Tool-Use and the Chimpanzee Brain: an Investigation of Gray and White Matter, and a Focused Study of Inferior Parietal Microstructure

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Tool-use and the Chimpanzee Brain: An Investigation of Gray and White Matter, and a Focused Study of Inferior Parietal Microstructure

by Laura Denise Reyes

A.B. in Psychology, June 2008, Dartmouth College M.A. in Anthropology, May 2011, New Mexico State University M.Phil in Hominid Paleobiology, June 2013, The George Washington University

A Dissertation submitted to

The Faculty of The Columbian College of Arts and Sciences of The George Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy

August 31, 2017

Chet C. Sherwood Professor of Anthropology

The Columbian College of Arts and Sciences of The George Washington University certifies that Laura D. Reyes has passed the Final Examination for the degree of Doctor of Philosophy as of May 3, 2017. This is the final and approved form of the dissertation.

Tool-use and the Chimpanzee Brain: An Investigation of Gray and White Matter, and a Focused Study of Inferior Parietal Microstructure

Laura Denise Reyes

Dissertation Research Committee:

Chet C. Sherwood, Professor of Anthropology, Dissertation Director

Kimberley Phillips, Professor of Psychology, Trinity University, Committee Member

Scott Mackey, Assistant Professor of Psychiatry, University of Vermont, Committee

Member

iii Acknowledgments

The author would like to thank her parents, Loretta and Francisco Reyes; her grandparents, Celia and Ralph Lopez and Paula and Francisco Reyes; and all of her family and friends who offered support during the course of her education, especially Amelia Villaseñor and Chrisandra Kufeldt.

The author acknowledges the dissertation committee, Brenda Bradley (Chair), Chet Sherwood

(Advisor), David Braun, Scott Mackey, Kimberley Phillips, and Sarah Shomstein, as well as the following funding sources: National Science Foundation Doctoral Dissertation Research

Improvement Grant BCS-1455629 and GWU Provost’s Fellowship.

iv Abstract of Dissertation

Tool-use and the Chimpanzee Brain: An Investigation of Gray and White Matter, and a Focused Study of Inferior Parietal Microstructure

The goal of this dissertation is to investigate neural correlates of tool-use in chimpanzees and provide the groundwork for comparisons with humans and other primates. The dissertation contains three studies that integrate different techniques to investigate brain structures associated with tool-use in chimpanzees. The first two studies use neuroimaging techniques to investigate gray and white matter differences associated with tool-use performance in a sample of captive chimpanzees. The first study uses tensor-based morphometry to study gray matter, while the second study uses diffusion tensor imaging and tract-based spatial statistics to study white matter tracts. The third study uses histological methods to focus on the microstructure of the inferior parietal lobe in the chimpanzee. This type of integrative approach allows for a comprehensive study of tool-use and how it is linked with specific aspects of chimpanzee cortical anatomy, and can inform our understanding of how the brain evolved and adapted to tool-use and tool-making in hominins.

The results from this dissertation show that the chimpanzee brain has specific tracts and areas that are associated with tool-use with distinct patterns of connectivity, similar to what is observed in humans. Chimpanzees, however, appear to rely more on areas involved in sensorimotor processing for tool-use than do humans. Human tool-use may therefore differ from that of chimpanzees in the recruitment of more areas associated with cognitive control and increased connectivity between areas involved in higher order cognitive functions. The additional cortical areas that have been identified in the human IPL that appear to lack chimpanzee homologues also may be essential for tool-related cognition in humans, especially cognition linked with complex processes such as stone tool-making. Investigations of both gray and white matter showed that chimpanzees also have notable sex differences in their neural correlates of tool-use. Male and female chimpanzees may process tool-use differently, and these differences may be linked with previously reported sex differences in tool-use behaviors.

v To fully understand evolutionary changes in the brain that occurred in the human lineage since its split from the Pan-Homo last common ancestor, comparative work must be performed between humans and chimpanzees, and especially work that can reveal the structure and function of the chimpanzee brain to ensure the validity of any comparisons. This dissertation has provided a characterization of tool-use in the chimpanzee brain based on multiple modalities, and these results can form the basis of direct comparisons between chimpanzees, humans, and other primate species in the future.

vi Table of Contents

Acknowledgements...... iv

Abstract of Dissertation...... v

List of Figures...... viii

List of Tables...... xi

Chapter 1: Introduction...... 1

Chapter 2: Gray Matter and Performance Time on a Tool-use Task in Chimpanzees: An Analysis of Voxel-based Morphometry and Cortical Thickness...... 8

Chapter 3: White Matter Correlates of Tool-use Performance in Chimpanzees Using Tract-Based Spatial Statistics...... 40

Chapter 4: Cyto- and Myeloarchitectural Parcellation of the Chimpanzee Inferior Parietal Lobe...... 61

Chapter 5: Conclusion...... 112

References...... 116

vii List of Figures

Chapter 2: Gray matter and performance time on a tool-use task in chimpanzees: an analysis of tensor-based morphometry and cortical thickness

1. Plots depicting the distributions of tool-use performance time (log transformed), age at tool-use, and brain volume by sex...... 27

2. Significant clusters for logJ analysis in females shown in left/right, superior/inferior, and anterior/posterior 3-D renderings, and in axial sections...... 28

3. Pearson’s r values for the logJ analysis in females shown in left/right, superior/inferior, and anterior/posterior 3-D renderings, and in axial sections...... 29

4. Significant clusters for logJ analysis in males shown in left/right, superior/inferior, and anterior/posterior 3-D renderings, and in axial sections...... 30

5. Pearson’s r values for the logJ analysis in males shown in left/right, superior/inferior, and anterior/posterior 3-D renderings, and in axial sections...... 31

6. Significant clusters for the cortical thickness analysis in females shown in left/right, superior/inferior, and anterior/posterior 3-D renderings...... 32

7. Pearson’s r values for the cortical thickness analysis in females shown in left/right, superior/inferior, and anterior/posterior 3-D renderings...... 33

8. Significant clusters for the cortical thickness analysis in males shown in left/right, superior/inferior, and anterior/posterior 3-D renderings...... 34

9. Pearson’s r values for the cortical thickness analysis in males shown in left/right, superior/inferior, and anterior/posterior 3-D renderings...... 35

10. Clusters in males and females where Pearson’s r shows a medium or larger effect for

TBM and cortical thickness results on 3-D rendered brains showing left, right, superior, inferior, anterior, and posterior views...... 36

11. Clusters from the TBM and cortical thickness analyses where Pearson’s r shows a medium or larger effect for TBM and cortical thickness results on 3-D rendered brains showing left, right, superior, inferior, anterior, and posterior views...... 38

viii Chapter 3: White Matter Correlates of Tool-use Performance in Chimpanzees Using Tract-Based

Spatial Statistics

1. Plots depicting the distributions of tool-use performance time (log transformed), age at tool-use, and brain volume by sex...... 57

2. Chimpanzee white matter atlas...... 58

3. Axial slices showing the location of significant clusters for the effect of tool-use performance time on fractional anisotropy and mean diffusivity for females...... 59

4. Axial slices showing the location of significant clusters for the effect of tool-use performance time on fractional anisotropy (FA) and mean diffusivity (MD) for males...... 60

Chapter 4: Cyto- and Myeloarchitectural Parcellation of the Chimpanzee Inferior Parietal Lobe

1. Parcellations of the chimpanzee IPL...... 88

2. Cytoarchitectural and myeloarchitectural features of the chimpanzee cortex...... 89

3. Cortical contours and image processing for the laminar sampling method...... 90

4. Nissl stained histological sections from six chimpanzee IPL areas in all four hemispheres...... 91

5. Silver stained histological sections showing the myeloarchitecture of the six chimpanzee

IPL areas...... 92

6. Representative histological sections from each hemisphere showing each IPL area...... 93

7. Plots of the boundaries between the IPL and surrounding cortical areas...... 94

8. Plots of the boundaries between the IPL areas...... 95

9. Plots of the differences in areal density between the IPL and surrounding cortical areas for each histological section...... 97

10. Plots of the mean density between areas within the IPL for each histological section...... 98

11. Plots of the first three principal components by subject and hemisphere...... 99

12. A hierarchical cluster dendrogram showing the major clusters of IPL and surrounding cortical areas...... 100

13. Density profiles for six cortical areas in the chimpanzee IPL and the surrounding areas...... 101

ix 14. 3D representation of the chimpanzee IPL areas and comparison with previous parcellations...... 102

15. 3D rendering of the chimpanzee IPL compared to macaque and human IPL...... 104

x List of Tables

Chapter 4: Cyto- and Myeloarchitectural Parcellation of the Chimpanzee Inferior Parietal Lobe

1. Comparison of models with different random effects...... 105

2. Estimated variance explained by random effects...... 105

3. Parameter estimates for the generalized linear mixed model for areas by layer...... 106

4. Post hoc least-squares means showing pairwise contrasts of bordering areas...... 107

5. Distinctive qualitative cytoarchitectural features in the chimpanzee inferior parietal lobe...... 109

6. Distinctive qualitative myeloarchitectural features in the chimpanzee inferior parietal lobe...... 110

7. Principal components analysis loadings and importance of components...... 111

xi Chapter 1: Introduction

The human species is distinctive among other animals for the variety of complex tools produced and used. Tool production in the human lineage began to differentiate by at least 3.3

Ma, when the Lomekwian stone tool technology appeared (Harmand et al. 2015). Further innovation in tool technology continued through the Oldowan and Acheulean industries that followed with the genus Homo (Ambrose 2001; Barsky et al. 2009; Semaw et al. 2009). The

Acheulean technology was particularly notable as it primarily consisted of symmetrical, and eventually bifacial, handaxes that were widespread in usage and uniform in production across space and time (Lycett 2008; Lycett and con Cramon-Taubadel 2008; Gowlett 2009), suggesting changes in cognition associated with tool-making (McPherron 2000; Shipton 2010; Moore 2011;

Stout 2011). The Mousterian (or Middle Stone Age) and Upper Paleolithic (or Late Stone Age) technologies that followed provided further innovations such as prepared core techniques, more refined blades, and, in the case of the Upper Paleolithic, specialized implements such as harpoons, needles, and even artwork (McBrearty and Brooks 2000; McBrearty 2003). Modern humans and their close hominin relatives have a proficiency at tool-use and tool-making that is unparalleled in other animals. However, how did this capability arise, and what are the parallels in other species?

Tool-use among animals is incredibly diverse and spans many distantly related taxa, including cephalopods, fish, marine mammals, birds, and nonhuman primates (Finn et al. 2008;

Shumaker et al. 2011; Bernardi 2012). Some forms of animal tool-use are relatively complicated and show many similarities to human tool-use. It is perhaps not surprising that chimpanzees, one of the closest living relatives of humans, exhibit a variety of tool-use capabilities that require cognitive capabilities once thought to be unique to the human lineage. It has been proposed that tool-use and tool-making in humans consists of a phased process called a chaîne opératoire, which is a sequence of material procurement, production, use, and disposal of tool materials

(Sellet 1993; Grace 1997; Shott 2003). Operational sequences are apparent in the archaeological evidence at hominin sites in the form of flakes from tool-making, cut marks on bones from tool-

1 use, and discarded tools including hammer stones and the produced tools (e.g. Roche et al.

1999; Goren-Inbar et al. 2008; Bar-Yosef et al. 2009). There is also evidence for tool transport in early hominins, which shows selectivity toward specific tool materials (Harmand 2007; Braun et al. 2008, 2009 a,b). Similar evidence of sequential tool-use behavior has also been identified in wild, extant chimpanzees. Chimpanzee nut cracking consists of using stones to break open nuts, and often leaves assemblages not unlike those observed in Oldowan archaeological sites, and show sequences of tool transport and nut cracking. The assemblages also show that chimpanzees have material preferences, with specific stone types less common in the environment found at a higher frequency than expected in the assemblages (Carvalho et al.

2008).

Certain species of crows also exhibit sophisticated tool-use and tool-making. New

Caledonian (NC) crows manufacture and use different types of hook-tools made from sticks to search for prey in wood (Hunt 1996). The type of tool-making commonly observed in NC crows has been proposed as more sophisticated that that seen in nonhuman primates, since they make a variety of different hook tools that each use specific materials and techniques (Hunt and Gray

2002). They also have the capability to rapidly adapt their tool-making to address specific foraging situations, demonstrating flexibility in tool-use (Auersperg et al. 2011). Initial failure on a tool-use task resulted in successful modification to the tool after very few trials, which contrasts with nonhuman primate tool-use which often requires extensive training (Hunt et al. 2006). Skilled tool-making and tool-use in NC crows may also have an element of social learning. Although many NC crows have been observed to make and use tools spontaneously, many NC crows require social interaction and observation of tool-use and tool-making to become fully proficient

(Logan et al. 2016). There is very strong between-site variation in tool materials used by NC crows (St. Clair et al. 2016), and this variation is reminiscent of the variation in types of tool-use observed at different chimpanzee sites (Whiten et al. 1999). The variation and adaptability of NC crow tool-use and tool-making reflects many of the same cognitive capabilities that were once thought to be specific to humans and their close relatives.

2 These examples show that human tool-use is not entirely unique and many tool capabilities are shared between humans and other animals. But unlike other animals, humans can create new tool technologies that drastically change the way they live and interact with each other and the environment (Tomasello 1999). Humans are capable of consistently innovating and pushing technology to its limits and fully integrating these technologies into daily life in a way not observed in any other animal. Therefore, it is not tool-use itself that differentiates humans, but the degree to which tools are used and innovated in different situations and environments.

To find out why humans have these unique capabilities, it is necessary to examine the human brain, how it evolved, and how it functions compared to other species. This can be relatively difficult, however, since there is little physical evidence of brain evolution in humans and other primates other than fossilized crania and associated endocasts. It is therefore especially helpful to compare brains of extant humans with other nonhuman primate species, since these comparisons can indicate which structural features and behaviors might be specific to humans or shared with other taxa (and likely their last common ancestor with humans).

Cranial capacity in the hominin lineage has increased dramatically, significantly more than expected based on body size increases alone (Jerison, 1973; Hofman, 1983; Hawks et al.

2000). While cranial capacity shows clear evidence of enlargement in the hominin fossil record, when and how brain reorganization, or changes in brain structure, occurred has been more difficult to determine since endocasts do not preserve internal brain structures.

Evidence for brain reorganization in hominins has been identified based on the size and shape of cortical regions. It has been claimed that the parietal lobe was one of the earliest structures to become reorganized, with the position of the lunate sulcus in australopithecine endocasts showing a more caudal modern human-like position, signifying a relative enlargement of the parietal lobes compared to what can be observed in living apes (Holloway et al. 2004).

Parietal reorganization was also identified in later hominins, with modern humans exhibiting a rounded expansion, or globularization, of the parietal region of the cranium compared with chimpanzees, Homo erectus, and Neandertals (Bruner, 2010; Bruner et al. 2003). The frontal lobe has also been identified as a region that is noticeably enlarged and widened in Neandertals

3 and modern humans compared to other hominins, partcularly in Broca's area, which is involved in human language (Bruner and Holloway, 2010).

Important information concerning the surface anatomy and external structure of the brain has been gained from morphological studies of endocasts, but they do not provide insight into changes that occurred internally. In endocasts, large anatomical structures that are essential for brain function such as the basal ganglia, which are subcortical nuclei that are involved in a variety of functions including voluntary movement and are highly interconnected with the cerebral cortex

(Bell and Shine 2016), and white matter tracts that connect different regions of the brain, are not discernable. Many interspecies differences have been identified by examining the microstructure, or small-scale anatomy of the brain including cytoarchitecture, or the organization of neurons and glia within neural tissue (Reyes et al. 2014). Endocasts, however, do not contain brain tissue due to mineralization and render the study of extinct hominin brain microstructure impossible.

Comparative neuroanatomical studies involving humans and extant nonhuman primates are therefore necessary to determine which traits may be specific to the hominin lineage.

Comparisons of cytoarchitecture between humans, great apes, and macaques have been particularly useful in defining microstructural differences between specific cortical areas between species. For example, some differences have been identified in the space between minicolumns in the cortex (Buxhoeveden et al. 2001). Minicolumns are single-neuron wide vertical arrays that traverse the cortical layers and are considered the functional units of the cerebral cortex

(Buxhoeveden and Casanova 2002). In general, the human cortex has lower neuron density and larger horizontal spacing between minicolumns than great apes. This pattern has been observed in the frontal pole (Semendeferi et al., 2001; 2011), Broca’s area (Schenker et al., 2008), insula

(Semendeferi et al., 2001; Spocter et al., 2012), planum temporale and fusiform gyrus in the temporal lobe (Buxhoeveden et al., 1996; Chance et al. 2013), and primary visual cortex

(Casanova et al., 2009).

Comparisons between humans and nonhuman primates can also indicate how brain structures might have changed to support specific capabilities that evolved in hominins. One such capability is complex stone tool-making, which likely first evolved among australopithecines in the

4 early Pliocene (McPherron et al. 2010, 2011; Harmand et al. 2015), and was relatively well developed by the appearance of Homo in the late Pliocene and early Pleistocene (Roche et al.

1999; Delagnes and Roche, 2005; Pelegrin et al. 2005; Stout et al. 2005; Semaw et al. 2006;

Goldman-Neuman and Hovers, 2012; Braun et al. 2009). Although extant great apes and some monkey species also use tools and even make modifications to them, their capacity for tool behaviors lack the skill, variety, and flexibility seen in modern humans (Osiurak et al. 2010;

Vaesen 2012; Gruber et al. 2015).

The human brain contains specific areas that are activated as a circuit during tool-related tasks (Lewis 2006; Peeters et al. 2009). Many of these regions, including those in the premotor cortex, inferior parietal lobe, and inferior frontal gyrus are also activated during experiments in which human subjects engage in, or observe others in the process of, stone tool-making (Stout et al. 2008, 2011). Comparisons between macaques and humans show that these two species share similarities in parieto-frontal circuits responsible for object manipulation and sensorimotor integration (Johnson-Frey 2003), but differ in the anatomical connectivity between regions involved in the tool circuit (Hecht et al. 2012). Humans also have a tool-specific area in the inferior parietal lobe (IPL) that has not been observed in macaques (Peeters et al. 2009).

Although comparisons between macaques and humans have been informative in identifying evolutionary changes involved in human tool-use, they do not indicate whether these brain structure changes are specific to humans or are shared by humans and apes. A comparison between humans and more closely related great ape species are necessary to show which neural character states are shared, and which are derived in humans and possibly hominins.

Chimpanzees are an ideal species for investigating the link between tool-use and brain evolution because they are closely related to humans and they exhibit tool-use behavior both in the wild and in captivity (Haslam 2013). Previous investigations of chimpanzee neuroanatomy have indicated that chimpanzee brains have specific features that may be associated with tool-use, particularly aspects of connectivity between the somatosensory cortex in both hemispheres, as well as between the frontal and parietal lobes (Hecht et al. 2012; Phillips et al. 2013).

5 The goal of this dissertation is to investigate the neural correlates of tool-use in chimpanzees and provide the groundwork for direct comparisons with humans and other nonhuman primates. The dissertation contains three studies: a tensor-based morphometry study of gray matter associated with performance on a tool-use task in chimpanzees (Chapter 2), a tract-based spatial statistics study of white matter tracts associated with performance on a tool- use task in chimpanzees (Chapter 3), and a parcellation of the chimpanzee inferior parietal lobe based on cytoarchitecture and myeloarchitecture (Chapter 4).

Chapters 2 and 3 used neuroimaging techniques to investigate gray and white matter regions associated with differences in tool-use performance time in a sample of captive chimpanzees. In both studies, tool-use skill was measured as the mean performance time of a chimpanzee on a simulated termite fishing task (Hopkins et al. 2009; Phillips et al. 2013). The task consisted of removing food from a tube attached to a cage, and performance time was measured as the length of time to successfully insert the stick into the tube. In chapter 2, structural magnetic resonance images (MRIs) were assessed using tensor-based morphometry, a method that identifies local differences in gray matter throughout the cortex, as well as a more specific measure of cortical thickness, both on a voxelwise basis (Ashburner and Friston 2000). In chapter 3, diffusion tensor images (DTIs) were assessed with tract-based spatial statistics, which performed a voxelwise comparison of the major white matter tracts of the brain (Smith et al.

2006). The purpose of these studies was to locate areas in the chimpanzee brain that vary among individuals in association with differences in tool-use performance, and determine if they might correspond to regions that have been associated with tool-use in humans (Lewis 2000).

Chapter 4 focused specifically on the IPL and provided a qualitative and quantitative parcellation of the region. The qualitative parcellation was performed by dividing the IPL based on differences between cortical areas from observations of cell size and distribution in cytoarchitecture and differences in axon fiber staining density and orientation in myeloarchitecture. The quantitative parcellation was performed using a method known as laminar sampling (Mackey and Petrides 2009), and tested whether the areas identified in the qualitative parcellation were quantitatively distinct based on analyses of cell density distributions across

6 cortical layers. The IPL was selected for parcellation because the human rostral IPL contains a tool-specific area that has been identified both functionally and anatomically, which is not present in macaque monkeys (Caspers et al. 2006, 2011; Stout et al. 2008, 2011; Peeters et al. 2009;

2013; Orban and Caruana 2014; Zhang and Li 2014; Orban 2016). Parcellation of the chimpanzee IPL was performed to provide a framework for comparison between chimpanzees, macaques and humans, and also ascertain whether chimpanzees share homologous cortical areas based on anatomy.

