Proc. R. Soc. B (2010) 277, 1011–1020

on February 24, 2010rspb.royalsocietypublishing.orgDownloaded from

* Autho

Electron1098/rsp

doi:10.1098/rspb.2009.1831

Published online 2 December 2009

ReceivedAccepted

Inhibitory interneurons of the humanprefrontal cortex display conserved

evolution of the phenotype andrelated genes

Chet C. Sherwood1,*, Mary Ann Raghanti2, Cheryl D. Stimpson1,

Muhammad A. Spocter1, Monica Uddin3, Amy M. Boddy4,

Derek E. Wildman4, Christopher J. Bonar5, Albert H. Lewandowski5,

Kimberley A. Phillips6, Joseph M. Erwin7 and Patrick R. Hof 8,9

1Department of Anthropology, The George Washington University, Washington, DC 20052, USA2Department of Anthropology and School of Biomedical Sciences, Kent State University, Kent, OH 44242, USA3Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA

4Center for Molecular Medicine and Genetics, Wayne State University School of Medicine,

Detroit, MI 48201, USA5Cleveland Metroparks Zoo, Cleveland, OH 44109, USA

6Department of Psychology, Trinity University, San Antonio, TX 78212, USA7Department of Biomedical Sciences and Pathobiology, Virginia-Maryland Regional College of Veterinary

Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA 24036, USA8Department of Neuroscience, Mount Sinai School of Medicine, New York, NY 10029, USA

9New York Consortium in Evolutionary Primatology, New York, NY, USA

Inhibitory interneurons participate in local processing circuits, playing a central role in executive cog-

nitive functions of the prefrontal cortex. Although humans differ from other primates in a number of

cognitive domains, it is not currently known whether the interneuron system has changed in the course

of primate evolution leading to our species. In this study, we examined the distribution of different

interneuron subtypes in the prefrontal cortex of anthropoid primates as revealed by immunohistochem-

istry against the calcium-binding proteins calbindin, calretinin and parvalbumin. In addition, we tested

whether genes involved in the specification, differentiation and migration of interneurons show evi-

dence of positive selection in the evolution of humans. Our findings demonstrate that cellular

distributions of interneuron subtypes in human prefrontal cortex are similar to other anthropoid pri-

mates and can be explained by general scaling rules. Furthermore, genes underlying interneuron

development are highly conserved at the amino acid level in primate evolution. Taken together,

these results suggest that the prefrontal cortex in humans retains a similar inhibitory circuitry to

that in closely related primates, even though it performs functional operations that are unique to

our species. Thus, it is likely that other significant modifications to the connectivity and molecular

biology of the prefrontal cortex were overlaid on this conserved interneuron architecture in the

course of human evolution.

Keywords: language; theory of mind; prefrontal cortex; chimpanzee; great ape

1. INTRODUCTIONDirect comparison of the human genome with that of

other species has led to important insight into the mol-

ecular changes underlying human brain evolution

(Dorus et al. 2004; Haygood et al. 2007; Uddin et al.

2008). Understanding how genetic evolution relates to

distinctive anatomical specializations of the human

brain, however, requires a more complete description of

the human neural phenotype in comparison to our closest

relatives, especially chimpanzees and other great apes.

r for correspondence (sherwood@gwu.edu).

ic supplementary material is available at http://dx.doi.org/10.b.2009.1831 or via http://rspb.royalsocietypublishing.org.

9 October 200911 November 2009 1011

Comparative studies of mRNA transcript levels using

high-throughput methodologies, such as microarrays,

have provided information about human-specific differ-

ences in gene expression and networks of co-expression

in the brain (Caceres et al. 2003; Khaitovich et al. 2004;

Uddin et al. 2004; Oldham et al. 2006), shedding light

on physiological pathways and molecular mechanisms

that have been important in human brain evolution. Yet,

the results of such microarray studies have been difficult

to link to species-specific variation in neuronal connec-

tivity. In part, this is due to complications associated

with interpreting species differences in overall levels of

mRNA within regions of the brain that are composed of

heterogeneous population of cells, distributed across

functionally distinct layers (Geschwind 2000). Because

This journal is q 2009 The Royal Society

1012 C. C. Sherwood et al. Evolution of interneurons in humans

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of these challenges, a parallel line of inquiry is needed to

elucidate how homologous neocortical areas vary between

humans and other species in terms of neuronal morph-

ology, distribution of neuron types, expression of

receptors and innervation by different neurotransmitter

systems (Preuss 2007; Sherwood et al. 2008).

It has been proposed that human cognition is

uniquely distinguished by traits such as an enhanced

ability to represent the mental states of oneself and

others in social cognition (Herrmann et al. 2007) and

to employ a hierarchical recursive syntactic organization

in language (Hauser et al. 2002). However, it is not

known to what extent the cerebral cortex carries out

these complex operations using machinery that is evolu-

tionarily conserved versus specialized. Beyond the more

than threefold enlargement in neocortical size during

human evolution (Holloway et al. 2004), how has its

architecture and connectivity changed to support

human-specific functions? One possibility, raised initially

by Ramon y Cajal (1923), is that human brain evolution

was associated with an increased number and diversity of

‘short-axon’ interneurons. Inhibitory GABAergic

interneurons are fundamental to the distribution of

thalamic input in the neocortex and comprise the local

circuitry that influences the activity of pyramidal cells

(Hendry 1987). The diverse cortical interneuron

population can be subdivided into subtypes on the

basis of morphology, biochemical phenotype, post-

synaptic target, developmental origin and expression of

transcription factors (Zaitsev et al. 2009). The calcium-

binding proteins, calbindin D-28k (CB), calretinin

(CR) and parvalbumin (PV), are particularly useful

markers for revealing the architecture of the cortical

interneuron network because they are colocalized with

GABA in 90–95% of interneurons and occur in

morphologically and physiologically distinct subtypes

(DeFelipe 1997). One important role of interneurons,

particularly PV-immunoreactive (ir) fast-spiking sub-

types, is to control the phase of rhythmic oscillations

across ensembles of pyramidal neurons (Sohal et al.

2009). Such coordination of spike timing is critical to

the temporal precision underlying processes ranging

from perception to cognition (Bartos et al. 2007).

We used stereological methods to quantify the density

of CB-, CR- and PV-ir interneurons within the cerebral

cortex from a diversity of anthropoid primates, focusing

special attention on prefrontal regions that are involved

in cognitive abilities such as working memory

(Brodmann’s area 9), mental state attribution (area 32)

and language (area 44) (Gallagher & Frith 2003;

Friederici et al. 2006), relative to the primary motor

cortex (area 4). We hypothesized that interneuron pro-

portions might be selectively increased in prefrontal

areas that underlie our species’ cognitive specializations

(areas 9, 32 and 44). In addition, we aligned vertebrate

orthologues of 20 genes that are important for the regu-

lation of neocortical interneuron development and

analysed their protein-coding sequence for evidence of

positive Darwinian selection along the human lineage.

Through examination of the phenotype and related

genes, we tested whether there is evidence of evolutionary

modification of the prefrontal interneuron system of

humans, or alternatively, whether this cellular network

is conserved.

Proc. R. Soc. B (2010)

2. MATERIAL AND METHODS(a) Specimens, preparation and

immunohistochemical staining

Formalin-fixed brain samples representing 23 anthropoid

primate species, from 51 individuals, were used for the phe-

notype analyses (see the electronic supplementary material).

A 1 : 10 series of 40 mm-thick sections was stained for Nissl

substance with a solution of 0.5 per cent cresyl violet. Immuno-

histochemistry was performed on adjacent 1 : 20 series of

sections with monoclonal antibodies against PV (dilution

1 : 10 000) and CB (dilution 1 : 8000), or with a polyclonal

antibody against CR (dilution 1 : 10 000; Swant, Bellinzona,

Switzerland) (see the electronic supplementary material).

(b) Quantification of neuron densities

All quantitative analyses were restricted to layers II and III

because CB- and CR-ir interneurons predominate in the

superficial layers of the primate neocortex (Hof et al.

1999). Our previous studies of species differences in quanti-

tative cyto- and chemoarchitecture of the cerebral cortex in

anthropoid primates provide detailed descriptions of the histo-

logical criteria used to identify cortical areas (Sherwood

et al. 2006; Raghanti et al. 2008b). Analyses of allometric scal-

ing and variance partitioning across the 23 species used

neuron densities in layers II and III of dorsolateral prefrontal

cortex (DLPFC), corresponding to area 9. We also analysed

the percentage of the total neuron population in layers II

and III that contains each calcium-binding protein in

humans, chimpanzees and macaque monkeys (n ¼ 6 each

species) from a larger number of frontal areas, including pri-

mary motor cortex (area 4 in the region of the hand

representation), DLPFC (area 9), anterior paracingulate

cortex (area 32) and inferior prefrontal cortex (area 44) (see

electronic supplementary material for more details).

Total neuron density in layers II and III was quantified

from adjacent series of Nissl-stained sections and was used

as a reference variable for analyses of interneuron subtype

densities. Calcium-binding protein-ir interneuron subtypes

were counted separately in quantitative analyses. Because

we were only interested in the distribution of interneurons,

we did not count lightly stained CB-ir somata with distinct

apical dendrites or pyramidal morphology. Densities of neur-

ons within layers II and III were estimated using an unbiased

stereological design employing the optical disector technique

combined with fractionator sampling (Mouton 2002). The

stereological analyses resulted in sampling an average of

143 counting frames per region in each individual for each

cell type, with a total of 59 472 counting frames investigated

and 24 557 neurons sampled (see the electronic supplemen-

tary material). It should be noted that our estimates of

densities for all neuron types in humans were considerably

higher than expected based on previous literature and in

comparison to the other species in this study. It is likely

that different tissue handling procedures at the Northwestern

University Alzheimer’s Disease Center Brain Bank contribu-

ted to this effect. Nonetheless, because of our concern about

uncontrolled and unknown degrees of tissue shrinkage for

the entire sample, all subsequent statistical analyses only con-

sidered interneuron subtype density relative to total neuron

density within the same individual, i.e. calculated as percen-

tages or as regressions of interneuron density against total

neuron density. Thus, equal amounts of fixation and

histological artefact are included in both independent

and dependent variables. Consideration of the effect of

Leontopithecus rosalia (2)

Saguinus oedipus (2)

Cebus apella (1)

Saimiri boliviensis (1)

Aotus trivirgatus (1)

Pithecia pithecia (1)

Ateles geoffroyi (2)

Alouatta caraya (1)

Colobus angolensis (1)

Trachypithecus francoisi (2)

Erythrocebus patas (2)

Cercopithecus kandti (1)

Macaca maura (6)

Cercocebus agilis (1)

Mandrillus sphinx (1)

Papio anubis (2)

Symphalangus syndactylus (1)

Hylobates muelleri (1)

Pongo pygmaeus (5)

Gorilla gorilla (4)

Pan troglodytes (6)

Pan paniscus (1)

Homo sapiens (6)

New

World m

onkeysO

ld World m

onkeyshom

inoids

Figure 1. Phylogenetic relationships among the primatespecies used in the analysis of phenotype. This phylogenywas used to calculate independent contrasts. Sample sizesfor each species used in stereological analyses are shown in

parentheses.

