorhinal cortex. Nat Neurosci 21(2):188-190.
Naya Y, Yoshida M, Miyashita Y (2003). Forward processing of long-term associative memory in
monkey inferotemporal cortex. J Neurosci 23(7):2861-2871.
Nelson ME, Kuryatov A, Choi CH, Zhou Y, Lindstrom J (2003). Alternate stoichiometries of α4ß2
nicotinic acetylcholine receptors. Mol Pharmacol 63:332-341.
Nemanic S, Alvarado MC, Bachevalier J (2004). The hippocampal/parahippocampal regions and
recognition memory: Insights from visual paired comparison versus object-delayed
nonmatching in monkeys. J Neurosci 24(8):2013-2026.
Newhouse PA, Kelton M (2000). Nicotinic systems in central nervous systems disease:
degenerative disorders and beyond. Pharm Acta Helv 74(2-3):91-101.
Nielson DM, Smith TA, Sreekumar V, Dennis S, Sederberg PB (2015). Human hippocampus
represents space and time during retrieval of real-world memories. Proc Natl Acad Sci USA
112(35):11078-11083.
177 Noël X, Van der Linden M, d’Acremont M, Bechara A, Dan B, Hanak C, Verbanck P (2007). Alcohol
cues increase cognitive impulsivity in individuals with alcoholism. Psychopharmacol (Berl)
192:291-298.
Nowicka A, Ringo J (2000). Eye position-sensitive units in hippocampal formation and in
inferotemporal cortex of the macaque monkey. Eur J Neurosci 12(2):751.
Nuechterlein KH, Barch DM, Gold JM, Goldberg TE, Green MF, Heaton RK (2004). Identification
of separable cognitive factors in schizophrenia. Schizophr Res 72(1):29-39.
Olton DS, Branch M, Best PJ (1978). Spatial correlates of hippocampal unit activity. Exp Neurol
58:387-409.
O’Keefe J, Burgess N (1996). Geometric determinants of the place fields of hippocampal neurons.
Nature 381:425-428.
O’Keefe J, Burgess N (2005). Dual phase and rate coding in hippocampal place cells: theoretical
significance and relationship to entorhinal grid cells. Hippocampus 15(7):853-866.
O’Keefe J, Dostrovsky J (1971). The hippocampus as a spatial map. Preliminary evidence from
unit activity in the freely-moving rat. Brain Res 34:171-175.
O’Keefe J, Nadel L (1978). The Hippocampus as a Cognitive Map. Clarendon, Oxford, UK.
O’Keefe J, Speakman A (1987). Single unit activity in the rat hippocampus during a spatial memory
task. Exp Brain Res 68:1-27.
Oki T, Takagi Y, Inagaki S, Taketo MM, Manabe T, Matsui M, Yamada S (2005). Quantitative
analysis of binding parameters of [3H] N-methylscopolamine in central nervous system of
muscarinic acetylcholine receptor knockout mice. Brain Res Mol Brain Res 133:6-11.
O’Mara SM, Rolls ET, Berthoz A, Kesner RP (1994). Neurons responding to whole-body motion in
the primate hippocampus. J Neurosci 14(11 Part 1):6511-6523.
Ono T, Nakamura K, Fukuda M, Tamura R (1991a). Place recognition responses of neurons in
monkey hippocampus. Neurosci Lett 121(1-2):194-198.
Ono T, Nakamura K, Nishijo H, Eifuku S (1993). Monkey hippocampal neurons related to spatial
and nonspatial functions. J Neurophysiol 70(4):1516-1529.
178 Ono T, Tamura R, Nakamura K (1991b). The hippocampus and space: are there “place neurons”
in the monkey hippocampus? Hippocampus 1:253-257.
Orbach J, Milner B, Rasmussen T (1960). Learning and retention in monkeys after amygdala-
hippocampus resection. AMA Arch Neurol 3:230-251.
Orr-Urtreger A, Goldner FM, Saeki M, Lorenzo I, Goldberg L, De Biasi M, Dani JA, Patrick JW,
Beaudet AL (1997). Mice deficient in the α7 neuronal nicotinic acetylcholine receptor lack
alpha-bungarotoxin binding sites and hippocampal fast nicotinic currents. J Neurosci 17:9165-
9171.
Overman WH, Ormsby G, Mishkin M (1990). Picture recognition vs. picture discrimination learning
in monkeys with medial temporal removals. Exp Brain Res 79:18-24.
Pang KC, Nocera R (1999). Interactions between 192-IgG saporin and intraseptal cholinergic and
GABAergic drugs: role of cholinergic medial septal neurons in spatial working memory. Behav
Neurosci 113(2):265-275.
Papke RL, McCormack TJ, Jack BA, Wang D, Bugai-Gaweda B, Schiff HC, Buhr JD, Waber AJ,
Stokes C (2005). Rhesus monkey α7 nicotinic acetylcholine receptors: Comparisons to human
α7 receptors expressed in Xenopus oocytes. Eur J Pharmacol 524:11-18.
Paredes-Olay C, Lôpez M (2002). Lithium-induced outcome devaluation in instrumental
conditioning: dose-effect analysis. Physiol Behav 75(5):603-609.
Parker A, Gaffan D (1998). Interaction of frontal and perirhinal cortices in visual object recognition
memory in monkeys. Eur J Neurosci 10:3044-3057.
Parkinson JK, Murray EA, Mishkin M (1988). A selective mnemonic role for the hippocampus in
monkeys memory for the location of objects. J Neurosci 8:4159-4167.
Parslow DM, Morris RG, Fleminger S, Rahman Q, Abrahams S, Recce M (2005). Allocentric spatial
memory in humans with hippocampal lesions. Acta Psychol (Amst) 118(1-2):123-147.
Parslow DM, Rose D, Brooks B, Fleminger S, Gray JA, Giampietro V, Brammer MJ, Williams S,
Andrew C, Vythelingum GN, Loannou G, Simmons A, Morris RG (2004). Allocentric spatial
179 memory activation of the hippocampal formation measured with fMRI. Neuropsychol 18(3):450-
461.