This dissertation provides an integrative approach in methodology, combining neuroimaging techniques to study gray and white matter in the entire brain, and histological techniques to characterize the microstructure of the IPL. This type of approach allows for a comprehensive study of how tool-use is linked with specific aspects of chimpanzee cortical anatomy, and can inform our understanding of how the brain evolved and adapted to tool-use and tool-making in hominins.

7 Chapter 2: Gray matter and performance time on a tool-use task in chimpanzees: an analysis of

tensor-based morphometry and cortical thickness

Abstract

Investigation of neuroanatomical variation linked with tool-use in chimpanzees allows for comparisons with other species, including humans. The current study used tensor-based morphometry (TBM) and measurement of cortical thickness to identify structural neural correlates of tool-use performance in a sample of captive chimpanzees (N = 63). Males and females were examined separately due to substantial differences in tool-use performance within the sample.

TBM and cortical thickness analyses resulted in only a few areas with shared effects in both males and females. For TBM they included small clusters with positive correlation in the right prefrontal and left and right inferior temporal cortex, and negative correlation in the left cerebellum, right temporal pole, and different parts of the inferior temporal cortex, and for cortical thickness only included negative correlation in the prefrontal and inferior frontal cortex. Even though the TBM and cortical thickness results showed only a few overlapping clusters, the correlation effects were generally localized to similar regions. The use of TBM and cortical thickness together provide different types of information about gray matter variation that are related to tool-use, and used together, can show a more comprehensive view of how gray matter co-varies with tool-use behavior. In both analyses, we found that tool-use performance showed dramatic differences between males and females. Faster tool-use performance was correlated with differences in motor, somatosensory, and parietal regions associated with hand and arm movements and sensorimotor integration in males, and differences in the premotor areas associated with motor control of reaching and grasping in females. Males and females had only a few shared areas with structural changes associated with tool-use performance for TBM and cortical thickness in the prefrontal cortex, inferior frontal gyrus, temporal pole, inferior temporal cortex, and cerebellum. These areas are involved in visual, sensorimotor, and cognitive control processes. Sex differences that were identified in the analyses are not unexpected and likely underlie many of the behavior differences in tool-use that have been observed in both wild and

8 captive chimpanzees. The overlapping areas in males and females that were correlated with tool- use performance have been previously identified as important for tool-use in both macaques and humans. However, macaques do not show the same types of structural changes in the inferior temporal cortex when receiving training on a tool-use task, suggesting that skilled tool-use in chimpanzees may rely on increased potential for structural change in object-related regions in the inferior temporal cortex. The current study reveals sex-based differences in tool-use in chimpanzees that may have been present in the last common ancestor of chimpanzees and humans, and shows potential regions, such as the inferior temporal cortex, that may have been integral in the evolution of tool-use and tool-making in the human lineage.

Introduction

Human evolutionary history is closely linked with the use and manufacture of tools

(Ambrose 2001). The direct study of these capabilities, however, is problematic since the only remaining evidence of the neural correlates underlying this behavior are endocranial casts

(Sherwood et al. 2008; Reyes and Sherwood 2015). Although direct investigation of neural changes that accompanied the evolution of stone tool-making is limited, neuroimaging techniques have allowed for the study of areas in the brains of human and other primates that are involved specifically in tool related tasks like tool-use and tool-making.

The human brain contains a group of interconnected regions that is activated during tool- related tasks, and includes bilateral activation with a leftward bias in the inferior frontal gyrus, premotor cortex, superior and inferior parietal lobes, middle temporal gyrus, and the fusiform cortex. This “tool circuit” is active across different aspects of tool processing, such as tool-use execution, pantomiming, imagining, planning, recognition, and observation (Lewis 2006; Peeters et al. 2009). Many of these regions, including those in the premotor cortex, inferior parietal lobe, and inferior frontal gyrus are also activated during experiments in which human subjects engage in stone tool-making and observe the process of stone tool-making (Stout et al. 2008, 2011).

Observing tool-use activates many of the same areas in both humans and macaques

(Peeters et al. 2009); however, extensive training on a tool-use task in macaques over six weeks

9 (two weeks habituation, two weeks intensive training, two weeks post-training) led to expanded gray matter in areas that are more closely associated with hand and arm movements rather than object manipulation and tool-use, including the superior temporal sulcus, secondary somatosensory cortex, and intraparietal sulcus (Quallo et al. 2009). Monkeys and humans do share similarities in parieto-frontal connectivity responsible for object manipulation and associated sensorimotor integration (Johnson-Frey 2003). However, anatomical connectivity among tool- related regions differs between humans and monkeys, with humans having increased connectivity between frontal, temporal, and parietal regions via the superior, middle, and inferior longitudinal fasciculi (Hecht et al. 2012).

These comparisons show substantial functional and anatomical differences in tool-related regions between humans and macaques, but do not directly show the type of neural changes that are specific to tool-use and tool-making in more recent periods of human evolution. Therefore, comparisons with a more closely related species is necessary. Chimpanzees are an ideal species for investigating the evolution of tool-use and the brain based on their well-documented tool-use both in the wild and in captivity (Haslam 2013). In the wild, chimpanzee tool-use varies by site with population specific tool-use behaviors that include termite fishing and nut cracking (Goodall

1986; Nishida 1990; McGrew 1992; Matsuzawa 1996; Whiten et al. 1999). In captivity, chimpanzees are able to complete a simulated termite fishing task (Hopkins et al. 2009).

Despite habitual tool-use in chimpanzees, however, there has been limited research on its anatomical and functional neural correlates, and many of the identified differences have been limited to white matter. For example, a DTI-based study indicated that faster performance on the simulated termite-fishing task (Hopkins et al. 2009) is linked with greater anisotropy of white matter fibers in parts of the corpus callosum associated with the somatosensory cortex (Phillips et al. 2013). The connectivity of tracts in the chimpanzee brain that link frontal, parietal, and temporal areas that are involved in tool-use is intermediate between macaques and humans, with chimpanzees sharing fronto-temporal connections in the external and extreme capsules, and chimpanzees and humans sharing connectivity between frontoparietal areas and the inferior

10 temporal cortex (Hecht et al. 2012). Although limited, these studies suggest that humans and chimpanzees may share specific brain regions that are involved in tool-use.

The current study examined the relationship between gray matter (GM) and performance time on a tool-use task in a sample of captive chimpanzees to identify structural neural correlates of tool-use. First, tensor-based morphometry (TBM) was applied in a whole brain voxelwise analysis to identify local GM differences. TBM localizes shape differences based on the deformation fields that map points from a subject image onto a template (Ashburner and Friston

2000). Differences in shape that are related to tool-use performance may indicate brain regions with structural changes associated with tool-use. Next, a whole brain voxelwise analysis of cortical thickness was performed to assess whether the differences identified in TBM were due to cortical thickness, since cortical thickness can reflect measures of cortical cytoarchitecture such as neuronal density (Narr et al. 2005; Narr et al. 2007).

Methods

Sample

The sample used for this study consisted of 63 chimpanzees (42 females, mean age = 25.8 years at time of MRI scanning, SD = 12.4 years; 21 males, mean age = 19.6 years, SD = 7.3 years) from Yerkes National Primate Research Center (YNPRC) in Atlanta, GA, USA that were housed according to institutional guidelines. This sample of chimpanzees has been included in previous research on communication, handedness, tool-use performance, and comparative neuroanatomy (e.g. Hopkins et al. 2008, 2010; Gómez-Robles et al. 2014, 2015; Hopkins and

Taglialatela 2013; Phillips et al. 2013).

Tool-use performance and covariates

Tool-use performance time was based on each individual’s mean performance time on a simulated termite fishing task (Hopkins et al. 2009; Phillips et al. 2013). The task consisted of removing food from a tube (approx. diameter = 4cm, approx. length = 20 cm) attached to the cage, and performance time was measured as the length of time for an individual to successfully

11 insert the stick into the tube. The mean performance time over multiple bouts was calculated for each individual. Performance time for both hands was pooled, since handedness in this sample can vary based on the motor demands of particular tasks (Hopkins and Pearson 2000).

Although we were primarily interested in the effects of tool-use, we accounted for other variables. Studies in the wild and in captivity both indicate that there are sex differences in chimpanzee tool-use (Boesch and Boesch 1990; Hopkins et al. 2009, Gruber et al., 2010; Koops et al. 2015). In the wild, male object manipulation is centered more around play activities, while female object manipulation is more diverse. Females also begin using tools at an earlier age than males (Koops et al. 2015). In captivity and in this particular sample, females exhibit faster tool- use performance than do males (Hopkins et al. 2009). There are also differences in cortical thickness in this sample of chimpanzees that can be attributed to sex in many of the ROIs used in this study, particularly in the precentral and postcentral gyri, and the superior parietal lobe

(Hopkins and Avants 2013). The current sample also shows substantial sex differences in the distribution of tool-use performance time (Figure 1A), and a preliminary analysis showed a significant interaction effect of sex and tool-use performance on TBM and cortical thickness. We therefore investigated brain structure correlates of tool-use within each sex separately.

Age and brain volume may also have confounding effects. In general, tool-use proficiency increases with age in chimpanzees (Koops et al. 2015). However, age at the time of tool-use was not associated with any differences in tool-use performance in the sample (Figure 1B). Age may also affect brain structures. Studies of the effects of age on cortical thickness in humans have shown that cortical thickness tends to decrease as age increases, with frontal and parietal regions experiencing a greater reduction in cortical thickness than temporal and occipital regions

(Thambisetty et al. 2010; Lemaitre et al. 2012). Age at the time of the scan was therefore included as a covariate in the analysis.

We also investigated a possible link between brain volume and tool-use performance prior to the analysis. Brain volume for each individual was measured in BrainVISA (Cointepas et al.

2001) and included the cerebrum and cerebellum. We found no relationship overall, or in either sex, between brain volume and tool-use performance (Figure 1C). Brain volume, however, has

12 been shown to affect brain structure. In chimpanzees, average whole-brain GM and white matter

(WM) volumes are negatively correlated with cortical thickness (Hopkins and Avants 2013), suggesting a decrease in overall brain volume as cortical thickness increases. Based on possible effects of brain volume, we included it as a covariate in the statistical model.

Scanning and preprocessing

T1 weighted magnetic resonance image (MRIs) scans were obtained in vivo for this sample of chimpanzees at YNPRC using procedures described previously (e.g., Hopkins et al. 2010,

Phillips et al. 2013). Prior to scanning, each chimpanzee was immobilized with a telazol injection

(2-6mg/kg) and subsequently anesthetized with propofol (10mg/kg/h). The chimpanzees were scanned with a 3.0 Tesla scanner (Siemens Trio; Siemens Medical Solutions, Inc., Malvern, PA,

USA). Acquisition time was approximately 1 hr. After completing the MRI scanning, each chimpanzee was housed in a single cage for 6 –12 hr to allow for the effects of anesthesia to wear off, after which they were returned to their home cage and social group. Guidelines from the

American Psychological Association for the ethical treatment of animals were followed during all aspects of this study, and institutional animal care and use approval was obtained prior to obtaining scans. Brain extraction and image normalization for the MRI scans were performed in

Analyze 8.0 (Mayo Clinic, Mayo Foundation, Rochester, MN, USA).

Tensor based morphometry

TBM was utilized to investigate local differences in GM in the cortex that were related to tool-use performance. First, we generated two templates to be used for the TBM analyses: a template for males only and a template for females only. Although the study was based on correlating TBM with a behavioral measure, the use of sample specific templates reduced any bias that might be introduced due to uneven male and female sample sizes, especially since TBM relies heavily on registration to the template.

The template building process was the same for the male and female samples. We used the baseline T1 images from all subjects in the sample (i.e. all males or all females) and

13 generated a template using a template building script in ANTs (Avants et al. 2010). The images were first rigidly aligned to the chimpanzee population template (Hopkins and Avants 2013) to put all images in the same space. As part of building each of the sample specific templates, the images underwent N4 bias correction (Tutison et al. 2010), and were processed using the SyN tool, which iteratively co-registers all the images to each other in diffeomorphic space for intensity averaging, and is then repeated recursively to refine the resulting template. For this study, this process was repeated six times to further refine the results. Individual brains were then registered to their respective template to obtain deformation fields for the TBM analysis.

To find the relationship between TBM and tool-use performance, we calculated the log of the Jacobian determinant of the deformation field (logJ) for each voxel, which shows volumetric increases (positive values) and decreases (negative values), and used this measurement in the statistical analysis. Differences in logJ associated with tool-use performance were then assessed using Pearson's r and resulting significance tests, described below.

Cortical thickness

Cortical thickness maps were obtained from T1-weighted structural MRI scans using diffeomorphic registration-based cortical thickness (DiReCT) implemented with Advanced

Normalization Tools (ANTs; Avants et al. 2008, 2010; Das et al. 2009; Hopkins and Avants 2013).

The maps showed the thickness of the cortex in millimeters across the cortical ribbon for each individual. Maps from each individual were registered to a chimpanzee population template that was produced using ANTs (Avants et al. 2011; Hopkins and Avants 2013), and included a sample of 71 chimpanzee T1 weighted images, a subsample of which were included in the current study.

These cortical thickness maps have been investigated previously to assess regional variation in the chimpanzee cortex (Hopkins and Avants 2013).

Statistical analyses

Whole brain, voxelwise analyses were performed for TBM and cortical thickness. A whole brain approach was selected since there is limited functional evidence for a relationship between

14 tool-use and specific brain areas in chimpanzees. The experimental designs employed in this study assessed the effects of tool-use within both sexes on measures of brain structure while controlling for brain volume and age. The same procedures were used for the TBM and cortical thickness analyses. Measures of tool-use performance time, brain volume, and age were log transformed to normalize their distributions, and mean centered. We performed regression analyses to assess the overall effects of tool-use and the effects of tool-use within each sex.

We considered four contrasts overall: greater female tool-use performance > lesser female tool-use performance and greater female tool-use performance < lesser female tool-use performance (female tool-use effect), greater male tool-use performance > lesser male tool-use performance and greater male tool-use performance < lesser male tool-use performance (male tool-use effect).

To obtain Pearson’s r values and corrected p values, a voxelwise GLM was applied using permutation-based non-parametric testing and threshold-free cluster enhancement (TFCE) using permutation analysis of linear models (PALM; Winkler et al. 2014). TFCE defined significant clusters and corrected for multiple comparisons on a voxel-wise basis (Smith and Nichols 2009;

Results

Tensor based morphometry

The TBM analysis showed substantial sex differences in brain regions associated with tool- use performance. Females had few significant clusters, with significant positive correlation (p <

0.025) between logJ and tool-use performance identified in small clusters in the frontal, motor, somatosensory, and parietal cortex, and clusters showing significant negative correlation (p <

0.025) in the prefrontal, inferior frontal, premotor, temporal, and occipital cortex and in cerebellar areas (Figure 2). Examination of Pearson's r showed additional positive correlation throughout the prefrontal, somatosensory and parietal cortex (Figure 3).

15 Males showed much larger and more widespread effects of tool-use performance on logJ than did females (Figures 4-5). Clusters with a significant positive correlation (p < 0.025) were observed in ventral areas of the premotor, motor, and somatosensory cortex, as well as the inferior parietal, temporal, and occipital regions on both sides, along with the right prefrontal cortex. Clusters with a significant negative correlation (p < 0.025) were identified bilaterally in dorsal parts of the premotor, motor, and somatosensory cortex, the superior parietal cortex, cerebellum, and in the left prefrontal cortex.

Cortical thickness

Examination of cortical thickness revealed sex differences similar to those found in the

TBM analysis. Females had few significant clusters associated with faster tool-use performance

(Figure 6), with significant positive correlation (p < 0.025) in the left premotor cortex, and significant negative correlation (p < 0.025) in diffuse clusters in the frontal, temporal, and occipital lobes. Pearson's r, however, shows that although not statistically significant, there were substantial effects (r > 0.3) throughout the brain. Negative correlation was identified throughout the prefrontal, premotor, and ventral motor and somatosensory areas, while positive correlation was seen in the dorsal motor and somatosensory areas (Figure 7).

In males, there was a large area in the premotor cortex, especially on the left side, that showed significant positive correlation (p < 0.025) with faster tool-use performance time.

Additional significant clusters with a positive correlation were observed in the orbitofrontal, temporal, and occipital cortex, along with the cerebellum (Figure 8). Clusters with significant negative correlation were identified in the prefrontal cortex. For Pearson's r, there was a clear pattern of large effects (r > 0.3) from positive correlation in the premotor cortex, and negative correlation in the parietal and occipital region (Figure 9). Although effects from negative correlation were seen throughout the parietal and occipital lobe, there were no clusters in these regions that reached statistical significance.

16 Discussion

The current study investigated the relationship between GM structure and tool-use performance in a sample of captive chimpanzees to identify brain regions that are structurally correlated with tool-use. We first used a TBM analysis to identify subtle changes in local GM morphology associated with tool-use performance, and then we investigated cortical thickness to assess if these differences could be due to changes in cortical thickness. Males and females were examined separately due to substantial sex differences in tool-use performance (Figure 1).

In both males and females, the TBM and cortical thickness results did not show large clusters of overlap, but correlation effects were generally present in similar regions. Males and females showed different effects of faster tool-use performance time. Males had positive correlation in premotor, occipital, and temporal regions, and showed negative correlation effects in prefrontal, motor, somatosensory, and parietal regions, whereas females had positive correlation effects in ventral premotor and somatosensory cortex, and negative correlation in the ventral premotor, inferior parietal, and inferior temporal cortex.

There were only a few areas with shared effects that differed slightly for TBM and cortical thickness results. For TBM they included small clusters with positive correlation in the right prefrontal and left and right inferior temporal cortex, and negative correlation in the left cerebellum, right temporal pole, and different parts of the inferior temporal cortex, and for cortical thickness only included negative correlation in the prefrontal and inferior frontal cortex.

TBM and cortical thickness

In both sexes, the results from cortical thickness show more regionally concentrated results (Figures 10-11), while TBM shows cortical differences that are more distributed. Like other measures of brain morphometry, TBM shows more generalized differences in GM that are due to a mixture of GM measures such as surface area and cortical folding, as well as cortical thickness, and allows for a wider view of GM differences at a given voxel. The cortical thickness analysis, in contrast, specifically investigates only one measure of GM (Hutton et al. 2009).

17 The TBM and cortical thickness analyses revealed few directly overlapping results in both sexes (Figure 10). Despite few overlapping clusters, the results from the both analyses were generally located in the same regions. Few of the GM differences shown with TBM were highly driven by cortical thickness, and these regions were in clusters in the premotor cortex in both sexes, the parietal cortex in females, and the temporal lobe and cerebellum in males. Clusters from the TBM analysis that did not correspond to the effects observed in cortical thickness were likely driven by expansion and contraction in another measure of GM such as surface area or cortical folding. This suggests that functional and behavioral differences may be linked with diverse neural correlates that do not correspond to one specific measure. It is therefore necessary to consider measurements such as TBM that give a more comprehensive examination of GM, as well as other measures such as cortical thickness which can add more specificity.

Human studies have shown that increasing gray matter and particularly cortical thickness is usually associated with higher intelligence or increased cognitive capacity for specific tasks

(e.g. Narr et al. 2005, 2007; Shaw et al. 2006; Ilg et al. 2008; Burgaleta et al. 2014; Chen and

Omiya 2014). The current study, however, indicates the opposite for many clusters associated with faster tool-use performance, where both males and females show clusters with negative correlation between cortical thickness and tool-use performance. Cortical thickness has been suggested as a means of measuring aspects of cortical cytoarchitecture such as neuronal density

(Narr et al. 2005; Narr et al. 2007), but the exact link between cortical thickness, processing capability, and proficiency at specific tasks has not yet been determined. Decreasing cortical thickness may be a sign of synaptic pruning or an increase in the WM underlying a cortical region that may affect the appearance of the GM-WM boundary in the MRI scan (Fjell et al. 2015). More detailed studies of the cortical architecture using histology and WM using imaging methods such as DTI are necessary to examine potential reasons that a decrease in GM is associated with faster tool-use performance time.

18 Sex differences in TBM and cortical thickness

Males and females had few shared areas with structural changes associated with tool- use performance for TBM and cortical thickness (Figure 11). While the shared clusters were small, their locations indicate that males and females have structural overlap in areas that are highly involved in visual, sensorimotor, and cognitive control processes. Areas involved in the ventral visual processing stream, in particular, had shared correlation in males and females. The ventral visual processing stream connects the primary visual cortex with temporal and prefrontal regions, and is responsible for stimulus identification, object recognition, and mapping this information onto conceptual representations (Ungerleider and Mishkin 1982; Cloutman, 2013).

Areas in the inferior temporal lobe including the inferior temporal gyrus, fusiform gyrus, lingual gyrus, are involved in the ventral stream and are activate during visuospatial attention and differentiating between different objects and faces (Bogousslavsky et al. 1987; Machielsen et al.

2000; Creem and Proffitt et al. 2001; Tyler et al. 2013; Roberts et al. 2013).

