Networks in the Brain
Genevieve Konopka,1,6,* Tara Friedrich,1Jeremy Davis-Turak,1Kellen Winden,1Michael C. Oldham,7Fuying Gao,1
Leslie Chen,1Guang-Zhong Wang,6Rui Luo,2Todd M. Preuss,5and Daniel H. Geschwind1,2,3,4,*
1Department of Neurology
2Department of Human Genetics
3Department of Psychiatry and Semel Institute
4Program in Neurobehavioral Genetics
David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
5Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, and Department
of Pathology, Emory University School of Medicine, Atlanta, GA 30329, USA
6Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
7Department of Neurology, The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California,
San Francisco, San Francisco, CA 94143, USA
*Correspondence: email@example.com (G.K.), firstname.lastname@example.org (D.H.G.)
Understanding human-specific patterns of brain
gene expression and regulation can provide key
insights into human brain evolution and speciation.
Here, we use next-generation sequencing, and
Illumina and Affymetrix microarray platforms, to
compare the transcriptome of human, chimpanzee,
and macaque telencephalon. Our analysis reveals
a predominance of genes differentially expressed
within human frontal lobe and a striking increase in
transcriptional complexity specific to the human
lineage in the frontal lobe. In contrast, caudate
nucleus gene expression is highly conserved. We
also identify gene coexpression signatures related
to either neuronal processes or neuropsychiatric
diseases, including a human-specific module with
CLOCKasits hubgene and anothermodule enriched
for neuronal morphological processes and genes
coexpressed with FOXP2, a gene important for
language evolution. These data demonstrate that
transcriptional networks have undergone evolu-
tionary remodeling even within a given brain region,
providing a window through which to view the foun-
dation of uniquely human cognitive capacities.
Identification of human-specific patterns of gene expression is
necessary for understanding how the brain was modified in
human evolution. Moreover, uncovering these human expres-
sion profiles is crucial for understanding human-specific neuro-
psychiatric and neurodegenerative disorders. Genetic changes
resulting in changes in the amino acid sequences of proteins
are probably too few to account for the phenotype differences
between humans and our closest relative, the chimpanzee,
prompting the suggestion that changes in gene expression are
likely to drive some of the major phenotypic differences between
humansand chimpanzees (CSAC, 2005; King and Wilson, 1975).
Recent detailed comparisons of human and chimpanzee
DNA differences have identified important differences related
to gene expression including human accelerated regions
(HARs) (Pollard et al., 2006a, 2006b) or conserved noncoding
sequences (CNSs) (Prabhakar et al., 2006), genomic neighbor-
hood differences (De et al., 2009), copy number variations
(CNVs) (Gazave et al., 2011; Perry et al., 2008), and promoter
and enhancer variations (Haygood et al., 2007; Planas and
Serrat, 2010) that could contribute substantially to differences
In addition to these DNA studies, several previous studies
have directly examined human-chimpanzee differences in gene
expression in the brain using microarrays to measure RNA tran-
script levels (Ca ´ceres et al., 2003; Enard et al., 2002a; Khaitovich
et al., 2004a). While these studies were an important first step in
uncovering human-specific patterns of gene expression in the
brain, microarray technology has several limitations that are
especially germane to evolutionary comparisons. First, microar-
ray analysis relies on a priori knowledge of the sequence of the
sample being measured, which precludes identifying unanno-
tated transcripts. The dynamic range of microarrays is also
narrow compared to that of new sequencing technologies
(Asmann et al., 2009; Feng et al., 2010). Perhaps most impor-
tantly, with respect to cross-species comparisons, is the
tremendous loss of usable probes due to sequence divergence
(Preuss et al., 2004).
To avoid these limitations, we utilized next-generation
sequencing (NGS) (Metzker, 2010) to compare gene expression
in the brains of three primates: humans, chimpanzees, and
rhesus macaques, employing 30digital gene expression (DGE)
tag-based profiling to assess levels of mRNA expression. DGE
has been shown to be both highly sensitive and reproducible
when assessing gene expression from human brain (Asmann
et al., 2009). Importantly, the present study included rhesus
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macaques as an outgroup, which provides a basis for inferring
occurred in the human lineage or the chimpanzee lineage. With
a few exceptions (Brawand et al., 2011; Ca ´ceres et al., 2003;
Liu et al., 2012; Somel et al., 2009, 2011), previous microarray
or NGS studies have not included an outgroup or only investi-
gated one brain region (Babbitt et al., 2010; Ca ´ceres et al.,
2003; Enard et al., 2002a; Khaitovich et al., 2004a, 2005; Liu
et al., 2011; Marvanova ´ et al., 2003; Somel et al., 2009; Uddin
et al., 2004; Xu et al., 2010a). We examine three brain regions
representing different developmental origins within the telen-
cephalon: subpallial (caudate), allocortical (hippocampus), and
neocortical (frontal pole). Frontal pole is of particular interest
because it was enlarged and structurally modified in human
evolution (Semendeferi et al., 2001, 2011), is involved in higher-
order cognitive functions (including mental multitasking, social
tions) (Dumontheil et al., 2008; Wendelken et al., 2011), has a
protracted course of development extending into adolescence
and beyond (Dumontheil et al., 2008; Rakic and Yakovlev,
1968; Wendelken et al., 2011), and appears to be affected in
diseases that affect higher-order cognition, including autism
and schizophrenia (see the review of Dumontheil et al., 2008).
We find many more expression changes using NGS than with
microarrays and use network biology to put the changes
observed into a systems-level context, showing high conserva-
tion of the caudate transcriptome, while identifying eight
human-specific gene coexpression modules in frontal cortex.
Moreover, we discover gene coexpression signatures related
to either neuronal processes or neuropsychiatric diseases, in
addition to a human-specific frontal pole module that has
CLOCK as its hub and includes several psychiatric disease
genes. Another frontal lobe module that underwent changes in
humans and chimpanzees
splicing regulation on the human lineage is enriched for neuronal
morphological processes and contains genes coexpressed with
FOXP2, a gene important for speech and language. By using
NGS, by including an outgroup, and by surveying several brain
regions, these findings highlight and prioritize the human-
specific gene expression patterns that may be most relevant
for human brain evolution.
