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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Neuron. Author manuscript; available in PMC 2011 October 21.
Published in final edited form as:
PMCID: PMC2993319

Human brain evolution: harnessing the genomics (r)evolution to link genes, cognition, and behavior


The evolution of the human brain has resulted in numerous specialized features including higher cognitive processes, such as language. The combination of our newfound communication expertise together with the process of transgenerational evolution at the epigenetic level has led to an exponential increase in human knowledge and abilities. In balance with these beneficent attainments though, the human brain has also acquired vulnerabilities to neuropsychiatric and neurodegenerative diseases, which reflect genetic and environmental factors. To understand the mechanisms of this disease susceptibility, a deeper appreciation of the developmental processes and their relationship to underlying features of brain evolution will be necessary. Knowledge of whole genome sequence and structural variation via high throughput sequencing technology provides an unprecedented opportunity to view human evolution at high resolution. However, phenotype discovery is a critical component of these endeavors and the use of non-traditional model organisms will also be critical for piecing together a complete picture. Ultimately, the union of developmental studies of the brain with studies of unique phenotypes in a myriad of species will result in a more thorough model of the groundwork the human brain built upon. Furthermore, these integrative approaches should provide important insights into human diseases.

Introduction: genome sequence and beyond

Attempts to understand the uniqueness of what it means to be human necessarily start with delving into the properties of the human brain. Genetics and genomics play a critical role in this regard, as identifying the genetic basis of phenotypic differences provides a causal foothold. Brain development, from its circuitry to cell and dendritic morphology is highly dependent on the environment. Without genetic reference points, it is challenging to ascribe observed differences between CNS features in humans and our closest relative, chimpanzee for example, to the forces of evolution (whether neutral or adaptive), or to differences in diet, lifestyle or other environmental features. Studying the brain from an evolutionary perspective and combining these results with those from development and pathology, connecting genetic variation to neural circuit development and functioning, will yield the best approximation of how natural forces shaped this organ. Aside from satisfying basic curiosity about the origins of our abilities, such endeavors have enormous implications for understanding human diseases involving cognition and behavior, ranging from intellectual disability and autism to neurodegenerative dementias.

On a macroscopic level, the evolution of the brain can be examined using several approaches: neuroanatomical comparisons, behavioral comparisons, and the study of skeletal remains from extinct species. These approaches are necessary for building a context in which molecular and cellular comparisons can be placed. In this era of genomics, we are poised to elaborate the numerous divergent properties of the human brain by collating gene expression and regulation data with phenotype and behavioral data. Here, we discuss insights into the genetics and genomics of human brain evolution generated through the use of technological advances in expression and sequencing platforms. These comparisons need to be interpreted carefully, however, as experimental validation of evolutionary studies are often challenging, and an openness to perform cross-species comparative studies is needed. Nonetheless, the integrated comparative analyses that can now be attained using appropriate analytical and technical approaches are providing fascinating insights into the story of human brain evolution.

Sequencing genomes: reading our evolutionary history in the code

The sequencing of the mouse genome followed quickly by the human genome, allowed us our first glimpse into evolutionary comparisons at a genome-wide level (Lander et al., 2001; Venter et al., 2001; Waterston et al., 2002). With at least 70 million years interceding since the common ancestor of mouse and human however, there were obviously many differences found between these genomes. Thus, the sequencing of the chimpanzee genome a few years later filled in many of those gaps, demonstrating approximately 4-fold more divergence between human and chimpanzee than had previously been appreciated, mostly due to structural chromosomal variation. Not unexpectedly, these drafts are a true work in progress. For example, the chimpanzee draft genome was based on one individual (Consortium, 2005). A recent reannotation of the chimpanzee genome using new technology has uncovered numerous novel transcripts (Wetterbom et al., 2010). Moreover, the human genome was based on only a few individuals, and until recently, only a handful of additional individuals have undergone complete genome sequencing. Not unexpectedly therefore, recent whole genome sequencing has uncovered much more diversity in the human genome than originally appreciated (Kidd et al., 2008; Pickrell et al., 2010). Thus, until the completion of the 1000 genomes project, which aims to identify most of the human genetic variants are have frequencies of at least 1% of the population ( and beyond, any comparative analyses using the reference human genome needs to take into consideration the caveats associated with incomplete documentation of the range of normal variation. This is especially relevant given the observation that a significant percentage of any individual’s genome is variable and diverges from normal diploid copy number.

