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! CROSS-SPECIES COMPARISONS OF THE IN PRIMATES: THROUGH AND NEUROPIL SPACE

A thesis submitted to Kent State University in partial fulfillment of the requirement for the degree of Master of Arts

by Mitch Sumner May, 2013

Thesis written by Mitch Andrew Sumner B.A., Indiana University of Pennsylvania, USA 2009

Approved by:

______Dr. Mary Ann Raghanti Advisor ______Dr. Richard Meindl Chair, Department of Anthropology ______Dr. Raymond A. Craig Associate Dean, Collage of Arts and Sciences

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TABLE OF CONTENTS

LIST OF FIGURES ...... v LIST OF TABLES ...... vi AKNOWLEDGEMENTS ...... vii ABSTRACT ...... viii

Chapter I. INTRODUCTION ...... 1

Declarative vs. nondeclarative ...... 4 and mental time travel in ...... 6 Memory in non- animals ...... 9 Connectivity and behavior ...... 13 Neuropil space ...... 15 The role of the retrosplenial cortex ...... 17

II. HYPOTHESES ...... 20

III. MATERIALS AND METHODS ...... 22

Specimens ...... 22 Histological identification of the retrosplenial cortex ...... 26 Tissue preparation ...... 26 Data collection ...... 27 Neuropil fraction ...... 27 Cell volume ...... 28 Statistical analysis ...... 28

IV. RESULTS ...... 32

Neuropil fraction ...... 32 Neuron size ...... 33 Regression analysis ...... 37

V. DISCUSSION ...... 41

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Principal Findings ...... 41 The emergence of mental time travel ...... 42

VI. CONCLUSIONS ...... 45

APPENDIX A. Individual neuropil fractions ...... 47 APPENDIX B. Individual neuron volumes ...... 48

REFERENCES ...... 50

! iv! LIST OF FIGURES

1. The anatomical position of Brodmann’s areas 29 and 30 ...... 2

2. An example Nissl stained section of a macaque ...... 3

3. Classifications of different types of memory ...... 5

4. Phylogeny of the primate species used in this study ...... 23

5. Photomicrograph of areas 29 and 30 ...... 24

6. Cytoarchitecture of each area in each species ...... 25

7. Example sites selected with fractionator sampling ...... 29

8. Example of the conversion method used to calculate neuropil ...... 30

9. An example of the method used to calculate neuron volume ...... 31

10. Differences in neuropil space among study species ...... 33

11. Differences in neuron volume in area 29 ...... 35

12. Differences in neuron volume in area 30 ...... 36

13. Regression analysis of volume for area 29, layer II and NF area 29 ...... 38

14. Regression analysis of volume for area 29, layer III and NF area 29 ...... 38

15. Regression analysis of volume for area 29, layers V/VI and NF area 29 ...... 39

16. Regression analysis of volume for area 30, layer II and NF area 30 ...... 39

17. Regression analysis of volume for area 30, layer III and NF area 30 ...... 40

18. Regression analysis of volume for area 30, layer V/VI and NF area 30 ...... 40

! v! LIST OF TABLES

1. Individuals used in this study ...... 23

2. Neuropil fractions for each species ...... 32

3. Neuron volume for each species ...... 34

! vi! ACKNOWLEDGEMENTS

First, I would like to thank Dr. Mary Ann Raghanti for support and expert advice while working towards the completion of this project, as well as for being an excellent science role model. I would also like to thank the other members of my committee, Dr.

Richard Miendl and Dr. F. Robert Treichler, as well as Dr. Muhammad Spocter, Dr. Chet

Sherwood, Chery Stimpson, Dr. Patrick Hof, Dr. Marilyn Norconk, Dr. Linda Spurlock, and Caroline Tannert for not only support and/or science advice, but for generally being incredibly helpful.

Finally, a special thanks goes to my friends in the Anthropology department, my family, Aidan Ruth, and Echo the Dog, all of whom provided valuable motivational support during the past two years.

! vii! ABSTRACT

Chronesthesia, or mental time travel (MTT), is the ability to be conscious of both past experiences and possible future scenarios. Behavioral studies have demonstrated that some non-human animals are capable of episodic-like memory, yet there exists no scientific consensus on the extent of memory abilities, including complex future thinking or planning, among non-human species. The retrosplenial cortex (Brodmann’s areas 29 and

30) plays a critical role in episodic memory, which is vital for MTT. Because MTT appears to be a uniquely human capacity, this region is of major interest for evolutionary studies.

However, comparative neuroanatomical data for these regions are scarce. The goal of the present analysis was to compare neuropil space among capuchins, macaques, , and humans to determine if humans significantly differ from the other species. The amount of neuropil space provides a proxy measure of connectivity because a large component of the neuropil is comprised of dendrites, synapses, and axons. Digital images were analyzed using ImageJ software to obtain a neuropil fraction. The results showed significantly higher neuropil fractions in humans relative to the other species examined. Further analysis showed that this difference could not be attributed to a decrease in cell volume. These results demonstrate a unique neuroanatomical reorganization of the human retrosplenial cortex. If it is true that an increase in neuropil space is indicative of an increase in connectivity, then it may be that this is a neuroanatomical substrate that evolved to support complex mental time travel in humans.

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CHAPTER ONE

Introduction

The ability to contemplate the future and reflect on past events, referred to as chronesthesia or mental time travel, has been hypothesized to be a uniquely human capacity (Tulving, 2002a). The emergence of mental time travel would have provided our ancestors with significant behavioral advantages in terms of anticipating and planning for the future, likely having a significant impact on the survivorship of our species.

Behavioral studies have demonstrated that some non-human primates and other animals seem to be capable of episodic-like memory (Schwartz and Evans, 2001; Correia, Sérgio

P. C. et al., 2007), yet there exists no scientific consensus on the extent of these memory abilities, including complex future thinking or planning, among non-human species. The neuroanatomical substrates that support mental time travel are of evolutionary interest yet comparative data for the brain areas associated with these functions are sparse.

To address this paucity of data, the present study used microscope-based image analysis techniques and advanced stereology to conduct a comparative examination of the retrosplenial cortex (i.e., Brodmann’s areas 29 and 30; Figures 1 and 2) (Brodmann,

1909), based on evidence of its role in the network of areas responsible for mental time travel (Addis et al., 2007; Botzung et al., 2008; Suddendorf, 2009; Vann et al., 2009).

Neuropil, the space not occupied by neuronal and glial cell bodies, was quantified and compared among humans, chimpanzees, macaques, and capuchins as an indirect measure

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of neural connectivity. Neuropil space has been used as a measure of connectivity because a large component of neuropil is occupied by dendrites, axons, and synapses.

Neuron volumes were then compared to better determine the basis for differences in neuropil space among species in the retrosplenial cortex.

Figure 1: An illustration of the medial surface of the with Brodmann’s areas 29 and 30 identified.

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Figure 2: An example of a nissl-stained coronal section of a macaque brain. The anatomical position of areas 29 and 30 are delineated by arrowheads. Scale bar = 5 mm.

The current study provides neuroanatomical evidence in support of the hypothesis that the human retrosplenial cortex underwent a unique neuroanatomical reorganization that resulted in an increase in neuropil space and thus a possible increase in connectivity

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independent of changes in neuron volume. This reorganization may have provided a neuroanatomical substrate for mental time travel to emerge out of preexisting rudimentary memory systems, such as semantic, spatial, or some other form of declarative memory, as a novel human ability.

A discussion of memory and its various subcategories, the characterization of memory in humans and other species, and the neuroanatomical substrates of memory will follow.

Declarative vs. nondeclarative memory

Understanding the retention and retrieval of has long been a goal of cognitive neuroscience. At its core, memory is the process in which knowledge obtained from our environment is stored and retrieved (Kandel and Schwartz, 1985) and throughout our lives, important behaviors are learned and experiences are retained.

Studies of patients with brain lesions (Squire, 2004), as well as imaging techniques such as positron emission tomography (PET) and functional magnetic resonance imaging

(fMRI), have provided much of our foundational knowledge of memory systems and their organization (Gabrieli, 1998). Through these studies, and a long history of psychological interpretation and experimentation (Squire, 2004), we have come to recognize several distinct memory systems.