At least four individuals from each species and each brain region
were assessed (see Table S1 available online) using DGE-based
sequencing and two different microarray platforms, Affymetrix
(AFX) and Illumina (ILM) (Figure 1). The total number of unique
genes available for analysis among the species was 16,813 for
DGE, 12,278 for Illumina arrays, and 21,285 for Affymetrix arrays
(Figure 1). Analysis of DGE data revealed an average of 50%
human, 43% chimp, and 39% macaque DGE reads mapping
to its respective genome, with two to three million total reads
mapping on average (Table S1); pairwise analysis of DGE
samples revealed high correlations (Table S1). Neither the total
number of reads nor the total number of mapped reads were
significantly different among species for a given region, elimi-
nating these as potential confounders in cross-species compar-
isons (total reads: FP, p = 0.993; CN, p = 0.256; HP, p = 0.123;
uniquely mapped reads: FP, p = 0.906; CN, p = 0.216; HP, p =
0.069; ANOVA). The samples were primarily segregated based
on species and brain region using hierarchical clustering (data
not shown). We also conducted thorough outlier analysis as
well as covariate analysis and do not find that factors such as
postmortem interval, sex, RNA extraction, library preparation
date, sequencing slide, or sequencing run are significant sample
covariates (see Supplemental Experimental Procedures).
Figure 1. Schematic of Experimental Design
Filtering strategy for DGE samples (A), Illumina microarrays (B), and Affymetrix microarrays (C). See also Table S1.
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DGE Identifies More Expressed Genes in Primate Brain
On average, DGE identified 25%–60% more expressed genes in
the brain than either microarray platform (Figures S1A and S1B).
Correlation plots of DGE-expressed genes versus AFX-ex-
pressed genes show an average correlation of 0.470 (Pearson
correlation; Figures S1C–S1K) and emphasize that a larger
number of genes are detected using DGE compared with AFX.
Further, weighted Venn diagrams demonstrate that DGE
captures the majority of the same genes detected by either mi-
croarray platform but identifies more than 50% additional genes
as present in human or chimp brain (Figure S2).
DGE Identifies More Differentially Expressed Genes
in the Human Brain
Next, we assessed differential expression, identifying more than
five times as many differentially expressed (DE) genes in the
brain between human and chimpanzee using DGE than AFX
and almost eight times more using DGE than ILM (Figure 2A).
The number of DE genes within the microarray data sets and
the FP DGE was consistent with what has been previously pub-
lished(Babbitt etal.,2010; Ca ´ceres etal.,2003; Khaitovich et al.,
frontal lobe between human and chimpanzee (p = 3.1 3 10?3,
Babbitt et al., 2010 and p = 2.7 3 10?2, Khaitovich et al.,
2004b). When we included the macaque outgroup data, using
both the DGE and AFX data sets, we identified approximately
five times as many human-chimp DE genes using DGE
compared with AFX (Figure 2B). As expected, the total number
of DE genes between humans and chimps decreased by about
50% upon inclusion of outgroup data, since many genes change
in their expression levels between the chimp and macaque
lineage. Correlation analysis of DE genes between platforms
showed significant concordance (0.37–0.52 Spearman; p =
9.6 3 10?78–1.2 3 10?105).
Due to the inclusion of three distinct brain regions, we were
also able to identify many genes differentially expressed in only
one of the regions examined (Figure 2C). Interestingly, FP had
the greatest number of region-specific differentially expressed
entially expressed genes in the FP (Figure 2B). Finally, we
confirmed a number of specific genes using a completely inde-
pendent platform, qRT-PCR (Figure 2E). These independent
qRT-PCR analyses demonstrate a 67% and 58% confirmation
rate with DGE and AFX, respectively, in line with published high
correlations between DGE and qRT-PCR (Asmann et al., 2009).
Thus, the use of NGS compared with microarray produces an
expressed in the human brain, reflecting the higher dynamic
range and lower variance of DGE, especially at lower levels of
expression, where arrays are known to suffer (Asmann et al.,
2009). Together, we were able to directly confirm the validity of
the DGE DE data using both an independent whole-genome
method, as well as a robust gene-specific method. Thus, DGE is
a more powerful method for identifying unique gene expression
signatures in the primate brain, providing a real-world example
demonstrating the power of next-generation sequencing for
analysis of a complex tissue such as brain.
Identification of Genes Specifically Changing in the
We next assessed the number of genes changing along each
species’ lineage in the DGE data (Figure 2D) using two distinct
methods, parsimony and an F-test, which insured robustness
of our results (see Supplemental Experimental Procedures).
Genes with similar expression values between chimpanzee
and macaques, but significantly different in humans, would be
indicative of those changing specifically on the human lineage
(hDE). Examination of hDE genes revealed several striking find-
ings. First, the number of hDE genes was greater in the FP
than in the two other brain regions examined. For example,
nearly 30% more hDE genes are detected in hFP (1,450 genes)
than hCN (1,087 genes) (Figure 2D). This could not simply be ex-
plained by a greater number of reads in these samples, as the FP
samples had fewer mapped reads on average than either CN or
HP (Table S1). Moreover, the FP predominance for the lineage-
specific DE genes is not observed in macaque and chimpanzee,
indicating that this is truly human specific. The increase in genes
changing in the frontal pole is of special interest given the recent
finding of an enrichment of evolutionary new genes in the human
lineage specifically within the prefrontal cortex using different
methods (Zhang et al., 2011). Thus, our data identify the
increasing number of genes changing specifically in the frontal
cortex compared to other noncortical regions in human brain
Gene ontology (GO) analyses identified enrichment of several
key neurobiological processes. In the FP, genes involved in
neuron maturation (FARP2, RND1, AGRN, CLN5, GNAQ, and
PICK1) and genes implicated in Walker-Warburg syndrome
(FKTN, LARGE,and POMT1),adisorder characterizedbyagyria,
abnormal cortical lamination, and hydrocephalus (Vajsar and
Schachter, 2006), were enriched. Filtering the FP list for those
specifically hDE in FP and not other brain regions revealed addi-
tional categories of interest including regulation of neuron
projection development (e.g., MAP1B, NEFL, PLXNB1, and
PLXNB2), the KEGG category for neurotrophin signaling (e.g.,
BAX, CSK, CALM2, and IRAK1), and the cellular component
category for axon (e.g., GRIK2, LRRTM1, NCAM2, MAP1B,
of genes involved in cell adhesion (e.g., CAV2, DSG2, SDC1,
SDC4, TJP2, CDH3, and NEDD9) and HP-specific analyses
demonstrated enrichment for neuron differentiation (e.g.,
EFNB1, MAP2, NNAT, REL2, and ROBO1) and the cellular
component category for synaptosome (e.g., ALS2, DLG4,
SYNPR, and VAMP3). CN-specific GO analyses identified
enrichment for genes involved in dendrites and dendritic shafts
(e.g., CTNNB1, EXOC4, GRM7, and SLC1A2), synapse (e.g.,
SYNGR3, SYT6, and CHRNA3), and sensory perception of
sound (e.g., SOX2, CHRNA9, USH2A, and KCNE1).