Pascalis O, Hunkin NM, Holdstock JS, Isaac CL, Mayes AR (2004). Visual paired comparison
performance is impaired in a patient with selective hippocampal lesions and relatively intact
item recognition. Neuropsychologia 42(10):1293-1300.
Pastalkova E, Itskov V, Amarasingham A, Buzsaki G (2008). Internally generated cell assembly
sequences in the rat hippocampus. Science 321:1322-1327.
Paterson D, Nordberg A (2000). Neuronal nicotinic receptors in the human brain. Prog Neurobiol
61:75-111.
Patterson TK, Craske MG, Knowlton BJ (2013). The effect of early-life stress on memory systems
supporting instrumental behavior. Hippocampus 23(11):1025-1034.
Penfield W, Milner B (1958). Memory deficits produced by bilateral lesions in hippocampal zone.
AMA Arch Neurol Psychiat 79:475-479.
Perry DC, Kramer JH (2015). Reward processing in neurodegenerative disease. Neurocase
21(1):120-133.
Perry EK, Court JA, Johnson M, Piggott MA, Perry RH (1992). Autoradiographic distribution of
[3H]nicotine binding in human cortex: relative abundance in subicular complex. J Chem
Neuroanat 5:399-405.
Perry E, Martin-Ruiz C, Lee M, Griffiths M, Johnson M, Pigott M, Haroutunian V, Buxbaum JD,
Nãsland J, Davis K, Gotti C, Clementi E, Tzartos S, Cohen O, Soreq H, Jaros E, Perry R,
Ballard C, McKeith I, Court J (2000). Nicotinic receptor subtypes in the human brain aging,
Alzheimer and Lewy body diseases. Eur J Pharmacol 393(1-3):215-222.
Picciotto MR, Caldarone BJ, Brunzell DH, Zachariou V, Stevens TR, King SL (2001). Neuronal
nicotinic acetylcholine receptor subunit knockout mice: physiological and behavioral
phenotypes and possible clinical implications. Pharmacol Ther 92:89-108.
Picciotto MR, Zoli M (2008). Neuroprotection via nAChRs: the role of nAChRs in neurodegenerative
disorders such as Alzheimer’s and Parkinson’s disease. Front Biosci 13(1):492-504.
180 Pihlajamäki M, Tanila H, Hänninen T, Könönen M, Mikkonen M, Jalkanen V, Partanen K, Aronen
HJ, Soininen H (2003). Encoding of novel picture pairs activates the perirhinal cortex: an fMRI
study. Hippocampus 13(1):67-80.
Pihlajamäki M, Tanila H, Könönen M, Hänninen T, Hämäläinen A, Soininen H, Aronen HJ (2004).
Visual presentation of novel objects and new spatial arrangements of objects differentially
activates the medial temporal lobe subareas in humans. Eur J Neurosci 19:1939-1949.
Plitman E, Iwata Y, Caravaggio F, Nakajima S, Chung JK, Gerretsen P, Kim J, Takeuchi H,
Chakravarty MM, Remington G, Graff-Guerrero A (2017). Kynurenic acid in schizophrenia: a
systematic review and meta-analysis. Schizophr Bull 43(4):764-777.
Poletti CE, Creswell G (1977). Fornix system efferent projections in the squirrel monkey: An
experimental degeneration study. J Comp Neurol 175:101-128.
Postle BR, Berger JS, Taich AM, D’Esposito M (2000). Activity in human frontal cortex associated
with spatial working memory and saccadic behavior. J Cogn Neurosci 12(Suppl 2):2-14.
Prendergast MA, Jackson WJ, Terry AV Jr, Decker MW, Arneric SP, Buccafusco JJ (1998). Central
nicotinic receptor agonists ABT-418, ABT-089, and (-)-nicotine reduce distractibility in adult
monkeys. Psychopharmacol (Berl) 136(1):50-58.
Pribram KW, Wilson W, Connors J (1962). Effects of lesions of medial forebrain on alternation
behavior of rhesus monkeys. Exp Neurol 6:36-47.
Quian Quiroga R (2012). Concept cells: the building blocks of declarative memory functions. Nat
Rev 13:587-597.
Quian Quiroga R, Reddy L, Kreiman G, Koch C, Fried I (2005). Invariant visual representation by
single neurons in the human brain. Nature 435(7045):1102-1107.
Quik M, O’Leary K, Tanner CM (2008). Nicotine and Parkinson’s disease: implications for therapy.
Mov Disord 23(12):1641-1652.
Quinn JJ, Pittenger C, Lee AS, Pierson JL, Taylor JR (2013). Striatum-dependent habits are
insensitive to both increases and decreases in reinforcer value in mice. Eur J Neurosci
37(6):1012-1021.
181 Raabe M, Fischer V, Bernhardt D, Greene MW (2013). Neural correlates of spatial working memory
load in a delayed match-to-sample saccade task. NeuroImage 71:84-91.
Reagh ZM, Yassa MA (2014). Object and spatial mnemonic interference differentially engage
lateral and medial entorhinal cortex in humans. Proc Natl Acad Sci USA 111(40):E4264-E4273.
Reed JM, Squire LR (1998). Retrograde amnesia for facts and events: findings from four new cases.
J Neurosci 18:3943-3954.
Rempel-Clower NL, Zola SM, Squire LR, Amaral DG (1996). Three cases of enduring memory
impairment after bilateral damage limited to the hippocampal formation. J Neurosci
16(16):5233-5255.
Ricciardi E, Bonino D, Gentili C, Sani L, Pietrini P, Vecchi T (2006). Neural correlates of spatial
working memory in humans: a functional magnetic resonance imaging study comparing visual
and tactile processes. Neuroscience 139(1):339-349.
Riches IP, Wilson FA, Brown MW (1991). The effects of visual stimulation and memory on neurons
of the hippocampal formation and the neighboring parahippocampal gyrus and inferior temporal
cortex of the primate. J Neurosci 11(6):1763-1779.