The human brain is capable of storing and retrieving two major categories of long-term memories: declarative and nondeclarative (Figure 3) (Kandel et al., 2000;

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Squire, 2004). Nondeclarative (named because the memories themselves are not verbalized), implicit, or procedural memory involves perceptual and motor skills that can be recalled without reference to the experience of acquisition (e.g., how to drive a car and how to tie your shoes) (Kandel and Schwartz, 1985). Insight gained from studies and patients with brain damage has shown that a wide collection of brain areas play roles in nondeclarative memory, including the striatum (skills and habits), cerebellum (motor skills), and (emotion response) (Kandel and Schwartz,

1985).

Figure 3: Classifications of the different types of long-term memory systems. (Adapted from Squire, 2004).

Nondeclarative memories have been experimentally demonstrated to be distinct from declarative memories based on tests that involve conditioning, skill , and priming, as examples (Gabrieli, 1998). Some priming tests ask participants to quickly

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complete a succession of word fragments based on previously studied words (i.e., study word: GLOBE; fragment: GLO__). Results showed that participants had a strong tendency to fill in the letters with those that spell the study word instead of other possible words. When amnesic patients were subjected to the same line of priming tests, they performed just as well as non-amnesic subjects, even when their condition prevented them from remembering the study period of the experiment or even the word itself

(Tulving and Schacter, 1990; Milner et al., 1998). These results support the idea of a fundamental divide between declarative and nondeclarative memory systems and how memories are stored and retrieved (Squire, 2004).

The other major category, declarative, or , is the type of memory usually referred to by the common usage of the word “memory” (Milner et al., 1998).

Declarative memories are the knowledge of facts () or events (episodic memory) and can be recalled willingly by an individual (e.g., the alphabet or your last birthday party) (Eichenbaum, 1997). Like nondeclarative memory, declarative memory involves a wide range of brain areas, evidenced by patients with neurological damage and disorders (Eichenbaum, 1997). Structures within the medial , including the and adjacent areas (Zola-Morgan and Squire, 1993) as well as the retrosplenial cortex (Shallice et al., 1994), are involved in declarative memory functions.

Episodic memory and mental time travel in humans

Mental time travel is the ability to mentally recollect and reflect on memories of past events as well as anticipate and contemplate possible future events. It is generally

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agreed upon that mental time travel relies on the two subsets of declarative memory: episodic and semantic (Suddendorf and Corballis, 2007). The first is memory of past events in your personal history, rather than just the knowledge retained from an event.

These memories can be recalled even in the absence of external cues (Tulving, 2002a).

Tulving (1972) originally suggested that a memory must meet a basic set of criteria of containing ‘what’, ‘where’, and ‘when’ information in order to define it as an episodic memory.

The second type, semantic memory, is made up of general facts and knowledge about the world not related to your personal (Tulving, 1983). For example, a memory of a family vacation is an episodic memory, while knowledge of the epochs in the geologic time scale is a semantic memory. Or, to use another example, remembering a trip to Yellowstone National Park with your family is an episodic memory, while the knowledge of what state Yellowstone is in, and that it is America’s first National Park

(Haines, 1996) is a semantic memory. By utilizing both types of memories, scenarios can be generated using prior knowledge about the world as well as particular pieces of personal information to create a scene that took place in either a person’s past or future

(Roberts and Feeney, 2009).

While episodic and semantic memories are similar in that they both contain information that can be verbally recalled, there is evidence to suggest that there is a distinct difference in the neuroanatomical areas governing their functions (Tulving,

2002a). One of the most compelling lines of evidence in support of this comes from

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clinical cases of patients with brain damage, especially from a patient referred to as K.C.

(Tulving, 2002a). After suffering a traumatic brain injury in mid-life that affected both anterior and posterior portions of his brain, including the left frontal-parietal and right parietal-occipital lobes (Tulving et al., 1988) as well as the hippocampus (Tulving,

2002b), K.C. showed signs of severe anterograde and . Despite his injuries, K.C. was able to read, write, and speak normally, could play instruments and games, could general facts about the world, and could conduct himself in a socially acceptable manner that gave little hint to his injury. In other words, his semantic memories seemed to have been unaffected by his accident (Tulving, 2002a).

However, K.C. had no memory of anything that had ever happened to him personally (Tulving et al., 1988). When prompted, he could not name or describe any situation that he had been in from the time of his birth to the present, yet could recall semantic memories from that period (Tulving, 2002a). Furthermore, he had trouble retaining new information. It seems that K.C.’s accident mostly affected his ability to retain and recall episodic memories (Tulving et al., 1988), possibly as a result of the damage to his hippocampus and/or adjacent areas (Zola-Morgan and Squire, 1993). He was neither able to recall anything from his own past, nor was he able to anticipate anything about his future and, when asked to do so, he described what was happening in his mind as “blank“ (Tulving 2002b). While bleak, K.C.’s condition provided an important line of evidence when arguing the distinction between semantic and episodic memories and the neuroanatomical areas associated with them. K.C. was capable of acquiring and recalling the former, but his accident seems to have damaged one or more

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of the components of the brain that make episodic memory and mental time travel possible (Tulving 2002b).

Memory in non-human animals

Tulving (2002) and Suddendorf (2009) have argued that humans alone possess episodic memory and the ability to mentally travel through time. Some researchers have suggested that non-human animals live in a permanent present with no mental concept of time beyond learned semantic memories and innate behaviors (Roberts and Feeney,

2009). Hibernation, migration, and food storage are examples of innate behaviors that rely on biological rhythm and day/night cycles, which evolved as adaptations for their specific environments (Rusak and Zucker, 1975). However, these behaviors have a physiological rather than a memory basis and are thus fundamentally different when compared to preparations influenced by forethought or mental time travel. Yet, many argue that some non-human animals are capable of utilizing past experiences to make conscious decisions about their present and future (Clayton and Dickinson, 1998, Zentall et al., 2001).

While non-human animals almost certainly rely on learning and memory to some extent for survival, the difference may be that they do not recognize subjective time, otherwise known as autonoetic or awareness (Tulving, 2002a). If an animal were able to recognize the passage of time and events in the past, but not their specific personal past experiences, then they would be experiencing a semantic rather than episodic version of the past and mental time travel would be impossible. However,

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these conditions have been difficult to test experimentally as we do not have access to other organism’s minds and possess no measureable way to recognize or record consciousness (Suddendorf and Corballis, 2007). Despite this, many studies have attempted to provide evidence of episodic memory and mental time travel in non-human animals, with varying degrees of success. However, only few have come close to showing the ability to recall ‘what’, ‘where’, and ‘when’ information, the basic set of criteria for episodic memory (Tulving 1972).

Clayton and Dickinson (1998) conducted several experiments with Scrub Jays

(Aphelocoma coerulescens) in an attempt to show that they are capable of episodic or episodic-like memory. The experimenters tested if the birds would preferentially choose between two types of food, one that degrades quickly over time (worms; their preferred food) and one that does not (peanuts), at different points in time depending on the stage of degradation. The birds chose the worms in an early trial, and without even inspecting the decayed worms, chose the peanuts in a later trial. The authors proposed that these behaviors suggested an awareness of the passage of time by the birds that chose the peanuts in the later trial, implying the birds knew the worms had degraded and chose their food accordingly (Clayton and Dickinson, 1998). The authors were careful to describe their abilities as ‘episodic-like’ because, as mentioned above, without speech or a criterion to identify autonoetic consciousness in animals, it is difficult to recognize true episodic behavior. It is also worth noting that the experimenters subjected the birds to training trials, which may have contributed to the observed behaviors and could perhaps be explained with semantic knowledge only.

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A similar experiment was attempted by Bird et al. (2003) in rats (Rattus norvegicus), where the animals were given two types of food to cache (cheese and pretzels). After several days the cheese had become degraded and inedible, yet the rats showed no behavior to suggest they were able to modify their actions based on an awareness of the passage of time, and went to the sites of both the cheese and pretzels equally as often. These results indicated that the rats had memories for ‘where,’ but not

‘when’ (Bird et al., 2003). This suggests that rats are not capable of episodic or episodic- like memory and the possibility that this ability evolved as a homoplasy in some birds and humans.