Genes that are hDE are also enriched for genes under positive
selection (dN/dS > 1 for human compared to chimpanzee), with
FP and HP containing more genes under positive selection than
CN: FP (56), HP (60), and CN (48). These genes are significantly
enriched for the GO categories’ cytokine receptor activity (p =
6.0 3 10?3) and the JAK/STAT signaling pathways (p = 1.0 3
10?3) in the FP (IL11RA, IL13RA2, and GHR), for carboxylic
acid catabolic process (p = 3.3 3 10?3) in the HP (ASRGL1,
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Figure 2. Identification of Genes Specifically Expressed in Human Brain
(A) Graph of number of differentially expressed genes using pairwise comparisons between humans and chimps without outgroup inclusion in each brain region
(upregulated genes = H > C; downregulated genes = H < C; Bayesian t test, FDR < 0.05, absolute fold change > 2).
(B) Graph of number of differentially expressed genes between humans and chimps with the inclusion of macaque outgroup data in each brain region (Bayesian
t test, FDR < 0.05, absolute fold change > 2).
(C) Graph of number of genes from (B) that are uniquely differentially expressed in only one brain region.
(D) Graph of number of genes that are changing along a specific lineage in each brain region (ANOVA; FDR < 0.05).
(E) qRT-PCR analyses of identified hDE genes in FP using DGE. See also Figures S1 and S2.
Human Brain Transcriptional Networks
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CYP39A1, and SULT2A1), and for synaptic transmission (p =
3.5 3 10?2) in the CN (LIN7A, MYCBPAP, and EDN1). Together,
these data suggest that human-specific gene evolution is impor-
tant for signaling pathways in the brain.
The Human Frontal Pole Has Increased Transcriptional
We next applied weighted gene coexpression network analysis
(WGCNA) (Oldham et al., 2008) to build both combined and
species-specific coexpression networks, so as to examine the
systems-level organization of lineage-specific gene expression
differences. We constructed networks in each species sepa-
rately and performed comparisons of these networks to insure
a robust and systematic basis for comparison (Oldham et al.,
2008). The human transcriptional network was comprised of 42
modules containing 15 FP modules, 6 CN modules, 2 HP
modules, and 19 modules not representing a specific brain
region (Figure 3 and Tables S2 and S3; Supplemental Experi-
mental Procedures). The FP samples correlated less with the
CN and HP samples, using a composite measure of module
gene expression, the module eigengene, or first principal
component (Oldham et al., 2008) (data not shown).
The chimp network analysis yielded 34 modules, including 7
FP modules, 9 CN modules, 7 HP modules, and 11 modules
that were unrelated to a specific brain region (Figure 3 and
Tables S2 and S3). The macaque analysis yielded 39 modules
with 6 FP modules, 8 CN modules, 5 HP modules, and 20
modules not related to a specific brain region (Figure 3 and
Tables S2 and S3). Thus, only in human brain were more
modules related to FP thaneither of the other regions, consistent
with increased cellular and hence transcriptional complexity in
FP relative to the other regions. While the smaller number of
chimpanzee (n = 15) and macaque (n = 12) samples compared
to human (n = 17) samples could potentially affect the outcome
of the network analysis, we used the same thresholding param-
eters, and there were equivalent numbers of human and
in human and macaque samples (42 and 39, respectively),
and proportionally more FP modules compared to total modules
in human samples (18/42 = 43%) compared to chimpanzee
Figure 3. Connectivity among Module Eigengenes
green. See also Table S2.
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(8/34 = 23%) or macaque (5/39 = 13%), mitigating this concern.
This indicates that even within a single region of human frontal
lobe, transcriptome complexity is increased with regards to
We next determined the conservation of the modules defined
in humans in the other species (see Supplemental Experimental
Procedures; Table S3). Interestingly, wefound that four out of six
of the human CN modules were highly preserved in chimps and
macaques, whereas only four of the FP and none of the human
tent with these data, the Hs_brown module, which is a highly
preserved caudate module, has high overlap with a previously
documented conserved caudate module between humans and
chimpanzees (Oldham et al., 2006). In addition, the conserved
CN module, Hs_darkcyan, has significant overlap with a recently
identified module in layer 6 of the rhesus macaque parietal lobe
(p = 2.12 3 10?54; hypergeometric overlap; Bernard et al., 2012)
annotated as containing genes important for oligodendrocytes
(Oldham et al., 2008). The conserved CN module, Hs_hotpink,
also has significant overlap with layers 2/3 in the macaque (p =
tated as an astrocyte module (Oldham et al., 2008). In fact, only
conserved modules from our data set had high overlap with
these recently described rhesus macaque cortical modules.
Together, these data highlight the power of our systems
approach to identify conserved cell-type-related networks
among primate brains. Interestingly, the gene CUX2 in the
Hs_hotpink module demonstrated conserved laminar expres-
sion by in situ hybridization in both primate and mouse cortex
(Bernard et al., 2012), providing additional evidence for confir-
mation of our network findings.
In contrast to our findings in the caudate, at least two of the
conserved human FP modules (Hs_orchid and Hs_magenta)
overlapped with two cortical modules denoted as nonconserved
in anearlier microarray-based dataset (Oldhametal., 2006), and
the Hs_magenta module also significantly overlapped an addi-
tional rhesus macaque frontal region module (p = 1.18 3
10?13; hypergeometric overlap) (Bernard et al., 2012), again
highlighting theincreasedpower of theDGEanalysisover micro-
arrays. In addition, two of the genes in the conserved Hs_tan FP
module, RORB and RXFP1, have conserved laminar expression
between primates and mice (Bernard et al., 2012).