Ridley RM, Hardy A, Maclean CJ, Baker HF (2001). Non-spatial acquisition and retention deficits
following small excitotoxic lesions within the hippocampus in monkeys. Neuroscience
107(2):239-248.
Ridley RM, Pearson C, Kershaw TR, Hodges H, Maclean CJ, Hoyle C, Baker HF (1997). Learning
impairment induced by lesion of the CA1 field of the primate hippocampus: attempts to
ameliorate the impairment by transplantation of fetal CA1 tissue. Exp Brain Res 115:83-94.
Riemer M, Shine JP, Wolbers T (2018). On the (a)symmetry between the perception of time and
space in large-scale environments. Hippocampus 28(8):539-548.
Ringo JL (1991). Memory decays at the same rate in macaques with and without brain lesions
when expressed in d’ or arcsine terms. Behav Brain Res 42:123-134.
Rivard B, Li Y, Lenck-Santini PP, Poucet B, Muller RU (2004). Representation of objects in space
by two classes of hippocampal pyramidal cells. J Gen Physiol 124:9-25.
182 Robertson RG, Rolls ET, Georges-François P (1998). Spatial view cells in the primate
hippocampus: effects of removal of view details. J Neurophysiol 79:1145-1156.
Robin J, Buchsbaum BR, Moscovitch M (2018). The primacy of spatial context in neural
representation of events. J Neurosci 38(11):2755-2765.
Robinson NTM, Priestley JB, Rueckemann JW, Garcia AD, Smeglin VA, Marino FA, Eichenbaum
H (2017). Medial entorhinal cortex selectively supports temporal coding by hippocampal
neurons. Neuron 94:677-688.
Rolls ET (1999). Spatial view cells and the representation of place in the primate hippocampus.
Hippocampus 9:467-480.
Rolls ET, Franco L, Stringer SM (2005). The perirhinal cortex and long-term familiarity memory. Q
J Exp Psychol 58B:234-245.
Rolls ET, Miyashita Y, Cahusac PMB, Kesner RP, Niki H, Feigenbaum J, Bach L (1989).
Hippocampal neurons in the monkey with activity related to the place in which a stimulus is
shown. J Neurosci 9:1835-1845.
Rolls ET, O’Mara SM (1995). View-responsive neurons in the primate hippocampal complex.
Hippocampus 5:409-424.
Rolls ET, Robertson RG, Georges-François P (1997). Spatial view cells in the primate hippocampus.
Eur J Neurosci 9:1789-1794.
Rolls ET, Sobotka S, Diltz MD, Bunce CM (1994). Eye movements modulate activity in hippocampal,
parahippocampal, and inferotemporal neurons. J Neurophysiol 71(3):1285-1288.
Rolls ET, Stringer SM (2005). Spatial view cells in the hippocampus, and their idiothetic update
based on place and head direction. Neural Netw 18(9):1229-1241.
Ron D, Barak S (2016). Molecular mechanisms underlying alcohol-drinking behaviors. Nat Publ Gr
17:576-591.
Rosenbaum RS, Gilboa A, Levine B, Winocur G, Moscovitch M (2009). Amnesia as an impairment
of detail generation and binding: Evidence from personal, fictional, and semantic narratives in
K.C. Neuropsychologia 47(11):2181-2187.
183 Rosenbaum RS, Köhler S, Schacter DL, Moscovitch M, Westmacott R, Black SE, Gao F, Tulving E
(2005). The case of K.C.: contributions of a memory-impaired person to memory theory.
Neuropsychologia 43(7):989-1021.
Rosene DL, Van Hoesen GW (1977). Hippocampal efferents reach widespread areas of cerebral
cortex and amygdala in the rhesus monkey. Science 198:315-317.
Rosene DL, Van Hoesen GW (1987). The hippocampal formation of the primate brain: a review of
some comparative aspects of cytoarchitecture and connections. Cerebral cortex (eds Jones
EG, Peters A). New York: Plenum. 345-456.
Rubboli F, Court J, Sala C, Morris C, Chini B, Perry E, Clementi F (1994). Distribution of nicotinic
receptors in the human hippocampus and thalamus. Eur J Neurosci 6:1596-1604.
Rubin A, Geva N, Sheintuch L, Ziv Y (2015). Hippocampal ensemble dynamics timestamp events
in long-term memory. eLife 4:e12247.
Rudebeck PH, Saunders RC, Prescott AT, Chau LS, Murray EA (2013). Prefrontal mechanisms of
behavioral flexibility, emotion regulation and value updating. Nat Neurosci 16(8):1140-1145.
Sagar HJ, Cohen NJ, Corkin S, Growdon JH (1985). Dissociations among processes in remote
memory. Ann N Y Acad Sci 444:533-535.
Sahakian B, Jones G, Levy R, Gray J, Warburton D (1989). The effects of nicotine on attention,
information processing, and short-term memory in patients with dementia of the Alzheimer type.
Br J Psychiat 154(6):797-800.
Sakai K, Miyashita Y (1991). Neural organization for the long-term memory of paired associates.
Nature 354:152-155.
Sakon JJ, Naya Y, Wirth S, Suzuki WA (2014). Context-dependent incremental timing cells in the
primate hippocampus. Proc Natl Acad Sci USA 111(51):18351-18356.
Saksida LM, Bussey TJ (1998). Toward a neural network model of visual object identification in
primate inferotemporal cortex. Soc Neurosci Abstr 24:1906.
184 Saksida LM, Bussey TJ, Buckmaster CA, Murray EA (2006). No effect of hippocampal lesions on
perirhinal cortex-dependent feature ambiguous visual discriminations. Hippocampus
16(4):421-430.
Saksida LM, Bussey TJ, Buckmaster CA, Murray EA (2007). Impairment and facilitation of
transverse patterning after lesions of the perirhinal cortex and hippocampus, respectively.
Cereb Cortex 17(1):108-115.
Saleem KS, Price JL, Hashikawa T (2007). Cytoarchitectonic and chemoarchitectonic subdivisions
of the perirhinal and parahippocampal cortices in macaque monkeys. J Comp Neurol 500:973-
1006.
