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Frontal White Matter Volume Is Associated with Brain Enlargement and Higher Structural Connectivity in Anthropoid Primates

  • Jeroen Bert Smaers ,

    Affiliation Leverhulme Centre for Human Evolutionary Studies, Department of Biological Anthropology, University of Cambridge, Cambridge, United Kingdom

  • Axel Schleicher,

    Affiliation Cécile and Oskar Vogt Institute for Brain Research, Heinrich-Heine-University, Düsseldorf, Germany

  • Karl Zilles,

    Affiliation Cécile and Oskar Vogt Institute for Brain Research, Heinrich-Heine-University, Düsseldorf, Germany

  • Lucio Vinicius

    Affiliation Leverhulme Centre for Human Evolutionary Studies, Department of Biological Anthropology, University of Cambridge, Cambridge, United Kingdom

Frontal White Matter Volume Is Associated with Brain Enlargement and Higher Structural Connectivity in Anthropoid Primates

  • Jeroen Bert Smaers, 
  • Axel Schleicher, 
  • Karl Zilles, 
  • Lucio Vinicius


Previous research has indicated the importance of the frontal lobe and its ‘executive’ connections to other brain structures as crucial in explaining primate neocortical adaptations. However, a representative sample of volumetric measurements of frontal connective tissue (white matter) has not been available. In this study, we present new volumetric measurements of white and grey matter in the frontal and non-frontal neocortical lobes from 18 anthropoid species. We analyze this data in the context of existing theories of neocortex, frontal lobe and white versus grey matter hyperscaling. Results indicate that the ‘universal scaling law’ of neocortical white to grey matter applies separately for frontal and non-frontal lobes; that hyperscaling of both neocortex and frontal lobe to rest of brain is mainly due to frontal white matter; and that changes in frontal (but not non-frontal) white matter volume are associated with changes in rest of brain and basal ganglia, a group of subcortical nuclei functionally linked to ‘executive control’. Results suggest a central role for frontal white matter in explaining neocortex and frontal lobe hyperscaling, brain size variation and higher neural structural connectivity in anthropoids.


Previous research has shown that the neocortex (or ‘neopallium’) has played a central role in the evolution of brain size and architecture in primates and other mammals [1]. Primates vary greatly in terms of overall brain size, and most of the variation among species is explained by the relative size increase (or ‘hyperscaling’) of the neopallium in larger primate brains [2], [3]. Overall, evolutionary changes in the size of neopallium and other brain structures are expressed in three ways: (1) a disproportionate increase of white versus grey matter; (2) a disproportionate increase of particular structures or areas; and (3) evolutionary changes in neural circuits of interconnecting structures.

The neopallium comprises both neocortical grey matter (consisting of neural cell bodies, their dendrites and parts of their axons as well as glial cells, and responsible for synaptic activity and the processing of information) and white matter (mostly consisting of bundles of myelinated or non-myelinated axons connecting cortical regions (i.e. grey matter) to each other and to subcortical structures). Barton and Harvey [3] found that the hyperscaling of the neopallium in primates is entirely due to white matter, with grey matter scaling only isometrically (in direct proportion with the rest of the brain). Zhang & Sejnowski [4] have shown that the hyperscaling of the neopallial white matter relative to grey matter occurs in all mammals, generalizing the finding of Allman [1] who had already described a scaling factor of about 4/3 between white and grey matter in primates. In larger brains, due to the larger distance between neurons and the length of the axons connecting them, longer axons need to be thicker and more heavily myelinated in order to maintain optimal conduction times, causing white matter volume to hyperscale with cortical volume [2], [4], [5], [6], [7]. In summary, because white matter consists of connective fibers linking neocortical regions to each other and to subcortical structures, it is of critical importance when investigating connectivity in the brain.