Most remarkably, eight out of 15 of the human FP modules
were human specific and were not preserved in either chim-
panzee or macaque (Figures 4A and 4B), whereas only three
out of seven FP modules in chimpanzee and one out of six
ing increased transcriptional complexity in human frontal pole. In
contrast, highly preserved modules among the three primate
brains include the modules with the strongest eigengene for
CN: Hs_brown and Hs_hotpink (Figures 4A–4C and Tables S2
and S3). After controlling for module size using a MedianRank
function (Langfelder etal.,2011;seeSupplemental Experimental
Procedures), we found similar module preservation results
across species as above (Table S3).
The conserved CN modules are very interesting as they high-
light a robust set of key conserved regulatory networks across
primates and likely other mammals. We explored the function
of the hub genes in these modules, as these genes are a primary
indicator of the module network function. The hub genes of the
Hs_hotpink module are significantly enriched for genes involved
in CNS development (p = 1.0 3 10?4; SERPINF1, PRDM8,
NEUROD2, RTN4R, CA10, and MEF2C). The hub genes of the
Hs_brown module are significantly enriched for genes involved
in regulation of G protein-coupled receptor protein signaling
(p = 3.0 3 10?7; RGS9, RGS14, RGS20, and GNG7). The most
highly connected gene in the Hs_brown module is PPP1R1B,
or DARPP-32, which is a critical mediator of dopamine signaling
in medium spiny neurons in the striatum (Walaas and Greengard,
1984). In addition, five other hubs in the Hs_brown module
(ADORA2A, GNG7, PDE10A, PRKCH, and RXRG) overlap with
the top 25 cell-type-specific proteins in Drd1 or Drd2 striatal
neurons in mouse characterized by translational profiling (Doyle
et al., 2008). The hub gene ADORA2A also overlaps with the top
differentially expressed genes from microarray profiling of stria-
tal neurons in mouse (Lobo et al., 2006). When considering all of
the genes in the conserved CN modules, we also find a high level
of confirmation: six genes overlap with striatal microarrays
(ADORA2A, CALB1, HBEGF, NRXN1, STMN2, and SYT6), eight
genes overlap with Drd1 translational profiling (ADORA2A,
BCL11B, GNG7, GPR6, GPR88, MN1, PDE10A, and RXRG),
and nine genes overlap with Drd2 translational profiling
(ADRA2C, ERC2, EYA1, KCNIP2, MYO5B, PDE10A, PDYN,
PRKCH, and WNT2). Interestingly, four CN hub genes have
been implicated in addiction, three are involved in alcohol addic-
tion (MEF2C, RGS9, and VSNL1), one is involved in nicotine
addiction (GABBR2) (Li et al., 2008), and two CN hub genes
have been linked to obsessive-compulsive disorder (HTR1D
and HTR2C) (Grados, 2010). Taken together, this cross-species
conservation and link to disease has implications for pharmaco-
therapeutics of neuropsychiatric diseases being developed in
rodent models because these data showing conservation
between primates and mice further validate rodents as appro-
priate models for striatal function in humans.
GO analysis of FP hub genes reveals an enrichment of genes
involved in neural tube development (FZD3, PAX7, PSEN2, and
SMO), and regulation of synaptic plasticity (ARC, KRAS, and
STAR). However, the majority of FP and HP hub genes are not
enriched for specific ontological categories. These results
emphasize the importance of these human-specific modules
as it suggests that due to their unique expression patterns in
the human brain, very little is known about the coordinated
function of these genes. Finally, at least one of the conserved
modules that was not associated with a particular brain region,
Hs_cyan, does overlap with a previously identified module
containing an enrichment of genes involved in ATP synthesis
that genes important for subcellular components important in all
brain regions throughout evolution may drive some of the
We next sought to integrate these findings showing regional
differences in network module conservation with changes
occurring at the DNA level in specific genes. Four out of eight
of the human-specific FP modules (six genes: BBS10, MTFR1,
TCP10L, FKBP15, KIAA1731, and TRIM22) and both of the
human-specific HP modules (two genes: CP110 and DFFA)
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contain hub genes (genes ranked in the top 20 for connectivity)
under strong positive selection (Supplemental Experimental
Procedures). In addition, six genes from human FP modules
with some level of conservation are under positive selection
(C15orf23, C20orf96, CYP8B1, GSDMB, REEP1, and UACA).
In contrast, only one hub gene from a CN module conserved
Figure 4. Module Preservation among Primate Brain Regions
(A and B) Graph of the module preservation score (Zsummary.pres) compared to the number of genes in a module (module size) for the human network as
reference compared to chimpanzee (A) and macaque (B). CN modules are in black, FP in red, HP in blue, and modules not correlated to region in gray. Modules
below the dashed line at 5 in both comparisons are human specific whereas those at or above 5 are conserved.
(C) Distribution of preservation scores using each species for the reference network. CN networks are conserved in all cases, whereas FP networks are only
specific when human is used as the reference genome. See also Figure S3 and Table S3.
Human Brain Transcriptional Networks
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between humans and chimpanzees is under positive selection
(APTX). Even considering all of the genes in a module for each
brain region, both FP (4.3%) and HP (6.9%) have more genes
under positive selection than CN (3.5%). Therefore, overall, non-
conserved modules tend to have more genes evolving faster.
These data again highlight the biological importance of the
network preservation findings: human-specific FP and HP
modules contain genes with fewer constraints to allow for new
cognitive functions, whereas highly preserved CN modules
contain genes with more constraint in order to participate in
essential brain functions necessary for all primates. These DNA
sequence data indicating positive selection of specific genes
more preferentially in the frontal lobe support the network data
based on gene expression, indicating that this region is most
divergent, and highlights specific hub genes with multiple levels
of evidence for their evolutionary importance.