Evolution of interneurons in humans C. C. Sherwood et al. 1013

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post-mortem interval, fixation condition and age on quanti-

tative results revealed no significant relationships (see the

electronic supplementary material).

(c) Allometric scaling analyses

Logarithm (base 10)-transformed species means were used

in allometric scaling analyses of interneuron density against

total neuron density in DLPFC. To determine the exponent

of scaling relationships, we used reduced major axis (RMA)

line-fitting to bivariate data to allow for error in both inde-

pendent and dependent variables. All RMA tests were

calculated using (S)MATR software v. 2.0.

Phylogenetic independent contrasts were also calculated

from the data to examine scaling relationships while control-

ling for the effects of phylogenetic relatedness in the dataset

(Felsenstein 1985). Standardized independent contrasts were

calculated using the PDAP:PDTREE module of MESQUITE

software v. 1.12 (Maddison & Maddison 2005) from

log-transformed data based on a phylogeny of primates in

Goodman et al. (2005) (figure 1). Branch lengths were

transformed according to the method of Pagel (1992),

which assigns all branch lengths to 1 with the constraint

that tips are contemporaneous.

We also examined whether human interneuron densities

represent significant deviations from allometric expectations

based on other anthropoid primates. We calculated least-

squares regression equations and 95 per cent prediction

intervals for humans based on the non-human data using

both contemporary ‘tip’ species data and independent con-

trasts according to the method of Garland & Ives (2000).

After logarithmic detransformation of predictions, the percen-

tage difference between observed and predicted values was

calculated as the ratio of (observed2predicted)/observed.

(d) Quantifying the partitioned variation

We employed a variance partitioning method to dissect

further the interaction between the phenotype and phylogeny

(Desdevises et al. 2003). Westboy et al. (1995) proposed the

theoretical partitioning of variance in a dataset into three

components: a, b and c—where a is a part strictly owing to

adaptation to the environment, b is a part owing to the

common influence of environment and phylogeny and c is

a part strictly owing to phylogeny. Desdevises et al. (2003)

described a multiple regression method for partitioning this

variation by expressing the phylogeny as a distance matrix.

In the current study, the decomposition of the variation for

interneuron subtypes across anthropoid species in DLPFC

was undertaken in accordance with the procedural steps

outlined by Desdevises et al. (2003). For further details see

the electronic supplementary material.

(e) Analysis of coding sequence evolution of

interneuron-important genes among mammals

Genes important to the transcriptional regulation of cortical

interneuron specification and differentiation were identified

from the literature (Wonders & Anderson 2006). Human

RefSeq IDs corresponding to these genes were identified

using NCBI’s Gene database. Where more than one tran-

script variant was available for a particular gene, the

longest variant was retained for further analysis. This process

resulted in a total of 20 human RefSeq IDs that were used in

subsequent investigations (see the electronic supplementary

material).

Multiple sequence alignments of the coding regions for

the 20 interneuron-important genes were obtained using

Proc. R. Soc. B (2010)

OCPAT, an online codon-preserved alignment tool for evol-

utionary genomic analysis of protein-coding sequences (Liu

et al. 2007). Briefly, the tool first identifies putative ortho-

logues from up to 14 taxa (chimpanzee, macaque, rabbit,

mouse, rat, dog, cow, tenrec, elephant, armadillo, opossum,

platypus, chicken and frog) that correspond to the queried

human RefSeq ID. Next, it creates an alignment from the

identified putative orthologues that retains the codon struc-

ture of all included sequences. Aligned sequences were

subsequently analysed for patterns of sequence evolution

using the PAML 3.15 package (Yang 1997). The phylo-

genetic relationships used to infer these patterns were

obtained from the literature (Hallstrom et al. 2007; Wildman

et al. 2007). Sequences were analysed by comparing the like-

lihood score of the data in a model that assumes the same

dN/dS (i.e. non-synonymous changes per non-synonymous

site/synonymous changes per synonymous site) ratio among

all branches (i.e. the one omega or M0 model) to the likeli-

hood score of the data in a model that permits dN/dS ratios

to vary freely among all branches in the phylogenetic tree

(i.e. the free ratio model or M1 model). All models were

run with three different starting omega values (0.5, 1 and 2)

to ensure optimal likelihood scores. Models were compared

using the likelihood ratio test, using the highest maximum like-

lihood value obtained among the three different starting

omega values. Patterns of selection were further investigated

for genes passing the M0 versus M1 test by mapping the

PAML-inferred M1 dN/dS ratios onto the phylogenetic tree.

log

CB

-ir

inte

rneu

ron

dens

ity

3.2

3.4

3.6

3.8

4.0

4.2

4.4(a)

(b)

(c)

log

CR

-ir

inte

rneu

ron

dens

ity

3.2

3.4

3.6

3.8

4.0

4.2

4.4

tern

euro

n de

nsity

3.8

4.0

4.2

4.4

gibbon

orangutan

bonobo

chimpanzee

siamang

gorillahuman

gibbon

orangutan

bonobo

chimpanzee

siamanggorilla

human

gibbon

orangutanchimpanzee

human

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3. RESULTS(a) Scaling relationships of interneurons in DLPFC

Figure 2 shows the allometric scaling of interneuron sub-

type densities against total neuron density from layers II

and III of DLPFC (area 9) across 23 anthropoid primate

species, including humans. The scaling exponents based

on independent contrasts for each interneuron subtype

were contained within the 95 per cent confidence inter-

vals of those calculated from contemporary tip species

data, indicating that there was not a strong effect of phylo-

genetic bias (table 1). We also tested for differences in

the slope and elevation of the scaling function for each

interneuron subtype among hominoids (n ¼ 7 species),

Old World monkeys (n ¼ 8 species) and New World

monkeys (n ¼ 8 species). There were no significant

differences among these phylogenetic groups as revealed

by a likelihood ratio test for common slopes or analysis

of variance (ANOVA) for elevation differences. Notably,

all interneuron subtypes scaled against total neuron

density with a positive allometric exponent as indicated

by both tip species data and independent contrasts

(table 1).

The variance partitioning analyses provided additional

support for the conclusion that phylogeny plays a rela-

tively weak role in determining the density of

interneuron types in the DLPFC (table 2). The exclusive

phylogenetic component (c) explained only a small pro-

portion of the variance in interneuron subtype densities

(between 7% and 13%), whereas a greater proportion of

the variance was explained by either total neuron density

alone (a) (between 13% and 28%), or the interaction

between neuron density and phylogeny (b) (19–38%).

It is notable, however, that the unexplained component

of variance (d) was usually greater than any of the defined

predictors.

We also tested whether brain size correlates with inter-

neuron distributions among species using variance

partitioning (table 2). When the percentage of interneuron

subtypes in DLPFC was considered in relation to brain

mass and phylogeny, a very large fraction of variance

remained unexplained (71–75%). Congruent with this

result, the simple bivariate relationship between species

mean brain mass and the percentage of each interneuron

subtype in the DLPFC also showed no significant corre-

lations using tip species data and independent contrasts.

log

PV-i

r in

3.2

3.4

3.6

log total neuron density

4.7 4.8 4.9 5.0 5.1 5.2 5.3

bonobo

siamang

gorilla

Figure 2. Scatterplots showing the allometric scalingrelationship between the density of (a) CB-ir interneurons,

(b) CR-ir interneurons and (c) PV-ir interneurons againsttotal neuron density in layers II and III of DLPFC. Solidlines indicate the RMA using tip species data. Dashed linesindicate the RMA using independent contrasts according to

the method of Garland & Ives (2000). Open circle, NewWorld monkeys; square, Old World monkeys and filledcircle, hominoids.

(b) Human allometric departures of interneuron

proportions in DLPFC

Given the regular scaling of interneurons, we tested

whether human interneuron densities in DLPFC deviate

from expectations for an anthropoid primate of the same

total neuron density (figure 2). Because there was no evi-

dence of a significant phylogenetic effect on the scaling

relationships, we calculated least-squares prediction

equations based on the total non-human anthropoid

sample. Human interneuron subtype densities were all

contained within the 95 per cent prediction intervals of

the non-human tip species data, but always fell below

the expected values (see electronic supplementary

material). Next, we used independent contrasts to gener-

ate predicted interneuron densities for a hypothetical

species attached to the branch leading to humans in the

phylogenetic tree. From this phylogenetically based

Proc. R. Soc. B (2010)

Table 2. Results of variance partitioning analysis of interneurons in DLFPC.

dependent variable

% explained by total neuron density

% unexplained% explained by phylogeny

a b c d

CB interneuron density 28.3 37.9 9.5 24.3

CR interneuron density 13.3 36.5 7.2 43.0PV interneuron density 20.2 18.9 13.4 47.5

% explained by brain mass

% CB interneurons 1.2 13.4 14.4 71.0% CR interneurons 0.7 8.9 15.3 75.1% PV interneurons 1.3 5.7 18.9 74.1

Table 1. The allometric scaling of interneuron subtype densities versus total neuron density in DLPFC of anthropoid

primates. RMA, reduced major axis; CI, confidence intervals.

interneuron

subtype

contemporary tip species data independent contrasts

r2 p-value

RMA

slope

lower

95% CI

upper

95% CI

difference fromH0 slope ¼ 1

r2 p-value RMA slopeF p-value

CB 0.66 0 1.84 1.41 2.38 25.86 0 0.74 0 1.69

CR 0.50 0 1.32 0.96 1.82 3.37 0.081 0.43 0 1.29PV 0.39 0.001 1.44 1.01 2.03 4.70 0.042 0.40 0.001 1.69

Evolution of interneurons in humans C. C. Sherwood et al. 1015

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prediction, the interneuron densities in humans also fell

within the 95 per cent prediction intervals and were

lower than expected (see electronic supplementary

material).