The evolution of primate brain size has also been associated with changes in the relative size of particular brain components. In primates, the neopallial frontal lobe (meaning frontal grey plus frontal white matter) has received particular attention in debates over the neuroanatomical foundations of ‘intelligence’. The frontal lobe has been linked to ‘executive functions’ [8], ‘theory of mind’ abilities [9], [10], [11] and ‘higher cognitive functions’ [12]. Semendeferi et al. [13], [14] collected frontal lobe volumetric data for humans, five other apes and two monkeys and showed that the frontal lobe hyperscales with the rest of neopallium; thus, a larger brain tends to exhibit a relatively larger neopallium, which in turn exhibits a relatively larger frontal lobe. Therefore, these studies suggest that the evolution of larger brains in primates is associated with a disproportionate enlargement in the frontal lobe, the neocortical region responsible for the processing of higher cognitive functions. The roles of white matter volume and frontal lobe volume in brain size variation suggest that they may have a joint effect in explaining variation in brain size. If the primate frontal lobe performs a ‘central executive’ or integrative function due to its neural connections to other brain regions, its relative enlargement is expected to be caused by a specific increase in the volume of its connective tissue (i.e. white matter). Schoenemann et al. [15] have demonstrated that both the total size of prefrontal cortex and specifically prefrontal white matter are exceptionally enlarged in humans in comparison with other hominoids, whereas prefrontal grey matter showed no significant difference. Their results have, however, been questioned by Sherwood et al [16] who pointed to the difficulties associated with identifying the prefrontal on the basis of gross anatomical rather than cytoarchitectonic features.

Because the frontal lobe is considered to exert executive type control over other structures it is expected to show significant connectivity to other brain structures. Brain structures are commonly known to work as part of functional systems in which interconnected structures work together in processing particular types of information. The ‘mosaic’ hypothesis of brain evolution suggests that selective pressures act directly on these interconnected neural circuits rather than on single brain areas [3] suggesting investigations into primate brain evolution would benefit from focussing on neural circuits of interconnected brain structures rather than on single structures in isolation [17]. Semendeferi et al. [14] indicated that since the total size of the human frontal lobe is not exceptionally large for great ape standards (i.e. the human frontal lobe is as large as predicted for a great ape with our brain size), our higher cognitive functions might be explained by the possibility that neural circuits are more richly interconnected within the frontal sectors themselves and/or between those sectors and other brain regions. They were, however, unable to test this hypothesis using their database. Of particular importance in the context of the frontal lobe is the cortico-basal ganglia neural circuit that interconnects mainly prefrontal areas to the basal ganglia [18], [19], [20], [21], [22], [23], [24], [25], [26] and is functionally associated to elements of ‘executive control’: control and selection of actions [27]; guidance of reward related behaviour [28], [29]; conscious, goal-directed behaviour [30], [31], [32]; and learning of complex motor skill behaviour [33], [34], [35].

The studies above have pointed to white matter volume and frontal lobe volume in the neopallium as crucial to the understanding of brain size variation in primates and suggest two areas of further improvement: First, it would be important to investigate the specific contribution of the frontal white, non-frontal white, frontal grey and non-frontal grey to brain size variation, which has not yet been possible due to the non-availability of data. Second, it would be relevant to empirically test the hypothesis that frontal (but not non-frontal) white matter volume across species should correlate with the volume of structures such as the basal ganglia.

In this study, new volumetric measurements of the neopallium and neopallial frontal, non-frontal, white and grey matter were obtained for 18 anthropoid primates (excluding humans) (see Table 1). The advantage of the current dataset is twofold: first, volumes are available for frontal grey, frontal white, non-frontal grey and non-frontal white matter in all species (previous studies either combined grey and white matter into one volumetric measurement [13], [14] or measured only the former [36]); Second, measurements were taken from the same specimens for which volumetric data are available for a wide range of other brain structures including the basal ganglia [37]. In the following, we investigate whether the neopallial scaling law between white and grey matter also applies separately for the frontal and non-frontal lobe; whether the hyperscaling of the frontal lobe is due to white or grey matter, or both; and how frontal (and non-frontal) grey and white matter scale to the basal ganglia and to the rest of the brain.

Table 1. Volumetric measurements of neopallium and frontal lobe white and grey matter.