Human-Specific Coexpression Networks Are Enriched
for Genes Involved in Neuronal Processes
and Neuropsychiatric Diseases
Further functional annotation of the human-specific FP modules
revealed several important findings relevant to evolution of
human brain function. One of the nonpreserved FP modules
was the human orange module (Hs_orange). Visualization of
the coexpressed genes in this module revealed that CLOCK,
such as bipolar disorder (Coque et al., 2011; Menet and
Rosbash, 2011), is a major hub and the most central gene
in the module (Figure 5 and Table S2). CLOCK is also differen-
tially expressed and is increased in human FP (Figure 2E). We
therefore asked whether the CLOCK protein was increased
in human FP and confirmed increased CLOCK protein expres-
sion in human FP compared to chimpanzee FP using immuno-
histochemistry (Figures 5C–5F). In addition, the Hs_orange
module is significantly enriched for other genes involved in
neuropsychiatric disorders, such as seasonal affective disorder
(p = 2.5 3 10?2), depression (p = 2.1 3 10?2), schizophrenia
(p = 4.7 3 10?2), and autism (p = 4.0 3 10?2) (e.g., HTR2A,
FZD3, HSPA1L, KPNA3, and AGAP1; Table S2). To determine
whether this might reflect differences in typical circadian genes
due todifferencesintimeofdeathorother factors,wecompared
the genes in the Hs_orange module to genes annotated as
circadian rhythm genes based on gene expression and other
functional studies (see Supplemental Experimental Procedures).
Interestingly, the genes in the Hs_orange module do not show
significant overlap with previously identified circadian rhythm
genes in the liver or brain of rodents, suggesting that we may
have identified unique targets of CLOCK in human brain. This
is especially interesting, as the histone acetyltransferase func-
tion of CLOCK is conserved from viruses to human (Kalamvoki
suggests potential transcriptional regulatory relationships with
other module genes.
Another FP module not preserved in chimp or macaque is
the Hs_darkmagenta module. Hs_darkmagenta is enriched for
genes involved in CNS development (e.g., BMP4, ADAM22,
KIF2A NRP1, NCOA6, PEX5, PCDHB9, SEMA7A, SDHA, and
TWIST1), growth cones (FKBP15), axon growth (KIF2A), cell
adhesion (ADAM22), and actin dynamics (EIF5A2) (Figure S3
and Table S2). These data are congruent with the finding that
human neurons have unique morphological properties in terms
of the number and density of spines (Duan et al., 2003; Elston
et al.,2001), providing a potential molecular basis for these ultra-
tion of these molecular data with the previous morphological
data support the hypothesis that in addition to the expansion
of cortical regions, the human brain has been modified by evolu-
tion to support higher rates of synaptic modification in terms of
growth, plasticity, and turnover (Ca ´ceres et al., 2007; Preuss,
An Exon-Level Analysis of Gene Connectivity Reveals
Accelerated Evolution in the Human Brain
We next examined each unique read individually to determine
whether there was information about the expression of alterna-
tive isoforms. Among the 22,761 Refseq genes detected, 86%
of those genes had more than one read aligning to it, demon-
strating that most transcripts had alternative forms detected.
Although some genes (about 40%) had a dominant variant that
accounted for more than 90% of the reads aligning to a specific
gene, more than half (57.3%) of genes had a dominant variant
that accounted for less than 90% of the expression detected.
We then examined the expression of these alternative variants
by calculating the Pearson correlation between all reads that
align to the same gene. We found that most pairs were slightly
negatively correlated and that the average correlation between
all pairs aligned to the same gene was zero (data not shown),
suggesting that these reads do indeed represent differentially
Based on these data that unique reads probably contained
information about alternative variants, we built a coexpression
network based upon aligning reads to specific exons rather
than only to whole genes to potentially uncover an enrichment
of gene coexpression patterns based on alternative splicing
(see Supplemental Experimental Procedures and Table S4).
whose module eigengene corresponded to the human frontal
pole. One of these, the olivedrab2 module (Figure 6), is an FP
module enriched for genes involved in neuron projections,
neurotransmitter transport, synapses, axons, and dendrites, as
well as genes implicated in schizophrenia. In addition, this
module contains many of the highly connected genes within
the Hs_darkmagenta module from the whole gene WGCNA
(n = 9, including the top five hubs out of a total of 62 genes in
preserved in the other species, 73% and 78% of exons exhibit
higher connectivity within the human data than the chimpanzee
or macaque data sets, respectively, demonstrating that these
genes have enhanced connectivity within the human brain (Fig-
ure S4). Connectivity is a measure of the extent to which the
expression of a gene (or exon) is coexpressed with all other
genes (or exons) that we and others have shown to be highly
preserved in human brain networks and is related to functional
properties of genes such as brain region, cellular types, and
disease states (Miller et al., 2010; Oldham et al., 2008). There-
fore, changes in this measure of connectivity among primate
Althoughthis module is
Human Brain Transcriptional Networks
608 Neuron 75, 601–617, August 23, 2012 ª2012 Elsevier Inc.
Figure 5. Visualization of a Human-Specific Gene Coexpression Network: The Hs_orange Module
(A and B) Visualization of the top 300 connections among the coexpressed genes in the module. Hub genes are denoted using purple circles.
(C–F) Immunohistochemistry for CLOCK in human FP (C) and chimpanzee FP (E). Corresponding negative control sections are shown in (D) and (F). Scale bars
represent 100 mm.
Human Brain Transcriptional Networks
Neuron 75, 601–617, August 23, 2012 ª2012 Elsevier Inc. 609
species may indicate changes in the function of these genes in
evolution. Strikingly, the conservation of connectivity of genes
within this human network and the other primates is less (r =
0.281, human-chimp; r = 0.182, human-macaque) than the
networks (r = 0.421) (Figure 7A). In view of the very close evolu-
tionary relationship of humans and chimpanzees, these results
indicate that patterns of gene connectivity in this module
Figure 6. Visualization of a Human-Specific Gene Coexpression Network: The olivedrab2 Module
(A–C) Visualization of the module eigengene in the olivedrab2 module in the human (A) samples and the expression of the same genes in the chimpanzee (B) and
macaque (C) samples.
(D) Visualization of the top 500 connections among the coexpressed genes in the module. FOXP2 and FOXP1 are highlighted in orange. Genes in purple are
specifically expressed along the human lineage in FP, genes in red are FOXP2 targets identified in human neural progenitors, genes in yellow are FOXP2 targets
identified in human fetal brain (Spiteri et al.,2007),human transformedneuronal cell lines (Konopkaet al.,2009),or mousebrain (Vernesetal., 2011),and genes in
green are both human specific and FOXP2 targets. See also Figure S4 and Table S4.
Human Brain Transcriptional Networks
610 Neuron 75, 601–617, August 23, 2012 ª2012 Elsevier Inc.
Figure 7. Brain Network Connectivity Differences
The human olivedrab2 FP module asan example of differential
connectivity in the primate brain.