Shared clusters were also present in areas that are associated with sensorimotor processing, particularly areas that combine motor, somatosensory, and in some cases, visual information. The inferior frontal gyrus receives input from the ventral visual stream, and is involved in attention, motor inhibition, and the observation of grasping motions (Aron et al. 2004;

Levy and Wagner 2011; Hecht et al. 2013; Lee et al. 2013). Although the temporal pole is considered a paralimbic regions, certain parts are associated with somatosensory and visual processing (Gloor et al. 1982; Pascual et al. 2013). The cerebellum, particularly the anterior portion, is involved in sensorimotor processing and contains a somatotopic map (Nitschke et al.

1996; Grodd et al. 2001; Stoodley and Schmahmann 2009; Buckner et al. 2011).

Shared clusters in the dorsolateral prefrontal cortex are involved in both sensorimotor and visual processing, along with higher order cognitive control. This portion of the prefrontal cortex receives input from the dorsal visual stream (Ungerleider and Mishkin 1982; Lee et al.

2013), which connects the primary visual cortex with the parietal and prefrontal cortex, and combines motor information in the spatial processing of motion and the guidance of actions toward objects in space (Culham et al., 2003; Cloutman, 2013), and likely uses this information

19 for working memory, particularly in visuospatial and object manipulation tasks (Procyk and

Goldman-Rakic 2006; Barbey et al. 2013). The dorsolateral prefrontal cortex is also involved in cognitive control processes including decision making and planning (Duncan and Owen 2000;

Kaller et al. 2011). Most of these areas are involved in either processing information about objects or combining object information with sensorimotor information. Males and females share structural differences associated with tool-use performance in these regions that might underlie similarities in how information concerning objects and object manipulation during tool-use is processed.

While a few shared clusters were identified in males and females, most effects were substantially different between the sexes. There were patterns of medium to large effects that differentiated males and females and were consistent even across TBM and cortical thickness, despite these measures having few clusters with direct overlap. Males have a decrease in GM in both TBM and cortical thickness in motor, somatosensory, and parietal regions that forms the most notable sex difference associated with tool-use performance. The parietal region is part of the dorsal stream of visual processing that is heavily involved in sensorimotor integration, particularly in visually guided reaching and grasping of an object (Chao et al. 2000; Fogassi et al.

2005; Grefkes and Fink 2005; Gardner et al., 2007; Rozzi et al., 2008; Bonini et al., 2010).

Another notable sex difference was found in GM in the female premotor cortex associated with faster tool-use performance. This difference, however, is shown as increased volume in TBM and decreased cortical thickness, which suggests that the effects in this region may be associated with additional measures of GM size and shape including cortical folding and surface area. The premotor cortex is generally involved in the guidance of movements and ventral parts of the premotor cortex have been specially adapted for the guidance of hand and arm movements based on visual information and complex movements of the arm and hand associated with objects (Rizzolatti et al. 1981,1988; Graziano et al. 1994, 2002; Fogassi et al. 1996; Murata et al.

1997). In macaques, parts of the ventral premotor cortex are part of the mirror neuron system, which is activated during the observation of reaching and grasping (Rizzolatti et al. 1988; diPellegrino et al. 1992; Murata et al. 1997; Graziano et al. 2002; Rizzolatti and Sinigaglia 2010).

20 These overall differences between males and females indicate that each sex may have a different means of processing tool-use information. A different relationship between tool-use performance and GM measures by sex is not unexpected, considering the notable behavioral differences regarding tool-use observed among male and female chimpanzees (Boesch and

Boesch 1990; Hopkins et al. 2009, Gruber et al., 2010; Koops et al. 2015). Female-based tool- use is evident across wild chimpanzee sites in a variety of tool-use types (McGrew 1979; Boesch and Boesch 1984; Hiraiwa-Hasegawa 1989). Immature chimpanzees also show sex-bias, but in the opposite direction with males showing higher rates of object manipulation than females

(Koops et al. 2015). Males, however, tended to engage with objects in a more play-dominated context, while female object manipulation was present in a greater variety of contexts.

These sex differences in behavior may be related to some of the structural differences identified in the current study. Females exhibited effects in the premotor cortex in areas that could be associated with the mirror neuron system. In monkeys, mirror neurons are involved in understanding action intention, or the goals of a particular action performed by a monkey that is being observed (Rizzolatti and Craighero 2004; Rizzolatti and Sinigaglia 2010). When juvenile females learn termite fishing, they tend to observe their mother more than juvenile males, and adopt a similar technique (Lonsdorf et al. 2004). Structural differences in regions containing mirror neurons may therefore support this type of observation and imitation that can more efficiently transmit tool-use behaviors between individuals (Iacoboni 1999; Iriki 2006). The major effects observed in the male motor, somatosensory, and parietal regions suggest that faster tool- use performance time may be related to changes in sensorimotor integration and dorsal stream visual processing, such as reaching and visually guided hand movements (Chao et al. 2000;

Fogassi et al. 2005; Grefkes and Fink 2005; Gardner et al., 2007; Rozzi et al., 2008; Bonini et al.,

2010). Whereas faster tool-use performance time produces differences in areas involved in motor planning in females, it is associated with changes in areas associated with integrating sensory and motor information in males. Because the current study focuses exclusively on linking behavior with structural differences, additional tests are necessary to more definitively link behavior and structure to functional differences in these regions.

21 One possible explanation for the different relationship between tool-use skill and GM measurements between males and females is an effect of sex hormones on brain structure.

There is evidence that the development of different brain structures can be affected by sex hormones. In rats, areas in the frontal, limbic, and occipital areas have a higher density of androgen receptors than areas in other brain regions (Kolb and Stewart 1991; Reid and Juraska

1992; Venkatesan and Kritzer 1999; Nuñez et al. 2000, 2001; Markham and Juraska 2002). In humans, similar regions exhibit sex differences during puberty, with thicker cortex in boys and thinner cortex in girls, even when matched for sexual maturity (Bramen et al. 2012).

There could be a similar effect of androgens or perhaps other sex hormones on specific areas in the chimpanzee brain, which may contribute to differences in brain structure associated with variation in behavior between males and females. Male chimpanzees with higher levels of testosterone have been shown to perform better on a variety of cognitive tasks, including tool- use, compared to males with lower levels of testosterone, an effect that was not seen in chimpanzee females, or in bonobos of either sex (Wobber and Herrmann 2015). This suggests that the behavior of chimpanzee males may be particularly sensitive to testosterone and possibly other sex hormones, and could possibly be related to male dominance hierarchies that are at the center of chimpanzee groups (Muehlenbein et al. 2004; Wobber and Herrmann 2015). Although this study investigated tool-use skill separately within each sex, further study of how specific brain regions directly compare between males and females in regard to tool-use may elucidate the relationship between sex, brain structure, and behavior.

Implications for tool-related behaviors in human evolution

The current study utilized structural neuroimaging techniques to investigate neural correlates of tool-use in chimpanzees. The results indicate that chimpanzees have notable sex differences, which may contribute to many of the sex-based behavioral differences that have been observed. These sex differences, however, make it difficult to draw comparisons with previous work in humans and other nonhuman primates where such large sex differences were either not observed or not directly investigated. As discussed previously, overlapping shared

22 effects associated with faster tool-use performance were identified in areas that are involved in object-related cognition and combining object and sensorimotor information, primarily located in the ventral visual stream.

The shared effects between both sexes in the ventral visual stream shows that more skilled chimpanzee tool-use relies heavily on regions involved in recognizing and interacting with objects. These inferior temporal areas in the ventral visual stream have been previously identified as important for tool use in both humans and macaques (Lewis 2006; Obayashi et al. 2001;

Johnson-Frey 2004). Although involvement of the inferior temporal cortex appears to be shared across macaques, chimpanzees, and humans, it is not a region that shows structural changes over the course of tool-use training in macaques. Instead, tool-use training in macaques resulted in GM expansion in areas associated with hand and arm movements along with biological motion, and did not show any effects in inferior temporal regions associated with object cognition (Quallo et al. 2009). This suggests that skill at tool-use in macaques may not have the same effects on object-related areas in the inferior temporal lobe as tool-use in chimpanzees, and may therefore involve improvement in sensorimotor capabilities rather than object cognition. This also suggests that object areas in the inferior temporal cortex may be particularly important for tool-use in chimpanzees and humans, and may account for the more varied and spontaneous tool interactions seen in both species. It must be noted that the current study examines neither function nor training effects, and is a cross-sectional analysis of the relationship between tool-use and brain structure in a single species. It does, however, provide a potential region of interest for further examining the longitudinal functional and structural changes during tool-use training in chimpanzees.

The substantial sex differences in chimpanzee tool-use and brain structure may also hold implications for the evolution of tool-use and tool-making in humans. A longstanding implication had been that the earliest stone tool-making hominins were male, but this notion was overturned with the wealth of behavioral evidence showing sex differences in chimpanzee tool-use (Ambrose

2001). Though sparse, there is evidence for potential sex-based differences in modern human tool-use. Children show distinct sex-based differences in object-based play that is highly

23 consistent across cultures and is shared with both macaques and chimpanzees (Hassett et al.

2008; Kahlenberg and Wrangham 2010), where boys have a strong preference for toys considered to be masculine (e.g. weapons and wheeled toys), and girls tend to play more with toys considered to be feminine (e.g. dolls), but do not show a strong preference for type

(Berenbaum and Hines 1992; Turner et al. 1993). In a toy-retrieval task in which only one of six tools presented to a three-year-old child could be used to retrieve a toy, boys engaged more in object-based play and were more likely to select the appropriate tool and retrieve the toy without a hint than were girls (Gredlein and Bjorklund 2005). An examination of how elementary students use objects and tools in a science class also revealed sex-based differences in tool-use, with girls tending to follow directions and use the tools as instructed, and boys tending to be more creative with tool-use and not necessarily relying on instructions (Jones et al. 2000). Chimpanzees show a similar sex differences in how juveniles learn termite fishing. Females tend to start termite fishing at an earlier age than males, and also use a similar technique to that used by their mothers, while males do not (Lonsdorf et al. 2004), suggesting that female chimpanzees, much like human female children, engage in direct observational learning, whereas males of both species tend to engage in learning based on individual trial and error. In general, these studies from human children reflect patterns of object-based play and early tool-use observed in juvenile chimpanzees.

Although there is evidence for sex-based differences in human children, there are few direct studies of the topic in human adults. Studies of tool-use and capabilities in adults tend to focus on the task itself and either used sex as a covariate or did not examine sex effects at all, rather than investigate sex differences directly (e.g. Imazu et al. 2007; Stout et al. 2008; Yoo et al.

2013; Hecht et al. 2015; Demirakca et al. 2016). Humans are highly flexible in cognition and based on these studies, males and females can both adapt easily to use of a new type of tool.

However, information about potential structural, functional, and behavioral differences between males and females that indicate how adaptation occurs in the brain are then lost when sex is not directly investigated. Sex differences in brain structures associated with skilled tool-use in chimpanzees, along with shared patterns of object manipulation and play in macaque and

24 chimpanzee juveniles and human children suggest that similar sex differences in juveniles likely existed in the last common ancestor of the two species. Sex differences in chimpanzee tool-use, however, have been observed to persist into adulthood, while the flexibility in human behavior and cognition that is present in both sexes may result in a relative lack of sex differences in tool- use capability seen in humans. The evolution of this type of flexibility in cognition and behavior likely set members of the human lineage apart from the chimpanzee-human last common ancestor, and allowed for an increased capacity for adaptation and innovation in tool-use and tool-making.

Conclusion

Complex tool-related behaviors have been a defining characteristic of the human lineage, but studying the evolution of these cognitive capabilities has proven difficult. The current study utilized structural neuroimaging techniques to investigate brain structure correlates of tool-use skill in chimpanzees. Differences in GM associated with tool-use performance were generally shown with both TBM and cortical thickness analyses, though the effects of these measurements showed very little overlap. There are therefore structural differences in GM associated with tool- use performance that are predominantly based on cortical thickness and do not produce local shape change in GM, and other structural differences that do produce local changes that are likely from surface area or cortical folding, but do not involve large differences in cortical thickness. This suggests functional or behavioral differences may result in GM differences that may be due to a number of measurements. A summary measurement such as TBM that takes into account local shape changes may provide a comprehensive means of identifying potential changes in GM, while the addition of a more targeted measurement such as cortical thickness can add specificity to the analysis and a more direct link with histological measures.

In both analyses, we found that tool-use performance showed substantial differences between males and females, with faster tool-use performance correlated with differences in motor, somatosensory, and parietal regions associated with hand and arm movements and sensorimotor integration in males, and differences in the premotor areas associated with motor

25 control of reaching and grasping in females. Despite these sex-differences, there were also shared areas that showed correlation with faster tool-use performance primarily in the inferior temporal cortex, but also regions in the prefrontal cortex, inferior frontal cortex, temporal pole, and cerebellum that are involved in involved in cognitive control, sensorimotor processing, and visual processing, and may work together to integrate information from objects and sensorimotor control. These areas have also been identified as important for tool-use in macaques and humans, but macaques lack structural changes in the inferior temporal cortex when receiving training on a tool-use task. This suggests that chimpanzee tool-use may be enhanced relative to macaques due to increased potential for structural changes in the inferior temporal cortex and a corresponding increase in object-related cognition. Further enhancement and flexibility in these structures may have persisted in the human lineage and may have ultimately resulted in the variety of technological implements associated with modern humans.

26 Figures

Figure 1. Plots depicting the distributions of tool-use performance time (log transformed), age at tool-use, and brain volume by sex. Tool-use performance time was substantially different between males and females (A), with the mean of each sex shown in red and the shape of the distribution shown in the outline. Age at the time of tool-use also had a different distribution in each sex (B, left), but showed no relationship with tool-use performance time for either sex (B, right). Brain volume was similar between males and females (C, left), and did not show a relationship with tool-use performance time in either sex (C, right).

Figure 2. Significant clusters for logJ analysis in females shown in left/right, superior/inferior, and anterior/posterior 3-D renderings (A), and in axial sections (B). The red-yellow scale indicates clusters with significantly higher logJ associated with faster tool-use performance, while the blue- green scale indicates clusters with significantly lower logJ associated with faster tool-use performance. Higher logJ is present in small clusters in the prefrontal, somatosensory, and superior parietal cortex. Lower logJ is present in small clusters in the inferior frontal, dorsal premotor, superior temporal, inferior parietal, and occipital lobe, along with the cerebellum.

Figure 3. Pearson’s r values for the logJ analysis in females shown in left/right, superior/inferior, and anterior/posterior 3-D renderings (A), and in axial sections (B). The scale indicates regions with a negative correlation (i.e. low logJ values and higher tool-use performance) in blue and regions with a positive correlation (i.e. high logJ values and higher tool-use performance) in red.

Much of the female brain shows little effect of tool-use performance on logJ values, shown by the large areas in green. Positive correlation between logJ and tool-use performance can be seen in frontal, motor, somatosensory, and parietal areas, while negative correlation can be seen in prefrontal, inferior frontal, premotor, temporal, occipital, and cerebellar areas.

Figure 4. Significant clusters for logJ analysis in males shown in left/right, superior/inferior, and anterior/posterior 3-D renderings (A), and in axial sections (B). The red-yellow scale indicates clusters with significantly higher logJ associated with faster tool-use performance, while the blue- green scale indicates clusters with significantly lower logJ associated with faster tool-use performance. There are numerous clusters that show both higher and lower logJ associated with tool-use performance, especially compared to females. Higher logJ is present most notably in the dorsal and ventral premotor cortex, inferior temporal cortex, occipital lobe, and cerebellum. Lower logJ is present in the prefrontal and orbitofrontal cortex, and in the motor, somatosensory and parietal cortex on both sides. The axial cross-sections (B) show the full extent of the significant clusters.

Figure 5. Pearson’s r values for the logJ analysis in males shown in left/right, superior/inferior, and anterior/posterior 3-D renderings (A), and in axial sections (B). The scale indicates regions with a negative correlation (i.e. low logJ values and higher tool-use performance) in blue and regions with a positive correlation (i.e. high logJ values and higher tool-use performance) in red.

Positive correlation between logJ and tool-use performance can be seen in prefrontal, dorsal and ventral premotor, temporal, superior parietal, occipital, and cerebellar regions. Substantial negative correlation can be seen in prefrontal, orbitofrontal, and middle temporal areas, and in a large region spanning motor and somatosensory cortex.

Figure 6. Significant clusters for the cortical thickness analysis in females shown in left/right, superior/inferior, and anterior/posterior 3-D renderings. The red-yellow scale indicates clusters with significantly higher logJ associated with faster tool-use performance, while the blue-green scale indicates clusters with significantly lower logJ associated with faster tool-use performance.

Higher logJ is associated with faster tool-use performance in a cluster in the left motor cortex.

Lower logJ is associated with faster tool-use performance small clusters in the temporal and occipital lobes.

Figure 7. Pearson’s r values for the cortical thickness analysis in females shown in left/right, superior/inferior, and anterior/posterior 3-D renderings. The scale indicates regions with a negative correlation (i.e. low cortical thickness and higher tool-use performance) in blue and regions with a positive correlation (i.e. high cortical thickness and higher tool-use performance) in red. Positive correlation between cortical thickness and tool-use performance can be seen in dorsal motor and somatosensory regions. Negative correlation can be seen in ventral prefrontal, premotor, and motor regions.

Figure 8. Significant clusters for the cortical thickness analysis in males shown in left/right, superior/inferior, and anterior/posterior 3-D renderings. The red-yellow scale indicates clusters with significantly higher logJ associated with faster tool-use performance, while the blue-green scale indicates clusters with significantly lower logJ associated with faster tool-use performance.

Higher logJ is associated with faster tool-use performance in prefrontal cortex, especially the on the left, orbitofrontal cortex, and small clusters in temporal lobe, occipital lobe, and cerebellum.

Lower logJ is associated with faster tool-use performance in prefrontal cortex, somatosensory cortex, and occipital lobe.

Figure 9. Pearson’s r values for the cortical thickness analysis in males shown in left/right, superior/inferior, and anterior/posterior 3-D renderings. The scale indicates regions with a negative correlation (i.e. low cortical thickness and higher tool-use performance) in blue and regions with a positive correlation (i.e. high cortical thickness and higher tool-use performance) in red. Positive correlation between cortical thickness and tool-use performance can be seen in dorsal premotor and prefrontal regions, orbitofrontal cortex, and the right temporal lobe and cerebellum. Negative correlation can be seen in somatosensory, parietal, occipital, and temporal pole areas.

Figure 10. Clusters in males and females where Pearson’s r shows a medium or larger effect (r ≥

0.3 and r ≤ -0.3) for TBM and cortical thickness results on 3-D rendered brains showing left, right, superior, inferior, anterior, and posterior views. TBM results are shown in blue, cortical thickness results are shown in red, and overlapping clusters are shown in purple and are indicated by black arrows. Both sexes show limited overlap between the TBM and cortical thickness results. In

36 males, overlapping positive correlation is seen in the right prefrontal cortex, left dorsal premotor cortex, right superior and inferior temporal lobe, right occipital lobe, and the cerebellum, while overlapping negative correlation is seen in the prefrontal and orbitofrontal cortex, and the right premotor cortex, temporal cortex, somatosensory cortex, and cerebellum. In females, positive correlation shows overlap in the left motor cortex and cerebellum, while negative correlation shows overlap in the prefrontal cortex and the right dorsal premotor cortex, inferior temporal cortex, and cerebellum.

Figure 11. Clusters from the TBM and cortical thickness analyses where Pearson’s r shows a medium or larger effect (r ≥ 0.3 and r ≤ -0.3) for TBM and cortical thickness results on 3-D rendered brains showing left, right, superior, inferior, anterior, and posterior views. Male results

38 are shown in green, female results are shown in purple, and overlapping clusters are shown in white and are indicated by black arrows. There is limited overlap between the results from males and females for both methods. In TBM, overlapping positive correlation is seen in the right prefrontal cortex, left occipital lobe, and the inferior temporal lobe, while overlapping negative correlation is seen in the inferior temporal lobe and the right cerebellum. For cortical thickness, negative correlation shows overlap in the left dorsal prefrontal cortex and the right ventral prefrontal cortex, while there is no notable overlap in associated with positive correlation.

39 Chapter 3: White Matter Correlates of Tool-use Performance in Chimpanzees Using Tract-Based

Spatial Statistics

Abstract

Comparing white matter (WM) tracts of the human tool-use circuit with that of closely related chimpanzees can reveal which pathways may have experienced evolutionary change in the human lineage related to increased tool-use capabilities. The human tool-use circuit has been linked with four WM tracts: the anterior thalamic radiation (ATR), inferior fronto-occipital fasciculus

(IFOF), superior longitudinal fasciculus (SLF), and the uncinate fasciculus (UF). We therefore examined the relationship between WM tracts and tool-use skill in a sample of captive chimpanzees using tract-based spatial statistics (TBSS) to perform a voxel-based whole-brain analysis of fractional anisotropy (FA) and mean diffusivity (MD). We performed regression analyses for each sex separately due to substantial sex-based differences in tool-use performance. Three tracts associated with human tool-use (ATR, IFOF, SLF) were also associated with tool-use performance in both male and female chimpanzees, with additional clusters in tracts that are have not been directly linked with tool-use in humans and are associated more closely with motor control. Compared to females, males had more clusters overall, including those associated with motor control. These observed differences in WM tracts between males and females could be related to previously observed sex-based behavioral differences in tool-use. Despite these sex-based differences, the WM tracts associated with tool- use were generally similar between humans and chimpanzees, though evidence from both GM and WM suggests that tool-use in chimpanzees may involve more activity in areas associated with general motor control and sensorimotor integration. Tool-use processing in the brain may have become more specialized in human evolution, with increased emphasis on connectivity between regions involved in higher-order cognitive function. These types of changes in neural circuitry underlying tool-use may have resulted in the complex tool-use characteristic of modern humans.