Scaling Law of White Versus Grey Matter

In terms of the scaling relationship between white and grey matter, results of PGLS regressions indicate a scaling coefficient or regression slope of b = 1.180 (95% C.I. = 1.062–1.298, α = 15.5, N = 18) when considering the neopallium as a whole. For the frontal lobe, the slope is b = 1.180 (95% C.I. = 1.082–1.278, α = 15.5, N = 18) and in the non-frontal, b = 1.170 (95% C.I. = 1.052–1.288, α = 15.5, N = 18).

Hyperscaling of Neocortex, White Matter and Frontal Lobe

Neocortical white matter hyperscales with the rest of the brain (calculated as brain volume minus neopallium volume), rendering a slope of b = 1.210 (95% C.I. = 1.034–1.386, α = 15.5, N = 18). Overall neocortical volume and neocortical grey matter, however, scale isometrically to the rest of the brain (b = 1.090, 95% C.I. = 0.972–1.208, α = 15.5, N = 18; b = 1.030, 95% C.I. = 0.932–1.128, α = 15.5, N = 18).

When the neopallium is divided into frontal and non-frontal lobe, frontal lobe volumes hyperscale with the rest of the brain (brain minus frontal lobe; b = 1.190, 95% C.I. = 1.151–1.229, α = 15.5, N = 18; Table 2). In contrast, the non-frontal lobe scales isometrically to the rest of the brain (brain minus non-frontal lobe; b = 0.990, 95% C.I. = 0.951-1.029, α = 6.2, N = 18). Furthermore, both frontal lobe white and grey matter significantly hyperscale with the rest of the brain (brain minus frontal lobe; b = 1.330, 95% C.I. = 1.232–1.428, α = 15.5, N = 18; b = 1.110, 95% C.I. = 1.051–1.169, α = 15.5, N = 18). For the non-frontal lobe, white matter hyperscales while grey matter hyposcales to the rest of the brain (brain minus frontal lobe; b = 1.120, 95% C.I. = 1.002–1.238, α = 4.77, N = 18; b = 0.930, 95% C.I. = 0.891–0.969, α = 7.04, N = 18).

Correlations with Basal Ganglia and Rest of Brain

A partial regression of both whole frontal lobe volume and frontal white matter with basal ganglia (controlling for the effects of brain minus frontal lobe and basal ganglia) yield significant positive associations (b = 0.260, 95% C.I. = 0.064–0.456, R2 = 0.442, α = 15.5, N = 11; b = 0.800, 95% C.I. = −0.004–1.604, R2 = 0.321, α = 2.1, N = 10), while no such association exists with whole non-frontal lobe volume or non-frontal white matter (b = 0.100, 95% C.I. = −0.214–0.414, R2 = 0.044, α = 15.5, N = 11; b = −0.020, 95% C.I. = −1.137–1.097, R2 = 0.000, α = 6.240, N = 11) (Table 3). Furthermore, a partial regression of both whole frontal lobe volume and frontal white matter indicate significant associations with the rest of the brain (brain minus neopallium) when the effects of the non-frontal lobe are controlled for (b = 0.360, 95% C.I. = 0.086–0.634, R2 = 0.283, α = 15.5, N = 18; b = 0.730, 95% C.I. = 0.201–1.259, R2 = 0.338, α = 15.5, N = 16), while whole non-frontal lobe volume and non-frontal white matter indicate no such association (after controlling for the effects of frontal lobe volume: b = 0.020, 95% C.I. = −0.313–0.353, R2 = 0.001, α = 15.5, N = 18; b = −0.360, 95% C.I. = −1.007–0.287, R2 = 0.069, α = 2.1, N = 18) (Table 3).

Table 3. Correlations between neocortical substructures and basal ganglia and rest of brain.