(A) Histogram of the difference in connectivity among genes in
the olivedrab2 module between each species. Human
networks compared to chimpanzee (blue) and macaque (red)
networks have greater differences than between chimpanzee
and macaque (gray).
(B–D) Unrooted distance trees demonstrate that while human
and chimpanzee are more similar as species overall (B) and in
absolute gene expression (C), the connectivity divergence of
genes in the human FP is greater (D).
Human Brain Transcriptional Networks
Neuron 75, 601–617, August 23, 2012 ª2012 Elsevier Inc. 611
underwent dramatic reorganization in the human lineage, after it
diverged from the chimpanzee lineage, without, it should
be noted, major changes in overall gene expression within
the module (Figures 7B–7D). Therefore, this new dimension of
gene expression analysis, only made possible using NGS, has
revealed a striking pattern of accelerated evolution of gene
connectivity in the human brain.
Human-Specific Coexpressed Gene Isoforms Are
Enriched for a Differentially Expressed Splicing Factor
gene corresponded to transcript variants of the gene, we at-
tempted to gain insight into the potential mechanism of regula-
tion of these transcripts by examining the exon to which the
read aligned for known RNA binding motifs. We calculated an
enrichment score for each module for the known RNA binding
motifs, and we then clustered modules based on their motif
enrichment patterns. We found that olivedrab2 clustered with
lavenderblush1 and thistle4 modules, which are among the top
scoring neuronal modules whencompared toaprevious annota-
tion of the human brain transcriptome (Oldham et al., 2008).
Moreover, the exons represented by the olivedrab2 module
showed an enrichment of ELAVL2 binding motifs within the
module gene membership (Table S4). ELAVL2 (alias HuB) is
also hDE in FP, increasing on the human lineage, consistent
with the parallel changes in splice isoforms observed in this
module. The adult CNS function of this splicing factor, ELAVL2,
is mostly unknown, but recent work indicates that it interacts
with microRNAs to regulate cortical neurogenesis via derepres-
sion ofFoxg1(Shibata etal.,2011).The evolutionarysignificance
of this pathway is highlighted by the recent finding that Foxg1
mutations in humans lead to a syndrome of microcephaly and
social and language impairment (Kortu ¨m et al., 2011). Table S4
provides a list of the significantly enriched ELAVL2 targets (p <
2.2 3 10?5) within this module in human frontal lobe (637 exons
in 521 genes). Importantly, we identify ELAVL1 as an enriched
target. Since ELAVL2 has already been shown to regulate
ELAVL1 (Mansfield and Keene, 2012), this provides validation
of our computational approach. These targets are enriched in
genes involved in calcium channels (p = 3.3 3 10?2; CACNB4,
CACNG2, and RYR2), synaptic vesicles (p = 4.4 3 10?2;
APBA1, RAB3B, SCAMP1, SYN2, and SYT11), postsynaptic
density proteins (p = 3.23 3 10?2; CAMK2N1, DLG2, DLG4,
GRIN2A, and MAP1B), as well as many other intrinsic CNS
properties. There is also an enrichment of genes involved in
Alzheimer’s disease (p = 4.2 3 10?2; BACE1, CYCS, GRIN2A,
GSK3B, and SDHC) and autism spectrum disorders (p = 1.1 3
10?4; APC, CNTN4, CNTNAP2, DLX1, EIF4E, FBXO33, FOXP2,
GABRB3, GALNT13, GRIN2A, HS3ST5, MAP2,
MECP2, MEF2C, MKL2, NRXN1, SLC9A6, and TSN). These
data provide a starting point for further mechanistic studies of
the molecular function of neurons in the frontal pole, especially
how they may relate to human cognitive disorders. Therefore,
examination of gene coexpression in primate brain has revealed
new biological connections and insights into the evolved human
brain: gene connectivity as evidenced by modified transcrip-
tional programs together with alternative splicing is likely critical
for human-specific frontal pole cognitive functions.
FOXP2 Is Coexpressed with Genes Involved in Neuronal
Projections in a Human-Specific Frontal Pole Module
contains FOXP2 among its more differentially connected genes
in the human brain (kMEHuman = 0.91, kMEChimp = 0.67,
kMEMacaque = 0.46; p = 1.24 3 10?5) (Figure 6 and Table S4).
FOXP2 is a transcription factor implicated in language and
cognition that has undergone accelerated evolution and has
human-specific functions (Enard et al., 2002b, 2009; Konopka
et al., 2009; Lai et al., 2001).
To validate the coexpression relationships in this module, we
assessed enrichment of known FOXP2 targets. We identified
13 genes that overlap with previously published targets of
FOXP2 from human brain, human cells, or mouse brain (Figure 6)
(Konopka et al., 2009; Spiteri et al., 2007; Vernes et al., 2007,
2011) with the genes in the olivedrab2 module including one of
the hub genes, TMEM55A (Figure 6D). We noted that FOXP1 is
scription(Li etal.,2004), FOXP1hasbeenimplicated in language
impairment, intellectual disability, and autism (Carr et al., 2010;
Hamdan et al., 2010; Horn et al., 2010; O’Roak et al., 2011).
We further examined FOXP2 targets in human neuronal cell
lines previously shown to exhibit patterns of gene expression
similar to those of forebrain neurons (Konopka et al., 2012). We
manipulated FOXP2 expression during the normal 4week period
of differentiation of these human cells by either forcing expres-
sion of FOXP2 or knocking down expression of FOXP2 using
RNA interference (Supplemental Experimental Procedures).
Using Illumina microarrays, we identified over 600 target genes
with expression going in the opposite direction with FOXP2
forced expression compared to FOXP2 knockdown (Figure S4).
Upon comparing this list of experimentally identified FOXP2
targets in human neural progenitors using microarrays with the
genes in the olivedrab2 module identified by DGE, we found
a significant overlap (13 overlapping genes, p = 4.0 3 10?4; Fig-
ure 6D). Interestingly, nine FOXP2 target genes overlap with hDE
genes in this module (Figure 6D). Strikingly, the FOXP2 targets in
the olivedrab2 module are enriched for genes involved in neuron
projections, synapse, and axonogenesis. These data fit with
work showing modulation of neurite outgrowth in mouse models
of Foxp2 (Enard et al., 2009; Vernes et al., 2011). Thus, while
regulation of neurite outgrowth by FOXP2 may be a conserved
mammalian function of FOXP2, the contribution of human
FOXP2 to modulation of this critical neuronal process may be
enhanced as evidenced by increased neurite length in human-
ized Foxp2 mice (Enard et al., 2009). Together, these data
identify a human-specific FP gene coexpression network that
is enriched in both genes involved in neurite outgrowth, binding
sites for a differentially expressed splicing factor on the human
lineage, and genes regulated by FOXP2.