One of the most exciting advances in the realm of sequence comparison was the unveiling of a first pass Neandertal genome (Briggs et al., 2009; Burbano et al., 2010; Green et al., 2010). While controversies such as experimental contamination and unknown admixture effects had stymied confidence in the initial analysis (Green et al., 2009; Wall and Kim, 2007), these data, together with the sequencing of other hominid species (Krause et al., 2010), should provide evidence for human-specific features that have promoted our continued evolution and survival. Although this represents a technical tour de force, interpretation at the whole genome level is limited by the relatively sparse average fold-coverage published. Detailed analysis of three genes in the Neandertal genome has been conducted: FOXP2 (Krause et al., 2007), the melanocortin 1 receptor (Lalueza-Fox et al., 2007), and microcephalin (Lari et al., 2010). None of these studies provided any insight into potential unique features of humans compared to Neandertals, as all three genes had variation in line with known human variation, suggesting that these changes occurred in an earlier ancestor. However, targeted resequencing of the Neandertal genome identified 83 genes with nonsynonymous substitutions that became fixed on the human lineage (Burbano et al., 2010). Functional analyses of the effect of these changes in the human proteins need to be undertaken.

The study of non-human primates is critical for understanding human brain evolution, and the use of multiple primate species is necessary for identifying changes that occur specifically in the human lineage (Khaitovich et al., 2006; Preuss et al., 2004; Varki et al., 2008). However, we expect that the inclusion of non-traditional model organisms in the study of brain evolution will further inform our understanding of specific brain processes or phenotypes, as well as uncover lineage-specific changes. To this end, a number of little-studied species on the molecular level have recently had their genomes sequenced such as honeybees, songbirds, and elephants (Table 1).

Table 1
Sequenced species that will improve our understanding of brain evolution

Once the complete sequences of genomes are in place, an often-utilized approach for determining the evolution of any given gene or region of the genome relies on direct sequence comparisons between two species. Measures of positive selection are calculated by dividing the number of nonsynonymous amino acid changes by what is considered neutral background, typically the number of synonymous changes or intronic variants; values greater than one are considered indicative of adaptive evolution (Figure 1). Of course, much of what has been considered neutral, is rapidly changing as non-coding regions of the genome are identified as functional (Varki et al., 2008). Additionally, for any specific gene, these metrics of adaptive evolution based on straight sequence comparisons do not take into account tissue specificity of expression. For example, strong evidence of adaptive evolution for brain-expressed genes at the gene level is often taken as evidence for that gene’s involvement in brain evolution. However, unless a gene is essentially brain specific, or some other form experimental evidence is provided (Konopka et al., 2009), one can not be certain that the sequence changes have been selected due to their brain function, rather than their function in other organ systems, Therefore, it is important to directly study genomics in the tissue of interest, and on multiple levels, not just examine the genome sequence to be able to make convincing evolutionary arguments.