Saleem KS, Tanaka K (1996). Divergent projections from the anterior inferotemporal area TE to the
perirhinal and entorhinal cortices in the macaque monkey. J Neurosci 16(15):4757-4775.
Salzmann E, Vidyasagar TR, Creutzfeldt OD (1993). Functional comparison of neuronal properties
in the primate posterior hippocampus and parahippocampus (area TF/TH) during different
behavioral paradigms involving memory and selective attention. Behav Brain Res 53:133-149.
Sargolini F, Fyhn M, Hafting T, McNaughton BL, Witter MP, Moser MB, Moser EI (2006). Conjunctive
representation of position, direction, and velocity in entorhinal cortex. Science 312:758-762.
Sato N, Nakamura K (2003). Visual response properties of neurons in the parahippocampal cortex
of monkeys. J Neurophysiol 90:876-886.
Saunders RC, Mishkin M, Aggleton JP (2005). Projections from the entorhinal cortex, perirhinal
cortex, presubiculum, and parasubiculum to the medial thalamus in macaque monkeys:
identifying different pathways using disconnection techniques. Exp Brain Res 167(1):1-16.
Saunders RC, Murray EA, Mishkin M (1984). Further evidence that amygdala and hippocampus
contribute equally to recognition memory. Neuropsychologia 22:785-796.
Saunders RC, Rosene DL (1988). A comparison of the efferents of the amygdala and the
hippocampal formation in the rhesus monkey: I. Convergence in the entorhinal, prorhinal, and
perirhinal cortices. J Comp Neurol 271(2):153-184.
185 Save E, Nerad L, Poucet B (2000). Contribution of multiple sensory information to place field
stability in hippocampal place cells. Hippocampus 10(1):64-76.
Scarr E, Hopper S, Vos V, Seo MS, Everall IP, Aumann TD, Chana G, Dean B (2018). Low levels
of muscarinic M1 receptor-positive neurons in cortical layers III and V in Brodmann areas 9 and
17 from individuals with schizophrenia. J Psychiat Neursci 43(5):338-346.
Scarr E, Seo MS, Aumann TD, Chana G, Everall IP, Dean B (2016). The distribution of muscarinic
M2 receptors in the human hippocampus. J Chem Neuroanat 77:187-192.
Scarr E, Um JY, Cowie TF, Dean B (2013). Cholinergic muscarinic M4 receptor gene
polymorphisms: a potential risk factor and pharmacogenomic marker for schizophrenia.
Schizophr Res 146(1-3):279-284.
Scharfen HE, Witter MP, Schwercz R (2000). Preface. The Parahippocampal Region: Implications
for Neurological and Psychiatric Diseases, vol 911 (eds Goldman BM, Cullinan J, Garry ML,
Kimball C). New York: New York Academy of Sciences. ix-xiii.
Schmitzer-Torbert N, Apostolidis S, Amoa R, O’Rear C, Kaster M, Stowers J, Ritz R (2015). Post-
training cocaine administration facilitates habit learning and requires the infralimbic cortex and
dorsolateral striatum. Neurobiol Learn Mem 118:105-112.
Schobel SA, Chaudhury NH, Khan UA, Paniagua B, Styner MA, Asllani I, Inbar BP, Corcoran CM,
Lieberman JA, Moore H, Small SA (2013). Imaging patients with psychosis and a mouse model
establishes a spreading pattern of hippocampal dysfunction and implicates glutamate as a
driver. Neuron 78:81-93.
Schwabe L, Bohbot VD, Wolf OT (2012a). Prenatal stress changes learning strategies in adulthood.
Hippocampus 22(11):2136-2143.
Schwabe L, Dalm S, Schächinger H, Oitzl MS (2008). Chronic stress modulates the use of spatial
and stimulus-response learning strategies in mice and men. Neurobiol Learn Mem 90:495-503.
Schwabe L, Dickinson A, Wolf OT (2011). Stress, habits, and drug addiction: a
psychoneuroendocrinological perspective. Exp Clin Psychopharmacol 19(1):53-63.
186 Schwabe L, Joëls M, Roozendaal B, Wolf OT, Oitzl MS (2012b). Stress effects on memory: An
update and integration. Neurosci Biobehav Rev 36:1740-1749.
Schwabe L, Wolf OT (2009). Stress prompts habit behavior in humans. J Neurosci 29(22):7191-
7198.
Schwabe L, Wolf OT (2010). Socially evaluated cold pressor stress after instrumental learning
favors habits over goal-directed action. Psychoneuroendocrinol 35(7):977-986.
Scoville WB, Milner B (1957). Loss of recent memory after bilateral hippocampal lesions. J Neurol
Neurosurg Psychiat 20:11-21.
Sebold M, Deserno L, Nebe S, Schad DJ, Garbusow M, Hägele C, Keller J, Jünger E, Katzmann
N, Smolka M, Rapp MA, Schlagenhauf F, Heinz A, Huys QJM (2014). Model-based and model-
free decisions in alcohol dependence. Neuropsychobiol 70:122-131.
Seger CA, Speiring BJ (2011). A critical review of habit learning and the basal ganglia. Front Syst
Neurosci 5:66.
Seltzer B, Pandya DN (1976). Some cortical projections to the parahippocampal area of the rhesus
monkey. Exp Neurol 34:212-225.
Shah DS, Prados J, Gamble J, De Lillo C, Gibson CL (2013). Sex differences in spatial memory
using serial and search tasks. Behav Brain Res 257:90-99.
Shapiro ML, Tanila H, Eichenbaum H (1997). Cues that hippocampal place cells encode: dynamic
and hierarchical representation of local and distal stimuli. Hippocampus 7:624-642.
Sharifzadeh M, Tavasoli M, Naghdi N, Ghanbari A, Amini M, Roghani A (2005). Post-training
intrahippocampal infusion of nicotine prevents spatial memory retention deficits introduced by
the cyclo-oxygenase-2-specific inhibitor celecoxib in rats. J Neurochem 95:1078-1090.