(c) Interneuron variation across the frontal cortex

of humans, chimpanzees and macaque monkeys

Our next analyses investigated whether humans, chimpan-

zees and macaque monkeys vary from each other in the

proportions of interneuron subtypes across different frontal

cortical areas. Individual data on the percentage of inter-

neuron subtypes in areas 4, 9, 32 and 44 of these species

(see electronic supplementary material) were analysed

using repeated-measures ANOVA. None of the ANOVAs

revealed a significant main effect of species (figure 3),

demonstrating an overall similarity in the proportions of

interneuron subtypes among these taxa. However, the per-

centage of CB-ir interneurons displayed an interaction

effect between species and cortical area. Results of Bonfer-

roni post hoc comparisons indicated that chimpanzees

diverged from the other two species in the regional distri-

bution of CB-ir interneurons, with the most remarkable

difference being a greater percentage of CB-ir interneuron

in primary motor cortex (area 4).

To determine further which variables distinguish among

species most clearly, we employed a forward stepwise dis-

criminant function analysis. The final discriminant

function was significant (Wilks’ L ¼ 0.020, F14,18 ¼ 7.90,

p , 0.00001) and classified 94.4 per cent of the cases to

the correct species. Among the seven variables that were

retained in the final discriminant function (see electronic

Proc. R. Soc. B (2010)

supplementary material), three represented the distri-

bution of CB-ir interneurons, three represented the

distribution of PV-ir interneurons and one represented

the distribution of CR-ir interneurons. The canonical vari-

ates plot shows that chimpanzees were the most distinct of

the three species in their frontal cortex interneuron pheno-

type (figure 4). The largest absolute correlations with

canonical root 1, which separates chimpanzees from the

other two species, were the percentage of CB-ir

interneurons in area 4 (r ¼ 0.19) and the percentage of

PV-ir interneurons in area 9 (r ¼ 0.11).

(d) Evolution of genes important to the

transcriptional regulation of cortical interneuron

specification and differentiation

For the 20 analysed genes, alignments were obtained that

included a minimum of eight species, with the majority

(n ¼ 11) of alignments including all 14 possible species.

Likelihood ratio tests indicated that for all but four

genes (i.e. DLX5, EMX2, LHX8 and NPAS1), the M1

model had a better fit to the data than did the M0

model (table 3) indicating that there was variation in

the rate of evolution of these genes across the lineages rep-

resented in our phylogenetic tree; for the four genes that

did not pass the M0 versus M1 test, inferred dN/dS

values ranged from a low of 0.013 to a high of 0.354

indicating a conserved rate of amino acid changing substi-

tutions (i.e. dN/dS , 1) which did not vary significantly

across the sampled phylogenetic lineages.

Among the remaining 16 genes, there was an over-

whelming pattern of conservation (i.e. dN/dS , 1)

0

1

2

3

4

5

6

7

8

9(a)%

CB

-ir

inte

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rons

2

4

6

8

10

12

14

16

18

% C

R-i

r in

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(b)

(c)

macaque monkeyschimpanzeeshumans

0

2

4

6

8

10

12

14

area 4 area 9 area 32 area 44

% P

V-i

r in

tern

euro

ns

Figure 3. Inter-regional variation in the proportion of (a)CB-ir (area * species: F6,45 ¼ 4.22, p ¼ 0.002; chimpanzee:area 4 . area 9, area 32), (b) CR-ir (area: F3,45 ¼ 4.88, p ¼0.005; area 44 . area 4, area 32) and (c) PV-ir (area:

F3,45 ¼ 4.88, p ¼ 0.005; area 4, area 44 . area 32) inter-neurons among humans, chimpanzees and macaquemonkeys. Means and 95% confidence intervals are shown.The significant effects of the repeated-measures ANOVA

models and Bonferroni post hoc results are displayed in thelegend for each interneuron subtype.

–8 –6 –4 –2 0 2 4 6 8 10canonical root 1

–3.5–3.0–2.5–2.0–1.5–1.0–0.5

00.51.01.52.02.53.0

cano

nica

l roo

t 2

Figure 4. Canonical variates plot of the full interneuron sub-type dataset from areas 4, 9, 32 and 44 in humans,chimpanzees and macaques. Note that chimpanzees showthe most distinct separation from the other species. Open

circle, macaque monkeys; square, chimpanzees and closedcircle, humans.

1016 C. C. Sherwood et al. Evolution of interneurons in humans

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observed among the majority of sampled mammalian

lineages, including primates. In most instances where

dN/dS was greater than 1, the actual number of PAML-

estimated non-synonymous and/or synonymous changes

was less than 1. For two genes that showed exceptions

to this general rule among primates (ETV1 and SHH),

results are tempered by the inclusion of bioinformatically

predicted ‘XM’ or ‘XR’ gene sequences, which do not

have experimentally verified transcripts as do ‘NM’- or

Ensembl-based sequences. Thus, among genes for

which higher quality non-human sequences were avail-

able, the overall pattern of conservation among primates

remained consistent.

Proc. R. Soc. B (2010)

4. DISCUSSIONThe present data demonstrate that cellular distributions

of interneurons in the human prefrontal cortex are similar

to those in other anthropoid primates and can be

explained by general scaling rules. Furthermore, genes

related to the transcriptional regulation of neocortical

GABAergic neuron development show evolutionary

conservation among primates.

(a) The distribution of inhibitory interneurons

across species

Given the crucial role played by local inhibitory circuits,

some investigators have suggested the existence of a cano-

nical network of neocortical interneurons that is relatively

invariant across species (Silberberg et al. 2002; Douglas &

Martin 2004). In the present study, scaling analyses and

partitioning of variance did not reveal a strong effect of

phylogeny (i.e. genetic relatedness) on the relationship

between interneuron subtype densities with respect to

total neuron density in DLPFC of anthropoid primates.

Interestingly, the positive allometric exponent of inter-

neuron scaling in DLPFC resembled that of visual areas

V1 and V2 from a previous study (Sherwood et al.

2007), suggesting that common scaling rules govern

interspecific variation in interneurons spanning disparate

regions of the neocortex. Despite such local network scal-

ing patterns, however, species differences in the

proportion of DLPFC interneurons did not correlate

with brain mass. Taken together, these results indicate

that the distribution of interneuron subtypes in the neo-

cortex of anthropoid primates may be constrained

within an optimal range of functionality regardless of

overall brain size. Indeed, the integrity of cortical inhibi-

tory interneurons appears to be required for normal

cognitive processing in humans, as evident by dysfunc-

tions of this system in patients with schizophrenia and

bipolar disorder (Benes & Berretta 2001;

Gonzalez-Burgos & Lewis 2008).

Table 3. M0 versus M1 likelihood ratio tests of interneuron-important genes. The likelihood of a model in which dN/dS is

equivalent on all branches (M0) was compared with the likelihood of a model in which dN/dS was allowed to vary on allbranches of a phylogenetic tree (M1) using a likelihood ratio test. If the likelihood of M1 was significantly greater (p � 0.05)than the likelihood of M0, the gene can be interpreted to have varying rates of amino acid changing substitutions during thedescent of mammals.

gene symbol RefSeq lnl1 M0 lnl2 M1 2(diff) ¼ chi square degrees of freedom probability

ARX NM_139058 25580.7 25566.1 29.2 15 0.0151ASCL1 NM_004316 21835.8 21805.5 60.6 21 0DLX1 v1 NM_178120 23410.0 23383.7 52.7 27 0.0022

DLX2 NM_004405 25330.3 25278.1 104.4 23 0DLX5 NM_005221 24403.8 24397.1 13.4 25 0.9717DLX6 NM_005222 22037.0 22016.8 40.3 27 0.0476EMX2 v1 NM_004098 22366.1 22355.6 20.9 27 0.7903

ETV1 v1 NM_004956 26952.5 26898.9 107.2 27 0GLI3 NM_000168 231 240.6 231 202.5 76.0 27 0GSX2 NM_133267 25211.0 25189.8 42.5 21 0.0036LHX6 v1 NM_014368 22502.1 22471.3 61.6 27 0.0001LHX8 NM_001001933 24484.9 24475.2 19.4 27 0.8548

NKX2-1 v2 NM_003317 24670.4 24646.8 47.1 19 0.0003NPAS1 NM_002517 25799.0 25792.7 12.6 13 0.4784NPAS3 v3 NM_173159 212 587.4 212 508.3 158.2 27 0NR2E1 NM_003269 24416.7 24383.5 66.5 27 0PAX6 v1 NM_000280 24790.2 24681.1 218.1 27 0

SHH NM_000193 28600.3 28541.4 117.9 21 0SIX3 NM_005413 23466.4 23438.7 55.4 27 0VAX1 v2 NM_199131 22168.5 22150.5 35.9 23 0.0423

Evolution of interneurons in humans C. C. Sherwood et al. 1017

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Although our narrow phylogenetic analysis of anthro-

poid primates showed remarkable consistency among

species, previous comparisons among representatives of

higher order taxonomic groups have demonstrated sub-

stantial variation in proportions of neocortical

interneurons (Glezer et al. 1993; DeFelipe et al. 2002).

In particular, primates have a larger percentage of inter-

neurons than rodents and other mammals (e.g.

afrotherians and xenarthrans) (Hendry et al. 1987;

Gabbott et al. 1997; Gonchar & Burkhalter 1997;

Sherwood et al. 2009). Additionally, most interneurons

in rodents originate from the ganglionic eminence,

whereas in primates there are also substantial numbers

of cortical GABAergic interneurons that migrate from

the lateral ventricular neuroepithelium (Letinic et al.

2002). Thus, evolutionary changes in the developmental

programme underlying neocortical interneuron

proliferation seem to have occurred during the divergence

of primates, but the complement of interneuron subtypes

has remained relatively stable in anthropoids since

that time.

Our evolutionary analyses of genes important to the

transcriptional regulation of cortical interneuron specifi-

cation and differentiation, furthermore, showed a strong

pattern of conservation in the primate lineage, and in

other mammals. This finding is consistent with the

known functional importance of these genes in the devel-

opment of cortical interneurons in mammals. Among

humans, mutations of such genes, including SHH,

SIX3, ARX, DLX2 and DLX5, have been linked to mul-

tiple diseases including holoprosencephaly, autism,

schizophrenia and anxiety disorders (Wonders &

Anderson 2006), further reinforcing the importance of

sequence conservation in these loci. Moreover, it has

been shown that several of the studied gene products cor-

egulate one another (Sussel et al. 1999; Gulacsi &

Proc. R. Soc. B (2010)

Anderson 2006). Nevertheless, the existence of pheno-

typic differences among mammals in the distribution of

interneurons suggests that mechanisms other than

sequence variation in protein-encoding loci underlie the

observed variation and differences in developmental

origin. Further studies should investigate the possibility

that interneuron diversity arises from changes in promo-

ter regions or from cell-specific and/or species-specific

epigenetic regulation (Farcas et al. 2009).