Figure 1
Combining comparative genomics with phenotype and expression data leads to disease insights

In addition to straightforward sequence comparisons, there are other comparisons that can be made at the genome level. Characterization of evolutionary breakpoints has provided a list of regions where chromosomal rearrangements may have led to both loss-of-function and gain-of-function mutations in genes important for mammalian evolution (Larkin et al., 2009; Murphy et al., 2005). The initial study uncovered 40 breakpoints that were likely primate-specific (Murphy et al., 2005), and further analysis derived 44 primate-specific breakpoints with two being human specific (Larkin et al., 2009). These data suggest that the human genome has achieved a good measure of stability, and therefore when new mutations arise within these breakpoints, disease can often occur. Other data suggests that some genomic rearrangements such as insertion of repetitive elements or mitochondrial DNA sequences into the nuclear genome were not the result of positive selection, but rather neutral drift (Gherman et al., 2007). But ultimately as King and Wilson’s prescient hypothesis suggested (King and Wilson, 1975), changes at the level of gene regulation is a greater driver of diversity between humans and chimpanzees than changes in coding sequences alone.

To uncover the complexity of gene expression and regulation during brain evolution, a multi-pronged approach needs to be taken (Figure 2). First, the power of next generation sequencing (NGS) needs to be leveraged to ascertain the changing landscape on many levels: noncoding sequences, epigenetics, splicing, transcriptional regulation, microRNA regulation, expression, and ultimately translation. These data need to be obtained using large-scale studies, appropriately analyzed, and completely integrated. Second, a greater understanding of developmental mechanisms needs to be unveiled, as these are likely integral to how the brain adapted. Third, a multitude of organisms should be investigated, not just traditional model organisms, and behavioral phenotypes with expression data in these organisms need to be combined. Many of these organisms are far more expert than humans at certain functions that may be related to the CNS, or have adapted to unique environments, so they provide excellent substrates for the comparative investigation of brain evolution. Finally, the incorporation of human brain disease attributes will highlight the most recent steps in brain evolution, and how they have yielded significant cognition and a more disease vulnerable organ (Crespi, 2010; Preuss et al., 2004; Vernier et al., 2004).

Figure 2
Multiple layers of regulation underlie human brain evolution


It is difficult to fathom how the relatively small number of genes in the human genome (a little over 20,000) has led to the highly complex social organism that is human. Changes in many parameters have built upon the archetypal mammalian brain to yield an organ of unprecedented complexity and versatility. Differences in gene regulation and expression have led to the emergence of new cell types and brain regions, and more remains to be discovered. These, in turn, have resulted in new behaviors and phenotypes, and have ultimately improved our cognitive abilities. Understanding the mechanisms underlying these changes in the human brain from both a genomic and developmental perspective should shed some light on the evolutionary process.

Gene Expression and Gene Splicing

Perhaps the most direct, truly genome-wide method for investigating evolutionary divergence in the brain is to measure differences in gene expression levels. Several studies have conducted comparative transcriptomic analyses in the brains of primates (Oldham and Geschwind, 2009; Preuss et al., 2004). Initial reports used microarrays for assessment and identified a few hundred genes that were different in human compared to chimpanzee brain (Caceres et al., 2003; Enard et al., 2002a; Khaitovich et al., 2005; Khaitovich et al., 2004a). Further studies have also utilized this approach of comparing the two species using analysis of microarray data (Khaitovich et al., 2006b; Nowick et al., 2009; Oldham et al., 2006; Somel et al., 2010). One of the more salient findings was an enrichment of KRAB-zinc finger transcription factors in human brain (Nowick et al., 2009). These genes have already been linked to both brain development and disease, and are therefore important for further consideration in functional analyses. Another compelling finding was the difference in gene expression with aging specifically in the human brain as a potential mechanism for the increased longevity of the human species (Somel et al., 2009). In addition, the genes most correlated with aging are also those demonstrating less evolutionary conservation (Somel et al., 2010). These data suggest that positive selection is having a significant effect on genes involved in aging, and likely neurodegenerative susceptibility, as well. Subcellular localization of expression may also play a pivotal role in evolution. At least one example, GLUD2, is a hominid-specific gene that localizes specifically to mitochondria in comparison with the ancestral from GLUD1 (Rosso et al., 2008). As GLUD2 has acquired some brain-specific functions, this change in localization may have been essential for these new properties in the primate brain.