This idea of homoplasy was supported by Zentall et al’s (2001) investigation of pigeons, where birds were taught to peck at certain colored stimuli to indicate previous behaviors. To distinguish between episodic and semantic responses, researchers posed

“questions” to the birds unexpectedly, where the only training the birds were subjected to was on how to answer the questions. The birds were prompted to either peck or not peck at blue or yellow colored response keys, and then were “asked” to answer the question

“What did you just do?” by pecking at a red or green colored response key. By pecking at the yellow response key they received an award and were then presented with the

“question” keys. The authors found that the birds were more likely to peck the keys in correct order, pecking the red response key after pecking the yellow reward response key, signaling, “I just pecked,” and the green response key after not pecking the blue response key, signaling “I just refrained from pecking.” Their actions were argued to be analogous

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to episodic memory because they were able to accurately report whether or not they had performed an action (Zentall et al., 2001; 2006).

Another example of possible episodic or episodic-like memory in a non-human species, evolutionarily much closer to our own, is Osvath and Osvath’s report (2008) on two chimpanzees and an orangutan that showed behavior associated with future thinking and planning beyond their immediate needs. The great in question were observed selecting and keeping tools for use an hour later. Choosing the appropriate tool from a selection of tools allowed the apes to access fruit-soup at a later time, showing that the apes had some understanding of what was required of them to obtain the future reward.

However, results could again be attributed to pre-testing periods where the experimenters

‘reminded’ the apes about the reward (Suddendorf et al., 2009). Yet, this study, coupled with anecdotal observations of the Franje collecting straw to make a warm nest well before she could have felt the cold outside (de Waal, 1982), seems to suggest some form future thinking in these primates.

The study of episodic memory and mental time travel in animals, especially primates, is difficult and is only in its infancy (Roberts and Feeney, 2009). Compounding this difficulty is that fact that non-human animals are not capable of speech, which is the source for much of the descriptive evidence for this ability. Thus, researchers have resorted to behavioral experiments that have produced a wide spectrum of results. While current behavioral results do seem to suggest that apes and other animals may possess episodic-like abilities, more studies with better controls need to be conducted in a way

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that can quantitatively establish forethought in non-human animals that is comparable to our own (Suddendorf et al., 2009). Until then, it remains unclear if the lack of conclusive behavioral evidence is indicative of the true capabilities of the animals under study or an inability to recognize and measure memory capabilities in the absence of verbal communication. As a result, no scientific consensus exists on the extent of memory abilities in non-human animals.

Connectivity and behavior

The human brain has been shown to be the most encephalized of all mammals

(Martin and Harvey, 1985), exhibiting a size that is more than three larger than expected for a haplorhine of the same body mass (Holloway, 1979). Fossil remains from early Miocene hominoids, such as Proconsul, show a relatively larger brain size when compared to mammals of similar body size (Walker et al., 1983), suggesting increased encephalization was present early in hominoid (i.e., apes and humans) evolution, but was still far below measurements of modern humans. Some have suggested that an enlarged brain alone may be responsible for the complex of distinct behavioral characteristics that define humans (e.g., Jerison, 1973), however it seems unlikely that only one variable is responsible for all of human cognitive abilities (Sherwood et al., 2012). While some cognitive and behavioral modifications were likely the result of overall brain expansion

(Sherwood et al., 2012) or changes in size of particular regions (Rilling and Seligman,

2002; de Sousa et al., 2010), it also appears that microstructural and molecular changes in brain organization may have contributed to major changes in behavior (Preuss et al.,

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2004; Raghanti et al., 2008; Craig and Halton, 2009; Sherwood et al., 2010; Raghanti et al., 2012). Larger and microstructural modifications, including increases in connectivity (Elston, 2003), combined with extended periods of growth and development

(Schultz, 1969) and complex social interactions (Cheney et al., 1986) exhibited by hominoids, suggest that, among other things, learning and memory are important features of the complex of adaptations in humans and other primates.

Some research into cortical cytoarchitecture and connectivity has shown that humans exhibit increased connectivity within prefrontal cortical areas involved in executive functions (Elston, 2003); a property that plays roles in many cognitive functions unique to humans (Miller and Cohen, 2001). Connectivity in this context refers to physical synaptic interactions between both individual neurons and among neural structures through which information is conveyed (Kandel et al., 2000) via action potentials and neurotransmission. Thus, an increase in connectivity, and an increase in the commutative capabilities of neural structures, would allow for the transfer and integration of larger amounts of information across a neural network, translating behaviorally to higher cognitive functions (Sporns et al., 2004), including mental time travel. Evidence suggests a decrease in overall connectivity results in impaired social and, communication skills and this deficit is associated with human-specific neuropathologies such as autism (Belmonte et al., 2004) and schizophrenia (Lynall et al., 2010).

One quantitative approach to investigating neocortical cytoarchitecture and connectivity is the measurement of the proportion of neuropil space in the cortex (Zilles

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and Rehkämper, 1988; Semendeferi et al., 2001; Sherwood et al., 2004; Spocter et al.,

2012). Neuropil is the space not occupied by neuronal and glial cell bodies and is largely occupied by connections (Semendeferi et al., 2001). In areas with more neuropil, the space is occupied by dendrites and axons, providing more opportunity for synaptic interconnections and the possibility of greater integrative capacity (Sherwood et al.,

2004), allowing for an enhanced degree of information processing from cortical inputs.

Thus, quantifying the neuropil provides a rough measure of interconnectedness among neurons within a region and gives some indication of the integrative capacity of the cortical area (Sherwood et al., 2004). However, neuropil is not a direct measure of connectivity, but is a simple estimation of a complex intercellular system that may give some indication of intrinsic and extrinsic connectivity in a region (Spocter et al., 2012).

Neuropil space

Comparative analyses have examined differences in neuropil space within cortical areas involved in a variety of cognitive functions. For example, Buxhoeveden and

Casanova (2000) recognized that the human brain does not have additional cortical areas compared to a macaque monkey, thus the development of specialized behaviors, such as language, must be the result of other evolutionary changes. Their data showed that area

Tpt in humans possessed an increase in neuropil space relative to chimpanzees and macaques, and the authors suggested that this microanatomical modification may have contributed to the emergence of language in our species.

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More recently, Semendeferi et al. (2011) reported that neuropil space in the among species is significantly larger in humans as a result of neural reorganization, suggesting that slight modifications in circuitry and connectivity may have played a large role in setting humans apart from other primates. An analysis by

Spocter et al. (2012) focused on six distinct areas of the neocortex, including the primary (area 4), frontopolar cortex (area 10), (area 22), primary (area 41/42), Broca’s area (area 45), and the frontoinsular cortex (area FI), to test the hypothesis that humans possess a unique distribution of neuropil and thus increased connectivity in one or more of these areas. Their results showed that the frontopolar cortex (area 10) and frontoinsular cortex (area FI) had significantly higher neuropil than other areas in humans compared to chimpanzees, suggesting that modifications to prefrontal cortical regions, which are involved in complex executive functions, including episodic memory (Shallice et al., 1994), are associated with the evolution of the human brain. In addition, their results also provided evidence that neuropil space can be used as a measure of total connectivity in a particular area

(Buxhoeveden and Casanova, 2000; Semendeferi et al., 2011; Spocter et al., 2012).

Further, results showed that variation in neuropil fraction among species is not correlated with brain size (Sherwood and Hof, 2007; Spocter et al., 2012).

Because cortical neuropil fractions are not correlated with brain size, this variable is a good candidate to investigate for neural reorganization. This reorganization could be influenced by a number of variables, including dendritic branching patterns (Semendeferi et al., 2011) or a narrowing of human minicolumns, which have been proposed to act as

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input/output processing structures that maintain connections (Mountcastle, 1978).

Narrower columns would function to divide input coming into a region into more elements, increasing the resolution of the connections (Gustafsson, 1997; Semendeferi et al., 2011). While wider minicolumns may individually contain more connections, their size limits their density in the cortex. Therefore, narrow minicolumns allow for a higher density and more interconnectedness (Semendeferi et al., 2011). Investigations of human

Brodmann’s areas 44 and 45, as well as the prefrontal cortex, have shown narrower than expected minicolumns, suggesting that this feature is an important for investigations of modifications to neural circuitry and connectivity (Semendeferi et al., 2011).

The role of the retrosplenial cortex

Based on anatomical, pathological, and experimental evidence, the retrosplenial cortex has been implicated in playing an important role in episodic memory function.