Sher E, Chen Y, Sharples TJ, Broad LM, Benedetti G, Zwart R, McPhie GI, Pearson KH,
Baldwinson T, De Filippi G (2004). Physiological roles of neuronal nicotinic receptor subtypes:
new insights on the nicotinic modulation of neurotransmitter release, synaptic transmission and
plasticity. Curr Top Med Chem 4:283-297.
187 Shine JP, Valdés-Herrera JP, Tempelmann C, Wolbers T (2019). Evidence for allocentric boundary
and goal direction information in the human entorhinal cortex and subiculum. Nat Commun
10(1):4004.
Shiozaki K, Iseki E, Hino H, Kosaka K (2001). Distribution of m1 muscarinic acetylcholine receptors
in the hippocampus of patients with Alzheimer’s disease and dementia with Lewy bodies - an
immunohistochemical study. J Neurol Sci 193(1):23-28.
Shorey-Kendrick LLEL, Ford MM, Allen DC, Kuryatov A, Lindstrom J, Wilhelm L, Grant KA, Spindel
ER (2015). Nicotinic receptors in non-human primates: analysis of genetic and functional
conservation with humans. Neuropharmacol 96:263-273.
Shrager Y, Gold JJ, Hopkins RO, Squire LR (2006). Intact visual perception in memory-impaired
patients with medial temporal lobe lesions. J Neurosci 26(8):2235-2240.
Sjoerds Z, de Wit S, van den Brink W, Robbins TW, Beekman ATF, Phenninx BWJH, Veltman DJ
(2013). Behavioral and neuroimaging evidence for overreliance on habit learning in alcohol-
dependent patients. Transl Psychiat 3(12):e337.
Sliwa J, Planté A, Duhamel JR, Wirth S (2016). Independent neuronal representation of facial and
vocal identity in the monkey hippocampus and inferotemporal cortex. Cereb Cortex 26(3):950-
966.
Smith KS, Graybiel AM (2016). Habit formation. Dialogues Clin Neurosci 18:33-43.
Sobotka S, Ringo JL (1993). Investigation of long-term recognition and association memory in unit
responses from inferotemporal cortex. Exp Brain Res 96:28-38.
Solstad T, Boccara CN, Kropff E, Moser MB, Moser EI (2008). Representation of geometric borders
in the entorhinal cortex. Science 322:1865-1868.
Spiers HJ, Burgess N, Hartley T, Vargha-Khadem F, O’Keefe J (2001a). Bilateral hippocampal
pathology impairs topographical and episodic memory but not visual pattern matching.
Hippocampus 11:715-725.
188 Spiers HJ, Burgess N, Maguire EA, Baxendale SA, Hartley T, Thompson PJ, O’Keefe J (2001b).
Unilateral temporal lobectomy patients show lateralized topographical and episodic memory
deficits in a virtual town. Brain 124:2476-2489.
Squire LR (1992). Memory and the hippocampus: a synthesis from findings with rats, monkeys,
and humans. Psychol Rev 99(2):195-231.
Squire LR, Knowlton B, Musen G (1993). The structure and organization of memory. Ann Rev
Psychol 44:453-495.
Squire LR, Stark CE, Clark RE (2004). The medial temporal lobe. Annu Rev Neurosci 27:279-306.
Squire LR, Wixted JT, Clark RE (2007). Recognition memory and the medial temporal lobe: a new
perspective. Nat Rev Neurosci 8:872-883.
Squire LR, Zola-Morgan S (1996). Structure and function of declarative and nondeclarative memory
systems. Proc Natl Acad Sci 93:13515-13522.
Stark CE, Squire LR (2000). Intact visual perceptual discrimination in humans in the absence of
perirhinal cortex. Learn Mem 7(5):273-278.
Starkstein SE, Jorge R, Mizrahi R, Robinson RG (2005). The construct of minor and major
depression in Alzheimer’s disease. Am J Psychiat 162(11):2086-2093.
Stauderman KA, Mahafy LS, Akong M, Velicelebi G, Chavez-Noriega LE, Crona JH, Johnson EC,
Elliott KJ, Gillespie A, Reid RT, Adams P, Harpold MM, Corey-Naeve J (1998). Characterization
of human recombinant neuronal nicotinic acetylcholine receptor subunit combinations α2ß4,
α3ß4, and α4ß4 stably expressed in HEK293 cells. J Pharmacol Exp Ther 284:777-789.
Staudigl T, Leszczynski M, Jacobs J, Sheth SA, Schroeder CE, Jensen O, Doleer CF (2018).
Hexadirectional modulation of high-frequency electrophysiological activity in the human
anterior medial temporal lobe maps visual space. Curr Biol 28:3325-3329.
Stefanacci L, Buffalo EA, Schmolck H, Squire LR (2000). Profound amnesia after damage to the
medial temporal lobe: A neuroanatomical and neuropsychological profile of patient E.P.. J
Neurosci 20(18):7024-7036.
189 Steinvorth S, Levine B, Corkin S (2005). Medial temporal lobe structures are needed to re-
experience remote autobiographical memories: evidence from H.M. and W.R.
Neuropsychologia 43(4):479-496.
Stepien LS, Cordeau JP, Rasmussen T (1960). Effect of temporal lobe and hippocampal lesions on
auditory and visual memory in monkeys. Brain 83:470-489.
Sugase-Miyamoto Y, Richmond BJ (2007). Cue and reward signals carried by monkey entorhinal
cortex neurons during reward schedules. Exp Brain Res 181(2):267-276.
Suzuki WA (1996). Neuroanatomy of the monkey entorhinal, perirhinal, and parahippocampal
cortices: Organization of cortical inputs and interconnections with amygdala and striatum. Sem
Neurosci 8(1):3-12.
Suzuki WA, Amaral DG (1990). Cortical inputs to the CA1 field of the monkey hippocampus
originate from the perirhinal and parahippocampal cortex but not from area TE. Neurosci Lett
115:43-48.
Suzuki WA, Amaral DG (1994a). Perirhinal and parahippocampal cortices of the macaque monkey:
cortical afferents. J Comp Neurol 350:497-533.