One of the difficulties inherent in analysis of gene expression data is determining positive selection from the neutral background (Khaitovich et al., 2004b; Oldham and Geschwind, 2009), as standard methods for analysis of differential expression cannot determine which changes are functional. Here, gene network analysis may serve as a framework for assessment of the functionality in changes in expression (Oldham and Geschwind, 2009; Oldham et al., 2006) and has been utilized to examine changes in gene co-expression networks between humans and both chimpanzees (Oldham et al., 2006) and mice (Miller et al., 2010). These analyses produced several important insights into the human brain transcriptome. The first insight was that gene connectivity in the newly evolved cortex was less preserved in other species compared to networks in the older caudate nucleus (Oldham et al., 2006). In addition, this measure of differential network connectivity between the species is more sensitive than differential expression in ascertaining evolutionary divergence (Miller et al., 2010; Oldham et al., 2006). Finally, this approach is able to pinpoint highly-connected genes in human-specific modules with known association to human-specific neurodegenerative disorders (Miller et al., 2010). Together, these studies highlight the importance of applying unbiased analytical methods to large-scale expression datasets to yield novel underlying structure (Geschwind and Konopka, 2009).

One of the interesting areas of convergence in gene expression studies is the identification of energy metabolism related genes (Caceres et al., 2003; Goodman et al., 2009; Grossman et al., 2001), mitochondria, and other related pathways suggesting differences in cellular energetics between humans and non human primates (Goodman and Sterner, 2010; Preuss et al., 2004). Some of these pathways, especially mitochondria, are also related to human aging and neurodegenerative disease (Miller et al., 2008). This suggests the interesting hypothesis that a potential consequence of having a human cerebral cortex and increased energy demands may be a predisposition to neurodegenerative conditions.

While some amount of diversity in the brain is certainly driven by changes in the amount, timing, and location of gene expression, it has become increasingly apparent that changes in the splicing of the mRNAs themselves is dramatically different among species, even among primates. Comparisons of splicing in primate transcriptomes have been conducted using both exon arrays for brain tissue (Calarco et al., 2007; Irimia et al., 2009; Lin et al., 2010) and NGS for liver tissue (Blekhman et al., 2010). One study found that human-specific splicing is likely regulated by cis-regulatory elements (Lin et al., 2010), and another found that while similar percentages of genes between humans and chimpanzees are alternatively spliced, these do not significantly overlap the differentially expressed genes (Calarco et al., 2007). Similar analyses are needed using NGS in the brains of primates, as NGS will allow for the discovery of previously unannotated isoforms.

Targets of known specific splicing factors such as Nova1/2 have also been examined in vivo at a genome-wide level in the brain, whereas a bioinformatic approach has been taken to examine the targets of the neuronal splicing factors Fox-1 (A2BP1) and Fox-2 (RBM9) in brain (Licatalosi et al., 2008; Ule et al., 2005; Zhang et al., 2010; Zhang et al., 2008). A discovery-based approach has also been taken to identify novel CNS-specific splicing factors. Using this methodology, a neural specific splicing factor, nSR100, was uncovered and shown to be integral for neuronal differentiation both by the identification of its target genes, which are important for CNS development, as well as in vivo loss of function studies (Calarco et al., 2009). Loss of Magoh, a gene involved in RNA splicing, leads to a disorganized, microcephalic brain with fewer neurons (Silver et al., 2010). Moreover, splicing of MAGOH itself has a human-specific pattern (Lin et al., 2010). Another example of how differential splicing can affect brain function can be found upon studying the Liprin-alpha protein family. These proteins have essential roles in both dendrites and synapses, and human-specific patterns of splicing occur, and are likely to regulate human-specific functions of this family of genes (Zurner and Schoch, 2009). Together, these data point to a critical role for both splicing in general and of genes such as MAGOH and Liprins in the evolution of the human brain.