Because of this, this area is important in the study of . Anterograde and retrograde axonal tracing studies in macaque monkeys (Kobayashi and Amaral, 2003;

2007) have shown that the retrosplenial cortex shares reciprocal connections with the (subiculum, presubiculum, and ), the parahippocampal region ( and areas TH and TF), and select thalamic nuclei (the anterior and lateral dorsal nuclei), all of which have been shown to be associated with memory (Kobayashi and Amaral, 2003; 2007; Vann et al., 2009).

Connections between the hippocampus and the retrosplenial cortex account for 20% of the input to the hippocampus in macaque monkeys and are higher in density than the

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connections between the hippocampus and adjacent cingulate area 23, suggesting a stronger role for the retrosplenial cortex in hippocampus-dependent functions than other posterior cingulate regions (Kobayashi and Amaral, 2003). Projections from the amygdala to the retrosplenial cortex suggest that the area also may play some role in integrating emotional information (Buckwalter et al., 2008).

Patients with lesions involving the retrosplenial cortex have exhibited significant memory problems (Svoboda et al., 2006). For example, Bowers et al. (1988) described a

39-year-old man who suffered a lesion in the left retrosplenial cortex and underlying white matter, resulting in profound retrograde amnesia. The lesion did not affect the hippocampus, amygdala, basal forebrain, thalamus, or other areas usually associated with amnesia. As a result of his condition, the patient lost all memory of the previous nine months, as well as the memory of the birth of his daughter four years earlier. He also experienced and was unable to retain recent episodic experiences or temporal information, suggesting a strong correlation between the retrosplenial cortex and episodic memory function. The author suggests that his memory impairment may be the result of a disruption of information from the site of the injury to the frontal lobes. His verbal, motor, and social skills remained intact, and investigations into other cognitive deficits were negative. After six months, his memory returned to normal (Bowers et al.,

1988).

Neuroimaging studies have shown that many of the same brain regions, including the retrosplenial cortex, are activated in both recalling past experiences and imagining

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possible future scenarios (Vann et al., 2009), suggesting that both abilities may draw on similar information from associated cortical areas. Addis et al. (2007) used fMRI to identify the neural correlates of remembering and imagining past and future scenarios in sixteen participants. Subjects were given cue words and asked to construct, in their mind, both past and future events that existed within a specified time period. Results showed that significant overlap existed in regions, including the retrosplenial cortex, as well as the left hippocampus, parahippocampus, and the during past and future construction. The most overlap occurred during elaboration of the constructed event

(Addis et al., 2007), which is consistent with lesion studies in amnesic patients who exhibit deficits in both past and future thinking (Tulving, 2002a). The results corroborate the hypothesis that remembering the past and imaging the future rely on common neural substrates (Addis et al., 2007).

Other functions of the retrosplenial cortex involve spatial navigation (Maguire,

2001) and possibly emotion (Maddock 1999). Lesion studies have shown that damage to this area impairs rodent’s abilities to successfully navigate a maze, and results in topographical disorientation in humans (Maguire, 2001). Projections from the amygdala to the retrosplenial cortex suggest that the area may play some role in integrating emotional information (Buckwalter et al., 2008).

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CHAPTER TWO

Hypotheses

The present study examines neuropil fraction and neuron size in the retrosplenial cortex of humans and non-human primate species. Using this fraction as a proxy measure of connectivity, this study tests the hypothesis that the human retrosplenial cortex has increased connectivity relative to the retrosplenial cortex of other primates. After the human-chimpanzee split, neuroanatomical reorganization may have resulted in an increase in connectivity (as evidenced by an increase in neuropil space) in in the retrosplenial cortex, one of the areas in the network of neuroanatomical structures associated with mental time travel.

Hypothesis 1: There will be an increase in neuropil fraction in humans relative to other primate species. This increase may be associated with the emergence of mental time travel.

This increase in neuropil space would be indicative of an increase in connectivity, which means that the human retrosplenial cortex would have an enhanced integrative capacity, making higher cognitive functions, such as mental time travel, possible.

The null hypothesis is that there will be no significant differences in neuropil fractions of retrosplenial cortical areas across species. If the null hypothesis stands, it could be that the retrosplenial cortex is only one part of a complex system and data from

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just this area may not accurately reflect the overall neuroanatomical changes that make mental time travel possible. Alternatively, an increase in neuropil space in areas 29 and

30 may not have been required for human mental time travel.

Hypothesis 2: Differences in neuropil space in the retrosplenial cortex are independent of changes in neuron size.

Further analysis was sought to determine more specifically the basis of differences in neuropil space in the retrosplenial cortex across species. The null hypothesis is that there will be no differences. If neuropil fraction increases independent of neuron size, then species differences in neuropil fraction may be the result of neural reorganization, possibly as a result of dendritic branching patterns or a narrowing of minicolumns.

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CHAPTER THREE

Methods and Methods

Specimens

The study sample included humans, chimpanzees, pigtailed macaques, and capuchin monkeys (Table 1, Figure 4). The capuchin and macaque sections derive from

Dr. Raghanti’s existing histological collection. Histological slides of chimpanzee brain tissue were borrowed from the Great Ape Aging resources through Dr. Patrick Hof at

Mount Sinai School of Medicine in New York, NY and Dr. Chet Sherwood at The

George Washington University in Washington, DC. Human samples were acquired from the Cognitive Neurology and Alzheimer’s Disease Center at Northwestern University in

Evanston, IL. The study sample was limited to non-geriatric adults (with the exception of one juvenile capuchin, age 3) free of gross neuropathological diseases or abnormalities.

The human subjects showed no signs of dementia before death. None of the subjects were part of any research protocol that may have contributed to their death and all died of natural causes. Sexes were as balanced as sample availability and histological slide quality allowed.

All samples were from the left hemisphere for two reasons: first, because primate brains are a limited resource, and it is difficult to obtain both hemispheres. Second, because evidence suggests that the left hemisphere of the retrosplenial cortex plays a role

! 22! 23! !

in acquiring new temporal info, which would be necessary in the formation of episodic memories (Bowers et al., 1988).

Table 1.Individuals used in this study. Species Age Sex Cebus apella 18 F Cebus apella 18 F Cebus apella 13 F Cebus apella 16 M Cebus apella 3 M Cebus apella 17 M Homo sapiens 57 F Homo sapiens 53 F Homo sapiens 43 F Homo sapiens 35 M Macaca nemestrina 15 F Macaca nemestrina 9 F Macaca nemestrina 6 F Macaca nemestrina 16 M Macaca nemestrina 7 M Pan troglodytes 41 F Pan troglodytes 12 F Pan troglodytes 35 F Pan troglodytes 19 M

Figure 4: Phylogeny of the primate species used in this study

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Figure 5: Photomicrograph of a Nissl-stained section showing Brodmann's areas 29 and 30 in a pigtailed macaque. CC = . Scale bar = 250 µm.

Sc

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Figure 6: Coronal nissl-stained sections showing the cytoarchitecture of the regions sampled in capuchin (A), macaque (B), chimpanzee (C), and human (D). Cortical layers are indicated by roman numerals. Scale bars = 250 µm.

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Histological identification of the retrosplenial cortex

Area identification of the region of interest (Brodmann’s areas 29 and 30; Figures

5 and 6) was based on previously published materials on the cytoarchitecture of cortical areas 29 and 30 of human (Morris et al., 2000) and macaque monkey (Kobayashi and

Amaral, 2000). The retrosplenial cortex is located deep in the midline of the brain and forms part of the posterior cingulate region, immediately posterior to, and forming an arch around the splenium (Figure 1), which is the most caudal part of the corpus callosum and is comprised of two areas, 29 and 30. Area 29 is recognizable based on dense, granular appearance of layer II, as opposed to area 30, which experiences a gradual loss of layers III-IV and thus a loss in cell density. Areas 29 and 30 are commonly described as granular and dysgranular (i.e., the presence or absence of layer IV), respectively

(Morris et al., 2000). The retrosplenial cortex has been described as an ‘intermediate’ cortex, based on the transitional laminar pattern that becomes six clearly defined layers in area 23, adjacent to the RSC (Vann et al., 2009).

Tissue preparation

All tissue samples were cyroprotected by immersion in graded sucrose solutions, frozen on dry ice, and then sectioned at 40 µm-thick using a freezing-sliding microtome.