When considering grey matter, frontal grey matter correlates with the rest of the brain (after controlling for the effects of non-frontal lobe size: b = 0.480, 95% C.I. = 0.186–0.774, R2 = 0.400, α = 15.5, N = 18) but not with basal ganglia (b = 0.090, 95% C.I. = −0.302–0.482, R2 = 0.025, α = 1.1, N = 11), while non-frontal grey matter does not indicate associations to either the rest of the brain (after controlling for the effects of frontal lobe size) or basal ganglia (b = 0.020, 95% C.I. = −0.114–0.514, R2 = 0.093, α = 15.5, N = 18; b = 0.090, 95% C.I. = −.106–0.286, R2 = 0.077, α = 15.5, N = 18).


The analyses presented here corroborate current scaling models of connectivity in brains of varying size [38], [39]. Using new data for anthropoid primates, we estimated a scaling parameter close to the suggested 4/3 [1], [4] between the entire neopallial white and grey matter, and also between white and grey matter separately in the frontal and the non-frontal lobe. Although the scaling parameter indicated in the current study is slightly lower than the suggested 4/3 (around 3.5/3), results show that the hyperscaling of white matter in the neopallium is consistent in both the frontal and non-frontal lobe and thus not due to a disproportionate effect in either lobe. Furthermore, results indicate that the hyperscaling of neopallium to the rest of the brain is mainly due to the effect of white matter, corroborating previous research [3].

Considering the possible separate contribution of the frontal and non-frontal lobe to brain size variation in anthropoids, our results are in line with studies indicating that the frontal lobe hyperscales with the rest of the brain [13], [14], and additionally show that the non-frontal lobe scales in direct proportion to the rest of the brain suggesting that the hyperscaling of the neocortex is due to the effects of the frontal lobe only. A combined effect of frontal lobe and white matter was also identified. The hyperscaling of the frontal lobe with the rest of the brain is mainly due to the effect of white matter (b = 1.33 for white matter; b = 1.11 for grey matter) and frontal white matter (but not non-frontal white matter) is found to correlate significantly with the rest of the brain (variation in the volume of frontal white matter explains 33.8% of the variation in the size of the rest of the brain after controlling for the effects of non-frontal lobe size, while volume changes in non-frontal white matter explain only 6.9% after controlling for the effects of frontal lobe size). Together, these results suggest that frontal white matter can be considered a principal component in explaining variation in brain size.

We also found a significant and exclusive correlation between variation in frontal white matter volume and variation in basal ganglia volume. Variation in frontal white matter explains 32.8% of the variation in the size of basal ganglia while non-frontal white matter explains 0%. This result provides comparative evidence for the cortico-basal ganglia neural circuit [19], [21] that is thought to underlie ‘executive functions’ such as learning [33], [34], [35] and suggests that it is mainly the frontal lobe connecting to the basal ganglia (making it a ‘frontal cortico-basal ganglia’ neural circuit) corroborating neuroanatomical studies showing that the main input into the basal ganglia comes from prefrontal areas [20], [21], [22].

We have presented novel volumetric measurements of frontal and non-frontal white and grey matter in 18 anthropoid primates and indicated that frontal white matter is a principal component in explaining variation in brain size and hyperscaling or relative enlargement of the neocortex in anthropoid evolution. We have also provided comparative neuroanatomical evidence for the presence of a frontal cortico-basal ganglia neural circuit (associated to ‘higher executive functions’ such as learning) by showing that only variation in frontal white matter size explains variation in the size of the basal ganglia. This finding implies that frontal white matter is at the heart of increased structural connectivity associated to brain enlargement and higher cognitive capacities. Future studies should focus on further delineation of frontal substructures based on cytoarchitectonic and including more taxa into the analysis (possibly going beyond the delineation of prefrontal areas proposed by Semendeferi et al.[40] and overcoming the contention of demarcation based on gross anatomical structures by Schoenemann et al.[15]).