Another level of species divergence lies in the regulation of gene expression. Understanding epigenetic regulation in the nervous system is an area of active study that should provide a first level of input into potential differences in regulation (Borrelli et al., 2008; Dulac, 2010). Changes at the epigenome, which include methylation and histone modifications, can derive from numerous sources including the environment, stress, and neuronal activity. In addition, epigenetics can lead to transgenerational evolution through effects on gene expression (Sharma and Singh, 2009). Other work has focused on identifying either cis-regulatory elements that have undergone positive selection, or the acquisition of novel transcriptional activity with evolution. Elegant work has detected both evolutionary conserved non-coding sequences (CNSs) (Pennacchio et al., 2006), followed by a further refined analysis to identify specific human-accelerated conserved non-coding sequences (haCNSs) (Prabhakar et al., 2006; Prabhakar et al., 2008) and human accelerated regions, HARs (Pollard et al., 2006a; Pollard et al., 2006b). Of note, haCNSs are enriched near neuronal cell adhesion genes (Prabhakar et al., 2006), genes spatially differentially expressed in human fetal brain (Johnson et al., 2009), and genes differentially regulated by human and chimpanzee FOXP2 (Konopka et al., 2009). One haCNS, HACNS1, has been shown to be important for human-specific digit and limb patterning, and therefore may have been instrumental in the development of features such as an opposable thumb in humans (Prabhakar et al., 2008). The most significant HAR, HAR1, forms an RNA gene that is expressed in Cajal-Retzius neurons (see below) in human fetal brain (Pollard et al., 2006b). Thus, the importance of newly evolved non-coding elements in the human genome cannot be underestimated in terms of their potential for regulating critical processes during brain development.

A well-characterized example of differential transcriptional activity in evolutionary terms is the study of the transcription factor FOXP2. This gene has undergone accelerated evolution along the human lineage due to changes at two amino acids (Enard et al., 2002b), and pathogenic variants of FOXP2 have been found in people with a specific form of verbal dyspraxia (Fisher and Scharff, 2009; Lai et al., 2001). However, FOXP2 is also highly expressed in the lung, so changes at the genome level cannot definitively be assigned to changes in brain function. Here is where experimental tests of changes at the genome level are important. In this regard, support for the relationship of the sequence changes with brain evolution was provided by comparisons of expression of the human form of FOXP2 in human cells versus the chimpanzee form. This difference led to a different transcriptional program, and these differentially expressed genes significantly overlap with differentially expressed genes in human and chimpanzee brains (Konopka et al., 2009). Remarkably, differentially expressed genes included many involved in craniofacial development, suggesting co-evolution of the CNS capacity for language with the vocal apparatus in humans (Konopka et al., 2009). Furthermore, mice expressing the human FOXP2 in place of the mouse gene display novel behavioral phenotypes, including changes in ultrasonic vocalization, and differences in neuronal morphology and activity (Enard et al., 2009). Together, these studies suggest that positive selection of even one transcription factor can potentially have a dramatic difference in the evolution of neuronal signaling and circuitry.