(b) The regional variation of interneurons

is similar among humans, chimpanzees

and macaques

Although calcium-binding protein immunohistochemis-

try has been used in the parcellation of cortical areas

(Nimchinsky et al. 1997; Ongur et al. 2003; Bourne

et al. 2007; Ding et al. 2009), there are relatively few

quantitative studies that examine the regional variation

of interneuron subtype distributions in primates

(Dombrowski et al. 2001). In the current study, we

obtained data from three different catarrhine primate

species, encompassing 25 Myr of evolution since the last

common ancestor of macaque monkeys and humans.

These data demonstrated that the percentage of CR-

and PV-ir interneurons across areas of the frontal cortex

does not differ among species, revealing certain funda-

mental characteristics of neurobiological organization

that are shared within this branch of primate evolution.

Remarkably, prefrontal regions that are involved in cogni-

tive functions that are unique in humans, such as ‘theory

of mind’ and language, did not show equally distinctive

interneuron distributions. Such regional characteristics

of GABAergic neuron populations might be related to

the specific processing requirements of each cortical

area. In particular, intercolumnar inhibition mediated

1018 C. C. Sherwood et al. Evolution of interneurons in humans

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by GABAergic interneurons has been implicated in shap-

ing the temporal pattern of activation across neuronal

ensembles (Constantinidis et al. 2002), and specializ-

ations of cortical inhibitory circuitry may contribute to

differences in processing among sensory modalities

(Pallas 2001). Similarly, our findings suggest that certain

computational processes of areas in the frontal cortex of

catarrhine primates are preferentially supported by

specific inhibitory architecture.

(c) Humans do not have specialized cellular

distributions of interneurons in the

prefrontal cortex

We did not find evidence that the distribution of inter-

neurons in the human prefrontal cortex is evolutionarily

specialized. Human interneuron densities were contained

within the 95 per cent prediction intervals for DLPFC

based on scaling to total neuron density in non-human

anthropoids. Humans also did not differ significantly

from chimpanzees or macaques in the regional distri-

bution of interneurons within the frontal cortex.

Multivariate discriminant function analysis, moreover,

demonstrated that the species with the most distinct fron-

tal cortex interneuron phenotype was chimpanzees, not

humans. In sum, we cannot conclude that alterations of

interneuron distributions in the prefrontal cortex have

made an important contribution to the evolution of

human species-specific cognition.

Although it has been argued by some investigators that

executive cognitive functions mediated by the prefrontal

cortex have been modified in human evolution (Coolidge &

Wynn 2005; Aboitiz et al. 2006), there is a paucity of

data addressing whether there has been corresponding

neuroanatomical reorganization. It is possible that the

prefrontal cortex (or subdivisions of it) has become dis-

proportionately enlarged in humans (Semendeferi et al.

2001; Schoenemann et al. 2005; Rilling 2006; Schenker

et al. 2009); however, many of the reported differences

are actually within the expected range for allometric scal-

ing at human brain size (Holloway 2002; Sherwood et al.

2005). Aside from the possible differential enlargement of

areas within the prefrontal cortex, there is currently only

minimal evidence of histological or connectional reorgan-

ization that would serve as a neurobiological basis for the

unique cognitive abilities of humans. Indeed, several

recent studies have demonstrated notable commonalities

in the neocortical architecture of humans relative to

other primates. For example, humans and chimpanzees

are similar in having a greater density of axons containing

serotonin, dopamine and acetylcholine innervating the

prefrontal cortex as compared with macaque monkeys

(Raghanti et al. 2008a,b,c). Furthermore, the total

number of neurons in the human neocortex accords

with allometric scaling predictions from other primate

brains (Azevedo et al. 2009).

Such evidence of continuity between humans and our

close relatives, however, is complemented by other data

indicating that corticocortical connectivity and descend-

ing subcortical projections have changed considerably in

recent human evolution (Kuypers 1958; Rilling 2008).

With the expansion of the forebrain in humans, novel

long-range neuronal projection patterns have emerged

that link previously unconnected processing modules,

Proc. R. Soc. B (2010)

such as areas in the inferior frontal cortex and the

middle temporal gyrus which are important in language

(Rilling et al. 2008). Interestingly, several molecules

involved in cell adhesion and axon guidance show evi-

dence of selection in their amino acid sequence and

surrounding non-coding regions in humans as compared

with other primates (Prabhakar et al. 2006; Uddin et al.

2008). In this light, the modern human neocortex

appears to combine both conserved and specialized archi-

tectural features into an evolutionary mosaic that

underlies our species’ uniqueness.

We thank Amy R. Garrison for assistance with histology.Brain materials used in this study were loaned by the GreatApe Ageing Project (NIH grant AG14308), the Foundationfor Comparative and Conservation Biology, the ClevelandMetroparks Zoo, the New England Primate ResearchCenter, Dr Antoine Mudakikwa from Office Rwandais duTourisme et des Parcs Nationaux, Dr Mike Cranfield fromthe Mountain Gorilla Veterinary Project, the NorthwesternUniversity Alzheimer’s Disease Center Brain Bank (NADCgrant P30 AG13854) and Dr Todd M. Preuss. This workwas supported by the National Science Foundation (BCS-0515484, BCS-0549117, BCS-0827531, BCS-0550209,BCS-0827546, DGE-0801634), the National Institutes ofHealth (NS42867), the Wenner-Gren Foundation forAnthropological Research and the James S. McDonnellFoundation (22002078).

REFERENCESAboitiz, F., Garcıa, R. R., Bosman, C. & Brunetti, E. 2006

Cortical memory mechanisms and language origins.Brain Lang. 98, 40–56. (doi:10.1016/j.bandl.2006.01.006)

Azevedo, F. A., Carvalho, L. R., Grinberg, L. T., Farfel,J. M., Ferretti, R. E., Leite, R. E., Jacob Filho, W.,Lent, R. & Herculano-Houzel, S. 2009 Equal numbersof neuronal and nonneuronal cells make the humanbrain an isometrically scaled-up primate brain. J. Comp.Neurol. 513, 532–541. (doi:10.1002/cne.21974)

Bartos, M., Vida, I. & Jonas, P. 2007 Synaptic mechanismsof synchronized gamma oscillations in inhibitory inter-neuron networks. Nat. Rev. Neurosci. 8, 45–56. (doi:10.1038/nrn2044)

Benes, F. M. & Berretta, S. 2001 GABAergic interneurons:implications for understanding schizophrenia and bipolardisorder. Neuropsychopharmacology 25, 1–27. (doi:10.1016/S0893-133X(01)00225-1)

Bourne, J. A., Warner, C. E., Upton, D. J. & Rosa, M. G.

2007 Chemoarchitecture of the middle temporal visualarea in the marmoset monkey (Callithrix jacchus): laminardistribution of calcium-binding proteins (calbindin, parv-albumin) and nonphosphorylated neurofilament. J. Comp.Neurol. 500, 832–849. (doi:10.1002/cne.21190)

Caceres, M., Lachuer, J., Zapala, M. A., Redmond, J. C.,Kudo, L., Geschwind, D. H., Lockhart, D. J., Preuss,T. M. & Barlow, C. 2003 Elevated gene expressionlevels distinguish human from non-human primate

brains. Proc. Natl Acad. Sci. USA 100, 13 030–13 035.(doi:10.1073/pnas.2135499100)

Constantinidis, C., Williams, G. V. & Goldman-Rakic, P. S.2002 A role for inhibition in shaping the temporal flow ofinformation in prefrontal cortex. Nat. Neurosci. 5,

175–180. (doi:10.1038/nn799)Coolidge, F. L. & Wynn, T. 2005 Working memory, its

executive functions, and the emergence of modern think-ing. Camb. Archaeol. J. 15, 5–26. (doi:10.1017/S0959774305000016)

Evolution of interneurons in humans C. C. Sherwood et al. 1019

on February 24, 2010rspb.royalsocietypublishing.orgDownloaded from

DeFelipe, J. 1997 Types of neurons, synaptic connectionsand chemical characteristics of cells immunoreactive forcalbindin-D28k, parvalbumin and calretinin in the neo-

cortex. J. Chem. Neuroanat. 14, 1–19. (doi:10.1016/S0891-0618(97)10013-8)

DeFelipe, J., Alonso-Nanclares, L. & Arellano, J. I. 2002Microstructure of the neocortex: comparative aspects.J. Neurocytol. 31, 299–316. (doi:10.1023/

A:1024130211265)Desdevises, Y., Legendre, P., Azouzi, L. & Morand, S. 2003

Quantifying phylogenetically structured environmentalvariation. Evolution 57, 2647–2652.