Materials and Methods

Brains and Volumetric Measurements

We examined brains from 18 anthropoid species, all of which are housed at the C. & O. Vogt Institute for Brain Research (Heinrich-Heine-University, Düsseldorf, Germany). The sample used in this study is part of a widely used collection in primate comparative neuroanatomy [3], [41], [42]. The collection consists of serial, Nissl-stained brain sections. The traditional procedure to delineate volumes from histological sections was used [Cavalieri method: 43,44]. Systematic random samples from each brain were taken, the position of the first section was chosen randomly and subsequent sections were chosen based on a regular sampling interval. 20 or more sections per brain (indicated by Zilles et al. [45] as a sufficient number to determine the volume of extremely irregular formed bodies) were used and digitized with a standard office flatbed scanner at 800dpi. Surface area was computed on each section and volumes were estimated by summing the areas across sections and multiplying by slice thickness. Left hemisphere data was computed and inferences were made for both hemispheres. Overall neopallial frontal lobe volumes of white and grey matter were computed and non-frontal volumes were defined accordingly. When a regularly spaced section was missing or of insufficient quality, an adjacent section was used. Shrinkage ratios were available for all specimens used and applied to estimate fresh volumes.

Demarcation of Boundaries


The boundary between white matter and all ventromedial non-neocortical structures was manually drawn making use of a brain atlas [46]. Neocortical volumes computed in this study were compared to the volumes of the same specimens as measured by Stephan et al. [37] (unpublished specimen specific data provided by Dr. H. Frahm). The average discrepancy between the two measures is 0.29% with a standard deviation of 1.36%. The highest difference was found for Pan troglodytes (3.08%).

Frontal lobe.

The boundary between the frontal and non-frontal lobe was delineated using cytoarchitectonic criteria. On its posterior end, the frontal lobe is defined as that part of the neopallium anterior to the boundary between the primary motor cortex (area 4) and the somatosensory cortex (area 3) [47]. Apart from the boundary of the primary visual cortex (area V1) in primates, this is the most easily recognizable and reliable cytoarchitectonic border in the neopallium [48], and has also been identified electrophysiologically in various primate species [49], [50], [51]. In anthropoid primates, the motor-somatosensory border is located in the fundus of the central sulcus [47], [52], [53], [54], [55]. Six cytoarchitectonic features change on the motor-somatosensory border [e.g., 36,47] and were used in the current analysis (for area 4 and area 3 respectively): cell-shape (pyramidal and elongated Betz cells versus absence of Betz cells); cell density (low versus high); cell size (large versus small); granular layer IV (absent versus present); border between white and grey matter (diffuse versus sharp); lamination (diffuse versus sharp). The posterior border of the frontal lobe (the motor-somatosensory border) was prolonged in a straight line transversely to the most posterior segment of the ventromedial end of frontal lobe white matter. The demarcation then follows the boundary between the frontal lobe white matter and the medial non-neocortical areas excluded from the analysis until above the corpus callosum (which is considered the posterior limit of the frontal lobe white matter on the medial surface). Although Semendeferi et al. [13] and Semendeferi et al. [14] obtained frontal lobe measurements from different specimens using a different delineation procedure (based on sulcal patterns using MRI-images), the ratios of frontal lobe to overall neopallial volume as obtained in these studies correspond to those obtained in the current study (see appendix 1).

Statistical Analysis

Phylogenetic controls.

We used phylogenetically controlled generalized least squares analysis (PGLS) to compute comparative correlations between the relative sizes of brain structures across taxa. This procedure is preferred over the traditionally more popular independent contrasts (IC [56]) approach because it allows the assumed model of evolution to deviate from Brownian Motion by using a maximum likelihood estimate of a parameter (α) that indicates the strength of the evolutionary constraint [57]. This approach is more general than IC because its results are identical to IC results when the estimated alpha parameter is zero. The phylogeny was taken from Smith & Cheverud [58] and supplemented with data for Pan paniscus from Purvis [59]. PGLS analyses were executed in COMPARE 4.6b [60].

Measures of ‘relative’ brain structure co-evolution.

The ‘relative’ sizes of brain structures were computed as residuals of a regression of a particular structure to the brain minus that structure. When the comparative correlation between the relative sizes of two brain structures was computed, a regression was computed using residuals of a regression of each structure to the brain minus both structures. E.g., for the comparative correlation between frontal lobe size and basal ganglia size, the independent variable constitutes the residuals of a regression of frontal lobe size to brain minus frontal lobe and basal ganglia size, the dependent variable comprises the residuals of a regression of basal ganglia size to brain minus frontal lobe and basal ganglia size. This approach ensures that two brain areas are always compared using the same control variable (so that the comparative platform is identical).