Studies of primates and other vertebrate species and the importance of outgroups

While all of the above studies have produced useful information as to which genes are different in the human brain, they are not able to determine which genes changed specifically along the human lineage unless an outgroup (e.g. rhesus macaque) is included. The use of an outgroup allows one to identify which lineage the observed changes occurred on. In comparisons of humans and chimpanzees, another primate with a common ancestor, such as rhesus macaque, is often included. Thus, any difference that holds between humans and chimpanzees as well as humans and macaques (but not chimpanzees and macaques) can be considered to have occurred more recently in time on the human lineage. The closer in evolutionary time two species are to one another, the more confident one can be about the relevance of the comparison. A few brain gene expression studies comparing humans and chimpanzees did indeed include outgroup data (Caceres et al., 2003; Enard et al., 2002a; Khaitovich et al., 2006b; Somel et al., 2009); however, the majority of these gene expression reports were still limited by either the number of brain regions or samples used in the analysis. Moreover, cross hybridization issues reduced the number of transcripts examined as the chimpanzee samples were queried on human microarrays. To alleviate this potential bias, NGS, which is agnostic to species, is now being used to conduct comparative transcriptome analysis by directly sequencing all expressed RNAs (RNA-seq). At least two RNA-seq studies have been completed comparing gene expression in the brains of humans, chimpanzees and macaques and have uncovered non-coding RNAs in the human lineage with potential functions in directing gene expression (Babbitt et al., 2010; Xu et al., 2010). All of these reports are important for understanding human-specific patterns of expression; however, as will become evident below different cells and areas of the brain conduct differentially evolved functions. Therefore, large-scale comparative expression analyses of many more brain regions are critical. Another vertebrate outgroup that may be critical for understanding brain development is the elephant shark. This vertebrate can be used for comparison with other bony vertebrates such as the mouse and zebrafish. In fact, the elephant shark genome is more similar to the human genome than zebrafish genome is to the human genome (Venkatesh et al., 2007). A recent study identified the homologous Dlx enhancer in the elephant shark genome, and showed that this enhancer had similar activity to the mouse enhancer in the forebrain but also displayed some unique characteristics (MacDonald et al., 2010). Further comparative expression studies using species such as the elephant shark should provide additional insights into unique and conserved brain evolution pathways in jawed vertebrates.

Moving from tissue to cells and beyond: cellular, synapse and circuit phenotype discovery

Cellular phenotype discovery is a critical aspect of the human evolutionary-genomics enterprise in this age of molecular and cellular neurobiology. Little is understood about the differences between cell types, such as neurons and glia, and key aspects of neuronal morphology, such as dendritic structures and the synapse, between humans and other vertebrates. Genomic investigations provide essentially unlimited opportunities for foundational investigations in this regard.

While it is debatable as to whether the pronounced increase in glia along the human lineage was more important for brain evolution than neuronal modifications; due to the unique physiology of neurons, investigations into the specific evolution of neurons are an essential line of inquiry. This approach has been thoroughly undertaken at the proteomic level. Comparison of the synaptic proteome in mouse and fly revealed core proteins that were further extended and elaborated in vertebrates to yield synapses with increasing functional abilities (Emes et al., 2008). Moreover, a significant number of proteins in the core synaptic proteome are enriched for genes already known to be implicated in several neuropsychiatric disorders, most prominently schizophrenia (Fernandez et al., 2009). This explains the debilitating effects that are seen when disruption to these genes occurs. Combining these proteomic data with that from genomic and transcriptomic studies will provide a greater understanding of how translational regulation may also be playing a role in divergent evolution. Furthermore, examination of the synaptic proteomic needs to be carried out in human and non-human primate brain, preferably during brain development, to identify any human-specific adaptations.

An even more challenging question in terms of human-specialization is whether there are different amounts or functions of particular neurons in the human brain. Even in the well-studied mouse, we have only just begun to appreciate and understand the diversity of neuronal subtypes in the brain. Thus, knowledge of neuronal subclasses in humans and other species is even sparser. Here, we illustrate two human neuronal subtypes with potential implications in human brain evolution. The first example are von Economo neurons (VENs) (von Economo, 1926) that are only present in the anterior cingulate cortex and frontoinsular cortex, regions important for social behavior. These neurons are present in greatest numbers in the human brain, and have only been identified in great apes, cetaceans, and elephants (Allman et al., 2010; Butti et al., 2009; Hakeem et al., 2009; Nimchinsky et al., 1999), all species that have passed tests for self-awareness (Byrne and Bates, 2010; Craig, 2009; Povinelli et al., 1993). There is a significant reduction in the number of VENs in the brains of patients with fronto-temporal dementia (FTD) (Seeley et al., 2006), a disease that presents with deterioration of social function, whereas there is an increase in the number of VENs in the brains of autistic patients (Santos et al., 2010), a disease with dysfunctional social interactions. It is therefore possible that VENs represent a specialized class of neurons with a role in social interactions. While expression of a few genes has been described in VENs (Allman et al., 2010; Allman et al., 2005), targeted whole genome expression profiling of VENs could provide some insight into their evolution and function in the human brain.