As the sections were cut, they were placed into sequentially numbered microcentrifuge tubes containing a freezer storage solution (30% each distilled water, ethylene glycol, and glycerol, and 10 % b. 0.244 M PBS) and stored at -20° C. Every 10th section containing

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the areas of interest (400 µm apart) was then stained for Nissl substance with a solution of 0.5% cresyl violet to reveal cell somata.

All samples were prepared using the same procedures; therefore any artifacts related to histological preparation are expected to be consistent across samples. Nissl- stained sections, which were the source of images for data collection, were analyzed at low power-magnification (4x) to define area boundaries. Then, at 20x, fractionator sampling was used to systematically collect images that spanned layers I to VI within the retrosplenial cortex. Images that fell outside of the study area or had tears in the tissue were omitted from analysis. The remaining images were then used to quantify the neuropil fraction of the area of interest. The fraction was defined as the regions that are darkly stained, comprised of basophilic cell bodies, compared to the unstained region, which is putatively comprised of dendrites, axons, and synapses.

Data collection

Neuropil fraction

Image collection was performed using an Olympus BX-51 photomicroscope equipped with a digital camera connected to a Dell PC running StereoInvestigator software (MicroBrightField Bioscience, Willston, VT). Following methods outlined in

Spocter et al. (2012), digital images (1,600 x 1,200 pixels in size) from the sections were captured and imported into ImageJ software (http://rsbweb.nih.gov/ij) (Figures 7 and 8).

An average of 16.81 images per section from each area was collected. Each image was

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then converted to binary (black and white) and the neuropil fraction was calculated by dividing the number of white pixels (representing neuropil) by the total number of pixels in the image.

Cell volume

Further analysis sought to determine more specifically the basis of differences in neuropil space in the retrosplenial cortex across species. Neuron volume was estimated to determine if differences in neuropil space were correlated with differences in neuron size.

The nucleator probe in the StereoInvestigator software was used as an unbiased estimator of neuron volume in layers II, III, and V/VI of the stained sections (Gundersen, 1988).

For each cortical area, the separate layers were outlined at low magnification (4x). At high magnification (60x), neurons were selected randomly by applying an optical fractionator sampling scheme to provide an unbiased representation of the distribution of neuron volumes (Figure 9). Only neurons with an identifiable nucleus were chosen for analysis. Each time a neuron was counted with the optical fractionator in a counting frame, a grid of six rays extended from the nucleus of the selected neuron in random directions. Each point where the lines met the boundary of the neuronal soma was marked. The mean line length was used as the radius to calculate the cross-sectional area and volume occupied by the neurons (Gundersen, 1988; see Figure 9).

Statistical Analysis

Data analysis was performed using SPSS software version 21.0 for Mac OS X

! ! 29! !

(http://www.01.ibm.com/software/analytics/spss). Analyses of variance (ANOVAs) were used to compare neuropil fraction for each cortical area among species. Differences in neuron volume for were also examined using ANOVAs. Least Significant Difference

(LSD) post-hoc tests were used to evaluate significant findings. To examine the relationship between changes in neuropil fraction and changes in cell size, linear regression analyses were performed. Alpha (α) was set at 0.05 for all statistical tests.

Figure 7: A representative section of the retrosplenial cortex showing the location of sites that were selected by fractionator sampling for image collection. Scale bar = 250 µm.

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Figure 8: An example of the conversion method used to calculate neuropil. Top: a captured image of retrosplenial cortical area 30 at 40x magnification. Bottom: the same image after conversion to binary. The black space represents cell bodies while the white is neuropil. Scale bar = 25 µm.

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Figure 9: An example of the method used to measure neuron volume using the nucleator probe. The borders of the region of interest were drawn at low magnification (A). At high magnification neuron volumes were measured with the nucleator probe (B).

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CHAPTER FOUR

Results

Neuropil fraction

For retrosplenial cortex areas 29 and 30 (Table 2, Figure 8), results indicated a significant difference in neuropil space across species (29: Brown-Forsythe F3,10.62 = 4.71, p < 0.05; 30: Brown-Forsythe F3,10.48 = 4.50, p < 0.05). Least Significant Difference

(LSD) post hoc analyses revealed that humans had a significantly larger neuropil fraction relative to all other species in areas 29 (all p’s < 0.05) and 30 (all p’s < 0.05). Differences among the nonhuman species were not detected.

Table 2. Neuropil fractions for each species. Data are presented as mean ± standard deviation.

Species N Area 29 Area 30 Cebus apella 6 0.57 ± 0.08 0.59 ± 0.08 Macaca nemestrina 5 0.58 ± 0.04 0.59 ± 0.04 Pan troglodytes 4 0.57 ± 0.02 0.55 ± 0.03 Homo sapiens 4 0.69 ± 0.07 0.70 ± 0.08

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Figure 10: Bar graph showing differences in neuropil space among study species. Error bars represent SDs. Significant differences between humans and other species are indicated by asterisks.

Neuron size

Estimated neuron volume (Table 3) calculated with the nucleator probe and analyzed using an ANOVA showed significant differences in neuronal some size across species in all layers and both cortical areas (Area 29, Layer II: F3,11 = 38.89, p < 0.05;

Layer III: F3,11 = 50.69, p < 0.05; Layers V/VI: F3,11 = 17.46, p < 0.05; See Figure 9; Area

30, Layer II = F3,11 = 6.91, p < 0.05; Layer III: F3,11 = 12.06, p < 0.05; Layers V/VI: F3,11

= 43.55, p < 0.05; See Figure 10).

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Table 3. Neuron volume for each species, layer and area. Data are presented as mean ± standard deviation. Species Layer Area 29 Area 30 N Homo sapiens II 268.06 ± 85.97 319.65 ± 114.02 3 III 401.99 ± 161.77 429.36 ± 116.93 3 V/VI 431.29 ± 132.95 475.79 ± 94.12 3 Pan troglodytes II 505.72 ± 82.43 491.37 ± 88.77 3 III 342.89 ± 74.34 323.30 ± 134.76 3 V/VI 539.09 ± 115.60 487.09 ± 107.89 3 Mac aca nemestrina II 916.97 ± 176.34 1017.01 ± 266.90 3 III 1230.07 ± 177.05 1350.58 ± 360.45 3 V/VI 1215.05 ± 159.08 1354.11 ± 224.35 3 Cebus apella II 1262.98 ± 114.70 787.38 ± 273.20 3 III 1484.04 ± 127.44 1352.62 ± 394.32 3 V/VI 1576.30 ± 387.11 1588.66 ± 1476.38 3

Least Significant Difference (LSD) post hoc analysis revealed that in Area 29, layer II, all species had significantly different neuronal volumes from each other (all p’s

< 0.05). However, in layers III and V/VI, chimpanzees and humans were not significantly different from each other (III: p = 0.62; V/VI: p = 0.56), nor were macaques and capuchins (III: p = 0.06; V/VI: p = 0.09). Yet, humans and chimpanzees are significantly different from both macaques and capuchins in layers III and V/VI (all p’s < 0.05)

(Figure 11). Similar results were found in Area 30, but humans/chimpanzees and

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macaque/capuchin were significantly different from each other layer II, as well.

Chimpanzees and humans were not significantly different from each other in any sampled layer (II: p = 0.33; III: p = 0.66; V/VI: p = 0.93), nor were macaques and capuchin (II: p

= 0.11; III: p = 0.99; V/VI: p = 0.09). Yet, humans/chimpanzees and macaques/capuchins were again significantly different from each other in all layers (all p’s < 0.05) (Figure 12).

Figure 11: Bar graph showing differences in neuronal volume (µm3) in area 29 among study species. Error bars represent SDs. Significant differences between capuchins and other species, and between macaque and other species are indicated by asterisks.

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Figure 12: Bar graph showing differences in neuronal volume (µm3) in area 30 among study species. Error bars represent SDs. Significant differences between capuchins and other species, and between macaque and other species are indicated by asterisks.

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Regression analysis

In order to quantify the relationship between neuropil fraction (NF) and neuronal volume, a series of linear regression analysis were performed. The regression equations for the relationships between neuropil fraction in area 29 and neuronal volume in layers II,

III, and V/VI are:

NF = 0.656 - 6.9 x 10-5 (Layer II), R2 = 0.18

NF = 0.641 - 4.2 x 10-5 (Layer III), R2 = 0.11

NF = 0.657 - 5.6 x 10-5 (Layers V/VI), R2 = 0.20

The R2 value shows a weak interaction between neuropil fraction in area 29 and neuronal volume in layers II, III, and V/VI, suggesting that much of the variation in neuropil space is explained by factors other than neuron volume (Figures 13, 14, and 15).