Suzuki WA, Amaral DG (1994b). Topographic organization of the reciprocal connections between
monkey entorhinal cortex and the perirhinal and parahippocampal cortices. J Neurosci
14:1856-1877.
Suzuki WA, Amaral DG (2003). Perirhinal and parahippocampal cortices of the macaque monkey:
cytoarchitecture and chemoarchitectonic organization. J Comp Neurol 463(1):67-91.
Suzuki WA, Miller EK, Desimone R (1997). Object and place memory in the macaque entorhinal
cortex. J Neurophysiol 78:1062-1081.
Suzuki WA, Zola-Morgan S, Squire LR, Amaral DG (1993). Lesions of the perirhinal and
parahippocampal cortices in the monkey produce long-lasting memory impairment in the visual
and tactual modalities. J Neurosci 13:2430-2451.
Taffe MA, Weed MR, Gold LH (1999). Scopolamine alters rhesus monkey performance on a novel
neuropsychological test batter. Cogn Brain Res 8:203-212.
190 Tamura R, Ono T, Fukuda M, Nakamura K (1990). Recognition of egocentric and allocentric visual
and auditory space by neurons in the hippocampus of monkeys. Neurosci Lett 109(3):293-298.
Tamura R, Ono T, Fukuda M, Nakamura K (1992). Spatial responsiveness of monkey hippocampal
neurons to various visual and auditory stimuli. Hippocampus 2(3):307-322.
Tang Y, Mishkin M, Aigner TG (1997). Effects of muscarinic blockade in perirhinal cortex during
visual recognition. Proc Natl Acad Sci USA 94:12667-12669.
Tavares RM, Mendelsohn A, Grossman Y, Williams CH, Shapiro M, Trope Y, Schiller D (2015). A
map for social navigation in the human brain. Neuron 87(1):231-243.
Teng E, Squire LR (1999). Memory for places learned long ago is intact after hippocampal damage.
Nature 400:675-677.
Terry AV Jr, Buccafusco JJ (2003). The cholinergic hypothesis of age and Alzheimer’s disease-
relate cognitive deficits: recent challenges and their implications for novel drug development. J
Pharm Exp Ther 306(3):821-827.
Thavabalasingam S, O’Neil EB, Lee ACH (2018). Multivoxel pattern similarity suggests the
integration of temporal duration in hippocampal event sequence representations. Neuroimage
178:136-146.
Thornton JA, Malkova L, Murray EA (1998). Rhinal cortex ablations fail to disrupt reinforcer
devaluation effects in rhesus monkeys (Macaca mulatta). Behav Neurosci 112(4):1020-1025.
Thornton JA, Rothblat LA, Murray EA (1997). Rhinal cortex removal produces amnesia for
preoperatively learned discrimination problems but fails to disrupt postoperative acquisition and
retention in rhesus monkeys. J Neurosci 17:8536-8549.
Tranel D, Brady DR, Van Hoesen GW, Damasio AR (1988). Parahippocampal projections to
posterior auditory association cortex (area Tpt) in Old-World monkeys. Exp Brain Res
70(2):406-416.
Tricomi E, Balleine BW, O’Doherty JP (2009). A specific role for posterior dorsolateral striatum in
human habit learning. Eur J Neurosci 29(11):2225-2232.
191 Tsao A, Sugar J, Lu L, Wang C, Knierim JJ, Moser MB, Moser EI (2018). Integrating time from
experience in the lateral entorhinal cortex. Nature 561:57.
Tulving E (1972). Episodic and semantic memory. Organization of Memory (eds Tulving E,
Donaldson W). New York: Academic Press. 381-403.
Turk-Browne NB, Simon MG, Sederberg PB (2012). Scene representations in parahippocampal
cortex depend on temporal context. J Neurosci 32(21):7202-7207.
Turner BH, Mishkin M, Knapp M (1980). Organization of the amygdalopetal projections from
modality-specific cortical association areas in the monkey. J Comp Neurol 191:515-543.
Vaidya AR, Pujara MS, Petrides M, Murray EA, Fellows LK (2019). Trends Cogn Sci 23(8):653-671.
Valenstein ES, Nauta WJH (1959). A comparison of the distribution of the fornix system in the rat,
guinea pig, cat and monkey. J Comp Neurol 3:337-363.
Valentin VV, Dickinson A, O’Doherty JP (2007). Determining the neural substrates of goal-directed
learning in the human brain. J Neurosci 27:4019-4026.
Van Hoesen GW (1980). The cortico-cortical projections of the posterior parahippocampal area in
the rhesus monkey. Anat Rec 196:195A.
Van Hoesen GW (1982). The parahippocampal gyrus: New observations regarding its cortical
connections in the monkey. Trends Neurosci 5:345-350.
Van Hoesen GW, Pandya DN (1975a). Some connections of the entorhinal (area 28) and perirhinal
(area 35) cortices of the rhesus monkey. I. Temporal lobe afferents. Brain Res 95:1-24.
Van Hoesen GW, Pandya DN (1975b). Some connections of the entorhinal (area 28) and perirhinal
(area 35) cortices of the rhesus monkey. III. Efferent connections. Brain Res 95:39-59.
Van Hoesen GW, Pandya DN, Butters N (1975). Some connections of the entorhinal (area 28) and
perirhinal (area 35) cortices of the rhesus monkey. II. Frontal lobe afferents. Brain Res 95:25-
38.
Van Hoesen GW, Pandya DN, Butters N (1972). Cortical afferents to the entorhinal cortex of the
rhesus monkey. Science 175:1471-1473.
192 Vanover KE, Veinbergs I, Davis RE (2008). Antipsychotic-like behavioral effects and cognitive
enhancement by a potent and selective muscarinic M1 receptor agonist, AC-260584. Behav
Neurosci 122(3):570-575.
Vargha-Khadem F, Gadian DG, Mishkin M (2001). Dissociations in cognitive memory: the syndrome
of developmental amnesia. Philos Trans R Soc Lond B Biol Sci 356:1435-1440.