Ding, S. L., Van Hoesen, G. W., Cassell, M. D. & Poremba, A.2009 Parcellation of human temporal polar cortex: acombined analysis of multiple cytoarchitectonic,chemoarchitectonic, and pathological markers. J. Comp.Neurol. 514, 595–623. (doi:10.1002/cne.22053)

Dombrowski, S. M., Hilgetag, C. C. & Barbas, H. 2001Quantitative architecture distinguishes prefrontal corticalsystems in the rhesus monkey. Cereb. Cortex 11,975–988. (doi:10.1093/cercor/11.10.975)

Dorus, S., Vallender, E. J., Evans, P. D., Anderson, J. R.,

Gilbert, S. L., Mahowald, M., Wyckoff, G. J., Malcom,C. M. & Lahn, B. T. 2004 Accelerated evolution of ner-vous system genes in the origin of Homo sapiens. Cell119, 1027–1040. (doi:10.1016/j.cell.2004.11.040)

Douglas, R. J. & Martin, K. A. 2004 Neuronal circuits of the

neocortex. Annu. Rev. Neurosci. 27, 419–451. (doi:10.1146/annurev.neuro.27.070203.144152)

Farcas, R. et al. 2009 Differences in DNA methylation pat-terns and expression of the CCRK gene in human and

nonhuman primate cortices. Mol. Biol. Evol. 26,1379–1389. (doi:10.1093/molbev/msp046)

Felsenstein, J. 1985 Phylogenies and the comparativemethod. Am. Nat. 125, 1–15. (doi:10.1086/284325)

Friederici, A. D., Bahlmann, J., Heim, S., Schubotz, R. I. &

Anwander, A. 2006 The brain differentiates human andnon-human grammars: functional localization andstructural connectivity. Proc. Natl Acad. Sci. USA 103,2458–2463. (doi:10.1073/pnas.0509389103)

Gabbott, P. L., Jays, P. R. & Bacon, S. J. 1997 Calretinin

neurons in human medial prefrontal cortex (areas24a,b,c, 32’, and 25). J. Comp. Neurol. 381, 389–410.(doi:10.1002/(SICI)1096-9861(19970519)381:4,389::AID-CNE1.3.0.CO;2-Z)

Gallagher, H. L. & Frith, C. D. 2003 Functional imaging of

‘theory of mind’. Trends Cogn. Sci. 7, 77–83. (doi:10.1016/S1364-6613(02)00025-6)

Garland, T. & Ives, A. R. 2000 Using the past to predict thepresent: confidence intervals for regression equations in

phylogenetic comparative methods. Am. Nat. 155,346–364. (doi:10.1086/303327)

Geschwind, D. H. 2000 Mice, microarrays, and the geneticdiversity of the brain. Proc. Natl Acad. Sci. USA 97,10 676–10 678. (doi:10.1073/pnas.97.20.10676)

Glezer, I. I., Hof, P. R., Leranth, C. & Morgane, P. J. 1993Calcium-binding protein-containing neuronal popu-lations in mammalian visual cortex: a comparative studyin whales, insectivores, bats, rodents, and primates.Cereb. Cortex 3, 249–272. (doi:10.1093/cercor/3.3.249)

Gonchar, Y. & Burkhalter, A. 1997 Three distinct families ofGABAergic neurons in rat visual cortex. Cereb. Cortex 7,347–358. (doi:10.1093/cercor/7.4.347)

Gonzalez-Burgos, G. & Lewis, D. A. 2008 GABA neuronsand the mechanisms of network oscillations: implications

for understanding cortical dysfunction in schizophrenia.Schizophr. Bull. 34, 944–961. (doi:10.1093/schbul/sbn070)

Goodman, M., Grossman, L. I. & Wildman, D. E. 2005Moving primate genomics beyond the chimpanzee

Proc. R. Soc. B (2010)

genome. Trends Genet. 21, 511–517. (doi:10.1016/j.tig.2005.06.012)

Gulacsi, A. & Anderson, S. A. 2006 Shh maintains Nkx2.1 in

the MGE by a Gli3-independent mechanism. Cereb.Cortex 16(Suppl. 1), i89–i95. (doi:10.1093/cercor/bhk018)

Hallstrom, B. M., Kullberg, M., Nilsson, M. A. & Janke, A.2007 Phylogenomic data analyses provide evidence that

Xenarthra and Afrotheria are sister groups. Mol. Biol.Evol. 24, 2059–2068. (doi:10.1093/molbev/msm136)

Hauser, M. D., Chomsky, N. & Fitch, W. T. 2002 The fac-ulty of language: what is it, who has it, and how did it

evolve? Science 298, 1569–1579. (doi:10.1126/science.298.5598.1569)

Haygood, R., Fedrigo, O., Hanson, B., Yokoyama, K. D. &Wray, G. A. 2007 Promoter regions of many neural-and nutrition-related genes have experienced positive

selection during human evolution. Nat. Genet. 39,1140–1144. (doi:10.1038/ng2104)

Hendry, S. H. C. 1987 Recent advances in understandingthe intrinsic circuitry of the cerebral cortex. In Higherbrain functions: recent explorations of the brain’s emergentproperties (ed. S. P. Wise), pp. 241–283. New York, NY:Wiley.

Hendry, S. H. C., Schwark, H. D., Jones, E. G. & Yan, J.1987 Number and proportions of GABA-immunoreactiveneurons in different areas of monkey cerebral cortex.

J. Neurosci. 7, 1503–1519.Herrmann, E., Call, J., Hernandez-Lloreda, M. V., Hare, B. &

Tomasello, M. 2007 Humans have evolved specializedskills of social cognition: the cultural intelligence hypoth-

esis. Science 317, 1360–1366. (doi:10.1126/science.1146282)

Hof, P. R., Glezer, I. I., Conde, F., Flagg, R. A., Rubin,M. B., Nimchinsky, E. A. & Vogt Weisenhorn, D. M.1999 Cellular distribution of the calcium-binding proteins

parvalbumin, calbindin, and calretinin in the neocortex ofmammals: phylogenetic and developmental patterns.J. Chem. Neuroanat. 16, 77–116. (doi:10.1016/S0891-0618(98)00065-9)

Holloway, R. L. 2002 Brief communication: how much

larger is the relative volume of area 10 of the prefrontalcortex in humans? Am. J. Phys. Anthropol. 118,399–401. (doi:10.1002/ajpa.10090)

Holloway, R. L., Broadfield, D. C. & Yuan, M. S. 2004 Thehuman fossil record, volume 3, brain endocasts—the paleo-neurological evidence. New York, NY: Wiley.

Khaitovich, P. et al. 2004 Regional patterns of geneexpression in human and chimpanzee brains. GenomeRes. 14, 1462–1473. (doi:10.1101/gr.2538704)

Kuypers, H. G. J. M. 1958 Some projections from the peri-central cortex to the pons and lower brainstem in monkeyand chimpanzee. J. Comp. Neurol. 110, 221–251. (doi:10.1002/cne.901100205)

Letinic, K., Zoncu, R. & Rakic, P. 2002 Origin of GABA-

ergic neurons in the human neocortex. Nature 417,645–649. (doi:10.1038/nature00779)

Liu, G., Uddin, M., Islam, M., Goodman, M., Grossman,L. I., Romero, R. & Wildman, D. E. 2007 OCPAT: anonline codon-preserved alignment tool for evolutionary

genomic analysis of protein coding sequences. SourceCode Biol. Med. 2, 5. (doi:10.1186/1751-0473-2-5)

Maddison, W. P. & Maddison, D. R. 2005 Mesquite: a mod-ular system for evolutionary analysis. Version 1.12. Seehttp://mesquiteproject.org.

Mouton, P. R. 2002 Principles and practices of unbiased stereol-ogy: an introduction for bioscientists. Baltimore, MD: TheJohns Hopkins University Press.

Nimchinsky, E. A., Vogt, B. A., Morrison, J. H. & Hof, P. R.1997 Neurofilament protein and calcium-binding

1020 C. C. Sherwood et al. Evolution of interneurons in humans

on February 24, 2010rspb.royalsocietypublishing.orgDownloaded from

proteins in the human cingulate cortex. J. Comp. Neurol.384, 597–620. (doi:10.1002/(SICI)1096-9861(19970811)384:4,597::AID-CNE8.3.0.CO;2-Y)

Oldham, M. C., Horvath, S. & Geschwind, D. H. 2006 Con-servation and evolution of gene coexpression networks inhuman and chimpanzee brains. Proc. Natl Acad. Sci. USA103, 17 973–17 978. (doi:10.1073/pnas.0605938103)

Ongur, D., Ferry, A. T. & Price, J. L. 2003 Architectonic

subdivision of the human orbital and medial prefrontalcortex. J. Comp. Neurol. 460, 425–449. (doi:10.1002/cne.10609)

Pagel, M. D. 1992 A method for the analysis of comparative

data. J. Theor. Biol. 156, 431–442. (doi:10.1016/S0022-5193(05)80637-X)

Pallas, S. L. 2001 Intrinsic and extrinsic factors that shapeneocortical specification. Trends Neurosci. 24, 417–423.(doi:10.1016/S0166-2236(00)01853-1)

Prabhakar, S., Noonan, J. P., Paabo, S. & Rubin, E. M. 2006Accelerated evolution of conserved noncoding sequencesin humans. Science 314, 786. (doi:10.1126/science.1130738)

Preuss, T. M. 2007 Primate brain evolution in phylogenetic

context. In The evolution of primate nervous systems. Evol-ution of nervous systems, vol. 4 (eds J. H. Kaas & T. M.Preuss), pp. 1–34. Oxford, UK: Academic Press.

Raghanti, M. A., Stimpson, C. D., Marcinkiewicz, J. L.,Erwin, J. M., Hof, P. R. & Sherwood, C. C. 2008aCholinergic innervation of the frontal cortex: differencesamong humans, chimpanzees, and macaque monkeys.J. Comp. Neurol. 506, 409–424. (doi:10.1002/cne.21546)

Raghanti, M. A., Stimpson, C. D., Marcinkiewicz, J. L.,

Erwin, J. M., Hof, P. R. & Sherwood, C. C. 2008b Corti-cal dopaminergic innervation among humans,chimpanzees, and macaque monkeys: a comparativestudy. Neuroscience 155, 203–220. (doi:10.1016/j.neuro-science.2008.05.008)

Raghanti, M. A., Stimpson, C. D., Marcinkiewicz, J. L.,Erwin, J. M., Hof, P. R. & Sherwood, C. C. 2008c Differ-ences in cortical serotonergic innervation among humans,chimpanzees, and macaque monkeys: a comparativestudy. Cereb. Cortex 18, 584–597. (doi:10.1093/cercor/

bhm089)Ramon y Cajal, S. 1923 Recuerdos de mi vida: Historia de mi

labor cientıfica. Madrid, Spain: Alianza Editorial.Rilling, J. K. 2006 Human and nonhuman primate brains:

are they allometrically scaled versions of the same

design? Evol. Anthropol. 15, 65–77. (doi:10.1002/evan.20095)

Rilling, J. K. 2008 Neuroscientific approaches and appli-cations within anthropology. Am. J. Phys. Anthropol. 137

(Suppl. 47), 2–32. (doi:10.1002/ajpa.20947)Rilling, J. K., Glasser, M. F., Preuss, T. M., Ma, X., Zhao,

T., Hu, X. & Behrens, T. E. J. 2008 The evolution ofthe arcuate fasciculus revealed with comparative DTI.Nat. Neurosci. 11, 426–428. (doi:10.1038/nn2072)

Schenker, N. M., Hopkins, W. D., Spocter, M. A., Garrison,A. R., Stimpson, C. D., Erwin, J. M., Hof, P. R. &Sherwood, C. C. 2009 Broca’s area homologue in chim-panzees (Pan troglodytes): probabilistic mapping,asymmetry, and comparison to humans. Cereb. Cortex.