The regression equations for the relationships between neuropil fraction (NF) in area 30 and neuronal volume in layers II, III, and V/VI are:

NF = 0.607 - 2 x 10-6 (Layer II), R2 = 0.0001

NF = 0.592 + 17 x 10-6 (Layer III), R2 = 0.02

NF = 0.610 - 4 x 10-6 (Layers V/VI), R2 = 0.001

The R2 value shows no interaction between neuropil fraction in area 30 and neuronal volume in layers II, III, and V/VI, suggesting that the variation in neuropil space is explained by factors other than neuron volume (Figures 16, 17, and 18).

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Figure 13: Regression of neuron volume for area 29, layer II on neuropil fraction of area 29. The equation NF = 0.656 - 6.9 x 10-5 shows a weak interaction (R2 = 0.18).

Figure 14: Regression of neuron volume for area 29, layer III on neuropil fraction of area 29. The equation NF = 0.641 - 4.2 x 10-5 shows a weak interaction (R2 = 0.11).

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Figure 15: Regression of neuron volume for area 29, layers V/VI on neuropil -5 fraction of area 29. The equation NF = 0.657 - 5.6 x 10 shows a weak interaction (R2 = 0.20).

Figure 16: Regression of neuron volume for area 30, layer II on neuropil fraction of -6 2 area 30. The equation NF = 0.592 + 2 x 10 shows no interaction (R = 0.02).

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Figure 17: Regression of neuron volume for area 30, layer III on neuropil fraction of area 30. The equation NF = 0.592 + 17 x 10-6 shows no interaction (R2 = 0.02).

).

Figure 18: Regression of neuron volume for area 30, layer II on neuropil fraction of area 30 .The equation NF = 0.607 - 4 x 10-6 shows no interaction (R2 = 0.0001).

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CHAPTER FIVE

Discussion

Principal findings

The present analysis represents the first comparative study of the retrosplenial cortex among human and nonhuman primate species. These results demonstrate an increase in neuropil space in the human retrosplenial cortex. Importantly, while there were differences in neuron volume across species, these differences did not correspond to differences in neuropil fraction, suggesting that an increase in neuropil was independent of changes in neuron size. These results suggest that the human-specific increase in neuropil may be attributed to other neuroanatomical changes, such as an reorganization of dendritic branching patterns or a narrowing of minicolumns (Semendeferi et al., 2011).

The present study was designed to examine this region based on evidence of its role in episodic memory and mental time travel. Some researchers have suggested these are unique capacities in humans. However, there is evidence that some animals are capable of episodic-like memory, such as Clayton and Dickinson’s (1998) investigation of Scrub Jays and Zentall et al’s (2001) experiments with pigeons. Yet, because of an inability to communicate with animals or observe cognition directly, the study of episodic memory and mental time travel in non-human animals has been difficult and results are often unconvincing or inconclusive, which is the reason why researchers have been

! 41! 42! !

careful to use the phrase ‘episodic-like”. Whether these results are the outcome of an incomplete understanding of how to capture these behaviors experimentally or is evidence of the true memory capabilities these animals remains to be seen. While it is possible some non-human animals have evolved episodic-like memory or future thinking abilities as homoplasies, there exists no scientific consensus on the extent of memory abilities in species other than humans. Therefore, comparative neuroanatomical investigations of the brain areas associated with these abilities are important. The data presented here demonstrated that humans possess an increase in neuropil space in the retrosplenial cortex, possibly indicative of an increase in connectivity to support the emergence of episodic memory and mental travel.

The emergence of mental time travel

Lesion and neuroimaging studies have shown that the retrosplenial cortex in rodents and primates plays a significant role in (Maguire, 2001), which is one of the components of episodic memory (i.e., “where”). Microstructural changes to the human retrosplenial cortex over evolutionary time may have modified the circuitry of that region to integrate other cortical inputs, leading to the capacity in humans to appreciate the “what” and “when” components of an episodic memory. Projections from the amygdala suggests that the retrosplenial cortex also plays a role in emotional information processing in non-human primates and humans, suggesting that the area may partially function as a site for emotional and spatial memory integration (Buckwalter 2008).

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This reorganization of the human retrosplenial cortex may have been the result of a modification to dendritic branching patterns, a narrowing of minicolumns, or some other neural reorganization that resulted in an increased capacity for connectivity and neural integration. This reorganization likely contributed to the evolution of an advanced capacity for memory manipulation, including episodic memory and mental time travel, out of preexisting memory systems, such as spatial, semantic, or some other rudimentary form of declarative memory. This finding lends support to the hypothesis by Tulving

(2002) and others (Suddendorf and Busby, 2003; Suddendorf and Corballis, 2007) that complex mental time travel is an ability that is unique to humans, yet does not suggest that all non-human animals are ‘stuck in time’ and have no scope of time beyond the present, as some have proposed.

The existence of multiple memory systems raises the question of why such systems would evolve in the first place. Semantic memory has obvious uses

(remembering everyday facts, such as locations and dangers), and perhaps evolved much earlier than episodic memory (Tulving, 2002a). It could be argued that the purpose of episodic memory is less clear, in that there is no obvious evolutionary benefit to reflecting on past events or experiences (Clayton et al., 2009), especially considering the unreliability and possibility or errors and distortions in one’s own memory (Suddendorf,

2009). However, it has been shown that past and future thinking rely on common neural areas (Addis et al., 2007). Perhaps episodic memory evolved from modifications to preexisting memory systems, such as spatial or semantic memory, to allow one to look ahead to possible future scenarios. It is also possible that this ability is a byproduct of a

! ! 44! !

reorganization of neural circuitry in the brain that evolved for some other means. If it is true that this ability is unique to humans, it may have afforded early humans an enhanced behavioral flexibility, and the ability to make preparations for the future that would aid in their survival and reproduction (Suddendorf and Corballis, 2007).

Humans prepare for the future in ways that are fundamentally different than those seen in most other animals. Behaviors such as food caching (Smith and Reichman, 1984), animal migration (Baker and Baker, 1978), hibernation (Lyman and Chatfield, 1955), and nest building (Collias and Collias, 1984) are adaptations to an organism’s specific environment and not decisions made based on knowledge of the past. If human ancestors had the ability to reflect on past situations from their own life and then use that knowledge to make long-term decisions and plans that contributed to their and their kin’s continued survival and reproduction, it would have provided them with a selective advantage and been a crucial aspect in the success of the human species.

In addition to the retrosplenial cortex, differences in neuropil space have been found in prefrontal cortical regions by Semendeferi et al. (2011) and Spocter et al. (2012), as well as in area Tpt by Buxhoeveden and Casanova (2000), suggesting that some human specific cognitive functions could be attributed to cortical reorganization and increases in connectivity. As more data becomes available on microstructural changes in the brain, it is becoming apparent that these changes are significant contributing factors to major changes in behavior over evolutionary time and important in our understanding of human brain evolution.

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CHAPTER SIX

Conclusions

The current analysis demonstrates that humans possess a relatively larger amount of neuropil space in the retrosplenial cortex, and thus potential for interconnectedness and information processing among neurons, relative to chimpanzees, macaques, or capuchins, suggesting evolutionary modifications within this region. An increase in neuropil did not correspond with an overall decrease in neuronal volume, suggesting that other factors, such dendritic branching or minicolumn size, influence the proportion of neuropil space in the retrosplenial cortex. This reorganization may be have allowed an advanced capacity for memory manipulation, including episodic memory and mental time travel, to emerge in the human lineage.

The retrosplenial cortex is one area in a network of areas responsible for episodic memory and mental time travel. Further studies should incorporate other areas, including the hippocampus, to better understand the extent of which neural reorganization has affected this network. Further work should also be done to identify which types of evolutionary changes could be attributed to such marked differences in neuropil space of the retrosplenial cortex and other areas of the human brain. While these data and other studies imply that an increase in neuropil space corresponds to an increase in connectivity, comparative diffusion tensor imaging (DTI) studies may better inform us of the extent and trajectory of connections in a region. Studies like this would provide us with a clearer

! 45! 46! !

picture of the evolutionary changes that took place that allowed the emergence of mental time travel in the human lineage.