We thank H. Frahm for useful comments, advice and making specimen specific data from Stephan et al. [37] available. We also thank two anonymous referees for insightful comments that have significantly improved a previous version of the paper.

Author Contributions

Conceived and designed the experiments: JBS AS KZ. Performed the experiments: JBS. Analyzed the data: JBS LV. Contributed reagents/materials/analysis tools: JBS AS KZ. Wrote the paper: JBS LV.


  1. 1. Allman JM (1999) Evolving Brains. New York: Freeman.
  2. 2. Kaas JH (2006) Evolution of the neocortex. Current Biology 16: R910–R914.
  3. 3. Barton RA, Harvey PH (2000) Mosaic evolution of brain structure in mammals. Nature 405: 1055–1058.
  4. 4. Zhang K, Sejnowski TJ (2000) A universal scaling law between gray matter and white matter of cerebral cortex. Proceedings of the National Academy of Sciences of the United States of America 97: 5621–5626.
  5. 5. Mitchison G (1992) Axonal trees and cortical architecture. Trends in Neurosciences 15: 122–126.
  6. 6. Van Essen DC (1997) A tension-based theory of morphogenesis and compact wiring in the central nervous system. Nature 385: 313–318.
  7. 7. Chklovskii D, Stevens CF (2000) Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.
  8. 8. Kolb B, Wilshaw IQ (1996) Fundamentals of human neuropsychology. San Francisco, CA: Freeman.
  9. 9. Siegal M, Varley R (2002) Neural systems involved in ‘theory of mind’. Nature Reviews Neuroscience 3: 463–471.
  10. 10. Adolphs R (2003) Cognitive neuroscience of human social behaviour. Nature Reviews Neuroscience 4: 165–178.
  11. 11. Adolphs R (2009) The Social Brain: Neural Basis of Social Knowledge. Annual Review of Psychology 60: 693–716.
  12. 12. Smith EE, Jonides J (1999) Neuroscience - Storage and executive processes in the frontal lobes. Science 283: 1657–1661.
  13. 13. Semendeferi K, Damasio H, Frank R (1997) The evolution of the frontal lobes: A volumetric analysis based on three-dimensional reconstructions of magnetic resonance scans of human and ape brains. Journal of Human Evolution 32: 375–388.
  14. 14. Semendeferi K, Lu A, Schenker N, Damasio H (2002) Humans and great apes share a large frontal cortex. Nature Neuroscience 5: 272–276.
  15. 15. Schoenemann PT, Sheehan MJ, Glotzer LD (2005) Prefrontal white matter volume is disproportionately larger in humans than in other primates. Nature Neuroscience 8: 242–252.
  16. 16. Sherwood CC, Holloway RL, Semendeferi K, Hof PR (2005) Is prefrontal white matter enlargement a human evolutionary specialization? Nature Neuroscience 8: 537–538.
  17. 17. Whiting BA, Barton RA (2003) The evolution of the cortico-cerebellar complex in primates: anatomical connections predict patterns of correlated evolution. Journal of Human Evolution 44: 3–10.
  18. 18. Wise SP, Murray EA, Gerfen CR (1996) The frontal cortex basal ganglia system in primates. Critical Reviews in Neurobiology 10: 317–356.
  19. 19. Alexander GE, Delong MR, Strick PL (1986) Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review of Neuroscience 9: 357–381.
  20. 20. Middleton FA, Strick PL (2002) Basal-ganglia ‘projections’ to the prefrontal cortex of the primate. Cerebral Cortex 12: 926–935.
  21. 21. Middleton FA, Strick PL (2000) Basal ganglia output and cognition: Evidence from anatomical, behavioral, and clinical studies. Brain and Cognition 42: 183–200.
  22. 22. Middleton FA, Strick PL (2001) A Revised Neuroanatomy of Frontal-Subcortical Circuits. In: Lichter DG, Cummings JL, editors. Frontal-Subcortical Circuits in Psychiatric and Neurological Disorders. New York: Guilford Publications, Inc.