(doi:10.1093/cercor/bhp138)Schoenemann, P. T., Sheehan, M. J. & Glotzer, L. D. 2005

Prefrontal white matter volume is disproportionatelylarger in humans than in other primates. Nat. Neurosci.8, 242–252. (doi:10.1038/nn1394)

Semendeferi, K., Armstrong, E., Schleicher, A., Zilles, K. &Van Hoesen, G. W. 2001 Prefrontal cortex in humans and

Proc. R. Soc. B (2010)

apes: a comparative study of area 10. Am. J. Phys. Anthro-pol. 114, 224–241. (doi:10.1002/1096-8644(200103)114:3,224::AID-AJPA1022.3.0.CO;2-I)

Sherwood, C. C., Holloway, R. L., Semendeferi, K. & Hof,P. R. 2005 Is prefrontal white matter enlargement ahuman evolutionary specialization? Nat. Neurosci. 8,537–538. (doi:10.1038/nn0505-537)

Sherwood, C. C. et al. 2006 Evolution of increased glia-

neuron ratios in the human frontal cortex. Proc. NatlAcad. Sci. USA 103, 13 606–11 611. (doi:10.1073/pnas.0605843103)

Sherwood, C. C., Raghanti, M. A., Stimpson, C. D., Bonar,

C. J., de Sousa, A. A., Preuss, T. M. & Hof, P. R. 2007Scaling of inhibitory interneurons in areas V1 and V2 ofanthropoid primates as revealed by calcium-bindingprotein immunohistochemistry. Brain Behav. Evol. 69,176–195. (doi:10.1159/000096986)

Sherwood, C. C., Subiaul, F. & Zawidzki, T. W. 2008 Anatural history of the human mind: tracing evolutionarychanges in brain and cognition. J. Anat. 212, 426–454.(doi:10.1111/j.1469-7580.2008.00868.x)

Sherwood, C. C., Stimpson, C. D., Butti, C., Bonar, C. J.,

Newton, A. L., Allman, J. M. & Hof, P. R. 2009 Neocor-tical neuron types in Xenarthra and Afrotheria:implications for brain evolution in mammals. BrainStruct. Funct. 213, 301–328. (doi:10.1007/s00429-008-0198-9)

Silberberg, G., Gupta, A. & Markram, H. 2002 Stereotypy in

neocortical microcircuits. Trends Neurosci. 25, 227–230.(doi:10.1016/S0166-2236(02)02151-3)

Sohal, V. S., Zhang, F., Yizhar, O. & Deisseroth, K. 2009

Parvalbumin neurons and gamma rhythms enhance corti-cal circuit performance. Nature 459, 698–702. (doi:10.1038/nature07991)

Sussel, L., Marin, O., Kimura, S. & Rubenstein, J. L. 1999Loss of Nkx2.1 homeobox gene function results in a ven-

tral to dorsal molecular respecification within the basaltelencephalon: evidence for a transformation of thepallidum into the striatum. Development 126, 3359–3370.

Uddin, M., Wildman, D. E., Liu, G., Xu, W., Johnson, R.M., Hof, P. R., Kapatos, G., Grossman, L. I. &

Goodman, M. 2004 Sister grouping of chimpanzees andhumans as revealed by genome-wide phylogenetic analysisof brain gene expression profiles. Proc. Natl Acad. Sci.USA 101, 2957–2962. (doi:10.1073/pnas.0308725100)

Uddin, M. et al. 2008 Distinct genomic signatures of adap-

tation in pre- and postnatal environments during humanevolution. Proc. Natl Acad. Sci. USA 105, 3215–3220.(doi:10.1073/pnas.0712400105)

Westboy, M., Leishman, M. R. & Lord, J. M. 1995 On mis-

interpreting the ‘phylogenetic correction’. J. Ecol. 83,531–534. (doi:10.2307/2261605)

Wildman, D. E. et al. 2007 Genomics, biogeography, andthe diversification of placental mammals. Proc. NatlAcad. Sci. USA 104, 14395–14400. (doi:10.1073/pnas.

0704342104)Wonders, C. P. & Anderson, S. A. 2006 The origin and

specification of cortical interneurons. Nat. Rev. Neurosci.7, 687–696. (doi:10.1038/nrn1954)

Yang, Z. 1997 PAML: a program package for phylogenetic

analysis by maximum likelihood. Comput. Appl. Biosci.13, 555–556.

Zaitsev, A. V., Povysheva, N. V., Gonzalez-Burgos, G.,Rotaru, D., Fish, K. N., Krimer, L. S. & Lewis, D. A.2009 Interneuron diversity in layers 2–3 of monkey

prefrontal cortex. Cereb. Cortex 19, 1597–1615. (doi:10.1093/cercor/bhn198)

Electronic Supplementary Material

Specimens, preparation, and immunohistochemical staining

The majority of specimens came from adults, with the exception of three juveniles that

had a brain size comparable to their species typical adult average (Trachypithecus francoisi,

Ateles geoffroyi, and Pithecia pithecia). Although some of the brains came from older

individuals, it is important to note that the human, chimpanzee, and macaque monkey samples,

which were used for the majority of phenotypic analyses, were all from non-geriatric adults.

None of the brains showed evidence of neuropathological abnormality on routine examination.

Most nonhuman primate postmortem brain specimens used in this study were provided by

zoological or research institutions subsequent to immersion fixation in 10% buffered formalin

after a variable postmortem interval (PMI), which never exceeded 14 hours. In addition, some

brains were obtained from animals that were perfused transcardially with 4% paraformaldehyde

in the context of unrelated experiments (Papio anubis, Macaca maura, Erythrocebus patas,

Saimiri boliviensis, Aotus trivirgatus, Ateles geoffroyi). Brains came from animals that were

housed in accordance with each institution’s guidelines. The golden guenon monkey brain was

provided by the Office Rwandais du Tourisme et des Parcs Nationaux and the Mountain Gorilla

Veterinary Project in compliance with CITES regulations. Human brain specimens were

provided by Northwestern University Alzheimer’s Disease Center Brain Bank (3 women, 3 men,

age range 35-54 years). For human cases, the PMI prior to formalin fixation ranged from 6 to 17

hours. The human cases showed no evidence of dementia before death and all individuals

received a score of 0 for the CERAD senile plaque grade (Mirra et al. 1991) and the Braak and

Braak (1991) neurofibrillary tangle stage. In all cases, brains were transferred to 0.1 M

phosphate buffered saline (PBS, pH 7.4) containing 0.1% sodium azide and stored at 4˚C as soon

as possible after acquisition to mitigate excessive antigen blockage and tissue shrinkage.

Brain mass of nonhuman specimens was measured either immediately after perfusion or

directly upon receipt from the donating institution. Human brain masses were measured at

autopsy and were provided by the brain bank. Some artifact in our total brain mass data due to

interindividual differences in fixation length was unavoidable. Nevertheless, shrinkage artifact is

likely to be minimal relative to the variance among species because our brain mass

measurements fall within the normal range of values reported in the literature from fresh brains

and in vivo MRI volumes measured from these taxa (Rilling & Insel 1999; Stephan et al. 1981;

Zilles & Rehkämper 1988).

For all nonhuman specimens, the entire left frontal lobe was sectioned in the coronal

plane. Human brain samples were dissected by the donating brain bank from the regions of

interest in the left hemisphere as 4 cm-thick slabs. Tissue blocks were cryoprotected by

immersion with increasing concentrations of sucrose solution up to 30%. Blocks were then

frozen on a slur of dry ice and isopentane, and sections were cut at 40 µm using a sliding

microtome.

Prior to immunostaining, sections were rinsed thoroughly in PBS, pretreated for antigen

retrieval by incubation in 10 mM sodium citrate buffer (pH 3.5) at 37° C in an oven for 30

minutes, then immersed in a solution of 0.75% hydrogen peroxide in 75% methanol to eliminate

endogenous peroxidase activity. Primary antibodies were diluted in a solution containing PBS

with 2% normal serum and 0.1% Triton X-100 and incubated for approximately 48 hours on a

rotating table at 4° C. After rinsing in PBS, sections were incubated in the secondary antibody

(either biotinylated anti-mouse IgG or biotinylated anti-rabbit IgG, dilution 1:200: Vector

Laboratories, Burlingame, CA) and processed with the avidin-biotin-peroxidase method using a

Vectastain Elite ABC kit (Vector Laboratories). Immunoreactivity was visualized using 3,3’-

diaminobenzidine (DAB) as a chromogen and intensified with nickel in some cases. For every

specimen, specificity of the immunoreaction was confirmed by processing control sections as

described above excluding the primary antibody. Immunostaining was completely absent in

control sections. Only sections with clear staining of neuronal perikarya and dendrites were

included in quantitative analyses. For each immunostained series, alternate sections were

counterstained with cresyl violet to visualize non-immunoreactive neurons, cytoarchitectural

boundaries, and cortical layers.

Stereologic analyses

Quantification of cellular densities within layers II-III was performed using a

computerized stereology system consisting of a Zeiss Axioplan 2 photomicroscope equipped

with a Ludl XY motorized stage, Heidenhain z-axis encoder, an Optronics MicroFire color

videocamera, a Dell PC workstation, and StereoInvestigator software (MBF Bioscience,

Wiliston, VT). Beginning at a random starting point, three equidistantly spaced sections were

selected for analysis for each neuron type. Only the central portion of each cortical region was

used for analyses, avoiding the boundaries at the transition between cytoarchitectonic areas.

Optical disector frames (30 µm × 30 µm for Nissl stain; 65 µm × 65 µm for calcium-binding

proteins) were placed in a systematic random fashion to cover the region of interest with

approximately 30 frames per section, and the exact spacing of disectors depending on the size of

the brain. Disector analysis was performed under Koehler illumination using a 63× objective

(Zeiss Plan-Apochromat, N.A. 1.4). In immunostained sections, only interneurons that showed

dark staining, a clear soma, and proximal dendritic segments were counted. Cells were counted

when their centroid was encountered within the optical disector frame according to standard

stereologic principles (Howard & Reed 1998; Mouton 2002). The thickness of optical disectors

was set to 6 µm to allow for a minimum 2-µm guard zone on either side of the section after z-

axis shrinkage from histological processing. Neuron numerical densities (Nv) were derived from

these optical disector counts and corrected by the number-weighted mean section thickness as

described in Sherwood et al. (2007).