Introduction

The origin of tool-use and tool-making behavior has been an integral aspect of the study of human evolution (Ambrose 2001; Stout 2011). Because we cannot directly observe the anatomy and behavior of extinct hominins, comparisons between humans and other primates are essential in attempting to identify conserved features as well as human-specific adaptations

(Nunn and Barton 2001). The comparative method is especially useful in the study of evolutionary neuroscience, where the brain can be compared across species at both the macro- and microscopic level to reconstruct the evolutionary history of different behaviors and their underlying neural circuitry (e.g. Semendeferi et al. 2001; Rilling and Seligman 2002; Sherwood et al. 2004;

Hecht et al. 2013). Comparisons between humans and chimpanzees are particularly valuable because the close evolutionary relationship between species can indicate which traits might have been shared with the last common ancestor with apes, and which might be specific to the human lineage. Additionally, chimpanzees are an ideal species for investigating the evolution of tool- based behaviors because they exhibit tool-use behavior both in the wild and in captivity (Haslam

2013). The characterization of brain structures associated with tool-use in chimpanzees can allow for more direct comparisons with the human tool-use circuit.

The neural circuits associated with tool-use in humans have been localized to a predominantly left lateralized functional network across the cerebral cortex involving areas associated with visual, somatosensory, and motor processing, sensorimotor integration, and cognitive control (Johnson-Frey et al. 2005; Lewis 2006). This circuit includes a number of areas that are activated during tool-use across different nonhuman primates (Peeters et al., 2009), and also experience relative expansion during tool-use training as shown in macaques (Quallo et al.

2009). These areas are associated with visually-guided hand and arm movements, reaching, grasping, and processing objects and biological motion (Grossman and Blake 2001; Beauchamp et al. 2004; Grefkes and Fink 2005).

41 A previous study characterized the effects of tool-use performance on gray matter (GM) in a sample of chimpanzees, and found that GM differences associated with tool-use were located primarily in motor and visual processing areas (Reyes et al. in prep). There were, however, differences between how tool-use performance affected GM in males and females, with males exhibiting a larger trend than females toward decreased GM in posterior parietal areas associated with faster tool-use performance. These results suggested that overall, tool-use performance in chimpanzees was related to changes in GM in many of the same areas associated with the tool-use circuit described in humans, but predominantly affected areas only involved in visual and motor processing. Sex differences that were detected in the GM of tool- related areas may underlie previously observed sex-based differences in tool-use behavior

(Hopkins et al. 2009; Gruber et al. 2010).

Evidence from human studies indicates that variation in white matter (WM) structure can also be correlated with behavior (Johansen-Berg 2010). Investigation of connectivity between specific cortical areas associated with tool-use in humans showed that the superior longitudinal fasciculus (SLF), connecting frontal, temporal, and parietal regions, and the extreme capsule, connecting frontal and occipital regions, were the major tracts linking areas in the tool-use circuit

(Ramayya et al. 2010; Hoeren et al. 2013). The mapping of functional deficits in tool-use processing and their corresponding cortical lesions onto tractography from healthy individuals revealed that the SLF, inferior fronto-occipital fasciculus (IFOF), anterior thalamic radiation (ATR) and uncinate fasciculus (UF) were all directly involved in processing tool-use information (Bi et al.

2015).

Each of these tracts connects areas that process and integrate motor and sensory information. The SLF is a large tract that can be divided into three parts (SLF I, II, III), with each connecting areas in the parietal, temporal, and frontal lobes (Petrides and Pandya 1984, 2002;

Thiebaut de Schotten et al. 2012; Hecht et al. 2015). The IFOF roughly corresponds to the extreme capsule (Mars et al. 2015), and links cortical areas associated with the ventral visual stream (Macko et al. 1982; Tusa and Ungerleider 1985; Makris et al. 1999; Catani and Thiebaut de Schotten 2008; Sarubbo et al 2013), which contains areas in the occipital, temporal, and

42 frontal lobes and is associated with object recognition and spatial processing, and attention

(Cloutman, 2013). The ATR has been associated with both executive functions and working memory, and connects sensory regions in thalamus with the prefrontal cortex (Mamah et al.

2010). The UF connects the orbitofrontal cortex with the temporal lobe and is part of the limbic system, and is hypothesized to modulate behavior by linking associative information from the temporal lobe with reward-based information from the orbitofrontal cortex (Von Der Heide et al.

2013).

The connectivity and morphology of many of these tracts have been investigated across humans and nonhuman primates, and are generally conserved across species. The internal capsule containing the ATR and the UF were similar in macaques and humans in their morphology and connectivity (Croxon et al. 2005; Von der Heide et al. 2013; Jbabdi et al. 2013).

Recent evidence from probabilistic tractography performed in both species indicated that the projections of the extreme capsule/IFOF were also similar between species, but also suggested that any interspecies differences in this tract may lie in WM microstructure (Mars et al. 2015). The mirror neuron system has also been investigated comparatively across species. The mirror neuron system connects frontal, temporal, and parietal regions that respond when observing another individual manually manipulating objects (Gallese et al., 1996; Rizzolatti and Craighero,

2004; Rozzi et al., 2008), and overlaps with many brain structures involved in tool-use. Patterns of connectivity within the mirror neuron system are largely conserved in macaques, chimpanzees, and humans. In humans, however, additional connectivity between frontal, parietal, and temporal regions was identified in a dorsal pathway through the SLF and inferior longitudinal fasciculus

(ILF; Hecht et al. 2013). The human SLF also exhibited a different pattern of connectivity with the inferior frontal gyrus compared with chimpanzees (Hecht et al. 2015).

Although these tracts have been examined separately in the chimpanzee brain, whether they are associated with tool-use in chimpanzees remains unexplored. The current study therefore examines WM connectivity associated with variation in performance on a tool-use task in chimpanzees. We used tract-based spatial statistics (TBSS) to perform a voxel-based whole- brain analysis of WM tracts using diffusion tensor imaging (DTI) scans. We analyzed two

43 measures of the diffusion tensor to characterize differences in WM related to tool-use performance: fractional anisotropy (FA) and mean diffusivity (MD). Based on the previous association between specific WM tracts and tool-use (Bi et al., 2015), we expected that variation in SLF, IFOF, UF, and ATR would be strongly associated with tool-use in this sample of chimpanzees.

Methods

Sample

The sample used for this study consisted of 44 chimpanzees (26 females, mean age =

18.4 years, SD = 7.6 years; 18 males, mean age = 17.7 years, SD = 5.6 years) from Yerkes

National Primate Research Center in Atlanta, GA, USA that were housed according to institutional guidelines. This sample of chimpanzees has been included in previous research on communication, handedness, tool-use performance, and neuroanatomy (e.g. Hopkins et al. 2008,

2010; Hopkins and Taglialatela 2013; Phillips et al. 2013; Gómez-Robles et al. 2014, 2015;

Reyes et al., in prep).

Scanning and preprocessing

Magnetic resonance images (MRIs) including both T1 weighted and diffusion tensor images (DTIs) were obtained at YNPRC using procedures described previously (e.g., Hopkins et al. 2010, Phillips et al. 2013). Prior to scanning, each chimpanzee was immobilized with a telazol injection (2-6mg/kg) and subsequently anesthetized with propofol (10mg/kg/h). The chimpanzees were scanned with a 3.0 Tesla scanner (Siemens Trio; Siemens Medical Solutions, Inc., Malvern,

PA, USA). Diffusion-weighted data was acquired with a single-shot EPI sequence with a b value of 1,000 s/mm2 with 60 diffusion directions plus one image without diffusion weighting (b value of

0 s/mm2). Acquisition time for both the MRI and DTI scans was approximately 1 hr. After completing the MRI and DTI scanning, each chimpanzee was housed in a single cage for 6 –12 hr to allow for the effects of anesthesia to wear off, after which they were returned to their home cage and social group. Guidelines from the American Psychological Association for the ethical

44 treatment of animals were followed during all aspects of this study, and institutional animal care and use approval was obtained prior to obtaining scans. Realignment, correction for head motion and eddy current distortion, and brain extraction were carried out with FDT and BET (Smith

2002), which are components of the FSL package (Smith et al. 2004). DTIfit, a function within

FDT, was used to reconstruct the diffusion tensors to create diffusion tensor images (DTIs).

DTI template and WM atlas

Further processing of the DTIs was performed with the Diffusion Tensor Imaging ToolKit

(DTI-TK; Zhang et al. 2006, 2007a, b), which checked for outliers, consistent voxel space across all volumes, and confirmed that the volumes were symmetric and positive-definite matrices. The entire sample of DTI scans was then normalized to produce a population DTI template (Zhang et al. 2007b, 2010). The spatial normalization process consisted of a number of steps: 1) bootstrapping an initial DTI template from a subsample of the DTI volumes, 2) a rigid alignment of all DTI volumes to the initial DTI template to create a rigid DTI template, 3) an affine alignment of all DTI volumes to the rigid DTI template to create an affine DTI template, 4) a fully deformable alignment of all DTI volumes to the affine DTI template, which produced the final version of the

DTI template. Each set of registrations iteratively refined the template to improve alignment. DTI volumes from each individual were then registered to template space using tensor-based registration that aligns images based on the diffusion tensors using a series of rigid, affine, nonlinear registrations, and results in normalized volumes for each individual (Zhang et al. 2006).

To facilitate the localization of significant results, an atlas of major WM tracts in the chimpanzee brain was constructed following the creation of the DTI template (Figure 1a). In preparation for locating specific tracts, an FA image was created from the template and a WM mask was obtained from the FA image. An FA value of 0.15 was used to threshold the image to create the WM mask. While a threshold of 0.2 is customary, a threshold of 0.15 allowed for better identification and delineation of complete WM tracts. The WM mask was then used as a seed mask to create a whole-brain tractography from the template.

The WM tracts included in the atlas are the anterior thalamic radiation (ATR; Figure 2),

45 cingulate portion of the cingulum bundle (CB; Figure 2), corticospinal tract (CST; Figure 2), inferior fronto-occipital fasciculus (IFOF; Figure 2), inferior longitudinal fasciculus (ILF; Figure 2), superior longitudinal fasciculus (SLF; Figure 2), and the uncinate fasciculus (UF; Figure 2). All tracts were produced separately for each hemisphere. We used ITK-SNAP (Yushkevich et al.

2006) to draw regions of interest (ROIs) to delineate the fibers in each tract. Both inclusion and exclusion ROIs were used; inclusion ROIs were drawn in areas where the tract was expected to pass, while exclusion ROIs were drawn in areas where fibers in the tract should not enter. The locations of these ROIs were based on published tracts in macaques and humans (Mori et al.

2002; Wakana et al. 2003; Adluru et al. 2012; Von Der Heide et al. 2013; Zakszewski et al. 2014), as well as chimpanzees when available (Hecht et al., 2015). The ROIs were manually refined continuously during this process to reduce errant tracts. The ROIs were then applied to the whole-brain tractography to extract the specific tract of interest (Zhang et al. 2010).

Tool-use performance and covariates

Tool-use performance was assessed based on each individual’s mean performance time on a simulated termite fishing task (Hopkins et al. 2009; Phillips et al. 2013). The task consisted of removing food from a tube attached to the cage using a thin probe, and performance time was measured as the length of time an individual spent extracting the food. The mean performance time over multiple bouts was calculated for each individual. Performance time for both hands was pooled, since handedness in this sample can vary based on the motor demands of particular tasks (Hopkins and Pearson 2000). Values for mean performance time were logged and centered.

46 use performance than do males (Hopkins et al. 2009). There are also differences in cortical thickness in this sample of chimpanzees that can be attributed to sex in many of the ROIs used in this study, particularly in the precentral and postcentral gyri, and the superior parietal lobe

(Hopkins and Avants 2013). The current sample also shows substantial sex differences in the distribution of tool-use performance time (Figure 1A), and a preliminary analysis showed a significant interaction effect of sex and tool-use for both FA and MD. We therefore investigated brain structure correlates of tool-use within each sex separately.

Age and brain volume may also have confounding effects. In general, tool-use proficiency increases with age in chimpanzees (Koops et al. 2015), but age at the time of tool-use was not associated with any differences in tool-use performance in the sample (Figure 1B). Age may also affect brain structures therefore age at the time of the scan was included as a covariate. We found no relationship overall, or in either sex, between brain volume and tool-use performance

(Figure 1C). However, brain volume may have effects on the measures of brain structure used in this study. In humans, there is a significant positive relationship between DTI measures such as

FA and MD and brain volume in several brain regions (Crawford et al. 2014). FA and MD are also affected by normal aging in humans (Sullivan and Pfefferbaum 2006; Bennett et al., 2010;

Westlye et al. 2010). Based on these findings, we also controlled for brain volume in all comparisons. Brain volume for each individual was measured in BrainVISA (Cointepas et al.

2001) and included the cerebrum and cerebellum.

Tract-based spatial statistics (TBSS) and measures of WM connectivity

Tract-based spatial statistics (TBSS) allows for a whole-brain approach to locating differences in WM structures. A whole-brain approach was selected since there is limited functional evidence for a relationship between tool-use and specific brain areas in chimpanzees.

TBSS was performed using the scripts available in FSL (Smith et al. 2006), and was modified to work with output from DTI-TK (Bach et al., 2014). DTI scans for each individual were registered to the DTI template using the deformation fields produced while creating the DTI template. DTI

47 scans for each individual underwent an affine alignment and a deformable alignment to warp them into template space using DTI-TK (Zhang et al., 2006).

Once registered to the template, FA and MD images were obtained from the DTI scans for each individual. Each of these modalities is a combination of the different eigenvalues that compose the diffusion tensor (Basser 1995; Basser and Pierpaoli 1996). FA expresses the fraction of the tensor’s diffusion that is anisotropic, and is the relative difference between the largest eigenvalue (the direction of anisotropy) and the two remaining eigenvalues (the radial spread of diffusion) (Basser 1995; Basser and Pierpaoli 1996; Basser and Jones 2002). Evidence suggests that anisotropy in WM reflects the packing density of axons within a tract that restricts water diffusion in directions perpendicular to the WM fibers. While not a primary component of anisotropy, the amount of myelination of a fiber can affect the degree of anisotropy present in a tract (Beaulieu 2002). MD is the mean of all three eigenvalues and is a measure of the total diffusion in a given voxel (Basser 1995; Jones et al. 2012). Because FA only indicates the degree of anisotropy present in a tract, it does not fully characterize the diffusion tensor. Investigation of

MD allows for a better understanding of changes present in the diffusion tensor and can suggest the level of organization of WM within a tract (Alexander et al. 2007).

Even though all these measures have been associated with microstructural changes in

WM (Johansen-Berg 2010; Kanai and Rees 2011), recent evidence suggests that each of these measures is a summation of a number of different features of the WM tracts and are not directly comparable to specific anatomical measures (Jones et al. 2012; Kingshott and Cercignani 2009).

Therefore, we limit our interpretation to increases or decreases in structural connectivity for these measures associated with tool-use performance without inferring specific microstructural changes

(Jones et al. 2012).

Within each modality, images for the entire sample were merged into a single 4-D file for analysis. An image showing the mean FA of the sample was also created and skeletonized to create the mean FA skeleton that constrained the analysis in all modalities to anisotropic voxels

(FA threshold = 0.15)

48 Statistical analyses

The experimental designs employed in this study assessed the effects of tool-use performance on FA and MD within each sex while controlling for brain volume and age at the time of the scan. Measures of tool-use performance time, brain volume, and age were log transformed to normalize their distributions, and mean centered. We performed regression analyses to assess the overall effects of tool-use and the effects of tool-use within each sex.

To obtain Pearson’s r values and corrected p values, voxel-wise GLM was applied using permutation-based non-parametric testing and threshold-free cluster enhancement (TFCE) using permutation analysis of linear models (PALM; Winkler et al. 2014). TFCE defined significant clusters and corrected for multiple comparisons on a voxel-wise basis (Smith and Nichols 2009;

Winkler et al. 2014). Corrections for multiple contrasts and multiple modalities were also obtained within each test. The number of permutations for all tests was set at n = 5000. A corrected p (corr p) value of 0.025 was considered significant for each one-tailed contrast. The locations of clusters were reported based on their positions relative to tracts delineated in the WM atlas produced for this study.

Results

There were significant effects of tool-use performance on FA and MD within both sexes.

In females, faster tool-use performance time was associated with increased FA in a cluster in the left ATR and decreased FA in clusters in the left IFOF and right cerebellum. Females also had increased MD in clusters in the left and right SLF and ATR, the left CST and cerebellum, the right

IFOF, and in clusters throughout the corpus callosum (Figure 3 A-B).

49 In males, faster tool-use performance time was associated with increased FA in the left and right ILF and cerebellum, the left ATR, IFOF and SLF, and in the splenium of the corpus callosum, and decreased FA bilaterally in the ATR, CST, and SLF, as well as in the body of the corpus callosum. Males also exhibited increased MD associated with faster tool-use performance in the left and right SLF and cerebellum, along with the right ATR, CST, and IFOF, and decreased

MD in the right CST and ILF (Figure 4 A-B).

Males and females shared few tracts with similar patterns associated with tool-use performance. For FA, both sexes had a negative correlation between clusters in the left IFOF and tool-use performance, and showed differences in the left ATR and right cerebellum, but these areas showed positive correlation between FA and faster tool-use performance time in females and negative correlation in males. MD showed more similarities, with both sexes exhibiting positive correlation between MD and faster tool-use performance time in the left and right SLF, and the right ATR and IFOF. The CST also showed positive correlation in both sexes, but clusters were located on the left in females and on the right in males.

Discussion

The current study investigated the relationship between WM tracts throughout the cortex and tool-use performance time in a sample of captive chimpanzees using a whole-brain TBSS analysis of FA and MD. We investigated effects within each sex. In both males and females, the same three tracts (ATR, IFOF, SLF) were associated with tool-use performance for both WM measures, but especially for MD. Males, however, had a greater number of clusters associated with faster tool-use performance time, with a greater number of clusters in tracts associated with motor control such as the CST and WM in the cerebellum.

Sex differences in tool-use performance

The relationship between tool-use performance time and WM tracts showed major differences between sexes. In general, males had a greater number of clusters in a wider variety of tracts associated with tool-use performance time than did females. These clusters in males

50 were in tracts typically associated with tool-use in humans, as well as tracts associated with motor control, such as the CST and WM in the cerebellum (Nudo and Masterson 1990; Buckner

2013). Females, however, showed significant clusters predominantly in tracts associated with the tool-use, with additional clusters in the cerebellum and corpus callosum. This suggests that faster tool-use performance in males may be more dependent on motor proficiency than in females.

Additional examination of these tracts within the context of both motor control and more complex tool-use would reveal a possible link between these types of structural differences and behavioral differences in tool-use behavior that has been previously observed in this sample of chimpanzees

(Hopkins et al. 2009).

Differences between males and females in the link between WM structure and tool-use performance may depend on differences in sex hormones. Sex hormones can have a profound effect on the WM structures throughout the brain. In humans, pubertal development results in changes to different WM tracts within each sex; males experience increased FA in WM associated with the CST and cortico-subcortical fibers and decreased MD in WM in tracts associated with frontal and temporal regions (Herting et al. 2012). Although these findings do not directly align with those obtained in this study, they do suggest that tracts associated with motor control such as the CST and those associated with frontal and temporal regions might be especially sensitive to sex hormones, particularly testosterone. Further study of the relationship between hormones and brain structure in chimpanzees would allow for an enhanced understanding of the interactions among sex, brain structure, and behavior.

Tracts associated with tool-use in chimpanzees

Based on the close relationship between the ATR, IFOF, SLF, and UF and specific areas associated with tool-use that were previously identified in humans (Bi et al. 2015), it was expected that any clusters identified in these tracts would show increased strength of connectivity through increased FA or decreased MD in relation to faster tool-use performance time. Instead, we observed a combination of increased FA and increased MD in tool-use related tracts. This disparity could be due to specific microstructural changes in the WM that could not be detected with the available MRI resolution. Both FA and MD are sensitive to various potential changes in

51 the microstructure of the tissue (Alexander et al. 2012), but cannot precisely identify what type of changes have occurred. There were tracts, however, that were consistently associated with tool- use in both males and females.

Bilateral clusters in the SLF, particularly for MD, were associated with faster tool-use performance. These clusters can be generally aligned with the SLF I and SLF III. The SLF I is a major tract connecting superior parietal and dorsal premotor areas (Petrides and Pandya 1984,

2002; Thiebaut de Schotten et al. 2012; Hecht et al. 2015). The superior parietal cortex integrates sensory and motor information and is a part of the dorsal visual stream (Culham et al., 2003;

Cloutman, 2013), which processes information about the spatial location of an object (Ungerleider and Mishkin, 1982; Goodale and Milner, 1992). The dorsal premotor area is located on the superior and middle frontal gyri, and is involved in motor planning and guidance for reaching

(Cisik and Kalaska 2005; Churchland et al. 2006). Connectivity between these two regions via the

SLF I allows for the integration of information concerning spatial awareness of objects with the guidance of motor actions, particularly reaching.