Since the sequencing of the human genome, a major goal of
evolutionary neuroscience has been to identify human-specific
patterns of gene expression and regulation in the brain. While
Human Brain Transcriptional Networks
612 Neuron 75, 601–617, August 23, 2012 ª2012 Elsevier Inc.
(Babbitt et al., 2010; Brawand et al., 2011; Ca ´ceres et al., 2003;
Enard et al., 2002a; Khaitovich et al., 2004a, 2005; Liu et al.,
2011; Marvanova ´ et al., 2003; Somel et al., 2009, 2011; Uddin
et al., 2004; Xu et al., 2010a), our study ascertains human-
and sufficient sample sizes in multiple species. Moreover, our
study identifies human-specific gene coexpression networks
with the inclusion of an outgroup. By including these data, we
find that gene coexpression or connectivity has rapidly evolved
in the neocortex of the human brain. In addition, the genes
with changing patternsof connectivityareimportant for neuronal
process formation, the structures that underlie neuronal func-
tional activity and plasticity. Therefore, the evolution of gene
connectivity in the human brain may have been critical for the
proposed uniqueabilities of human neurons for synaptic integra-
tion (Ca ´ceres et al., 2007; Preuss, 2011).
specific FP gene coexpression modules. Since FP is a region of
the neocortex that was recently enlarged and modified in human
evolution (Dumontheil et al., 2008; Semendeferi et al., 2011),
human-specific FP networks may provide particular insight into
human brain evolution. Previous work has highlighted the evolu-
tion of prefrontal cortex in terms of its expansion, enlargement of
select subdivisions, its cellular organization, and its connectivity
(Rakic, 2009; Semendeferi et al., 2011). In fact, strong evidence
supports the protomap model, which by connecting neuronal
ular basis for the evolutionary addition of new brain regions
(Donoghue and Rakic, 1999; Rakic et al., 2009). Here, we
demonstrate that even within a single specific cortical region,
transcriptional regulation and complexity have dramatically
increased on the human lineage. These changes may not be
specific to the frontal lobe; it is possible that profiling of addi-
tional cortical areas will uncover a general trend for increased
transcriptional connectivity in human cortex overall relative to
nonneocortex. This network connectivity may reflect elaboration
of signaling pathways within neurons, neuronal and synaptic
ultrastructural elements, or even new cell types. For example,
critical for neuronal processes, such as spines, dendrites, and
These findings are striking in light of data demonstrating that
human neurons contain a greater number and density of spines
compared to other primates (Duan et al., 2003; Elston et al.,
2001). A number of the genes identified in the Hs_olivedrab2
module support the hypothesis that our network approach is
useful for prioritizing large-scale comparative genomics data
sets as well as potentially providing insight into human-specific
neuronal processes. STMN2 (or SCG10) has previously been
shown to be an important regulator of NGF-induced neurite
outgrowth (Xu et al., 2010b). Thus, the human-specific increase
in STMN2 may be involved in the human-specific increase in
spine number. In addition, STMN2 also acts to retard the
multipolar transition of neurons and subsequent migration of
neurons (Westerlund et al., 2011), suggesting a potential role
lating human cortical expansion. MAP1B is both increasing on
the human lineage in the FP as well as a FOXP2 target in human
neural progenitors. MAP1B has primarily been associated with
axon growth and guidance and was recently shown to be neces-
sary for the maturation of spines, since loss of MAP1B causes
a deficiency in mature spines (Tortosa et al., 2011). Therefore,
increased expression of MAP1B in human brain may also be
involved in the increased number or density of spines in human
neurons. The transcription factor LMO4, a previously identified
FOXP2 target (Vernes et al., 2011), is also coexpressed in the
olivedrab2 module. LMO4 has preferential increased expression
in the right human fetal cortex (Sun et al., 2005), perhaps due to
repression by FOXP2 in the left cortex. Moreover, coexpression
in this human FP module, the distinct expression pattern in the
right cortex, and potential regulation by FOXP2 together suggest
animportant role for LMO4 regulation of genes involved in asym-
metrically developed cognitive processes such as language.
Several other hub genes in the Hs_darkmagenta have also
been directly implicated in neuronal processes such as axons
and dendrites. FKBP15 (or FKBP133), which is increased in the
human FP, promotes growth cone filipodia (Nakajima et al.,
2006). In contrast, KIF2A is an example of a hub gene that is
coexpressed in a human-specific FP module. KIF2A negatively
regulates growth cones (Noda et al., 2012). Together, these
data suggest that human-specific expression of genes leads to
positive growth and maturation of neuronal processes, while
those highly coexpressed but not showing human-specific
expression may have either negative or refining effects on
neuronal process formation. Thus, our data provide a molecular
basis for connecting anatomical changes to their underlying
genomic origins, furthering our understanding of human brain
evolution and providing predictions that can be tested in model
systems. Moreover, our data support the hypothesis that human
brain evolution has not only relied upon the expansion and
modification of cortical areas but also on increasing molecular
and cellular complexity within a given region. Such complexity
is exemplified in findings of neuronal subtypes like the von
Economo neurons that have evolved in animals of complex
cognition such as primates and expanded in the human brain
(Allman et al., 2010; Stimpson et al., 2011).
Previous attempts to identify unique properties of the human
brain have focused on changes in brain size, anatomy, regional
connectivity, and gene expression (Preuss, 2011; Sherwood
et al., 2008). Consistent with recent findings (Brawand et al.,
2011), our study finds that patterns of gene expression differ-
ences across species are generally consistent with known
species phylogeny (Figures 7B and 7C). However, there are
some remarkable differences between the gene coexpression
connectivity tree and the species tree: the relative distance of
human genes to chimpanzee and macaque genes is much larger
gene connectivity, and hence gene regulation, in the human
brain. Previously, we have found that connectivity is a more
sensitive measure of evolutionary divergence than gene expres-
sion (Miller et al., 2010; Oldham et al., 2006). Therefore, by using
new technology and multiple primate species, we have shown
a rapidly evolving mechanism for the coordination of gene
expression patterns in the human brain. Here we demonstrate,
at a genomic level, that increased transcriptional diversity of
Human Brain Transcriptional Networks
Neuron 75, 601–617, August 23, 2012 ª2012 Elsevier Inc. 613
a single brain region accompanies the cortical expansion known
to occur in human evolution.