Area 9 of dorsolateral prefrontal cortex (DLPFC) was selected for our broad interspecific

analyses because it has been described as anatomically homologous in a diverse range of

anthropoids, e.g., Callithrix jacchus (Burman et al. 2006), Saimiri oerstedii (Rosabal 1967),

Macaca sp. (Petrides & Pandya 1999; von Bonin & Bailey 1947; Walker 1940), Papio

hamadryas (Watanabe-Sawaguchi et al. 1991), Pan troglodytes (Bailey et al. 1950), Homo

sapiens (Petrides & Pandya 1999; Rajkowska & Goldman-Rakic 1995). Cortical areas 4, 9, 32,

and 44 in humans, chimpanzees, and macaque monkeys were identified based on their

characteristic cytoarchitecture using descriptions from previous parcellations of these areas

(Bailey et al. 1950; Paxinos et al. 2000; Petrides et al. 2005; Petrides & Pandya 1999; Sherwood

et al. 2003; Sherwood et al. 2004; Sherwood et al. 2006; Vogt et al. 1995).

Consideration of fixation conditions and postmortem interval

Given the opportunistic nature of the brain collection for this study, it is important to

consider the effect of fixation condition and postmortem interval (PMI) on the quantitative

results. We examined directly whether fixation condition had a significant effect on the

immunostaining quality of neurons in our sample by performing a restricted comparison among

Old World monkey brains that had been prepared by immersion-fixation (n = 6) and perfusion-

fixation (n = 10). We selected the Old World monkeys for this comparison because this

phylogenetic group contained the most equal representation of brains that were fixed by each

method. The percentage of the total layer II-III neuron population was calculated for each

interneuron subtype in DLPFC and evaluated for differences between fixation conditions using

independent samples t tests. Results showed no significant differences in the percentage of

interneuron subtypes based on fixation method (CB: t = -0.25, P = 0.81; CR: t = 1.14, P = 0.30;

PV: t = 1.61, P = 0.15). Thus, potential artifact in immunostaining against calcium-binding

proteins from immersion-fixation with a 14-hour PMI is statistically indistinguishable from

perfused tissue.

To further assess whether the length of PMI affected the quality of immunohistochemical

staining, nonparametric Spearman’s correlation coefficients were calculated for PMI and the

percentage of each interneuron subtype in each cortical area in humans. Data on specific PMI

were not available for the other species in the sample. Out of twelve possible comparisons, there

were two significant correlations with PMI (CB-ir interneurons in area 9: rs = 0.81, P = 0.05;

CR-ir interneurons in area 44: rs = -0.81, P = 0.05); however these correlations were in opposite

directions and neither was significant after applying a sequential Bonferroni correction of α for

multiple tests. It is notable that Lavenex and colleagues (2009) also did not find a difference in

the number of neurons immunoreactive for CB, CR, and PV in the hippocampal formation of

rhesus monkeys when comparing brains that were paraformadehyde-fixed by perfusion versus

immersion with varying PMIs extending as long as 48 hours.

We also used Spearman’s rank order correlations to test whether there was a relationship

between age at death and the percentage of each interneuron subtype in each cortical area within

macaques, chimpanzees, or humans. Out of 36 possible comparisons, there was only one

significant correlation with age (PV-ir interneurons in area 44 of macaques: rs = -0.85, P = 0.03);

however this correlation was not significant after Bonferroni correction of α. Thus, we conclude

that PMI and age did not contribute significantly to the observed immunostaining patterns of

interneurons.

Statistical analyses

For reduced major axis (RMA) scaling analyses and regression predictions, we used total

neuron density as the independent variable to compare with interneuron subtype densities. In

such bivariate plots, part of the independent variable is comprised by the dependent variable

because interneurons contribute to the total neuron density. Some authors have raised the

concern that this part-whole relationship may be particularly problematic for dependent variables

that constitute a large proportion of the independent variable. When this is not taken into

account, autocorrelation artificially inflates the correlation between variables and reduces the

sensitivity of the regression to detecting departures from allometric expectations (Deacon 1990;

Holloway 1979). To examine the possibility that such effects were present in our data, we re-

analyzed each scaling relationship for interneuron densities versus total neuron density by

subtracting the dependent value from the independent value. All relationships retained essentially

the same patterns of statistical significance, coefficients of determination, and scaling exponents

as when the analyses were performed with total neuron density as the independent variable.

For independent contrasts analyses, alternative methods of branch length transformation

did not significantly alter the results and independent contrasts were uncorrelated with their

standard deviations, indicating that branch lengths meet statistical assumptions (Garland et al.

1992).

Quantifying the partitioned variation

Treating each interneuron subtype separately, the first step in variance partitioning

requires the calculation of fraction a+b, which is obtained from the regression of the predictor

variable on the dependent variable. The resultant coefficient of determination (r2) is entered as

the fraction a+b and represents the total uncorrected variance attributed to the undifferentiated

components representing the interaction of phylogeny and the predictor. Step 2 is the calculation

of fraction b+c by computing a multiple regression of the dependent variable on principal

coordinates, which are derived from a distance matrix representing the phylogeny. Step 3 is the

determination of fraction a+b+c by computing a multiple regression of the dependent variable

on the predictor variable and the principal coordinates representing the phylogeny. Step 4 is the

calculation of the decomposed component values for a, b, c, and d (i.e., unexplained variance) by

subtraction from previous results, e.g., a = r2 (Step 3) – r2 (Step 2); c = r2 (Step 3) – r2 (Step 1).

All multiple regressions were calculated using SPSS version 11.0 whereas the resultant distance

matrix was calculated using Compare 2.0.

References

Bailey, P., von Bonin, G. & McCulloch, W. S. 1950 The Isocortex of the Chimpanzee. Urbana,

IL: University of Illinois Press. Braak, H. & Braak, E. 1991 Neuropathological stageing of Alzheimer-related changes. Acta

Neuropathol 82, 239-59. Burman, K. J., Palmer, S. M., Gamberini, M. & Rosa, M. G. 2006 Cytoarchitectonic

subdivisions of the dorsolateral frontal cortex of the marmoset monkey (Callithrix jacchus), and their projections to dorsal visual areas. J Comp Neurol 495, 149-72.

Deacon, T. W. 1990 Fallacies of progression in theories of brain-size evolution. International Journal of Primatology 11, 193-235.

Garland, T., Harvey, P. H. & Ives, A. R. 1992 Procedures for the analysis of comparative data using phylogenetically independent contrasts. Syst Biol 41, 18-32.

Holloway, R. L. 1979 Brain size, allometry, and reorganization: toward a synthesis. In Development and Evolution of Brain Size: Behavioral Implications (ed. M. E. Hahn, C. Jensen & B. C. Dudek), pp. 59-88. New York: Academic Press.

Howard, C. V. & Reed, M. G. 1998 Unbiased Stereology - Three-Dimensional Measurement in Microscopy. New York: Springer-Verlag.

Lavenex, P., Lavenex, P. B., Bennett, J. L. & Amaral, D. G. 2009 Postmortem changes in the neuroanatomical characteristics of the primate brain: hippocampal formation. J Comp Neurol 512, 27-51.

Mirra, S. S., Heyman, A., McKeel, D., Sumi, S. M., Crain, B. J., Brownlee, L. M., Vogel, F. S., Hughes, J. P., van Belle, G. & Berg, L. 1991 The Consortium to Establish a Registry for Alzheimer's Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer's disease. Neurology 41, 479-86.

Mouton, P. R. 2002 Principles and Practices of Unbiased Stereology: An Introduction for Bioscientists. Baltimore and London: The Johns Hopkins University Press.

Paxinos, G., Huang, X. F. & Toga, A. W. 2000 The Rhesus Monkey Brain in Stereotaxic Coordinates. San Diego, CA: Academic Press.

Petrides, M., Cadoret, G. & Mackey, S. 2005 Orofacial somatomotor responses in the macaque monkey homologue of Broca's area. Nature 435, 1235-8.

Petrides, M. & Pandya, D. N. 1999 Dorsolateral prefrontal cortex: comparative cytoarchitectonic analysis in the human and the macaque brain and corticocortical connection patterns. Eur J Neurosci 11, 1011-36.

Rajkowska, G. & Goldman-Rakic, P. S. 1995 Cytoarchitectonic definition of prefrontal areas in the normal human cortex: I. Remapping of areas 9 and 46 using quantitative criteria. Cereb Cortex 5, 307-22.

Rilling, J. K. & Insel, T. R. 1999 The primate neocortex in comparative perspective using magnetic resonance imaging. Journal of Human Evolution 37, 191-223.

Rosabal, F. 1967 Cytoarchitecture of the frontal lobe of the squirrel monkey. J Comp Neurol 130, 87-108.

Sherwood, C. C., Broadfield, D. C., Holloway, R. L., Gannon, P. J. & Hof, P. R. 2003 Variability of Broca's area homologue in African great apes: implications for language evolution. Anat Rec 271A, 276-285.

Sherwood, C. C., Holloway, R. L., Erwin, J. M., Schleicher, A., Zilles, K. & Hof, P. R. 2004 Cortical orofacial motor representation in Old World monkeys, great apes, and humans. I. Quantitative analysis of cytoarchitecture. Brain Behav Evol 63, 61-81.

Sherwood, C. C., Raghanti, M. A., Stimpson, C. D., Bonar, C. J., de Sousa, A. A., Preuss, T. M. & Hof, P. R. 2007 Scaling of inhibitory interneurons in areas V1 and V2 of anthropoid primates as revealed by calcium-binding protein immunohistochemistry. Brain Behav Evol 69, 176-195.

Sherwood, C. C., Stimpson, C. D., Raghanti, M. A., Wildman, D. E., Uddin, M., Grossman, L. I., Goodman, M., Redmond, J. C., Bonar, C. J., Erwin, J. M. & Hof, P. R. 2006 Evolution of increased glia-neuron ratios in the human frontal cortex. Proc Natl Acad Sci U S A 103, 13606-1311.

Stephan, H., Frahm, H. D. & Baron, G. 1981 New and revised data on volumes of brain structures in insectivores and primates. Folia Primatol. 35, 1-29.

Vogt, B. A., Nimchinsky, E. A., Vogt, L. J. & Hof, P. R. 1995 Human cingulate cortex: surface features, flat maps, and cytoarchitecture. J Comp Neurol 359, 490-506.

von Bonin, G. & Bailey, P. 1947 The Neocortex of Macaca Mulatta. Urbana, IL: University of Illinois Press.

Walker, A. E. 1940 A cytoarchitectural study of the prefrontal area of the macaque monkey. J Comp Neurol 73, 59-86.

Watanabe-Sawaguchi, K., Kubota, K. & Arikuni, T. 1991 Cytoarchitecture and intrafrontal connections of the frontal cortex of the brain of the hamadryas baboon (Papio hamadryas). J Comp Neurol 311, 108-33.