The SLF III connects inferior parietal and ventral premotor areas (Hecht et al. 2015). The inferior parietal cortex is a region that likely connects and integrates information from the ventral and dorsal streams (Singh-Curry and Husain 2009), and is involved in visually-guided reaching and grasping along with the perception of peripersonal space (Fogassi et al., 2005; Gardner et al., 2007; Rozzi et al., 2008; Bonini et al., 2010; di Pellegrino and Làdavas 2015). The ventral premotor cortex is involved in controlling movement with respect to objects and maintaining peripersonal space (Fogassi et al. 1996; Murata et al. 1997; Graziano and Cooke 2006), and is also involved in hand to mouth movements (Graziano et al. 2002). Both the inferior parietal cortex and parts of the ventral premotor cortex also have specific regions that are involved in the mirror neuron system (Rizzolatti et al. 1996; Gallese et al. 2002; Orban and Caruana 2014), which is active both while executing a motor action and observing another individual perform that same action (Rizzolatti and Craighero 2004), and has been considered as the possible neural basis of imitation (Iacoboni 1999; Iriki 2006).

52 The IFOF also consistently showed correlation with faster tool-use performance time in both sexes. The IFOF connects occipital, temporal, and frontal areas involved in the ventral visual stream (Macko et al. 1982; Tusa and Ungerleider 1985; Makris et al. 1999; Catani and Thiebaut de Schotten 2008; Sarubbo et al 2013). The ventral visual stream processes information about the visual properties of objects and constructs a perceptual representation of objects within a space (Ungerleider and Mishkin, 1982; Goodale and Milner, 1992; Cloutman, 2013). Similar correlation between the ventral visual stream and measures of gray matter were found consistently in both sexes (Chapter 2), suggesting that this region and its associated tracts are crucial for skilled tool-use in chimpanzees.

These results, however, might be affected by the specific task employed to measure tool- use performance. The simulated termite fishing task and its corresponding measurement of performance time may not reflect all the demands that a similar task would place on a chimpanzee in the wild. The current task tests for speed of performance, which relies on efficient motor planning and execution, but does not necessarily measure other additional cognitive aspects of tool-use such as decision-making or problem-solving. Future work including different tool-use tasks and measures of performance might allow for a more complete neural representation of the chimpanzee tool-use circuit. Nevertheless, the results from the current study suggest that faster tool-use performance for this particular type of task relies extensively on sensorimotor processing, while visual information about objects may not be essential.

In addition to clusters in tool-use-related tracts, we also observed significant clusters in tracts associated with general connectivity and motor control. Effects were observed throughout the corpus callosum, but specifically in parts that have been mapped to fibers crossing from premotor, motor, and somatosensory areas in the cortex (Phillips et al. 2013). These results suggest that faster tool-use performance time is linked with increased interhemispheric connectivity between premotor, motor, and sensory regions that may facilitate the encoding of more efficient motor action plans.

The CST and cerebellum also showed effects associated with faster tool-use performance. The CST is a tract that arises in motor regions of the cerebral cortex and terminates

53 on motor neurons in the spinal cord (Nudo and Masterson 1990). The CST, however, has not been specifically linked with tool-use, and is instead involved with motor control of the distal limbs and hand (Heffner and Masterson 1983). Clusters with increased MD in the cerebellum are likely located in WM tracts containing input from the cortex via the pons that provide topographically organized input to the cerebellum from several different functional regions (Buckner et al. 2011).

These differences in motor-related tracts generally are consistent with GM regions that were identified in chimpanzees to be associated with this same tool-use task (Chapter 2).

The cerebellum is known to play a major role in motor control, although it also serves in many higher cognitive functions and is highly interconnected with the frontal cortex (Buckner

2013). These changes in FA and MD of cerebellar WM may therefore reflect differences seen in tracts in the cerebral cortex that underlie tool-use related functions such as motor control, attention, and visual processing. The specific organization of the chimpanzee cerebellum, however, has not been fully investigated and mapping of the chimpanzee cerebellum for both GM and WM is necessary to fully understand how its organizational structure relates to that of the cerebral cortex.

These results indicate that tracts more directly related to motor control are also involved in tool-use performance. Even though motor tracts such as the CST and those in the cerebellum have not previously been directly associated with the tool-use circuit (Bi et al. 2015), they are undoubtedly involved in the initiation and execution of all motor actions. Changes in these tracts observed in the current study might therefore be a result of differences in overall motor abilities rather than tool-use proficiency during execution of the simulated termite fishing task. Testing the sample of chimpanzees with a comparable motor task is therefore necessary to further differentiate changes of interest associated with the tool-use circuit from changes that are more closely related to motor control.

Evolution of the structural connectivity of tool-use

In humans, the ATR, IFOF, SLF, and UF have been identified as tracts that are strongly associated with tool-use based on lesion-based mapping (Bi et al. 2015). The current study has

54 shown that three of these tracts, the ATR, and the IFOF and SLF, in particular, are associated with faster tool-use performance time in both male and female chimpanzees, suggesting that many of the WM tracts that are highly involved in tool-use may be similar in humans and chimpanzees.

Although several similarities to human tool-use WM tracts were found in our study, it is noteworthy that we did not find clusters in the UF associated with faster tool-use time in either male or female chimpanzees. The UF connects the orbitofrontal cortex with the temporal lobe and is associated with the limbic system (Von Der Heide et al. 2013). Anatomical connectivity of the

UF is largely conserved between humans and macaques. The human UF, however, is one of the last tracts to fully develop, with development often extending beyond age 30 (Lebel et al. 2008,

2012; Lebel and Beaulieu 2011; Olson et al. 2015). In humans, the UF has been implicated in episodic memory, language, and social functions, but is more likely involved in more specialized circuits that allows for the modulation of behavior by linking associative information from the temporal lobe with reward-based information from the orbitofrontal cortex (Von Der Heide et al.

2013). Connecting areas in the temporal lobe that supply specific information about objects and tools with these orbitofrontal reward regions might influence tool-use, either through motivation to use tools or recognizing a potential reward following tool-use. Based on its relatively late maturation, the human UF may allow for improved modulation of these pathways compared to both macaques and chimpanzees. Further research specifically targeting the UF may be a promising prospect for further elucidating structural differences underlying interspecific differences in tool-use behavior.

Sophisticated tool-use and tool-making are defining characteristics of human behavior, but studying the evolution of these cognitive capabilities has been challenging. Only limited evidence of hominin brain structure remains in the form of endocasts, which can show gross anatomical features of the brain but give no information concerning microstructure or function

(Sherwood et al. 2008; Falk 2012; Neubauer 2014; Reyes and Sherwood 2015). Furthermore, brain size differences between humans and other primates can introduce confounds that complicate direct comparisons between species and make it difficult to determine which structural

55 differences are related to function, and which are an artifact of size. Focusing on a single species such as chimpanzees allows for the investigation of structural variation without needed to account for large-scale size differences, and can indicate whether this variation can be linked with significant differences in function.

Conclusion

The current study utilized structural neuroimaging techniques to investigate connectivity in the chimpanzee brain that can be linked with tool-use. In males and females, the same three tracts (ATR, IFOF, SLF) were associated with tool-use performance, with additional clusters in the CST, corpus callosum, and cerebellum that are associated more closely with motor control.

Males, however, had more clusters associated with faster tool-use performance time in the CST and the cerebellum than did females. The results indicate that many of the WM tracts associated with tool-use are similar between humans and chimpanzees, though evidence from both GM and

WM suggests that tool-use in chimpanzees may involve more activity in areas associated with general motor control and sensorimotor integration. These findings suggest that tool-use processing in the brain may have become more refined and specialized in human evolution, with increased emphasis on connectivity among brain regions involved in higher-order cognitive functions rather than motor control. Such changes in neural circuitry underlying tool-use may have ultimately resulted in the variety of complex technological implements associated with modern humans.

56 Figures

Figure 2. Chimpanzee white matter atlas (A). To assist with isolating the locations of significant clusters, seven tracts were identified in the chimpanzee brain: the anterior thalamic radiation (B), cingulum portion of the cingulum bundle (C), uncinate fasciculus (D), superior longitudinal fasciculus (E), corticospinal tract (F), inferior fronto-occipital fasciculus (IFOF), and the inferior longitudinal fasciculus (ILF). These tracts were constructed for the atlas using DTI-TK and ITK-

SNAP.

58 59

Figure 3. Axial slices showing the location of significant clusters (corrected p ≤ 0.025) for the effect of tool-use performance time on (A) fractional

anisotropy (FA) and (B) mean diffusivity (MD) for females. Clusters on the red-yellow scale show where faster tool-use performance time is

associated with increased FA or MD, while clusters on the blue-green scale where faster tool-use performance time is associated with decreased

FA or MD. Increased FA was observed in the left ATR, while decreased FA was seen in the left IFOF and right cerebellum. Increased MD was

identified bilaterally in the SLF and ATR, along with the left CST and cerebellum, right IFOF, and the corpus callosum. There were no decreases in

MD associated with tool-use performance.

Figure 4. Axial slices showing the location of significant clusters (corrected p ≤ 0.025) for the effect of tool-use performance time on (A) fractional

anisotropy (FA) and (B) mean diffusivity (MD) for males. Clusters on the red-yellow scale show where faster tool-use performance time is

associated with increased FA or MD, while clusters on the blue-green scale where faster tool-use performance time is associated with decreased

FA or MD. Increased FA was observed bilaterally in the ILF and cerebellum, as well as in the left ATR, IFOF, SLF, and in the splenium of the

corpus callosum. Decreased FA was identified in bilaterally in the ATR, CST, SLF, and in the body of the corpus callosum. Increased MD was

seen bilaterally in the SLF and cerebellum, and in the right ATR, CST, and IFOF, while decreased MD was observed in the right CST and ILF. Chapter 4: Cyto- and Myeloarchitectural Parcellation of the Chimpanzee Inferior Parietal Lobe

Abstract

The inferior parietal lobe (IPL) is a brain region associated with sensorimotor integration.

The IPL has undergone substantial evolutionary change in the human lineage, including expansion and specialization for tool-use. At present, however, it is not known whether these aspects of IPL organization are unique to humans or if they are also shared with closely related species. Chimpanzees are an ideal comparative species, since they are among the closest living relatives of humans and use tools. The goal of this study was to parcellate the chimpanzee IPL using a combination of qualitative descriptions of cyto- and myeloarchitecture, and quantitative comparisons between cytoarchitectural features using the Laminar Sampling Method developed by Mackey and Petrides (2009). This method measures the density of cell somata within specific layers, to compare between each cortical area. The sample consisted of four cerebral hemispheres from two adult male chimpanzees. The qualitative parcellation based on both cytoarchitecture and myeloarchitecture identified four major areas on the lateral convexity of the

IPL (PF, PFG, PG, OPT) and two opercular areas (PFOP, PGOP). Analysis of quantitative profiles of cytoarchitecture showed that cell density was significantly different in a combination of layers III, IV, and V between bordering cortical areas. Quantitative examination of the density profiles of these six areas supports their classification as distinct areas. These six areas in the

IPL correspond to those that have been identified in macaques, suggesting that these species share homologous IPL areas. The results also suggest that there could have been evolutionary change in the IPL between chimpanzees and humans. Because the IPL is so strongly linked with tool-use and tool-making capabilities, this type of anatomical change may underlie the striking differences in tool behavior that has been observed in these species.

Introduction

The inferior parietal lobe (IPL) is a region of the brain that is involved in a number of integrative sensorimotor functions (Fogassi et al. 2005). Activity in the IPL is associated with

61 grasping, reaching, and object oriented actions such as tool-use in both humans and nonhuman primates (Rizzolatti and Craighero 2004; Stout et al., 2008; 2011; Peeters et al., 2009; Hecht et al., 2013). The rostral IPL is involved in execution and observation of grasping and hand-to-face movements, while the caudal IPL is important for visually-guided arm and hand movements

(Fogassi et al., 2005; Gardner et al., 2007; Rozzi et al., 2008; Bonini et al., 2010; Caspers et al.,

2010). Mirror neurons that respond to the observation of manual manipulation of objects have also been identified in rostral portions of the macaque IPL (Gallese et al., 1996; Rizzolatti and

Craighero, 2004; Rozzi et al., 2008), and similar functionality has been observed in neuroimaging studies in chimpanzees and humans (Iacoboni et al., 1999; Ramayya et al., 2010; Hecht et al.,

2013, 2015). The IPL is also a region that has experienced substantial evolutionary change in the human lineage, showing disproportionate expansion relative to macaques (Hill et al., 2010).

Investigation of the structural organization of cells and other anatomical features in the nervous system can be used to divide, or parcellate, the brain into distinct regions (Toga et al.,

2006). A comparison of parcellated regions between species is important for investigating the link between microstructure and function (Geyer et al., 2011). The macaque and human IPL have both been parcellated into cortical areas based on qualitative descriptions of laminar patterns of cytoarchitecture. Comparisons of these descriptions show that these two species differ in the number of identified cortical areas (Pandya and Seltzer, 1982; Gregoriou et al., 2006; Rozzi et al.,

2006; for review of human parcellations see Zilles et al. 2001), further supporting evidence for the

IPL as a region of potential evolutionary change in the human lineage (Bruner et al., 2003, 2011;

Neubauer et al., 2010; Gunz et al., 2010; Hill et al., 2010; Yeo, Krienen et al., 2011; Zhang et al.,

2013). Parcellations of the macaque IPL based on cytoarchitecture, myeloarchitecture, and immunohistochemistry are consistent in describing four major areas (PF, PFG, PG, Opt) and two adjacent areas in the parietal operculum (PFop, PGop; Pandya and Seltzer, 1982, Gregoriou et al., 2006; Rozzi et al., 2006). Human IPL maps, however, tend to be more variable (Zilles and

Palomero-Gallagher, 2001), describing between two areas (areas 39 and 40), as identified in

Brodmann (1909), and six (PF, PFt, PFm, PFcm or PFc, PGa, PGp, plus one opercular area

PFop) as shown in Von Economo and Koskinas (1925) and Caspers (2006).

62 Thus, many of the IPL parcellations, especially those performed more recently, indicate that humans have a greater number of subdivisions than do macaques. The relative increase in areas appears most markedly in the rostral IPL, which contains two areas in macaques, PF and the transitional PFG. By contrast, Caspers et al. (2006) identified four distinct rostral IPL areas in humans (PF, PFt, PFm, PFcm). These structural and architectural differences may be linked to evolutionary change in the function of the IPL in the human lineage. In macaques, functional studies have shown that the rostral IPL is involved in execution and observation of grasping and hand-to-face movements (Fogassi et al., 2005; Gardner et al., 2007; Rozzi et al., 2008; Bonini et al., 2010; Caspers et al., 2011). The human rostral IPL, however, contains a specific region that has been strongly associated with tool-use, as it is activated during the observation of object manipulation by a tool-like implement, a pattern of activation that was not observed in tool-trained macaques (Peeters et al., 2009).

One important and unresolved question is whether the tool-specific area in the rostral IPL is unique to the human brain or if it is shared with closely related taxa such as apes.

Chimpanzees are an ideal comparative species to address this question, since they are among the closest living relatives of humans and they engage in a wide range of tool-use behaviors both in the wild and in captivity (Goodall 1986; Nishida 1990; McGrew et al. 1992; Matsuzawa 1996;

Whiten et al. 1999; Hopkins et al. 2009; Haslam 2013). The chimpanzee IPL has been parcellated previously based on cytoarchitecture and myeloarchitecture, with the most well-known maps including those by Shevchenko (1936), Gerhardt (1938), and Bailey and von Bonin (1950).

These parcellations have identified two areas in the chimpanzee IPL with associated subareas, though they do not agree on the location of the identified areas (Figure 1). Based on these descriptions, each of the subareas were identified as a region that shared cytoarchitectural features with the major cortical area, but differed in a slight but notable manner. For example, 89t, a subarea of 89 in Gerhardt’s (1938) parcellation, differs from area 89 in the cell composition of layer III.

Questions also remain regarding the classification of specific parts of the chimpanzee

IPL, particularly the areas in the parietal operculum and in its caudalmost portion. Bailey and von

63 Bonin (1950) did not identify two distinct opercular areas, and instead identified a transitional area, PFD, near the somatosensory cortex, as an area likely homologous to the rostral opercular area, PFop, identified in macaques. The existence of an area homologous to macaque area Opt in the caudal portion of the IPL is also debated. Bailey and von Bonin (1950) classified the caudal portion of the angular gyrus as area OA, which is part of the occipital lobe. Gerhardt (1938), however, included this part of the angular gyrus in the IPL, although assigned it to area PG. The use of a quantitative method to directly compare areas identified in a qualitative parcellation introduces an element of objectivity into the analysis and can be used to test hypotheses about the presence or absence of cortical areas.

The use of quantitative methods can improve upon the use of qualitative descriptions by providing the ability to directly compare measurable features between cortical areas, (e.g.,

Mackey and Petrides, 2009, 2010, 2014; Geyer et al., 2011). Quantitative methods of parcellation measure the differences in the density and distribution of cells between cortical areas using image processing techniques. A method of laminar sampling was recently developed by Mackey and Petrides (2009), and provides a proxy measurement of cell density using the gray value of pixels within an image. A series of processing steps are performed on images of Nissl-stained sections to create density profiles that characterize each cortical area. The use of this type of density measurement (Zilles et al. 1978; Wree et al. 1982) decreases the effects of staining artifacts, since density measurements are made on images where the cells of interest (in this case neurons) are extracted from the cellular matrix. The density profiles and their associated measurements of areal density can then be compared between areas that have been identified qualitatively. For each cortical area, density profiles are created by sampling cell density along transverse lines spanning the thickness of the cortex (Hudspeth et al. 1976; Ryzen and Campbell

1955, Ryzen, 1956; Hopf 1965, 1968). Density profiles should therefore differ between different cortical areas, and should also correspond to many of the differences observed qualitatively, especially those associated with cell density and size.

The goal of the current study was to parcellate the chimpanzee IPL using a combination of qualitative descriptions of cyto- and myeloarchitecture and the quantitative laminar sampling

64 method. The descriptive parcellations were made by observing patterns of cytoarchitecture such as cell size, density, and distribution of cells, patterns of myeloarchitecture such as staining intensity, and the presence or absence of the bands of Baillarger to distinguish between different cortical areas. Density profiles were then obtained for each identified cortical area and were compared to test their classification as distinct areas, and if IPL areas were different from those in surrounding somatosensory, intraparietal sulcus, and occipital areas.

Methods

Sample

The sample for this study included four fixed cerebral hemispheres from two male chimpanzees (Justin, 26 years 11 months; Jolson, 19 years and 4 months) obtained from Yerkes

National Primate Research Center (Atlanta, GA, USA) and curated as part of the National

Chimpanzee Brain Resource (funded by NIH grant NS092988). A total of 85 histological sections were used for the cytoarchitectural portion of this study, with approximately 20 sections for each hemisphere, and each identified IPL area represented by at least 5 sections. A total of 40 histological sections were used for parcellation with myeloarchitecture. Guidelines from the

Histological preparation

Both brain specimens were immersion-fixed in 10% buffered formalin for approximately 2 weeks, transferred to a PBS solution containing 0.1% sodium azide, and stored at 4°C. For sectioning, each hemisphere was cut coronally into three blocks (frontal, temporal-parietal, and occipital). The IPL was contained within the temporal-parietal and occipital blocks. Photographs of the dissections were used as a reference to ensure that anatomical information was retained between blocks. In preparation for sectioning, each block was cryoprotected by immersion in buffered sucrose solutions up to 30%, embedded in tissue medium, frozen in a slurry of dry ice

65 and isopentane, and sectioned coronally at 40 µm with a sliding microtome (Leica SM2000 R,

Nussloch, Germany). Although cut in the coronal plane, there was variation in the exact orientation of the blocks during sectioning. Every 10th section (400 µm) was stained for Nissl substance with a solution of 0.5% cresyl violet to visualize cytoarchitecture. A 1 in 20 series (800

µm) was stained using a modified Gallyas silver impregnation technique to reveal myelinated axons.

Qualitative parcellation

Examination of each section was carried out using a Zeiss Axioplan 2 photomicroscope equipped with a Ludl XY motorized stage (Ludl Electronics, Hawthorne, N.Y., USA), Heidenhain z-axis encoder (Heidenhain, Schaum- burg, Ill., USA), an Optronics MicroFire color videocamera

(Optronics, Goleta, Calif., USA) and a Dell PC workstation running StereoInvestigator software, version 10 (MBF Bioscience, Williston, VT, USA). The qualitative parcellation was guided by previous descriptions of cytoarchitecture and myeloarchitecture in IPL cortical areas in the macaque, chimpanzee, and human (von Economo and Koskinas, 1925; Bailey and von Bonin,

1950; Pandya and Seltzer, 1982, Caspers et al., 2006; Gregoriou et al., 2006; Rozzi et al., 2006).

Cytoarchitectural features considered for the parcellation included observed cell density, patterns of cell distribution in each of the cortical layers, typical cell size and shape, and the relative thickness and general appearance of the cortical layers. For myeloarchitecture, the features included the degree of myelination shown by the amount of staining, visibility and size of the inner and outer bands of Baillarger and their relative staining intensity, and the presence and size of vertical fibers. Features for the qualitative parcellation can be seen in Figure 2.

