inhibitory interneurons of the human prefrontal...
TRANSCRIPT
Proc. R. Soc. B (2010) 277, 1011–1020
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* 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 ([email protected]).
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.
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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
rneu
rons
2
4
6
8
10
12
14
16
18
% C
R-i
r in
tern
euro
ns
(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).
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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.
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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
CBCBCBCBCBCBCBCBCCBCBBBBBBB CRCCRCRCRCRCRCRCRCRCRCRCRRCRRCRCRCRCRRCCRRCCRRCRCCRCCRCCC PVPVPVPVPVPVPVPVVPVPVPVVPPPVVPPVVVVVPPPV
Alou
atta
car
aya
Sym
phal
angu
ssy
ndac
tylu
sG
orilla
gor
illaM
acac
a m
aura
Hom
o sa
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