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APPENDIX A

Neuropil fractions from each area of each study subject

Species Area Neuropil Fraction N Human 29 0.75 88 30 0.74 116 Human 29 0.68 115 30 0.67 107

Human 29 0.74 50

30 0.77 56

Human 29 0.61 61 ! ! 30 0.60 66 Chimpanzee 29 0.59 42 ! 30 0.58 78 Chimpanzee 29 0.57 71 ! 30 0.54 114 ! Chimpanzee 29 0.54 35 30 0.54 49 ! Chimpanzee 29 0.57 19 30 0.53 33 ! Macaque 29 0.57 32 ! 30 0.60 74 Macaque 29 0.56 31 ! 30 0.55 92 ! Macaque 29 0.55 29 30 0.56 42 ! Macaque 29 0.57 39 30 0.57 77 ! Macaque 29 0.65 34 ! 30 0.66 71 Capuchin 29 0.66 29 ! 30 0.69 91 Capuchin 29 0.52 47 ! 30 0.55 90 ! Capuchin 29 0.56 44 30 0.58 106 Capuchin 29 0.51 36 30 0.55 116 Capuchin 29 0.69 27 30 0.69 66 Capuchin 29 0.49 10 30 0.49 37

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APPENDIX B

Nucleator data from each cortical layer of each study subject

Species Areas Layers N Avg. Volume (µm3) Est. Volume CE Human 29 II 29 171.12 0.012 III 65 222.45 0.007 V/VI 44 285.53 0.022 30 II 43 193.44 0.008 III 116 304.92 0.006 V/VI 137 368.85 0.004 Human 29 II 75 335.06 0.007 III 123 536.40 0.006 V/VI 73 545.90 0.009 30 II 96 415.22 0.005 III 136 446.19 0.004 V/VI 109 512.48 0.006 Human 29 II 66 298.01 0.006 III 129 447.14 0.005 V/VI 73 462.44 0.007 30 II 100 350.27 0.005 III 136 536.96 0.006 V/VI 131 546.05 0.005 Chimpanzee 29 II 35 472.79 0.014 III 18 264.17 0.041 V/VI 59 572.90 0.008 30 II 40 394.14 0.013 III 25 171.66 0.029 V/VI 57 407.59 0.013 Chimpanzee 29 II 54 444.85 0.009 III 17 352.60 0.049 V/VI 45 410.36 0.01 30 II 65 511.88 0.006 III 37 368.86 0.016 V/VI 58 443.79 0.009 Chimpanzee 29 II 52 599.53 0.007 III 28 411.89 0.021 V/VI 66 634.01 0.008 30 II 43 568.09 0.011 III 20 429.38 0.039 V/VI 41 609.89 0.013 Macaque 29 II 24 1120.59 0.017

! ! 49! !

III 34 1232.36 0.01 V/VI 55 1278.96 0.008 30 II 32 1286.50 0.013 III 49 1747.34 0.009 V/VI 49 1570.80 0.011 Macaque 29 II 35 814.27 0.014 III 24 1405.97 0.017 V/VI 69 1033.95 0.007 30 II 41 1011.67 0.011 III 45 1261.09 0.01 V/VI 47 1122.81 0.01 Macaque 29 II 28 816.05 0.014 III 25 1051.89 0.015 V/VI 54 1332.23 0.009 30 II 54 752.87 0.01 III 59 1043.30 0.007 V/VI 80 1368.73 0.006 Capuchin 29 II 27 1164.13 0.012 III 18 1427.13 0.018 V/VI 49 1405.37 0.013 30 II 35 1102.21 0.014 III 32 1801.18 0.02 V/VI 48 1743.69 0.012 Capuchin 29 II 33 1236.08 0.01 III 24 1631.16 0.011 V/VI 82 2019.45 0.007 30 II 31 612.78 0.01 III 29 1060.65 0.016 V/VI 31 1571.93 0.019 Capuchin 29 II 49 1388.74 0.009 III 38 1396.84 0.008 V/VI 63 1304.08 0.018 30 II 19 647.15 0.019 III 23 1196.02 0.019 V/VI 47 1450.36 0.024 !

!

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References

Addis DR, Wong AT, Schacter DL (2007) Remembering the past and imagining the future: Common and distinct neural substrates during event construction and elaboration. Neuropsychologia 45:1363.

Amunts K, Schmidt-Passos F, Schleicher A, Zilles K (1997) Postnatal development of interhemispheric asymmetry in the cytoarchitecture of human area 4. Anatomy Embryology (Berl) 196:393–402.

Baker RR, Baker RR (1978) The evolutionary ecology of animal migration. Holmes and Meier, New York.

Belmonte MK, Allen G, Beckel-Mitchener A, Boulanger LM, Carper RA, Webb SJ (2004) Autism and Abnormal Development of Brain Connectivity. Journal of Neuroscience 24:9228–9231.

Bird LR, Roberts WA, Abroms B, Kit KA, Crupi C (2003) Spatial memory for food hidden by rats (Rattus norvegicus) on the radial maze: Studies of memory for where, what, and when. Journal Of Comparative 117:176.

Botzung A, Denkova E, Manning L (2008) Experiencing past and future personal events: Functional neuroimaging evidence on the neural bases of mental time travel. Brain & Cognition 66:202–212.

Bowers D, Verfaellie M, Valenstein E, Heilman KM (1988) Impaired acquisition of temporal information in retrosplenial amnesia. Brain & Cognition 8:47–66.

Breedlove SM, Watson NV, Rosenzweig MR (2010) Biological psychology: an introduction to behavioral, cognitive, and clinical neuroscience. Sinauer Associates Inc.

Brodmann K (1909) Vergleichende Lokalisationslehre der Großhirnrinde. Leipzig: Barth (reprinted as Brodmann'sLocalisation in the , translated by L.J. Garey, London: Smith-Gordon, 1994).

Buckwalter JA, CM Schumann, GW Van Hoesen (2008) Evidence for direct projections from the basal nucleus of the amygdala to retrosplenial cortex in the Macaque monkey. Experimental Brain Research 186.1: 47-57.

Buxhoeveden D, Casanova MF (2000) Comparative lateralization patterns in the language area of human, chimpanzee, and rhesus monkey brains. Laterality 5:315–330.

Cheney D, Seyfarth R, Smuts B (1986) Social relationships and social cognition in nonhuman primates. Science 234:1361–1366.

Clayton NS, Dickinson A (1998) Episodic-like memory during cache recovery by scrub jays. Nature 395:272.

! 50! 51! !

Clayton NS, Russell J, Dickinson A (2009) Are Animals Stuck in Time or Are They Chronesthetic Creatures? Topics in Cognitive Science 1:59–71.

Collias NE, Collias EC (1984) Nest building and bird behavior. Princeton University Press.

Correia, Sérgio PC, Dickinson A, Clayton NS (2007) Western Scrub-Jays Anticipate Future Needs Independently of Their Current Motivational State. Current Biology 17:856–861.

Craig IW, Halton KE (2009) Genetics of human aggressive behaviour. Human Genetics 126:101– 113. de Sousa AA, Sherwood CC, Mohlberg H, Amunts K, Schleicher A, MacLeod CE, Hof PR, Frahm H, Zilles K (2010) Hominoid visual brain structure volumes and the position of the lunate . Journal of Human Evolution 58:281–292. de Waal FBM (1982) Chimpanzee Politics. London: Jonathan Cape.

Eichenbaum H (1997) Declarative Memory: Insights from Cognitive Neurobiology. Annual Review of Psychology 48:547–572.

Elston GN (2003) Cortex, cognition and the cell: New insights into pyramidal neuron and prefrontal function. Cerebral Cortex 13:1124–1138.

Gabrieli JDE (1998) Cognitive neuroscience of human memory. Annual Review of Psychology 49:87–115.

Gundersen HJ (1988) The nucleator. Journal of Microscopy 151 (Pt. 1):3–21.

Gustafsson L (1997) Inadequate cortical feature maps: A neural circuit theory of autism. Biological Psychiatry 42:1138–1147.

Haines AL (1996) The Yellowstone story: A history of our first national park. Vol. 1: Yellowstone Association for Natural Science, History, and Education.

Holloway RL (1979) Brain size, allometry, and reorganization: toward a synthesis. In: Development and Evolution of Brain Size: Behavioral Implications (Hahn ME, Jensen C, Dudek BC, eds), pp. 59–88. New York: Academic Press.