Another specialized neuronal cell type that is not unique to primate, but displays differential characteristics along the primate lineage are the Cajal-Retzius neurons. These cells, as well as Reelin which is expressed in these cells, are both critical for cortical lamination (Marin-Padilla, 1998). There is an increase in Reelin along the primate lineage (Abellan et al., 2010a; Zecevic and Rakic, 2001), and LIM-homeodomain transcription factors also display differential expression in Cajal-Retzius neurons in the cortex of multiple species (Abellan et al., 2010a; Abellan et al., 2010b). Finally, while a few studies have examined gene expression profiles specifically in these neurons (Soriano and Del Rio, 2005), agnostic developmental time-course whole genome expression studies in multiple species are warranted to identify the entire coterie of primate and human-specific expression patterns. In addition, cross-species comparisons of neuronal plasticity, activity-dependent gene expression, and dendritic structures are also needed.

Brain size isn’t everything, but it is relevant

While clearly a relatively gross phenotype, brain size is correlated with overall intellectual function (Deaner et al., 2007; Roth and Dicke, 2005; Rushton and Ankney, 2009). So, the identification of genes involved in brain size has also provided insight into the molecular mechanisms underlying human brain evolution. However, one logical fallacy, not often appreciated, is that the relatively weak, but positive correlation between brain size and intelligence does not mean that every gene contributing to brain size is related to intelligence, or to its evolution. Study of common genetic variation in each of these genes to assess its contributes to normal variation in brain size, and to intelligence in turn will be necessary. Several genetic loci associated with microcephaly were first identified followed by the further identification of mutations in several genes in these regions including ASPM, CDK5RAP2, CENPJ, MCPH1, SLC25A19, and STIL. Interestingly, there is some evidence for positive selection of ASPM and MCPH1 in humans (Evans et al., 2005; Mekel-Bobrov et al., 2005), however, other reports have suggested different factors as the driving force for variation in these genes (Currat et al., 2006; Timpson et al., 2007; Woods et al., 2006; Yu et al., 2007). This type of controversy highlights the difficulty in conducting comparative genomic studies and drawing conclusions about the evolutionary implications. A recent report used the new technology of whole exome sequencing to define the causative gene in one microcephaly locus: WDR62 (Bilguvar et al., 2010). Of note, WDR62 is expressed in the nucleus of neural progenitors and therefore may be important for regulating the transition from precursor to postmitotic neuron. Loss of function studies in model systems should address this potential mechanism.

Another parameter with a potential effect on brain size is the control of the cell cycle in neural progenitor pools (Kriegstein et al., 2006). Changes in cell cycle duration in these cells in primates are thought to be one adaptive mechanism for the increase in neocortical size in primates (Dehay and Kennedy, 2007; Rakic, 1995). Area-specific modulation of cell cycle length can also result in different sensitivities to proliferation versus differentiation signals (Lukaszewicz et al., 2005). In vivo manipulation of genes important in cell cycle progression results in mouse cortical tissue subsuming a thickened more primate-like phenotype (Chenn and Walsh, 2002; Pilaz et al., 2009). Recently, a primate-specific PAX6 target, RFPL1, was shown to influence cell cycle progression with a potential implication for this function in brain development (Bonnefont et al., 2010). The microcephaly-related genes ASPM and MCPH1 are also involved in regulating brain size through mitotic spindle activity (Bond et al., 2002) and cell cycle progression (Xu et al., 2004), respectively. The identification of additional primate or human-specific mRNA patterns for genes involved in cell cycle progression should permit further study of the role of the cell cycle in the evolution of the brain, and regional differences in expression are probably key.