Of particular note in this regard is the olivedrab2 human FP-
specific coexpression module, which is enriched in genes
involved in neurite outgrowth and has as a hub the gene for
FOXP2, a transcription factor involved in human language and
cognition (Lai et al., 2001). Whereas FOXP2 levels themselves
are low in the adult brain and FOXP2 is not an hDE gene,
FOXP2 is enriched in frontal cortex in developing human brain
(Johnson et al., 2009) and it underwent sequence evolution
(Enard et al., 2002b) so that it binds a number of new human-
specific transcriptional targets (Konopka et al., 2009). Impor-
tantly, we experimentally validate an enrichment of human
FOXP2 target genes identified during progenitor development
in vitro in this human FP module in adults. Thus, the significant
overlap with FOXP2 targets in the olivedrab2 module is consis-
tent with a human-specific transcriptional program for FOXP2
in frontal pole (Table S4), which is supported by the graded
reduction in FOXP2’s centrality in this network from human to
chimp to macaque. So although FOXP2 is highly expressed in
the striatum, these data suggest that the key evolutionary
changes are most relevant in the cerebral cortex. These data
provide strong in vivo evidence for FOXP2 evolution in human
cognition, complementing previous in vitro analyses (Konopka
et al., 2009).
Another important observation is the enrichment of ELAVL2
binding sites within this module. ELAVL2 has been shown to
promote a neuronal phenotype (Akamatsu et al., 1999) and has
been modestly associated with schizophrenia (Yamada et al.,
2011). Indeed, we find that the ELAVL2 target genes in the olive-
drab2 module are enriched for genes involved in nervous system
function and disease. For example, numerous genes involved in
neuronal function such as ion channels as well as genes critical
for synapses, dendrites, and axons are among the genes with
ELAVL2 binding motif enrichment. There are also a significant
number of autism candidate genes among these potential
binding targets (see Results). Therefore, these data have uncov-
ered potential novel mechanisms for linking alternative splicing,
gene coexpression, and neuropsychiatric disorders.
To date, most research on human brain evolution has focused
on changes in brain size, although the past decade has seen
contributions from comparative neuroimaging (e.g., Rilling
et al., 2008), revealing human specializations of fiber-tract orga-
nization, and from comparative histology, revealing human
specializations of cell and tissue organization (e.g., Preuss and
Coleman, 2002). However, the number of well-documented
human-specific brain phenotypes is currently quite small
(Preuss, 2011). The present study demonstrates evolutionary
changes at another level of organization, the level of gene inter-
tions can help us understand how evolution crafted changes in
brain morphology and physiology manifested at the levels of
cells and tissues. What is more, the discovery of human-specific
gene coexpression networks, such as the ones in the cerebral
cortex that are described here, can drive ‘‘phenotype discovery
providing information about changes in patterns of molecular
expression that can be used to uncover human specializations
of human brain structure and function’’ (Preuss, 2012; Preuss
et al., 2004). In addition, the enrichment of genes associated
with neuropsychiatric diseases within these networks provides
affirmation of the relevancy of human-specific gene expression
patterns providing insight into these cognitive disorders.
We recognize that due to the inherent methodology of this
study (profiling from tissue pieces), we are unable to fully deter-
mine the anatomical expression of transcripts within a particular
brain region. For example, while we attempted to only use gray
matter, we still find a number of gene coexpression modules
driven by astrocyte or oligodendrocyte genes. Therefore, these
data provide a road map for future immunohistochemical work
that will be needed to ascertain the expression of these high-
lighted genes within different cell types in the brain. Additionally,
tissue-level expression profiling may miss low-abundance tran-
scripts expressed in small subsets of cells. The use of NGS
provides significantly improved sensitivity in this regard over
microarrays, yet still could miss very low-abundance transcripts.
We apply WGCNA, which permits in silico dissection of whole
tissue into cell-level expression patterns (Oldham et al., 2008).
Therefore, some of the frontal pole modules may indeed corre-
spond to specific subpopulations of neurons that may be unique
to humans.Future work using
section will be useful to uncover transcriptional profiles of addi-
tional human-specific gene expression changes at a cellular
level. Nevertheless, this work provides a key foundation for
connecting human-specific phenotypes to evolved molecular
mechanismsat thelevel of newsignaling pathways and genomic
complexity in the human brain. Application of the approaches
introduced hereto otherbrain regions hasthepotential togreatly
enrich our understanding of human brain organization and
For Experimental Procedures, please see Supplemental Experimental Proce-
dures available online.
The NCBI Gene Expression Omnibus (GEO) accession number for the next-
generation sequencing data reported in this paper is GSE33588.
Supplemental Information includes four figures, Supplemental Experimental
Procedures, and four tables and can be found with this article online at
WethankDr. GiovanniCoppola for providing code for microarray and WGCNA
analyses and Lauren Kawaguchi for laboratory management. Thiswork is sup-
(G.K.), a NARSAD Young Investigator Award (G.K.), the National Center for
Research Resources (RR00165) and Office of Research Infrastructure
Programs/OD (P51OD11132), and a James S. McDonnell Foundation grant
(JSMF 21002093) (T.M.P., D.H.G.). Human tissue was obtained from the
NICHD Brain and Tissue Bank for Developmental Disorders at the University
of Maryland (NICHD contract numbers N01-HD-4-3368 and N01-HD-4-
3383). The role of the NICHD Brain and Tissue Bank is to distribute tissue
and therefore cannot endorse the studies performed or the interpretation of
Human Brain Transcriptional Networks
614 Neuron 75, 601–617, August 23, 2012 ª2012 Elsevier Inc.
results. G.K., M.O., T.M.P., and D.H.G. conceived the project. G.K. and L.C.
conducted experiments. G.K., T.F., J.D.-T., K.W., M.O., F.G., G.-Z.W., and
R.L.analyzeddata.T.M.P.performed IHCandtissuedissectionsand provided
nonhuman primate samples. G.K. and D.H.G. wrote the manuscript. All
authors discussed the results and commented on the manuscript.
Accepted: May 31, 2012
Published: August 22, 2012
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