Jerison HJ (1973) and Intelligence. New York: Academic Press.

Kandel ER, Schwartz JH (1985) Principles of Neural Science, Second Edition. Elsevier Science Publishing Co., Inc.

Kandel ER, Schwartz JH, Jessell TM (2000) Principles of neural science. Fourth Edition. McGraw-Hill, New York.

Kobayashi Y, Amaral DG (2000) Macaque monkey retrosplenial cortex: I. Three-dimensional and cytoarchitectonic organization. The Journal of Comparative Neurology 426:339–365.

! ! 52! !

Kobayashi Y, Amaral DG (2003) Macaque monkey retrosplenial cortex: II. Cortical afferents. The Journal of Comparative Neurology 466:48–79.

Kobayashi Y, Amaral DG (2007) Macaque monkey retrosplenial cortex: III. Cortical efferents. The Journal of Comparative Neurology 502:810–833.

Lyman CP, Chatfield PO (1955) Physiology of hibernation in mammals. Physiological reviews 35:403–425.

Lynall M-E, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, Bullmore E (2010) Functional Connectivity and Brain Networks in Schizophrenia. Journal of Neuroscience 30:9477–9487.

Maguire E (2001) The retrosplenial contribution to human navigation: a review of lesion and neuroimaging findings. Scandinavian Journal of Psychology 42:225–238.

Martin RD, Harvey PH (1985) Brain size allometry: ontogeny and phylogeny. In: Size and Scaling in Primate Biology (Jungers WL, ed), pp. 147–173. New York: Plenum.

Miller EK, Cohen JD (2001) An Integrative theory of prefrontal cortex function. Annual Review of Neuroscience 24:167.

Milner B, Squire LR, Kandel ER (1998) Cognitive Neuroscience Review and the Study of Memory. Neuron 20:445–468.

Morris R, Paxinos G, Petrides M (2000) Architectonic analysis of the human retrosplenial cortex. The Journal of Comparative Neurology 421:14–28.

Mountcastle V (1978) An organizing principle for cerebral function: The unit model and the distributed system. In: Schmitt FO, Worden FG, editors. The neurosciences: the fourth program. Cambridge (MA): MIT Press. p. 21-41.

Preuss TM, Cáceres M, Oldham MC, Geschwind DH (2004) Human brain evolution: insights from microarrays. Nature Reviews Genetics 5:850–860.

Raghanti MA, Conley T, Sudduth J, Erwin JM, Stimpson CD, Hof PR, Sherwood CC (2012) Neuropeptide Y‐immunoreactive Neurons in the Cerebral Cortex of Humans and Other Haplorrhine Primates. American Journal Of Primatology. doi: 10.1002/ajp.22082.

Raghanti MA, Stimpson CD, Marcinkiewicz JL, Erwin JM, Hof PR, Sherwood CC (2008) Cortical dopaminergic innervation among humans, chimpanzees, and macaque monkeys: A comparative study. Neuroscience 155:203–220.

Rilling JK, Seligman RA (2002) A quantitative morphometric comparative analysis of the primate temporal lobe. Journal of Human Evolution 42:505–533.

Roberts WA, Feeney MC (2009) The comparative study of mental time travel. Trends in Cognitive Sciences 13:271–277.

! ! 53! !

Rusak B, Zucker I (1975) Biological rhythms and animal behavior. Annual Review of Psychology 26:137–171.

Schultz A (1969) The Life of Primates. London: Weidenfeld & Nicholson.

Schwartz BL, Evans S (2001) Episodic memory in primates. American Journal Of Primatology 55:71–85.

Semendeferi K, Armstrong E, Schleicher A, Zilles K, Van Hoesen GW (2001) Prefrontal cortex in humans and apes: a comparative study of area 10. American Journal of Physical Anthropology 114:224–41.

Semendeferi K, Teffer K, Buxhoeveden DP, Park MS, Bludau S, Amunts K, Travis K, Buckwalter J (2011) Spatial organization of neurons in the frontal pole sets humans apart from great apes. Cerebral Cortex (New York, NY: 1991) 21:1485–1497.

Shallice T, Fletcher P, Frith CD, Grasby P, Frackowiak R, Dolan RJ (1994) Brain regions associated with acquisition and retrieval of verbal episodic memory. Nature 368:633–635.

Sherwood CC, Bauernfeind AL, Bianchi S, Raghanti MA, Hof PR (2012) Human brain evolution writ large and small. Progress In Brain Research 195:237–254.

Sherwood CC, Hof PR (2007) The evolution of neuron types and cortical histology in apes and humans. In: The Evolution of Primate Nervous Systems. Evolution of Nervous Systems, Vol. 4 (Preuss TM, Kaas JH, eds). Academic Press.

Sherwood CC, Holloway RL, Erwin JM, Schleicher A, Zilles K, Hof PR (2004) Cortical orofacial motor representation in Old World monkeys, great apes, and humans. I. quantitative analysis of cytoarchitecture. Brain, Behavior, and Evolution 63:61–81.

Sherwood CC, Raghanti MA, Stimpson CD, Spocter MA, Uddin M, Boddy AM, Wildman DE, Bonar CJ, Lewandowski AH, Phillips KA (2010) Inhibitory interneurons of the human prefrontal cortex display conserved evolution of the phenotype and related genes. Proceedings of the Royal Society B: Biological Sciences 277:1011–1020.

Smith CC, Reichman OJ (1984) The evolution of food caching by birds and mammals. Annual Review of Ecology and Systematics: 329–351.

Spocter MA, Hopkins WD, Barks SK, Bianchi S, Hehmeyer AE, Anderson SM, Stimpson CD, Fobbs A, Hof PR, Sherwood CC (2012) Neuropil distribution in the cerebral cortex differs between humans and chimpanzees. The Journal of Comparative Neurology. 520.13 (2012): 2917-2929.

Sporns O, Chialvo DR, Kaiser M, Hilgetag CC (2004) Organization, development and function of complex brain networks. Trends in Cognitive Sciences 8:418–425.

Squire LR (2004) Memory systems of the brain: a brief history and current perspective. Neurobiology of Learning & Memory 82:171–177.

! ! 54! !

Suddendorf T (2009) Mental time travel and the shaping of the human mind. Philosophical Transactions of the Royal Society B: Biological Sciences 364:1317–1324.

Suddendorf T, Busby J (2003) Mental time travel in animals? Trends in Cognitive Sciences 7:391.

Suddendorf T, Corballis MC (2007) The evolution of foresight: What is mental time travel, and is it unique to humans? Behavioral And Brain Sciences 30:299–313.

Suddendorf T, Corballis MC, Collier-Baker E (2009) How great is great ape foresight? Animal Cognition 12:751–754.

Svoboda E, McKinnon MC, Levine B (2006) The functional neuroanatomy of : a meta-analysis. Neuropsychologia 44:2189–2208.

Tulving E (1983) Elements of episodic memory. Oxford: Clarendon Press.

Tulving E (2002a) Episodic Memory: From Mind to Brain. Annual Review of Psychology 53:1- 25.

Tulving E (2002b) Chronesthesia: Conscious Awareness of Subjective Time. Principles of function. Oxford University Press, USA.

Tulving E, Schacter DL (1990) Priming and human memory systems. Science 247:301–306.

Tulving E, Schacter DL, McLachlan DR, Moscovitch M (1988) Priming of semantic autobiographical knowledge: a case study of retrograde amnesia. Brain & Cognition 8:3–20.

Vann SD, Aggleton JP, Maguire EA (2009) What does the retrosplenial cortex do? Nature Reviews Neuroscience 10:792–802.

Walker A, Falk D, Smith R, Pickford M (1983) The skull ofProconsul africanus:reconstruction and cranial capacity. Nature 305:525–527.

Zentall TR, Clement TS, Bhatt RS, Allen J (2001) Episodic-like memory in pigeons. Psychonomic Bulletin and Review 8:685–690.

Zentall TR (2006) Mental time travel in animals: A challenging question. Behavioral Processes 72:173-183.

Zilles K, Rehkämper G (1988) The brain, with special reference to the telencephalon. In: Orang- utan Biology (Schwartz JH, ed), pp 157–176. New York: Oxford University Press.

Zola-Morgan S, Squire LR (1993) Neuroanatomy of memory. Annual Review of Neuroscience 16:547–563.

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