Nomenclature was adopted from the Pandya and Seltzer (1982) macaque parcellation, which adapted the naming conventions used by von Economo and Koskinas (1925). The only exception is the use of the abbreviation A2 (Brodmann’s area 2) for the somatosensory cortex, which was taken solely from Pandya and Seltzer (1982). This study applied the macaque IPL area names to the chimpanzee IPL rather than other names previously used in chimpanzee cortical maps since the macaque parcellations are widely known and have been used in a

66 number of studies. Furthermore, use of a similar naming convention will facilitate future direct comparisons between the two species.

Laminar sampling method

A quantitative method was used to assess differences in density profiles between cortical areas following an image processing and laminar sampling protocol previously reported (Mackey and Petrides, 2009, 2010, 2014). Image montages of sections containing the IPL were obtained using the computer-coupled microscopy system described above, with a resolution of 1.344

µm/pixel. To prepare the images for analysis, the inner and outer contours of the cortex along with layers II-VI were delineated on the images in Adobe Photoshop and were transformed into individual contour files to be used for laminar sampling. The cortical layers of interest included layers III shallow, III deep, IV, and V, and were selected based on features identified in the qualitative parcellation. For layer IV, density was measured as in Mackey and Petrides (2009,

2010, 2014). Briefly, layer IV was identified based on the drawn cortical contour, and fixed region around the contour, -1 to 1% of the total length of the transverse line, was used to calculate density for layer IV.

The laminar sampling method was performed with a customized ImageJ plugin based on

Mackey and Petrides (2009, 2010, 2014). Each of the acquired images underwent a six step process: 1) the acquired image and its associated contours were loaded into the program and resampled to a resolution of 1.315 um/pixel, then resampled again to 1/10 of their dimensions, 2) an adaptive threshold (Mackey and Petrides 2009) was applied to the acquired image, 3) after adaptive thresholding, the image was automatically segmented to produce a binary image with the minimum threshold algorithm in ImageJ, and the watershed algorithm was employed to separate individual cells that were not separated with the minimum threshold, 4) using a spatial binning algorithm, the image was segmented into one image containing granule cells (bin size =

50-120 µm), and another image containing pyramidal neurons that was produced by subtracting the granule cell image. These segmented images were then blurred with a Gaussian filter (60 pixels full-width half-maximum), 5) the sampling location was selected interactively in the laminar

67 sampling program, then a series of transverse lines were generated at equidistant 10 pixel (131.5

µm) intervals along layer IV that spanned the shortest distance between the inner and outer contours and passing through one of the generated points on layer IV. This series of steps is illustrated in Figure 3.

Following these processing steps, the values of the pixels under each transverse line were sampled serially from the outer to inner contour and exported to a text file, with one column of values for each transverse line. This process was performed for each of the eight outputs from the processing steps (contours image, granule cell density, pyramidal neuron density, total density, layer II/VI, layer III/V, layer IV). Profiles were standardized to a common length by linear interpolation in R (R Development Core Team 2008), which allowed for the comparison of profiles sampled from cortex with different absolute thickness (Hudspeth et al. 1976; Schleicher et al

1986; Mackey and Petrides 2009).

The laminar sampling method takes a representative sample of the density for each cortical layer of interest and the cortex between layers II and VI for the pyramidal neuron and granule cell images. For layers III and V, density was calculated based on the entire width of the layers based on the drawn contours. For layer III, shallow and deep portions were then taken by dividing this width in half and calculating the density for each half. The densities of each layer were standardized against the density of the cortex. The density of the cortex was measured from the layer II/III contour to the layer VI contour for both the pyramidal and granule cell images. At each transverse line, the raw density values for each layer were divided by the mean density value for the entire transverse line. This was performed separately for each layer against these values for both pyramidal and granule cells to normalize the density values.

Identification of cytoarchitectural boundaries

Potential cytoarchitectural boundaries were identified during the qualitative parcellation using Nissl stained images. The qualitative boundary identification was performed three different times by a single observer (LDR). Using Adobe Photoshop, a line was drawn on the image to denote the location of a boundary on a different digital layer each time. The largest distance

68 between these lines was recorded for each boundary to calculate the mean overall intra-observer variation. The mean was 753.8 µm, which was considered acceptable as this was equivalent to less than eight transverse lines produced by the laminar sampling method. The final boundary was set at the midpoint of the three preliminary boundaries.

The identified border was matched with the closest transverse line produced by the laminar sampling method on each slide. The line was set as the midpoint, and it along with the

100 transverse lines surrounding it (50 on either side) were then averaged across all sections that contained the same boundary. This was performed on each slide for layers III (shallow and deep),

IV, and V. The values were plotted for each boundary to visualize general trends in areal density that indicated a change between cortical areas (figures 7 & 8). The mean areal density for cortical areas that shared a boundary were also plotted for each histological section to assess whether the difference in areal density for each cortical area and between bordering cortical areas were consistent across sections (figures 9 & 10).

Analysis

A principal components analysis (PCA) was performed on the data to visualize the variation in area density in the sample by examining the Euclidean distances between areal density measurements in each hemisphere. The analysis used the mean areal density value in layers III (shallow and deep), IV, and V for each cortical area for each individual chimpanzee. The distances between the points in the resulting PCA plot indicate the level of similarity between each of the data points, with closer points showing greater similarity than more distant points. A hierarchical cluster analysis was also performed to identify potential clusters in the data due to individual, hemisphere, or cortical area. Euclidean distance and UPGMA were used to perform the cluster analysis. The pvclust package in R was used to calculate the approximate unbiased values and bootstrap probability (Suzuki et al. 2006; Bates et al. 2015).

A statistical comparison was performed to test if differences identified qualitatively would translate into statistically significant differences in areal density. Layers selected for statistical comparison were thus based on the layers that were consistently different across areas in the

69 qualitative parcellation, and included pyramidal neuron density for layers III and V, and granular cell density for layer IV. The density values were measured along the transverse lines produced by the laminar sampling method, and areas were divided based on the cytoarchitectural borders identified in the qualitative parcellation and checked by plotting densities as described previously.

We employed a linear mixed effects model using the lme4 package in R, with post-hoc pairwise tests performed using the lsmeans package (Lenth 2016). A linear mixed effects model contains fixed effects and random effects, where the fixed effects are the explanatory predictor variables that are specifically compared in the model, and random effects are variables that are part of a larger population and contain only a small sample of possible levels. Mixed effect models are also useful in study designs with repeated measures, where multiple measurements are made from an individual or a sample. Including the grouping of the repeated measure as a random effect can control for the variance introduced by each of these groups.

The linear mixed effects model can be given as density ~ layer + area + layer:area +

(1|subject) + (1|hemisphere) + (1|slide). The dependent variable is the measure of areal density, while fixed effects are layer, area, and the interaction effect between layer and area. The random effects accounted for repeated measures within each subject and accounted for variability introduced by hemisphere and histological section. Hemisphere and histological section were included as random effects because the purpose was to characterize the variance introduced by hemisphere and section in general, and not to compare these variables directly. A mixed effects model was elected following a comparison with a model that did not have the random effects, as the mixed effects model had lower AIC and BIC and a higher log likelihood value (Table 1). Post- hoc pairwise tests were performed to directly compare density between each area by cortical layer. A Tukey correction was used for multiple comparisons, and results were considered significant at p ≤ 0.05.

Results

Cortical areas of the inferior parietal lobe

70 The qualitative parcellation using both cytoarchitecture and myeloarchitecture identified four major areas on the lateral convexity of the IPL (PF, PFG, PG, OPT) and two parietal opercular areas (PFOP, PGOP). Examination of density profiles also indicated that there were significant differences in areal density across layers III, IV, and V between bordering cortical areas. Cytoarchitecture of cortical areas in each hemisphere can be seen in Figure 4, and representative myeloarchitecture images can be seen in Figure 5. Representative histological sections showing the locations of the six IPL areas are shown in Figure 6.

Overall cell density in PF was lower compared to somatosensory and opercular areas, with all layers clearly identifiable. Layer II was well differentiated from upper layer III and was less cell dense relative to the somatosensory cortex. Layer III had a slight superficial to deep size gradient in pyramidal neurons, with the superficial portion of layer III containing small pyramidal neurons that transitioned to medium pyramidal neurons in the deep portion of layer III. The size of the pyramidal neurons in layer III was much smaller than in the somatosensory cortex and in the operculum (PFOP). Medium pyramidal neurons in deep layer III occasionally intruded into layer

IV, but not as frequently as in the somatosensory cortex. Layer IV was broad and darkly stained, and appeared denser and more continuous than in both the somatosensory cortex and operculum, since it only had occasional intrusion from pyramidal neurons from the surrounding layers. Layer V appeared similar to the somatosensory cortex, and had two sublayers. The upper portion, Va, contained medium pyramidal neurons that occasionally intruded into layer IV. The lower portion, Vb, contained smaller pyramidal neurons and was denser than in Va. Overall, layer

V appeared to have greater cell density compared to the operculum. Layer VI was not easily differentiated from Vb. The upper portion, VIa, was only slightly more darkly stained than Vb, and they appeared homogeneous mainly because the cell size and densities appeared similar. Layer

VI appeared similar in density to layer VI in the somatosensory cortex.

The myeloarchitecture of PF showed light myelination, especially in comparison to the somatosensory cortex. The bands of Baillarger were visible but not very distinct. The inner band

71 was darker than the outer band, and the two were close together but not merged. Vertical fiber bundles were present throughout PF, but were thin and did not extend past the outer band of

Baillarger.

PFG

Based on cytoarchitecture, PFG was characterized by easily distinguished layers with larger layer III and layer V pyramidal neurons than in areas PF and PG. Layer II was not well differentiated from upper layer III, but was denser than in PF. Layer III lacked a superficial to deep size gradient, and instead had an abrupt change from smaller pyramidal neurons in the upper portion to larger pyramidal neurons in deep layer III, which appeared larger than those in the somatosensory cortex, PF, and PFOP. Layer III also appeared to have increased density compared to the intraparietal sulcus (IPS). Unlike in PF, the pyramidal neurons did not intrude into layer IV, making the border between these two layers more distinct. Layer IV was broad and well differentiated from the surrounding layers, but was not as well defined as in PG. Layer Va contained medium and scattered large pyramidal neurons, while Vb contained medium and small pyramidal neurons. The difference between Va and Vb appeared more distinct in PFG compared to PF, but these areas also appeared to have similar layer V density overall. Layer VI was more apparent and darkly stained than PF and also had larger pyramidal neurons, making layer V appear more differentiated. As in PF, the infragranular layers appeared less cell dense than in the somatosensory cortex.

The myeloarchitecture of PFG showed relatively heavy myelination, especially compared to PF and PG. The bands of Baillarger were evident, but were close together and nearly merged.

Myelination throughout PFG was fairly uniform, but vertical fiber bundles were apparent and more darkly stained than in both PF and PG. The bundles were thin and did not extend past the outer band of Baillarger, similar to those seen in PF.

72 The cytoarchitecture of PG showed that the cell size in this area was more homogeneous than other IPL areas, especially in layers V and VI, and can be characterized by a very broad and differentiated layer IV. Layer II was thin, darkly stained, and easily differentiated from layer III.

The upper portion of layer III was homogeneous with small pyramidal neurons, and deep layer III contained medium and a few larger pyramidal neurons. In general, the size of the cells was smaller than in area PFG, especially in deep layer III. Layer III lacked a superficial to deep size gradient and the change in layer III cell size appeared abrupt, with deep layer III forming a distinct band above layer IV, and appeared less cell dense than in PFG. There was no intrusion of pyramidal neurons from deep layer III into layer IV, making layer IV appear strongly defined especially compared to PFG, and also the IPS. Layer IV was cell dense, more than in neighboring areas PFG, PGOP, and OPT, and had clear boundaries with deep layer III as well as Va.

Sublayers were identified in layer V, but were difficult to distinguish. Layer V had a notable decrease in cell density compared to PGOP, but layer V cell density appeared to be similar to both PFG and OPT. Overall, layer V was not easily differentiated from layer VI, mainly because cell size was uniform throughout the two layers.

The myeloarchitecture of PG showed that it was most lightly myelinated of all IPL areas.

Both the inner and outer bands of Baillarger were evident, especially the inner band which was more darkly stained. The bands were also nearly merged, with the lower portion of the outer band nearly touching the upper inner band. The vertical fiber bundles were thinner than in PFG, but were a prominent feature due to the overall light myelination in PG.

OPT

The cytoarchitecture of OPT is characterized by more of an apparent layered structure, especially compared to surrounding areas PG and OA, with heterogeneity of cell size throughout all layers. Layer II was darkly stained but appeared less dense than in PG. The border between layers II and III was not well defined. Layer III had a more superficial to deep size gradient than

PG, the IPS, and OA, with smaller pyramidal neurons in superficial layer III and an increase to medium and occasionally large pyramidal neurons in deep layer III. Compared to OA, cell size in

73 layer III was generally larger in Opt. Layer IV was also distinct from both layers III and V with no invading pyramidal neurons, but appeared less differentiated than in both the IPS and PG. Cell density in layer IV of OPT also appears reduced compared to the IPS, but is greater than in OA.

As in PG, layer V had evident sublayers, but the two sublayers were easier to distinguish since

Va had medium pyramidal neurons, whereas Vb contained smaller pyramidal neurons. Layer V in

OPT had more visible sublayers than OA. Layer VI was broad, dense, and had clearly visible sublayers, which differentiated OPT from PG. Layer VIa contained larger, more darkly stained cells than VIb, which made the sublayers more distinct, and it appeared denser than in both PG and OA.

The myeloarchitecture of OPT showed that it was slightly more myelinated than PG, but not as much as PFG. Both bands of Baillarger were evident and clearly differentiated, remaining separate throughout Opt. Both bands appeared equally darkly stained, although in some areas, the outer band appeared darker. OPT had the thickest vertical fiber bundles out of all of the IPL areas, and they frequently extended past the outer band of Baillarger.

PFOP

Cytoarchitecture showed that overall, PFOP was less cell dense than both the somatosensory cortex and PGOP, though it was still characterized by large pyramidal neurons in deep layer III. Layer II in PFOP was broad and not well differentiated from layer III. Layer III contained a slight superficial to deep size gradient, with upper layer III containing small and medium pyramidal neurons and deep layer III containing medium and large pyramidal neurons.

The pyramidal neurons in deep layer III were much larger than those in PF, PFG, and PGOP, but were similar to those seen in the somatosensory cortex. Layer IV was broad and lacked the intrusion from layer III pyramidal neurons that is seen in PF. Layer IV appeared similar to that of the somatosensory cortex. Layer V was less cell dense than the other layers, and had clear sublayers. Layer Va contained medium pyramidal neurons that occasionally intruded into layer IV, whereas Vb contained medium and small pyramidal neurons. Compared to PF, layer V in PFOP was much more distinct. Layer V in PFOP also had larger cells and decreased cell density

74 compared to PGOP. Layer VI was more compact and darkly stained than in the somatosensory cortex, PF, and PGOP, and had a clear border with layer V.

The myeloarchitecture of PFOP indicated that its myelination was fairly light and similar to that of PF. The bands of Baillarger were not evident, although there were vertical fiber bundles that were prominent and extended beyond the outer band.

PGOP

The cytoarchitecture of PGOP showed that it was more cell dense than PFOP and its layers were more easily identifiable than in PG. Layer II was broader than in PG and appeared to be more cell dense than in both PFOP and PG. Layer III lacked a clear deep layer III, and instead had a more uniform appearance due to smaller pyramidal neurons throughout. Layer IV was thinner and less distinct than in PG, with pyramidal neurons that intruded from the surrounding layers. Layer V appeared more distinct than in both PFOP and PG. Compared to PG, it was less homogeneous, with medium pyramidal neurons in Va and smaller pyramidal neurons in Vb. Layer

VI was broader than in PFOP and more darkly stained than in both PFOP and PG.

The myeloarchitecture of PGOP showed that its myelination was lighter than that of

PFOP, but heavier than that of PG. Both bands of Baillarger were more visible than in PFOP and appeared separately, but were lightly stained not easily distinguishable from the background staining. The vertical fiber bundles in PGOP were thick, but not especially prominent and did not extend past the outer band.

Laminar sampling and quantitative differences between cortical areas in layers III, IV, and V

Regions of the cortex that were likely to contain a cytoarchitectural boundary were initially identified qualitatively using images of Nissl-stained sections. Plots of the boundaries for layers

III-V aggregated across all sections are shown in Figures 7-8. The plots of the boundaries based on the mean across slides show that there are general trends present between cortical areas, but this trend is not consistent across all the cortical layers. The changes observed between areas are further supported by the results of the linear mixed effects model and post-hoc pairwise tests.

75 The hypothesized locations of the cortical areas based on the qualitative parcellation were largely validated by changes in areal density. Sampled differences in areal density between cortical areas were often not consistent across each histological section (Figures 9-10). This could be due to sampling error or misclassification of the cortical area. The inconsistency, however, is not entirely unexpected in association cortex where differences between cortical areas can be subtle

(Mackey and Petrides 2014).

The PCA showed that there were three principal components (PCs) that accounted for

97% of the variation in density across the nine different cortical areas (6 IPL areas and 3 surrounding areas) that were measured (Table 7). The primary loadings for PC1 were layers V,

IV, and shallow layer III, while the primary loading for PC2 was deep layer III, and the primary loading for PC III was layer IV (Table 7). The two individual chimpanzees tend to cluster separately along PC2, although PC1 and PC3 do not show a such a strong trend toward clustering within individuals and show a slight split between rostral and caudal areas, with rostral areas (somatosensory cortex, PF, PFG, PFOP, PGOP) generally loading more positively on PC1 and negatively on PC3, and caudal areas (PG, OPT, OA) loading more negatively on PC1 and positively on PC3 (Figure 11). The PCA suggests that there is a large amount of variability between individuals primarily in deep layer III that is greater than the variability present between the cortical areas. The hierarchical cluster analysis showed no major trends in clustering, either by individual, hemisphere, or cortical area (Figure 12).

Differences in areal density in layers III, IV, and were measured quantitatively between

IPL areas (PF, PFG, PG, OPT, PFOP, PGOP), as well as between these areas and neighboring cortical areas (somatosensory cortex, IPS, OA) using the laminar sampling method (Mackey and

Petrides 2009, 2010, 2014). Density profiles for each of these areas are shown in Figure 13. A linear mixed effects model was performed that included subject, hemisphere, and histological section as random effects. The use of subject as a random effect accounted for repeated measures within each chimpanzee and controlled for the differences shown by the PCA in deep layer III, while the inclusion of hemisphere and histological section as random effects characterized the variance introduced by these variables.

76 Post-hoc Tukey’s HSD pairwise tests following the linear effects mixed effects model showed that there were significant (p ≤ 0.05) differences between bordering areas in at least one of the measured layers (Tables 4-6). Overall, deep layer III, layer IV, and layer V show the most notable differences between areas. These layers, particularly layer IV, consistently differentiated the IPL from surrounding areas in the somatosensory cortex, IPS, and OA. Within the IPL, areas in the lateral convexity were significantly different from areas in the parietal operculum. Deep layer III and layer IV were especially useful in differentiating between areas in the lateral convexity and the operculum, as well as between areas within the lateral convexity. Within the operculum, layer V was the only layer that showed a significant difference between the two areas.

Discussion

The current study used qualitative and quantitative methods to parcellate the chimpanzee

IPL. We identified six areas in the IPL, two in the operculum (PFOP, PGOP) and four on the lateral convexity of the IPL (PF, PFG, PG, OPT). An examination of the areal density of the cytoarchitecture of these six areas supports their classification as distinct areas and also differentiated the IPL from surrounding cortical areas.

The six chimpanzee IPL areas identified in this study are primarily based on cytoarchitectural differences in layers III, IV and V, along with differences in patterns of myeloarchitecture. Shallow layer III contains small to medium pyramidal neurons, while deep layer III contains medium to large excitatory pyramidal neurons (Douglas and Martin 2004) that project locally to other excitatory pyramidal neurons in layer III and layer V, as well as to layer III and IV in other cortical areas (Gilbert and Wiesel 1979). Layer III, especially the deep portion, is evident in myeloarchitecture as the outer band of Baillarger, a prominent bundle of tangential fibers, and its projections are contained within the vertical fiber bundles. Although myeloarchitectural descriptions usually place the outer band within layer IV, it contains the neurites of both deep layer III pyramidal neurons and stellate cells in layer IV (Gilbert and Wiesel

1979; Krstic 1997; Nieuwenhuys 2013). Based on cytoarchitectural features including cell size and cell distribution as well as areal density, deep layer III was especially useful in differentiating

77 between surrounding cortical areas (somatosensory cortex, IPS) from the IPL, IPL areas on the convexity from those in the operculum, and between the opercular areas (Tables 4-5).

Differences within IPL areas were also reflected in myeloarchitecture, with variation between these areas in the intensity and location of the outer bands of Baillarger (Table 6). Areas on the convexity generally had darker, more evident outer bands than those seen in the two opercular areas. In the current parcellation, cell size and areal density both correspond to the degree of staining seen in the outer band of Baillarger, with areas having larger deep layer III cell size exhibiting darker outer band staining. This suggests differences in local pyramidal neuron connectivity between these cortical areas (Douglas and Martin 2004). Differences in pyramidal neuron size and the degree of staining in the outer band of Baillarger may therefore indicate differences in the patterns of local and corticocortical connectivity in layer III in each of the different areas.