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

Figure Legend

Fig. ESM1 – Examples of interneuron morphology revealed by immunostaining against calcium-

binding proteins in layers II-III of DLPFC from various anthropoid primates. Scale bar = 100

µm.

Table ESM1. Sample used for neuron counting Taxonomic group Species Sex Age Fixation Hominoids Homo sapiens F 40 I Homo sapiens F 43 I Homo sapiens F 53 I Homo sapiens M 35 I Homo sapiens M 48 I Homo sapiens M 54 I Pan paniscus F 25 I Pan troglodytes F 19 I Pan troglodytes F 27 I Pan troglodytes F 35 I Pan troglodytes M 17 I Pan troglodytes M 19 I Pan troglodytes M 41 I Gorilla gorilla F 50 I Gorilla gorilla M 13 I Gorilla gorilla M 21 I Gorilla gorilla M 49 I Pongo pygmaeus F 23 I Pongo pygmaeus F 31 I Pongo pygmaeus M 11 I Pongo pygmaeus M 33 I Pongo pygmaeus M 39 I Symphalangus syndactylus M 33 I Hylobates muelleri M 19 I Old World monkeys Papio anubis M 11 P Papio anubis M 11 P Mandrillus sphinx F 33 I Cercocebus agilis F 19 I Macaca maura F 5 P Macaca maura F 7 P Macaca maura F 7 P Macaca maura F 8 P Macaca maura M 8 P Macaca maura M 10 P Erythrocebus patas F 12 P Erythrocebus patas M 12 P Cercopithecus kandti M adult I Colobus angolensis M 18 I Trachypithecus francoisi M 1 I Trachypithecus francoisi M 16 I New World monkeys Alouatta caraya M 21 I Ateles geoffroyi F 26 P Ateles geoffroyi M 1 P Cebus apella M 4 I

Saimiri boliviensis F 12 P Aotus trivirgatus M >18 P Saguinus oedipus F 6 I Saguinus oedipus M 15 I Leontopithecus rosalia F 11 I Leontopithecus rosalia M 6 I Pithecia pithecia F 1 I Age in years; I = immersion-fixed, P = perfusion-fixed

Table ESM2. Results of stereologic estimates of cellular densities in layers II-III of DLPFC, area 9 (cells per mm3). Species means are shown.

Taxonomic group Species N Brain

mass, g Total

neurons CB CR PV Hominoids Homo sapiens 6 1,373.3 93,432 3,471 7,297 5,711 Pan paniscus 1 337.0 64,224 2,422 11,145 4,475 Pan troglodytes 6 336.2 73,755 2,115 8,260 6,112 Gorilla gorilla 4 520.6 80,599 3,477 6,356 4,255 Pongo pygmaeus 5 367.4 62,385 2,221 3,638 5,508 Symphalangus syndactylus 1 138.7 74,776 4,614 7,308 5,630 Hylobates muelleri 1 101.8 53,618 2,440 5,568 7,318 Old World monkeys Papio anubis 2 155.8 91,806 5,121 10,614 10,225 Mandrillus sphinx 1 159.2 135,417 7,361 18,462 17,479 Cercocebus agilis 1 95.3 70,198 2,182 11,802 3,483 Macaca maura 6 92.6 144,649 6,115 14,149 7,945 Erythrocebus patas 2 102.3 143,783 8,343 13,057 14,278 Cercopithecus kandti 1 71.6 89,538 6,117 8,603 12,527 Colobus angolensis 1 74.4 67,555 2,375 4,650 11,319 Trachypithecus francoisi 2 96.7 84,667 3,986 13,318 7,279 New World monkeys Alouatta caraya 1 55.8 93,326 4,001 6,700 5,482 Ateles geoffroyi 2 103.0 172,818 13,034 14,482 15,115 Cebus apella 1 91.2 106,630 7,804 12,954 8,064 Saimiri boliviensis 1 24.1 127,266 10,361 15,651 16,351 Aotus trivirgatus 1 13.2 84,727 12,214 10,677 7,704 Saguinus oedipus 2 10.3 118,002 6,147 11,348 7,604 Leontopithecus rosalia 2 12.2 105,365 5,457 10,400 7,228 Pithecia pithecia 1 30.0 71,549 3,680 10,405 8,532

*Note – these values contain an unknown degree of artifact from tissue shrinkage during fixation and histological processing; therefore our analysis considered only the relative relationships between these cell type densities.

Table ESM3. Human predictions for interneuron densities in DLPFC based on least-squares regression (LSR) against total neuron density from nonhuman anthropoid primate data

Method Interneuron

subtype LSR

Slope Intercept

Observed log

density

Predicted log

density Lower 95% PI

Upper 95% PI

% Difference

Tip species data CB 1.50 -3.75 3.54 3.69 3.37 3.99 -40% CR 0.94 -0.66 3.86 3.99 3.72 4.26 -35% PV 0.90 -0.56 3.76 3.91 3.58 4.23 -43% Independent CB 1.47 -3.61 3.54 3.67 3.44 3.90 -35% contrasts CR 0.91 -0.50 3.86 4.01 3.77 4.25 -40% PV 1.02 -1.16 3.76 3.91 3.56 4.26 -42%

PI = prediction intervals

Table ESM4. The percentage of interneuron subtypes in areas 4, 9, 32, and 44 of humans, chimpanzees, and macaques

Area 4 Area 9 Area 32 Area 44

Species Sex Age, years

Brain mass, g CB CR PV CB CR PV CB CR PV CB CR PV

Macaca maura F 5 83 4.9 7.6 6.4 3.6 10.3 4.7 3.6 8.0 5.8 3.9 12.9 9.5 Macaca maura F 7 86 3.1 8.4 7.0 4.5 13.3 5.8 5.1 8.8 3.7 6.2 15.0 6.0 Macaca maura F 7 89 2.2 9.8 5.0 7.2 10.0 7.1 4.3 7.1 6.0 4.2 13.5 7.0 Macaca maura F 8 97 2.8 6.7 11.7 3.3 5.7 4.3 5.6 8.1 5.1 6.7 11.1 6.2 Macaca maura M 8 106 2.3 6.8 8.1 3.9 10.2 7.3 4.3 9.4 7.4 2.7 7.1 4.9 Macaca maura M 10 95 2.1 9.7 8.0 3.5 10.9 4.6 3.5 11.8 4.2 3.6 14.5 4.7 Pan troglodytes F 19 229 4.5 8.0 6.4 3.9 12.9 9.6 5.3 6.7 5.2 3.2 9.0 2.2 Pan troglodytes F 27 314 7.5 10.3 15.0 3.6 18.4 15.7 2.9 12.6 10.0 3.7 14.0 14.0 Pan troglodytes F 35 348 5.8 10.5 15.3 2.0 8.0 5.1 1.8 8.2 6.7 5.3 17.8 10.0 Pan troglodytes M 17 384 8.4 9.7 9.3 2.4 8.6 6.5 2.1 14.0 4.9 3.7 10.7 8.5 Pan troglodytes M 19 365 3.9 6.8 7.6 2.5 6.4 7.2 2.4 4.8 2.2 1.6 11.2 5.0 Pan troglodytes M 41 377 6.7 6.6 6.3 3.2 14.9 7.7 2.7 9.5 5.0 1.6 7.7 5.7 Homo sapiens F 40 1,250 2.5 7.9 4.4 6.3 8.1 6.6 4.7 6.7 7.3 2.7 6.7 2.7 Homo sapiens F 43 1,280 2.4 8.3 3.2 1.6 4.6 4.2 1.7 3.5 0.9 3.4 12.9 7.0 Homo sapiens F 53 1,350 5.5 13.4 7.1 3.6 8.2 8.1 7.8 7.7 5.5 2.9 9.6 6.3 Homo sapiens M 35 1,460 2.6 6.6 6.6 1.4 7.6 6.0 3.3 5.8 6.2 4.6 13.9 7.8 Homo sapiens M 48 1,450 6.0 6.4 8.8 4.5 11.0 5.7 5.6 8.5 3.3 5.5 7.7 11.4 Homo sapiens M 54 1,450 3.7 11.0 4.1 4.2 6.4 5.6 3.1 8.1 2.7 9.0 8.1 11.0

Table ESM5. Predictors retained in the discriminant function analysis of interneurons in the frontal cortex of humans, chimpanzees, and macaque monkeys Variable Wilks' Λ P % CB in area 44 0.039 0.048 % PV in area 4 0.052 0.012 % CR in area 32 0.070 0.003 % PV in area 9 0.077 0.002 % PV in area 44 0.129 0.000 % CB in area 32 0.161 0.000 % CB in area 4 0.214 0.000

Table ESM6. Genes analyzed in this study

RefSeq Gene Symbol Gene Title

NM_139058 ARX Homo sapiens aristaless related homeobox NM_004316 ASCL1 Homo sapiens achaete-scute complex homolog 1 NM_178120 DLX1 Homo sapiens distal-less homeobox 1 (DLX1), transcript variant 1 NM_004405 DLX2 Homo sapiens distal-less homeobox 2 NM_005221 DLX5 Homo sapiens distal-less homeobox 5 NM_005222 DLX6 Homo sapiens distal-less homeobox 6 NM_004098 EMX2 Homo sapiens empty spiracles homeobox 2 (EMX2), NM_004956 ETV1 Homo sapiens ets variant gene 1 NM_000168 Gli3 Homo sapiens GLI-Kruppel family member GLI3 NM_133267 GSX2 Homo sapiens GS homeobox 2 NM_014368 LHX6 Homo sapiens LIM homeobox 6 NM_001001933 LHX8 Homo sapiens LIM homeobox 8 (LHX8) NM_003317 NKX2-1 Homo sapiens NK2 homeobox 1 (NKX2-1), transcript variant 2, NM_002517 NPAS1 Homo sapiens neuronal PAS domain protein 1 NM_173159 NPAS3 Homo sapiens neuronal PAS domain protein 3 (NPAS3), transcript variant 2 NM_003269 NR2E1 Homo sapiens nuclear receptor subfamily 2, group E, member 1 NM_000280 PAX6 Homo sapiens paired box 6 (PAX6), transcript variant 1 NM_000193 SHH Homo sapiens sonic hedgehog homolog (Drosophila) NM_005413 SIX3 Homo sapiens SIX homeobox 3 NM_199131 VAX1v2 Homo sapiens ventral anterior homeobox 1 (VAX1), transcript variant 2

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