  23. 23. Cavada C, Goldman-Rakic PS (1991) Topographic segregation of corticostriatal projections from posterior parietal subdivisions in the macaque monkey. Neuroscience 42: 683–696.
  24. 24. Goldman PS, Nauta WJH (1977) Intricately patterned prefronto-caudate projection in rhesus-monkey. Journal of Comparative Neurology 171: 369–385.
  25. 25. Goldman-Rakic PS (1984) Modular organization of prefrontal cortex. Trends in Neurosciences 7: 419–424.
  26. 26. Johnson TN, Rosvold HE, Mishkin M (1968) Projections from behaviorally-defined sectors of prefrontal cortex to basal ganglia, septum and diencephalon of monkey. Experimental Neurology 21: 20–&.
  27. 27. Gurney K, Prescott TJ, Redgrave P (2001) A computational model of action selection in the basal ganglia. I. A new functional anatomy. Biological Cybernetics 84: 401–410.
  28. 28. Schultz W, Tremblay L, Hollerman JR (2000) Reward processing in primate orbitofrontal cortex and basal ganglia. Cerebral Cortex 10: 272–283.
  29. 29. Schultz W, Tremblay L, Hollerman JR (1998) Reward prediction in primate basal ganglia and frontal cortex. Neuropharmacology 37: 421–429.
  30. 30. Weinberger DR (1993) A connectionist approach to the prefrontal cortex. Journal of Neuropsychiatry and Clinical Neurosciences 5: 241–253.
  31. 31. Schultz W, Romo R (1992) Role of primate basal ganglia and frontal cortex in the internal generation of movements. 1. Preparatory activity in the anterior striatum. Experimental Brain Research 91: 363–384.
  32. 32. Hollerman JR, Tremblay L, Schultz W (2000) Involvement of basal ganglia and orbitofrontal cortex in goal-directed behavior. Cognition, Emotion and Autonomic Responses: The Integrative Role of the Prefrontal Cortex and Limbic Structures 126: 193–215.
  33. 33. Doyon J, Gaudreau D, Laforce R, Castonguay M, Bedard PJ, et al. (1997) Role of the striatum, cerebellum, and frontal lobes in the learning of a visuomotor sequence. Brain and Cognition 34: 218–245.
  34. 34. Doyon J, Penhune V, Ungerleider LG (2003) Distinct contribution of the cortico-striatal and cortico-cerebellar systems to motor skill learning. Neuropsychologia 41: 252–262.
  35. 35. Exner C, Koschack J, Irle E (2002) The differential role of premotor frontal cortex and basal ganglia in motor sequence learning: Evidence from focal basal ganglia lesions. Learning & Memory 9: 376–386.
  36. 36. Bush EC, Allman JM (2004) The scaling of frontal cortex in primates and carnivores. Proceedings of the National Academy of Sciences of the United States of America 101: 3962–3966.
  37. 37. Stephan H, Frahm H, Baron G (1981) New and revised data on volumes of brain structures in insectivores and primates. Folia Primatologica 35: 1–29.
  38. 38. Hofman MA (1989) On the evolutoin and geometry of the brain in mammals. Progress in Neurobiology 32: 137–158.
  39. 39. Ringo JL (1991) Neuronal interconnection as a function of brain size. Brain Behavior and Evolution 38: 1–6.
  40. 40. Semendeferi K, Armstrong E, Schleicher A, Zilles K, Van Hoesen GW (2001) Prefrontal cortex in humans and apes: A comparative study of area 10. American Journal of Physical Anthropology 114: 224–241.
  41. 41. Dunbar RIM (2003) The social brain: Mind, language, and society in evolutionary perspective. Annual Review of Anthropology 32: 163–181.
  42. 42. Joffe TH, Dunbar RIM (1997) Visual and socio-cognitive information processing in primate brain evolution. Proceedings of the Royal Society of London Series B-Biological Sciences 264: 1303–1307.