Layer IV contains stellate and pyramidal neurons and is the internal granular layer of the cortex (DeFelipe et al. 2002; Valverde 2002). Layer IV receives afferents from the thalamus and intrahemispheric cortical regions and also shows substantial local connectivity with the other cortical layers, sending the majority of its efferent fibers to layers II and III (Gilbert and Wiesel

1979; Douglas and Martin 2004). In sensory areas such as the primary visual cortex, layer IV receives the sensory input of the cortex directly from the thalamus and is the starting point of the processing circuit. Even in association cortex such as the IPL, layer IV receives major sensory input from the thalamus as well as input from layer III in the same and other cortical areas

(Douglas and Martin 2004). Because layer IV is highly involved in both thalamocortical, corticocortical, and local connectivity, variation in cell density may reflect the degree of connectivity of a particular area, and may also indicate differences in function.

Layer IV was useful in differentiating the majority of identified cortical areas, particularly the areas of the IPL lateral convexity, areas on the convexity and opercular areas, the IPS and the IPL, and OPT and OA. The only areas that showed similar layer IV areal density were the somatosensory cortex, PFG, and the areas in the operculum (Tables 4-5). Layer IV differences between areas were apparent in both the qualitative cytoarchitectural descriptions and in the

78 results of the quantitative analysis. As with deep layer III, the differences in layer IV were also reflected in differences in the intensity and location of the outer bands of Baillarger (Table 6).

The parietal operculum, which includes areas PFOP and PGOP, is usually included in the secondary somatosensory cortex, which responds directly to specific types of tactile stimulation based on electrophysiological and functional neuroimaging studies (e.g. Ridley and Ettlinger

1976, 1978; Woolsey et al. 1979; Ferretti et al. 2003). Layer IV is likely similar between the somatosensory cortex and the opercular areas because they both receive direct sensory input and participate in sensory processing. PFG may also be involved more directly in sensory processing than other IPL areas. The myeloarchitecture of these regions, however, shows a stark contrast between the intensity of the staining in the outer band of Baillarger in PFG versus the operculum, suggesting that the two areas may receive similar sensory input, but differ in the degree of local connectivity in layers III and IV.

Opt and OA also differ in layer IV areal density. This difference suggests that OPT and

OA receive different types of input from the thalamus and may not participate in sensory processing to the same degree. OA is closely associated with the primary visual cortex and contains extrastriate areas that are involved in processing higher order visual information (Orban

2008). Based on similarities in cytoarchitecture between OPT and other IPL areas, it may serve a similar role as association cortex, and is likely involved more in integrative processing rather than direct sensory processing.

Differences in layer IV density between the IPS and the IPL may reflect differences in the type of information processed by these two areas. Both are located in the association cortex and are involved in sensorimotor integration (Andersen and Bruneo 2003). The IPS has been investigated extensively with electrophysiological recordings in primates, and is involved in visual processing and reaching of the forearm (Vingerhoets 2014; Archambault et al. 2015) and the dorsal stream of visual processing that gives information about where objects are in space

(Ungerleider and Mishkin 1982; Goodale and Milner 1992; Culham et al. 2003). Generally, the

IPL is involved in similar activities, but is not specifically included in the dorsal stream that indicates object location, nor the ventral stream that processes information about object features

79 (Ungerleider and Mishkin 1982; Goodale and Milner 1992). Instead, the IPL lies at the center and integrates the "where" and "what" object information from both of the streams (Singh-Curry and

Husain 2009; Cloutman et al. 2013). Differences in layer IV between the IPS and IPL may reflect this functional difference since each region would receive different types of thalamic and corticocortical inputs.

Layer V pyramidal neurons are a major output of the cortex and send substantial efferents to subcortical structures and other cortical areas, and also project locally to other cortical layers (Douglas and Martin 2004; Constantinople and Bruno 2013). Projections of layer V pyramidal neurons are evident in myeloarchitecture as the inner band of Baillarger, the prominent deep bundle of tangential fibers (Krsti´c 1997; Nieuwenhuys 2013). Layer V was generally similar throughout the lateral convexity of the IPL in terms of both areal density and descriptions, but differed between the areas on the lateral convexity and those in the operculum, areas within the operculum, and the IPS and the IPL in cell size and cell distribution, as well as in areal density

(Tables 4- 6).

Layers IV and V were similar in the areas they were able to differentiate. Both layers differed between the IPL lateral convexity and operculum, as well as between the IPS and IPL.

Because layer V contains the significant output of the cortex (Constantinople and Bruno 2013), it is possible that its patterns of connectivity and function would be similar to that of layer IV and would differ between cortical areas in much the same way. The inner band of Baillarger showed a dramatic difference between the IPL areas on the lateral convexity and those in the operculum.

The inner band contains neurites from excitatory pyramidal neurons in layer V. The inner band was dark and apparent in all areas on the convexity, but was only lightly stained in the opercular areas. The light staining of the inner band of Baillarger also resulted in a more prominent array of vertical fiber bundles, which differentiates the operculum from the lateral convexity. This suggests different patterns of local connectivity between the two regions. Because the operculum is involved more in sensory processing (e.g. Ridley and Ettlinger 1976, 1978; Woolsey et al. 1979;

Ferretti et al. 2003), local connections may not be as prominent as they are in a highly integrative region such as areas in the IPL convexity.

80 Additionally, layer V differed between the opercular areas PFOP and PGOP in cyroarchitectural features. The parietal operculum contains the secondary somatosensory cortex, which is organized topographically much like the primary somatosensory cortex (Ferretti et al.

2003). It is possible that this topographic organization calls for different subcortical and corticocortical connectivity between the two opercular areas and thus results in different layer V organization. These differences in cytoarchitecture and myeloarchitecture across each of the cortical areas in the IPL lend support to their classification as distinct areas.

Comparison with other parcellations

The descriptions of the IPL cortical areas from the current study identify many of the same features of the chimpanzee IPL that were previously identified by Bailey and von Bonin

(1950), particularly the indistinct boundary between layers II and III, and the two sublayers

(superficial and deep) of layer III. The chimpanzee IPL has also been described as a relatively homogenous region; for example, Bailey and von Bonin (1950) designated areas PF and PG for mainly topographical reasons, and interpreted changes in cytoarchitecture within the IPL as a gradient of change from rostral to caudal. The quantitative analysis showed that the differences between the six areas identified in the current study were subtle, which can be seen most clearly in the density profiles (Figure 13). However, the descriptions of both cytoarchitecture and myeloarchitecture identified qualitative features that consistently differentiated each of the six cortical areas between hemispheres and across individuals, and the quantitative analysis significantly differed between all areas.

The current study considers all areas to be distinct from each other, and unlike previous parcellations such as Shevchenko (1936) and Gerhardt (1938), does not specifically name any subareas, which are regions that share cytoarchitectural features with the major cortical area, but differ slightly in one of the features, such as layer III cell size or layer V cell density. All six areas contained identifiable differences from the surrounding cortical areas (i.e. somatosensory cortex,

IPS, OA) as well as each other, and many of these differences were further supported quantitatively using the laminar sampling method. The differences between regions within the IPL

81 were perhaps most striking in myeloarchitecture. Myelination is an important aspect of a cortical area because it reflects the organization of axons and shows the extent of connectivity within a region of cortex. Furthermore, previous parcellations likely delineated subareas based on subtle differences with the main cortical area that could not be compared directly. For example,

Gerhardt (1938) included 89t as a subarea of area 89 based on cell size and density differences in layers III, IV, and V, but did not include it as a distinct area likely due to its overall similarity to area 89.

The six cortical areas identified in this study outnumber those in the prior parcellations of the chimpanzee IPL (Figure 14). These parcellations show two main areas in the IPL with a varying number of associated subareas. The results from the PCA indicated a subtle difference between rostral and caudal areas in the current study, and support the existence of two main clusters of areas. These two rostral and caudal clusters likely represent the functional differences that have been identified in the IPL, which include more involvement with somatosensory functions in the rostral IPL and visual functions in the caudal IPL (Fogassi et al., 2005; Gardner et al., 2007; Rozzi et al., 2008; Bonini et al., 2010; Caspers et al., 2010).

Based on the qualitative descriptions, areas PF and PFG in the current study align with area PF in Bailey and von Bonin (1950), while these areas along with the rostral portion of PG match with 89 in Gerhardt (1936). PG in both the current parcellation and Bailey and von Bonin

(1950) appear homologous, while the caudal part of the IPL in Gerhardt (1936) (area 90) corresponds to OPT in the current study. The parcellation of the lateral convexity of the IPL in current study appears most similar to that of Shevchenko (1936). This parcellation includes two areas (40 and 39) and one subarea of each. The most rostral subarea of area 40 appears to correspond to PFG in the current study, while most of area 39 corresponds to PG. Area 39 appears to be the same area as OPT in the current study, and area 90 in Gerhardt (1938). The remaining subarea of area 39 was not included in the IPL in the current study and was instead considered part of the superior temporal gyrus. One notable difference between the current parcellation and that of Shevchenko (1936) is that the most rostral portion of the IPL, in this study

82 identified as PF, was not included as part of the IPL, nor were any areas in the parietal operculum.

The current parcellation included two opercular areas, PFOP and PGOP, in addition to the four areas on the lateral convexity. Although Shevchenko did not delineate any areas in the operculum, both Bailey and von Bonin (1950) and Gerhardt (1936) each show an opercular area,

PFD and 72, respectively. These areas, however, are not explicitly included as part of the IPL, though Bailey and von Bonin (1950) do include PFD as part of the parietal lobe rather than the somatosensory cortex. The current parcellation includes the parietal operculum in the IPL based on location and similarities with the lateral convexity in overall appearance, specifically a superficial to deep size gradient in layer III, larger pyramidal neurons in deep layer III, and reduced cell density in layer V relative to the somatosensory cortex. The operculum was differentiated from the areas in the convexity primarily by a relative reduction in layer IV density that was seen qualitatively and shown quantitatively with the laminar sampling method. Unlike the prior parcellations, the parietal operculum was shown as two areas, PFOP and PGOP, that were differentiated qualitatively by layer III cell size, prominence of layer IV, and layer V cell density.

The laminar sampling method showed that areal density between the two areas differed in layer V density. The current parcellation indicates that the parietal operculum can be included with the

IPL areas in the convexity based on similarities in cytoarchitecture, but also forms a distinct region within the IPL.

The existence of an IPL area on the caudal portion of the lateral convexity was debated by Bailey and von Bonin (1950), who placed this region in the occipital lobe as part of OA. They classified this portion of the angular gyrus as part of OA based on the small columns present throughout all of the layers, small cells in the deep portion of layer III, high layer IV density, and reduced cell density in layer V. They also argued that areas 39 and 90 (Gerhardt, 1938 and

Shevchenko, 1936 respectively) corresponded to OA based on their description. The current parcellation, however, described this part of the angular gyrus as a separate area than OA, based on a more apparent superficial to deep size gradient, reduced layer IV density, and a more pronounced difference between the superficial and deep portions of layer V in Opt. Comparison

83 of areal density in layers III, IV, and V using the laminar sampling method supports the differences observed in layer IV cell density between the two areas, but did not find significant differences in layers III and V. Differences in layer IV, however, suggest a possible functional difference between areas OPT and OA, and would be expected to have higher density in an occipital area such as OA, which is more closely associated with visual processing compared to

OPT (Orban, 2008).

Evolutionary implications for the evolution of tool-use and tool-making

The parcellation from the current study is identical to what has been identified in macaques (Pandya and Seltzer 1982; Gregoriou et al. 2006; Rozzi et al. 2006; Figure 15). Along with descriptions of the chimpanzee IPL cytoarchitecture, descriptions of macaque IPL cytoarchitecture were used to aid in identifying the areas. All the areas from the macaque IPL were identified in the chimpanzee IPL, and there were no additional subareas or areas observed in the qualitative parcellation. Pandya and Seltzer (1982) identified a rostral to caudal gradient in macaques where caudal areas became increasingly laminated. In general, the chimpanzee IPL did not exhibit an apparent rostral to caudal gradients in cell size or patterns of lamination, but did show a slight tendency in more caudal areas (PG and OPT) toward a broader and more apparent layer IV, and increasing layer V and VI sublayers, especially in OPT.

The areas identified in the chimpanzee were also similar to those that have been identified in some of the human IPL parcellations that show more numerous areas than the commonly used Brodmann (1909) parcellation. These include the cytoarchitectural parcellations of von Economo and Koskinas (1925) and Caspers (2006), and the myeloarchitectural parcellations of Vogt (1911) and colleagues (Gerhardt 1940, Batsch 1956), which show 5-6 cortical areas in the human IPL.

Currently, qualitative descriptions can be used to find corresponding areas in the IPL across all three species. Comparisons of the descriptions and images in von Economo and

Koskinas (1925) with the results from the current study and previous macaque parcellations might show which human areas (PFop, PFt, PF, PFc, PFm, PG) are comparable to areas in the

84 macaque and chimpanzee IPL. It should be noted, however, that von Economo and Koskinas

(1925) considered many of these areas to be subareas of PF; however, quantitative assessment by Caspers (2006) showed that these areas were substantially different and should be considered distinct areas.

Based on the connectivity of areas parcellated using cytoarchitecture, Caspers et al.

(2011) propose homologies between human areas PFt, PF, PGa, and PGp with macaque areas

PF, PFG, PG, and Opt, respectively. However, the human PFt lacks the connectivity with Broca’s area (area 44) that characterizes the macaque PF and is shared with human PF (Petrides and

Pandya 2009). Furthermore, a connectivity-based parcellation based on probabilistic tractography that was performed blind to cytoarchitecturally parcellated areas did not identify PFt as a distinct area based on its connectivity, but instead grouped it with area PF (Mars et al. 2012). PFm also had a pattern of strong connectivity with the prefrontal cortex that was distinctive of the macaque area PFG (Mars et al., 2012).

Comparisons between the qualitative descriptions of macaques (Pandya and Seltzer

1982; Gregoriou et al. 2006; Rozzi et al. 2006) and humans (von Economo and Koskinas 1925) also suggest a different set of possible homologies. The description of human PF from von

Economo and Koskinas (1925) is similar to that of PF in macaques from Pandya and Seltzer

(1982) based on small to medium pyramidal neurons, with larger neurons in deep layer III, a broad layer IV, and a notably unremarkable layer V with small and medium pyramidal neurons.

The description for human PFm is similar to that of the macaque PFG, mainly because PFm is a magnocellular area, and like PFG, is characterized by an increase in the size of pyramidal neurons compared to area PF in layers III and V. The rostral portion of PG (area PGa in Caspers et al. 2006) is similar to PG in macaques especially in its abrupt changes in pyramidal neuron size in layer III, a well-defined layer IV, and a homogenous appearance in layers V and VI. In the von Economo and Koskinas (1925) parcellation, PG includes an un-named transitional area near the occipital lobe with larger pyramidal neurons in deep layer III, which may correspond to OPT in macaques and humans, and likely corresponds to area PGp in Caspers et al. (2006). The

85 description for and location of PFc suggests that this area might be correspond to PGOP in macaques.

In this case, the human area that does not appear to have a counterpart in the macaque and chimpanzee is area PFt, which is in the anterior portion of the supramarginal gyrus (Figure

13). This area is of interest because it is the hypothesized location of the human-specific tool-use area (Peeters et al. 2009, 2013; Orban and Caruana 2014; Orban 2016). This area is proposed to be a tool-specific area that is functionally distinct from other hand-action circuits in the posterior parietal cortex (Peeters et al. 2013), and is activated in humans but not macaques during the observation of a mechanical hand manipulating an object (Peeters et al. 2009). It has also been proposed as one of the regions specifically activated during skilled tool-making in humans (Stout et al. 2008) as well as observation of tool-making (Stout et al. 2011). Functional experiments of tool-use that compare humans and macaques have shown that there is a change that occurred at some point along the human lineage that resulted in the evolution of a tool-specific area, PFt

(Orban and Caruana, 2014; Orban 2016), that may have also resulted in structural changes and the formation of a new cortical area as well.

While our data suggest that area PFt in humans lacks an anatomically similar region in chimpanzees, this hypothesis remains to be more fully tested with functional neuroimaging methods to compare these species with spatial resolution fine enough to detect anatomical differences in such a small area. There are currently no direct comparisons of IPL cortical architecture between species using the same methods. Future studies should employ a multimodal approach to compare IPL histological structure, anatomical connectivity, cortical thickness variation, and receptor distributions in humans, chimpanzees, and other primates in a systematic manner to further investigate structural homology between species. The laminar sampling method would allow for a direct comparison of areal density in the various IPL cortical areas between species, and would provide quantitative support for structural homologies that can further our understanding of how the IPL evolved.

The current study provides an initial description and quantitative analysis of the chimpanzee IPL using the laminar sampling method. The laminar sampling method has been

86 shown to be effective in locating cytoarchitecturally comparable areas across species, specifically macaques and humans, in the orbitofrontal cortex (Mackey and Petrides 2011). Future work can therefore expand on the current study and use a similar method of qualitative parcellation and testing with the laminar sampling method in the macaque and human IPL, which can be directly compared to the results of the current study. Such a comparison is essential because chimpanzees are one of the closest living relatives of humans and are prominent tool users

(Goodall 1986; Nishida 1990; McGrew et al. 1992; Matsuzawa 1996; Whiten et al. 1999; Hopkins et al. 2009; Haslam 2013). Directly comparing the IPL across macaques, chimpanzees, and humans can thus determine which cortical areas are homologous between species, and can identify IPL areas that are unique to humans.

87 Figures 88

Figure 1. Parcellations of the chimpanzee IPL. All three parcellations identified two major areas on the lateral convexity. In Shevchenko (1936), the

two areas were area 40 (horizontal lines) and area 39, and included subareas. Gerhardt (1938) parcellated two major areas, 89 and 90, along with

a subarea, 89t. Bailey and von Bonin (1950) divided the IPL into PF and PG with no subareas. None of these parcellations included IPL areas in

the parietal operculum. All images were adapted from their original sources.

Figure 2. Cytoarchitectural and myeloarchitectural features of the chimpanzee cortex. The qualitative parcellation was guided by previous descriptions of cytoarchitecture and myeloarchitecture in IPL cortical areas in the macaque, chimpanzee, and human IPL (Von

Economo and Koskinas, 1925; Bailey and Von Bonin, 1950; Pandya and Seltzer, 1982, Caspers et al., 2006; Gregoriou et al., 2006; Rozzi et al., 2006), and focused on distinctive features that could be identified consistently across different areas and brains. Cytoarchitectural features considered for the parcellation included the presence of a superficial to deep pyramidal neuron size gradient in layer III, pyramidal neuron size and the presence of sublayers in layers V and VI, and cell density throughout all layers and especially in layer IV. For myeloarchitecture, the features included the degree of myelination shown by the amount of staining, the presence and size of vertical fiber bundles (1), visibility and size of the outer (2) and inner (3) bands of

Baillarger and their relative staining intensity, and the amount of separation between the bands of

Baillarger.

Figure 3. Cortical contours and image processing for the laminar sampling method. The laminar sampling method requires the preparation of high resolution images of the cortex. Contours are drawn onto the raw image to cover layer I and the white matter and to delineate the boundary between layers II and III, the deepest row of pyramidal neurons in layer III, the center of layer IV, the most superior band of pyramidal neurons in layer V, and the center of the superior darkly stained band of cells in layer VI. The layer IV contour is then used to produce the transverse lines, and the remaining contours are used to define the density sampling along the transverse lines. To process the images for sampling, an adaptive threshold is applied to the raw image that normalizes the background, and a second threshold is applied to binarize the image. Cells in the binarized image are then binned according to size, and are split into a blurred image with pyramidal neurons, and a blurred image with granule dells. These blurred images contain can be sampled for areal density along the transverse lines using the laminar sampling method.

Figure 4. Nissl stained histological sections from six chimpanzee IPL areas in all four hemispheres. Differences in staining intensity and individual variability result in variation in how each cortical area appears between individuals, hemispheres, and histological section. However, the major features such as pyramidal cell size and observed cell density that differentiated each cortical area were consistent, especially in layers III, IV, and V, and similarities in the cytoarchitecture of the same cortical area in a different hemisphere and individual are apparent. A complete list of features for each cortical area are listed in Table 5 and full descriptions are given in the text.

Figure 5. Silver stained histological sections showing the myeloarchitecture of the six chimpanzee IPL areas. Myeloarchitecture was more consistent across hemisphere and individual compared to cytoarchitecture. Shown here are representative histological sections from the right parietal lobe of Justin. There are striking differences between all of the areas, especially in the overall darkness of the stain and the staining and location of the bands of Baillarger. These differences in myelination indicate that the areas have different patterns of local and long range connectivity and suggest functional differences between these areas as well.

Figure 6. Representative histological sections from each hemisphere showing each IPL area.

Four areas (PF, PFG, PG, OPT) can be seen on the lateral convexity of the IPL, while two areas

(PFOP, PGOP) are located in the parietal operculum.

Figure 7. Plots of the boundaries between the IPL and surrounding cortical areas. Boundaries are shown for layers III (shallow and deep), IV and V. Mean standardized areal density for all histological sections containing a specific boundary was plotted for 50 transverse lines on either side of the hypothesized boundary. The patterns of change between cortical areas was not uniform across all of the cortical layers, suggesting that differences between cortical areas may be based on changes in a subset of the cortical layers.