Verplanken B, Sui J (2019). Habit and identity: behavioral, cognitive, affective, and motivational
facets of an integrated self. Front Psychol 10:1504.
Vidyasagar TR, Salzmann E, Creutzfeldt OD (1991). Unit activity in the hippocampus and
parahippocampal temporobasal association cortex related to memory and complex behaviour
in the awake monkey. Brain Res 544:269-278.
Vilaro MT, Palacios JM, Mengod G (1990). Localization of m5 muscarinic receptor mRNA in rat
brain examined by in situ hybridization histochemistry. Neurosci Lett 114:154-159.
Vogt BA, Pandya DN (1987). Cingulate cortex of the rhesus monkey: II. Cortical afferents. J Comp
Neurol 262(2):271-289. von Bonin G, Bailey P (1947). The neocortex of Macaca mulatta. Urbana: University of Illinois Press.
Voytko ML (1986). Visual learning and retention examined with reversible cold lesions of the
anterior temporal lobe. Behav Brain Res 22:25-39.
Wada E, Wada K, Boulter J, Deneris E, Heinemann S, Patrick J, Swanson LW (1989). Distribution
of α2, α3, α4, and ß2 neuronal nicotinic receptor subunit mRNAs in the central nervous system:
a hybridization histochemical study in the rat. J Comp Neurol 284:314-335.
Waguespack HF, Malkova L, Forcelli PA, Turchi J (2018). Effects of systemic cholinergic
antagonism on reinforcer devaluation in macaques. Neurosci Lett 678:62-67.
Wallis JD, Anderson KC, Miller EK (2001). Single neurons in prefrontal cortex encode abstract rules.
Nature 411:953-956.
Wang HY, Lee DH, D’Andrea MR, Peterson PA, Shank RP, Reitz AB (2000). beta-Amyloid(1-42)
binds to alpha7 nicotinic acetylcholine receptor with high affinity. Implications for Alzheimer’s
disease pathology. J Biol Chem 275(8):5626-5632.
193 Watanabe T, Niki H (1985). Hippocampal unit activity and delayed response in the monkey. Brain
Res 325:241-254.
Watkins ER, Nolen-Hoeksema S (2014). A habit-goal framework of depressive rumination. J
Abnorm Psychol 123(1):24-34.
Watson P, Wiers RW, Hommel B, de Wit S (2014). Working for food you don’t desire. Cues interfere
with goal-directed food-seeking. Appetite 79:139-148.
Webster MJ, Ungerleider LG, Bachevalier J (1991). Connections of inferior temporal areas TE and
TEO with medial temporal-lobe structures in infant and adult monkeys. J Neurosci 11(4):1095-
1116.
Wellman LL, Gale K, Malkova L (2005). GABAA-mediated inhibition of basolateral amygdala blocks
reward devaluation in macaques. J Neurosci 25(18):4577-4586.
Weniger G, Irle E (2006). Posterior parahippocampal gyrus lesions in the human impair egocentric
learning in a virtual environment. Eur J Neurosci 24(8):2406-2414.
Weniger G, Siemerkus J, Schmidt-Samoa C, Mehlitz M, Baudewig J, Dechent P, Irle E (2010). The
human parahippocampal cortex subserves egocentric spatial learning during navigation in a
virtual maze. Neurobiol Learn Mem 93:46-55.
West EA, DesJardin JT, Gale K, Malkova L (2011). Transient inactivation of orbitofrontal cortex
blocks reinforcer devaluation in macaques. J Neurosci 31(42):15128-15135.
West EA, Forcelli PA, Murnen AT, McCue DL, Gale K, Malkova L (2012). Transient inactivation of
basolateral amygdala during selective satiation disrupts reinforcer devaluation in rats. Behav
Neurosci 126(4):563-574.
Westmacott R, Leach L, Freedman M, Moscovitch M (2001). Different patterns of autobiographical
memory loss in semantic dementia and medial temporal lobe amnesia: a challenge to
consolidation theory. Neurocase 7:37-55.
Wevers A, Burghaus L, Moser N, Witter B, Steinlein OK, Schütz U, Schnitz B, Krempel U, Nowacki
S, Pilz K, Stoßt J, Lindstrom J, De Vos RA, Jansen Steur EN, Schröder H (2000). Expression
194 of nicotinic acetylcholine receptors in Alzheimer’s disease: postmortem investigations and
experimental approaches. Behav Brain Res 113(1):207-215.
Whalen PJ, Rauch SL, Etcoff NL, McInerney SC, Lee MB, Jenike MA (1998). Masked presentations
of emotional facial expressions modulate amygdala activity without explicit knowledge. J
Neurosci 18(1):411-418.
White HK, Levin ED (1999). Four-week nicotine skin patch treatment effects on cognitive
performance in Alzheimer’s disease. Psychopharmacol (Berl) 143(2):158-165.
Wicker E, Turchi J, Malkova L, Forcelli PA (2018). Mediodorsal thalamus is required for discrete
phases of goal-directed behavior in macaques. eLife 7:e37325.
Wiener SI, Paul CA, Eichenbaum H (1989). Spatial and behavioral correlates of hippocampal
neuronal activity. J Neurosci 9:2737-2763.
Wilkins LK, Girard TA, Konishi K, King M, Herdman KA, King J, Christensen B, Bohbot VD (2013).
Selective deficit in spatial memory strategies contrast to intact response strategies in patients
with schizophrenia spectrum disorders tested in a virtual navigation task. Hippocampus
23(11):1015-1024.
Wilming N, König P, König S, Buffalo EA (2018). Entorhinal cortex receptive fields are modulated
by spatial attention, even without movement. eLife 7:e31745.
Wirth S, Avsar E, Chiu CC, Sharma V, Smith AC, Brown E, Suzuki WA (2009). Trial outcome and
associative learning signals in the monkey hippocampus. Neuron 61(6):930-940.
Wirth S, Baraduc P, Planté A, Pinède S, Duhamel JR (2017). Gaze-informed, task-situated
representation of space in primate hippocampus during virtual navigation. PLoS Biol
15:e2001045.