  43. 43. Cavalieri B (1635) Geometria degli Indivisibili. Torino: Unione Tipografico-Editrice Torinese.
  44. 44. Howard CV, Reed MG (1998) Unbiased Stereology. Three-dimensional measurement in microscopy. New York: Springer-Verlag.
  45. 45. Zilles K, Schleicher A, Pehlemann FW (1982) How many sections must be measured in order to reconstruct the volume of a structure using serial sections? Microscopica Acta 86: 339–346.
  46. 46. Stephan H, Baron G, Schwerdtfeger WK (1980) The Brain of the Common Marmoset: A Stereotaxic Atlas. Berlin, Heidelberg, New York: Springer-Verlag.
  47. 47. Sherwood CC, Holloway RL, Erwin JM, Schleicher A, Zilles K, et al. (2004) Cortical orofacial motor representation in old world monkeys, great apes, and humans - I. Quantitative analysis of cytoarchitecture. Brain Behavior and Evolution 63: 61–81.
  48. 48. Brodmann K (1994) Brodmann's localization in the cerebral cortex. Garey LG, translator. London: Smith-Gordon.
  49. 49. Fitzpatrick KA, Carlson M, Charlton J (1982) Topography, cytoarchitecture, and sulcal patterns in primary somatic sensory cortex (SMI) of the prosimian primate, Perodictues potto. Journal of Comparative Neurology 204: 296–310.
  50. 50. Stepniewska I, Preuss TM, Kaas JH (1993) Architectonics, somatotopic organization, and ipsilateral cortical connections of the primary motor area (M1) of owl monkeys. Journal of Comparative Neurology 330: 238–271.
  51. 51. Fogassi L, Gallese V, Gentilucci M, Luppino G, Matelli M, et al. (1994) The frontoparietal cortex of the prosimian Galago - patterns of cytochrome-oxidase activity and motor maps. Behavioural Brain Research 60: 91–113.
  52. 52. Sanides F, Krishnamurti A (1967) Cytoarchitectonic sub-divisions of sensorimotor and prefrontal regions and of bordering insular and limbic fields in slow ioris (Nyctice-bus coucang coucang). J Hirnforsch 9: 225–252.
  53. 53. Geyer S, Matelli M, Luppino G, Zilles K (2000) Functional neuroanatomy of the primate isocortical motor system. Anatomy and Embryology 202: 443–474.
  54. 54. Rivara CB, Sherwood CC, Bouras C, Hof PR (2003) Stereologic characterization and spatial distribution patterns of Betz cells in the human primary motor cortex. Anatomical Record Part a-Discoveries in Molecular Cellular and Evolutionary Biology 270A: 137–151.
  55. 55. Sherwood CC, Lee PWH, Rivara CB, Holloway RL, Gilissen EPE, et al. (2003) Evolution of specialized pyramidal neurons in primate visual and motor cortex. Brain Behavior and Evolution 61: 28–44.
  56. 56. Felsenstein J (1985) Phylogenies and the comparative method. American Naturalist 125: 1–15.
  57. 57. Martins EP, Hansen TF (1997) Phylogenies and the comparative method: A general approach to incorporating phylogenetic information into the analysis of interspecific data. American Naturalist 149: 646–667.
  58. 58. Smith RJ, Cheverud JM (2002) Scaling of sexual dimorphism in body mass: A phylogenetic analysis of Rensch's rule in primates. International Journal of Primatology 23: UNSP 0164–0291/0102/1000–1095/0160.
  59. 59. Purvis A (1995) A Composite Estimate of Primate Phylogeny. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 348: 405–421.
  60. 60. Martins EP (2004) COMPARE, version 4.6b. Computer programs for the statistical analysis of comparative data. Distributed by the author at In: Department of Biology IU, editor. Bloomington IN.
  61. 61. MacLeod CE, Zilles K, Schleicher A, Rilling JK, Gibson KR (2003) Expansion of the neocerebellum in Hominoidea. Journal of Human Evolution 44: 401–429.