Figure 8. Plots of the boundaries between the IPL areas. Boundaries are shown for layers III

(shallow and deep), IV and V. Mean standardized areal density for all histological sections containing a specific boundary was plotted for 50 transverse lines on either side of the hypothesized boundary. The patterns of change between cortical areas was not uniform across all of the cortical layers, suggesting that differences between cortical areas may be based on

95 changes in a subset of the cortical layers. Differences between IPL areas at the hypothesized boundary were also more consistent than those between the IPL and surrounding areas (Figure

5), but were generally more subtle in the observed amount of change in density.

Figure 9. Plots of the differences in areal density between the IPL and surrounding cortical areas for each histological section. Differences in mean areal density were not consistent across all of the histological sections for a given layer and cortical area. However, each of the plots shows a general trend that is reflected in the aggregated plots shown in Figure 6. The IPL is part of the association cortex which contains subtle differences between cortical areas that are not always evident in measurements of areal density (Mackey and Petrides 2014).

Figure 10. Plots of the mean density between areas within the IPL for each histological section.

Similar to the data shown in Figure 7, differences in mean areal density were inconsistent between sections for a given layer and cortical area. There are trends for each of the boundaries with the majority of the many of the plotted lines sharing a similar slope. These trends are reflected in the aggregated plots shown in Figure 6. The IPL is part of the association cortex which contains subtle differences between cortical areas that are not always evident in measurements of areal density (Mackey and Petrides 2014).

98 99

Figure 11. Plots of the first three principal components by subject and hemisphere. The PCA showed three principal components (PCs) that

accounted for 97% of the variation across the nine cortical areas. The primary loadings for PC1 were layer V, layer IV, and shallow layer III, while

the primary loading for PC2 was deep layer III, and the primary loading for PC III was layer IV. PC1 and PC3 show substantial overlap between

each of the individual chimpanzees and hemispheres with poor separation of each cortical area. However, the rostral areas (PF, PFG), opercular

areas (PFOP, PGOP), and the somatosensory cortex tend to cluster positively on PC1 and negatively on PC3, while the more caudal areas (PG,

OPT), IPS, and OA show the opposite pattern. PC2, however, shows a clear separation between individuals, suggesting that the variability for

loadings on PC2 (primarily deep layer III) is greater between individuals than between cortical areas.

100

Figure 12. A hierarchical cluster dendrogram showing the major clusters of IPL and surrounding cortical areas. Euclidean distance and UPGMA

were used to form the clusters. The red values (left) show the approximately unbiased values and the green values (right) show the bootstrap

probability. The cluster analysis showed no major trends in clustering, either by individual, hemisphere, or cortical area.

Figure 13. Density profiles for six cortical areas in the chimpanzee IPL and the surrounding areas.

The laminar sampling method measures areal density in delineated cortical layers along transverse lines. The mean areal density for each layer can then be plotted in density profiles to visually compare similarities and differences between the identified cortical areas. These density profiles show standardized areal density measurements in layers III (shallow and deep), IV, and

V, the same areas used in the quantitative analysis. The differences across all of the areas are generally subtle. Deep layer III and especially layer IV show the most evident differences between areas, similar to what is shown in the results of the linear mixed effects model.

101

Figure 14. 3D representation of the chimpanzee IPL areas and comparison with previous parcellations. The parcellations for both hemispheres in each chimpanzee are shown in A and B in a 3D rendering and in coronal sections. The 3D rendering is based on an the estimated location of the cytoarchitectural borders on a T1 weighted MRI scan. The parcellations were generally similar between hemisphere and between chimpanzee. Areas PFOP, PF, PFG, PG, and OPT are visible on the lateral surface of the cortex, but PGOP is not. PGOP is contained entirely within the operculum and can be seen in the coronal sections in orange. The current parcellation (A, B) is similar in extent to all three prior parcellations (C). The actual parcellation of cortical areas is most similar to Shevchenko (1936), even though Shevchenko (1936) lacks the rostralmost areas of the IPL. Also evident in the current parcellation is the inclusion of OPT (pink), an area on the caudal aspect of the angular gyrus that is not included in Bailey and von Bonin

102 (1950), but is present in the other two parcellations. The current parcellation also includes areas in the parietal operculum, PFOP and PGOP (red and orange), which are not included in any of the previous parcellations.

103

Figure 15. 3D rendering of the chimpanzee IPL compared to macaque and human IPL. The extent of the chimpanzee IPL (A) is intermediate between the macaque and the human (B). The size of the occipital lobe appears to affect the extent of the IPL, which is evident in the macaque and human parcellations. The number and location of areas identified in the macaque and chimpanzee were the same. Humans differed in both the number and naming of the IPL areas; however, descriptions and locations of the areas suggest that many of the areas are similar to those in the macaque and chimpanzee, and that humans may only have one area, PFt, that is not found in the other species. This area is the hypothesized location of the tool-specific area in humans, and has been implicated as an area of major change in human evolution.

104 Tables

Table 1. Comparison of models with different random effects

Degrees of log Model freedom AIC BIC Likelihood

density ~ area + layer + area:layer + (1|subject) + (1|side) + (1|slide) 31 -74206.12 -73915.22 37005.85 *

density ~ area + layer + area:layer 28 -70548.19 -70285.45 35302.1 *model selected for analysis

Table 2. Estimated variance explained by random effects

Groups Variance Standard deviation

Slide 0.0013190 0.0363200

Side 0.0000237 0.0048730

Subject 0.0000278 0.0052700

Residual 0.0250628 0.1583120

105 Table 3. Parameter estimates for the generalized linear mixed model for areas by layer Degrees Standard of p Predictor Level Estimate error freedom t value value

Area IPS -0.0179 0.0056 16456 -3.2093 0.001 *

Area OA -0.0297 0.0064 6960 -4.6009 0.000 *

Area OPT -0.0220 0.0058 6845 -3.7754 0.000 * Area Area PF -0.0023 0.0039 87646 -0.5880 0.557 (reference: A2) Area PFG 0.0133 0.0044 42954 2.9985 0.003 *

Area PFOP 0.0016 0.0034 87833 0.4791 0.632

Area PG -0.0220 0.0046 17752 -4.7436 0.000 *

Area PGOP 0.0125 0.0056 59867 2.2128 0.027

Layer Layer IV 0.6693 0.0036 87776 188.2178 0.000 * (reference: III) Layer V -0.0204 0.0036 87776 -5.7505 0.000 *

Area IPS : Layer IV 0.0868 0.0061 87776 14.1785 0.000 *

Area OA : Layer IV 0.1251 0.0063 87776 19.9095 0.000 *

Area OPT : Layer IV 0.0649 0.0057 87776 11.4724 0.000 *

Area PF : Layer IV 0.0706 0.0053 87776 13.2603 0.000 *

Area PFG : Layer IV -0.0062 0.0051 87776 -1.1980 0.231

Area PFOP : Layer IV -0.0057 0.0046 87776 -1.2263 0.220

Area by Area PG : Layer IV 0.1206 0.0048 87776 25.3693 0.000 * layer Area PGOP : Layer IV -0.0081 0.0070 87776 -1.1682 0.243 interaction (references Area IPS : Layer V 0.0136 0.0061 87776 2.2181 0.027 : A2, III) Area OA : Layer V 0.0326 0.0063 87776 5.1823 0.000 *

Area OPT : Layer V 0.0424 0.0057 87776 7.4881 0.000 *

Area PF : Layer V 0.0152 0.0053 87776 2.8571 0.004 *

Area PFG : Layer V 0.0089 0.0051 87776 1.7298 0.084

Area PFOP : Layer V -0.0018 0.0046 87776 -0.3953 0.693

Area PG : Layer V 0.0356 0.0048 87776 7.4878 0.000 *

Area PGOP : Layer V 0.0204 0.0070 87776 2.9322 0.003 * *Results are statistically significant at a level of p < 0.01

106 Table 4. Post hoc least-squares means showing pairwise contrasts of bordering areas

Standard Degrees of p Contrast Layer Estimate error freedom t ratio value

A2-PF III shallow 0.003 0.004 116804.35 0.763 0.998

A2-PFG III shallow 0.006 0.004 54412.41 1.576 0.818

A2-PFOP III shallow 0.009 0.003 117086.94 2.753 0.130

IPS-PFG III shallow -0.028 0.004 74120.18 -6.484 <0.001 *

IPS-PG III shallow 0.003 0.004 107599.86 0.669 0.999

IPS-OPT III shallow 0.000 0.005 93539.76 -0.09 1.000

OA-OPT III shallow -0.011 0.005 99284.3 -2.29 0.348

PF-PFG III shallow 0.003 0.004 73582.9 0.874 0.994

PFG-PG III shallow 0.031 0.003 101095.82 9.146 <0.001 *

PG-OPT III shallow -0.003 0.004 32774.93 -0.75 0.998

PF-PFOP III shallow 0.006 0.003 117131.24 1.779 0.696

PFG-PFOP III shallow 0.002 0.004 50618.11 0.667 0.999

PFG-PGOP III shallow 0.008 0.005 117147.1 1.704 0.745

PG-PGOP III shallow -0.023 0.005 112029.51 -5.016 <0.001 *

PFOP-PGOP III shallow 0.005 0.005 77928.98 1.139 0.968

A2-PF III deep 0.006 0.004 116804.35 1.62 0.794

A2-PFG III deep -0.023 0.004 54412.41 -5.875 <0.001 *

A2-PFOP III deep -0.010 0.003 117086.94 -3.263 0.030 *

IPS-PFG III deep -0.015 0.004 74120.18 -3.484 0.015 *

IPS-PG III deep 0.009 0.004 107599.86 2.185 0.416

IPS-OPT III deep 0.016 0.005 93539.76 3.464 0.016 *

OA-OPT III deep 0.002 0.005 99284.3 0.461 1.000 - PF-PFG III deep -0.029 0.004 73582.9000 7.3680 <0.001 *

PFG-PG III deep 0.024 0.003 101095.82 7.084 <0.001 *

PG-OPT III deep 0.007 0.004 32774.93 1.725 0.731

PF-PFOP III deep -0.016 0.003 117131.24 -4.874 <0.001 *

PFG-PFOP III deep 0.013 0.004 50618.11 3.52 0.013 *

PFG-PGOP III deep -0.004 0.005 117147.1 -0.863 0.995

PG-PGOP III deep -0.028 0.005 112029.51 -6.084 <0.001 *

107 PFOP-PGOP III deep -0.017 0.005 77928.98 -3.483 0.015 *

A2-PF IV -0.066 0.004 116804.35 -18.49 <0.001 *

A2-PFG IV -0.005 0.004 54412.41 -1.209 0.955

A2-PFOP IV 0.005 0.003 117086.94 1.566 0.823

IPS-PFG IV 0.070 0.004 74120.18 16.133 <0.001 *

IPS-PG IV -0.029 0.004 107599.86 -7.21 <0.001 *

IPS-OPT IV 0.030 0.005 93539.76 6.436 <0.001 *

OA-OPT IV 0.056 0.005 99284.3 11.908 <0.001 *

PF-PFG IV 0.062 0.004 73582.9 15.809 <0.001 * - PFG-PG IV -0.099 0.003 101095.82 29.348 <0.001 *

PG-OPT IV 0.059 0.004 32774.93 14.162 <0.001 *

PF-PFOP IV 0.071 0.003 117131.24 21.747 <0.001 *

PFG-PFOP IV 0.010 0.004 50618.11 2.645 0.168

PFG-PGOP IV 0.006 0.005 117147.1 1.255 0.944

PG-PGOP IV 0.105 0.005 112029.51 22.866 <0.001 *

PFOP-PGOP IV -0.004 0.005 77928.98 -0.783 0.997

A2-PF V -0.011 0.004 116804.35 -3.043 0.059

A2-PFG V -0.020 0.004 54412.41 -5.074 <0.001 *

A2-PFOP V 0.001 0.003 117086.94 0.324 1.000

IPS-PFG V -0.018 0.004 74120.18 -4.097 <0.001 *

IPS-PG V -0.017 0.004 107599.86 -4.274 <0.001 *

IPS-OPT V -0.021 0.005 93539.76 -4.579 <0.001 *

OA-OPT V -0.014 0.005 99284.3 -2.853 0.100

PF-PFG V -0.009 0.004 73582.9 -2.275 0.358

PFG-PG V 0.001 0.003 101095.82 0.2 1.000

PG-OPT V -0.004 0.004 32774.93 -0.932 0.991

PF-PFOP V 0.012 0.003 117131.24 3.642 0.008 *

PFG-PFOP V 0.021 0.004 50618.11 5.736 <0.001 *

PFG-PGOP V -0.008 0.005 117147.1 -1.647 0.779

PG-PGOP V -0.008 0.005 112029.51 -1.805 0.679 PFOP-PGOP V -0.028 0.005 77928.98 -5.908 <0.001 * *Results are statistically significant at a level of p < 0.05

108 Table 5. Distinctive qualitative cytoarchitectural features in the chimpanzee inferior parietal lobe Layer PF PFG PG OPT PFOP PGOP

Clear boundary Unclear boundary Unclear boundary Unclear boundary Clear boundary with Unclear boundary II with layer III, low with layer III, low with layer III, broad, with layer III, dense layer III, thin with layer III, broad cell density cell density cell dense

Slight gradient, Slight gradient, Slight gradient, No gradient, uniform No gradient, No gradient, increase increase from increase from increase from small appearance, small III increase from small from small to small/medium to small to medium to medium/large and medium cells to large cells medium/large cells medium/large/very cells cells throughout large cells

Very well defined, Broad and cell Broad and cell Well defined, cell Broad and cell IV very broad, cell Not well defined, thin dense dense dense dense dense 109 Clear sublayers, Clear sublayers, Sublayers, Homogeneous Clear sublayers, decrease from decrease from Clear sublayers, decrease from appearance, decrease from V medium/large cells medium to decrease from medium cells to sublayers present but medium to small to medium/small medium/small cells, medium to small cells small cells difficult to locate cells cells low cell density Homogeneous Well defined, Well defined, Well defined, clear appearance, cell size Well defined and Well defined and VI broad, cell dense, small cell size sublayers and density similar to thin broad clear sublayers layer V Table 6. Distinctive qualitative myeloarchitectural features in the chimpanzee inferior parietal lobe Feature PF PFG PG OPT PFOP PGOP Overall myelination Light Dark Very light Light Light Very light level

Outer band of Baillarger Light Dark Light Very dark Very light Light staining

Outer band of Close to inner Close to inner Close to inner Separate from Separate from Baillarger band, nearly band, nearly - band inner band inner band position merged merged

Inner band of Baillarger Dark Dark Dark Dark Very light Light staining 110

Evident but thin, Evident and thick, Evident but thin, Evident and very Thick but not Evident and Vertical fiber and do not extend and do not extend and do not thick, and extend prominent do not extend past the bundles past the outer past the outer extend past the past the outer extend past the outer band band band outer band band outer band Table 7. Principal components analysis loadings and importance of components Component 1 Component 2 Component 3 Component 4 Layer III shallow loading 0.55 -0.39 -0.53 -0.52 Layer III deep loading 0.11 0.85 -0.51 Layer IV loading -0.54 0.14 -0.82 0.15 Layer V loading -0.63 -0.32 0.23 -0.67 Standard deviation 1.41 1.14 0.77 0.34 Proportion of Variance 0.50 0.33 0.15 0.03 Cumulative Proportion 0.50 0.82 0.97 1.00

111 Chapter 5: Conclusion

The overall goal of this dissertation was to investigate the neural correlates of tool-use in chimpanzees, and provide data that can be compared with humans and nonhuman primates.

These aims were addressed using an integrative approach that combined neuroimaging and histological methods. The dissertation comprised three studies that all investigated neuroanatomical correlates of tool-use in chimpanzees. Chapters 2 and 3 addressed differences in gray and white matter structure that were linked with tool-use performance time in a captive sample of chimpanzees, while Chapter 4 focused exclusively on characterizing the histology of the inferior parietal lobe, an area highly associated with hand motion, object manipulation, and tool-use.

Chapter 2 used a whole-brain analysis of tensor-based morphometry (TBM) and cortical thickness to detect differences in gray matter associated with faster tool-use performance time in a captive sample of chimpanzees. We found substantial differences between males and females.

Faster tool-use performance was correlated with differences in motor, somatosensory, and parietal regions associated with sensorimotor integration in males, and differences in the premotor areas associated with motor control of reaching and grasping in females. There were only a few areas with structural changes shared between the sex that were associated with tool- use performance for TBM and cortical thickness. These shared regions were in the prefrontal cortex, inferior frontal gyrus, temporal pole, inferior temporal cortex, and cerebellum, areas are highly involved in visual, sensorimotor, and cognitive control processes. Sex differences that were identified in the analyses are not unexpected and likely underlie many of the behavior differences in tool-use that have been observed in both wild and captive chimpanzees. These sex-based differences in chimpanzee tool-use may have been present in the last common ancestor of chimpanzees and humans. There are also potential regions, such as the inferior temporal cortex, that may have been integral in the evolution of tool-use and tool-making in the human lineage.

Chapter 3 examined the relationship between white matter tracts and tool-use performance in the same sample of captive chimpanzees using tract-based spatial statistics

(TBSS) to perform a voxel-based, whole-brain analysis of different diffusion tensor imaging (DTI)

112 metrics, including both fractional anisotropy (FA) and mean diffusivity (MD). There are four tracts in the human brain that are strongly involved in tool-use: the anterior thalamic radiation (ATR), inferior fronto-occipital fasciculus (IFOF), superior longitudinal fasciculus (SLF), and the uncinate fasciculus (UF). In chimpanzees, faster tool-use performance time in both sexes was associated with significant clusters in the ATR, SLF, and IFOF, suggesting that white matter tracts connecting tool-related cortical areas are similar between chimpanzees and humans. In addition to these three tracts, variation in tool skill among chimpanzees was significant in clusters in the corticospinal tract (CST) and white matter tracts in the cerebellum, tracts associated with motor control that suggests differences in motor proficiency within the sample. There were also sex differences, with males exhibiting a greater number of significant clusters in tracts associated with motor control such as the CST and the cerebellum.

Chapter 4 provided a parcellation of the chimpanzee IPL using qualitative descriptions of histological architecture and a quantitative comparison of the identified cortical areas using the laminar sampling method. Qualitative parcellations were made based on four hemispheres from two adult male chimpanzees by observing patterns of cytoarchitecture and myeloarchitecture to distinguish between cortical areas. In the quantitative analysis, density profiles were obtained for each cortical area and were compared using the laminar sampling method. The qualitative parcellation identified six total areas in the IPL, four on the lateral aspect of the parietal lobe and two within the parietal operculum. Further examination of the density profiles of each of these areas showed that there were significant differences in layers III, IV, and V between bordering areas, supporting their classification as distinct cortical areas.

The results from this dissertation show that, much like humans, the chimpanzee brain has specific tracts and areas that are associated with tool-use with distinct patterns of connectivity. Chimpanzees, however, may rely more on areas involved in sensorimotor processing for tool-use than do humans. Tool-use in humans may therefore differ in the recruitment of more areas associated with cognitive control, and increased connectivity between areas involved in higher order cognitive functions. The additional cortical areas that have been identified in the human IPL that appear to lack chimpanzee homologues also may be essential for

113 tool-related cognition in humans, especially cognition linked with complex processes such as stone tool-making. Investigations of both gray and white matter showed that chimpanzees also have notable sex differences in their neural correlates of tool-use. Male and female chimpanzees may process tool-use differently, and these differences may be linked with previously reported sex differences in tool-use behaviors.

To fully understand evolutionary changes in the brain that occurred in the human lineage since its split from the Pan-Homo last common ancestor, comparative work must be performed between humans and chimpanzees. However, it is necessary to understand the structure and function of the chimpanzee brain to ensure the validity of these comparisons. This dissertation has provided a characterization of tool-use in the chimpanzee brain based on multiple modalities, and these results can form the basis of direct comparisons between chimpanzees, humans, and other primate species in the future. The results of this dissertation can also inform paleoanthropology, particularly by complementing the study of endocasts. Cortical maps of the whole IPL from humans and chimpanzees can be compared to the inferior parietal regions in fossil hominin endocasts to assess the position and potential size differences between these species. Insights into structures and cortical areas involved in chimpanzee and human tool-use can also guide morphometric studies that are seeking potential areas of expansion in the hominin lineage.

Tool-use and tool-making have been considered defining features of the human lineage, but studying how and when these capacities evolved has proven difficult. Comparative studies, especially those including humans and closely related primates, have been useful for showing aspects of tool-use cognition and neural circuitry that may be unique to humans. The purpose of this dissertation was to expand on this previous work and identify the neural correlates of tool-use in chimpanzees and to provide the methodological groundwork for future cross-species comparisons of neuroanatomy. The findings of this dissertation suggest that there may be a general tool-use circuit that is conserved across chimpanzees and humans that likely became further specialized in the human lineage as the capacity for tool-use and tool-making increased, and appear to have included the evolution of a tool-specific area in humans that is not seen in

114 other primates including chimpanzees. These types of changes in neural circuitry associated with tool-use may underlie the variety and flexibility of the technology seen in modern human societies.

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