Wirth S, Yanike M, Frank LM, Smith AC, Brown EN, Suzuki WA (2003). Single neurons in the
monkey hippocampus and learning of new associations. Science 300(5625):1578-1581.
Witter MP (2010). Connectivity of the hippocampus. Hippocampal Microcircuits: Springer Series in
Computational Neuroscience, vol 5 (eds Gutsuridis V, Graham B, Cobb S, Vida I). New York:
Springer. 5-26.
195 Witter MP, Amaral DG (1991). Entorhinal cortex of the monkey: V. Projections to the dentate gyrus,
hippocampus, and subicular complex. J Comp Neurol 307:437-439.
Witter MP, Van Hoesen GW, Amaral DG (1989). Topographic organization of the entorhinal
projection to the dentate gyrus of the monkey. J Neurosci 9:216-228.
Wonnacott S (1997). Presynaptic ACh receptors. Trends Neurosci 20:92-98.
Wonnacott S, Barik J (2007). Nicotinic ACh receptors. Tocris Biosci Sci Rev 28:1-20.
Wood ER, Dudchenko PA, Eichenbaum H (1999). The global record of memory in hippocampal
neuronal activity. Nature 397(6720):613-616.
Wood F, Ebert V, Kinsbourne M (1982). Human Memory and Amnesia (ed Cermak LS). Hillsdale,
New Jersey: Erlbaum. 167-193.
Wood RA, Moodley KK, Lever C, Minati L, Chan D (2016). Allocentric spatial memory testing
predicts conversion from mild cognitive impairment to dementia: an initial proof-of-concept
study. Front Neurol 7:215.
Wu W, Saunders RC, Mishkin M, Turchi J (2012). Differential effects of m1 and m2 receptor
antagonists in perirhinal cortex on visual recognition memory in monkeys. Neurobiol Learn
Mem 98:41-46.
Wunderlich K, Dayan P, Dolan RJ (2012). Mapping value based planning and extensively trained
choice in the human brain. Nat Neurosci 15:786-791.
Wyss JM, Swanson LW, Cowan WM (1979). Evidence for an input to the molecular layer and the
stratum granulosum of the dentate gyrus from the supramammillary region of the hypothalamus.
Anat Embryiol (Berl) 156:165-176.
Xiang JZ, Brown MW (1998). Differential neuronal encoding of novelty, familiarity and recency in
regions of the anterior temporal lobe. Neuropharmacol 37(4-5):657-676.
Yang T, Bavley RL, Fomalont K, Blomstrom KJ, Mitz AR, Turchi J, Rudebeck PH, Murray EA (2014).
Contributions of the hippocampus and entorhinal cortex to rapid visuomotor learning in rhesus
macaque. Hippocampus 24(9):1102-1111.
196 Yanike M, Wirth S, Smith AC, Brown EN, Suzuki WA (2009). Comparison of associative learning-
related signals in the macaque perirhinal cortex and hippocampus. Cereb Cortex 19(5):1064-
1078.
Yanike M, Wirth S, Suzuki WA (2004). Representation of well-learned information in the monkey
hippocampus. Neuron 42(3):477-487.
Yin HH, Knowlton BJ (2006). The role of the basal ganglia in habit formation. Nat Rev Neurosci
7:464-476.
Yin HH, Knowlton BJ, Balleine BW (2004). Lesions of dorsolateral striatum preserve outcome
expectancy but disrupt habit formation in instrumental learning. Eur J Neurosci 19:181-189.
Yin HH, Knowlton BJ, Balleine BW (2005). Blockade of NMDA receptors in the dorsomedial striatum
prevents action-outcome learning in instrumental conditioning. Eur J Neurosci 22:505-512.
Yonelinas AP (2001). Components of episodic memory: the contribution of recollection and
familiarity. Philos Trans R Soc Lond B Biol Sci 356(1413):1363-1374.
Yonelinas AP, Kroll NE, Quamme JR, Lazzara MM, Sauvé MJ, Widaman KF, Knight RT (2002).
Effects of extensive temporal lobe damage or mild hypoxia on recollection and familiarity. Nat
Neurosci 5(11):1236-1241.
Zola SM, Squire LR (2001). Relationship between magnitude of damage to the hippocampus and
impaired recognition memory in monkeys. Hippocampus 11:92-98.
Zola SM, Squire LR, Teng E, Stefanacci L, Buffalo EA, Clark RE (2000). Impaired recognition
memory in monkeys after damage limited to the hippocampal region. J Neurosci 20:451-463.
Zola-Morgan SM, Squire LR (1985). Medial temporal lesions in monkeys impair memory on a
variety of tasks sensitive to human amnesia. Behav Neurosci 99:22-34.
Zola-Morgan SM, Squire LR (1986). Memory impairment in monkeys following lesions limited to
the hippocampus. Behav Neurosci 100:155-160.
Zola-Morgan SM, Squire LR (1990). The primate hippocampal formation: evidence for a time-
limited role in memory storage. Science 250:288-290.
197 Zola-Morgan S, Squire LR, Amaral DG (1989a). Lesions of the hippocampal formation but not
lesions of the fornix or the mammillary nuclei produce long-lasting memory impairment in
monkeys. J Neurosci 9(3):898-913.
Zola-Morgan S, Squire LR, Amaral DG, Suzuki WA (1989b). Lesions of perirhinal and
parahippocampal cortex that spare the amygdala and hippocampal formation produce severe
memory impairment. J Neurosci 9:4355-4370.
Zola-Morgan S, Squire LR, Clower RP, Rempel NL (1993). Damage to the perirhinal cortex
exacerbates memory impairment following lesions to the hippocampal formation. J Neurosci
13:251-265.
Zola-Morgan S, Squire LR, Mishkin M (1982). The neuroanatomy of amnesia: amygdala-
hippocampus versus temporal stem. Science 218:1337-1339.
Zola-Morgan S, Squire LR, Rempel NL, Clower RP, Amaral DG (1992). Enduring memory
impairment in monkeys after ischemic damage to the hippocampus. J Neurosci 12(7):2582-
2596.
198