Certain human-specific expression patterns may also result in specialized cell types that underlie cortical size. The recent demonstration of outer subventricular zone (OSVZ) cells undergoing asymmetric division to generate neurons is of great interest due to the substantial increase in the number of OSVZ neurons along the primate lineage (Fietz et al., 2010; Hansen et al., 2010). One of these reports also demonstrated the existence of OSVZ-derived neurons in the cortex of ferret. Since the ferret has a gyrencephalic cortex, this supports the intriguing hypothesis that OSVZ progenitors are a critical determinant of brain folding. Thus, the increase in the number of OSVZ progenitors in human brain may have been instrumental in generating a larger brain due to an increase in surface area from folding.

While the function of a number of genes involved in brain folding (e.g. LIS1, DCX, RLN) has been actively studied in rodent brain, the mouse brain is lissencephalic and therefore does not lend itself to studies of brain folding. Thus, several groups have been using either ferret, as mentioned above, or the pig. To this end, a recent report detailed tissue-specific transcriptomic findings in the ferret brain (Bruder et al., 2010). Another group examined gene expression in the pig cortex before and after the onset of convolution using microarrays and identified differentially expressed genes with a potential role in cortical folding (Nielsen et al., 2010). Manipulation of these genes in a genetically tractable organism such as mouse should provide insight some mechanistic information on the involved signaling pathways. Furthermore, deep sequencing of both the pig and ferret genomes (Table 1) should also assist in interpreting future gene expression studies using NGS in these species.

Lastly, but perhaps most importantly, while brain size and brain folding are relevant to the evolution of the brain, it is clear that the expansion of specific regions in the primate brain have had a tremendous impact on human brain evolution. In particular, the increase in the number of association cortex areas and the development of the prefrontal cortex has been instrumental for cognitive abilities (Krubitzer, 2007; Krubitzer and Kaas, 2005). Again, regional size is not necessarily the important factor, but it is the evolution of the cortical networks among the prefrontal cortex and other cortical areas that has likely driven adaptations in cognition (Semendeferi et al., 2002). This also includes the development of perisylvian cortical asymmetries related to language ((Geschwind and Miller, 2001); see below). In addition, the expression and timing of expression of particular genes within specific regions is also critical for the development of specific cortical areas (Krubitzer and Kaas, 2005). Examples of genes with critical roles in arealization include Emx2, Pax6, and COUP-TFI (Nr2f1) (Krubitzer, 2007). Not surprisingly, variation in some of these genes leads to cognitive deficits including schizencephaly (EMX2) (Brunelli et al., 1996; Faiella et al., 1997), or cerebellar ataxia and intellectual disability (Gillespie syndrome; PAX6) (Graziano et al., 2007; Ticho et al., 2006). A better understanding of the genes important for arealization in mammalian brain evolution should provide insight into the gene networks underlying higher cognition.


Since ontogeny recapitulates phylogeny to some degree, it is critical to study the developing human brain. Moreover, proper arealization or regionalization during brain development is necessary for setting up the brain circuitry needed for higher cognitive functions. Therefore, the elucidation of regional expression patterns during human brain development and comparison of these patterns to those in other species is an important aim. One of the caveats to this approach is the paucity of fetal human and great ape brain tissue. Despite this challenge, a recent report examined gene expression using exon microarrays in thirteen regions in human fetal brain (Johnson et al., 2009). Almost a third of expressed genes are either regionally differentially expressed and/or differentially spliced. In addition, a significant number of differentially expressed genes have proximal non-coding regions that have undergone accelerated evolution along the human lineage. These data suggest that positively selected regulatory elements may have had a significant impact on driving human brain regionalization and thus, human cognition. Another study examined gene expression patterns in peri-sylvian cortex by comparing two regions in human fetal brain and identified numerous differentially expressed genes, including several patterning or cell adhesion molecules (Abrahams et al., 2007). Of note, CNTNAP2 was significantly enriched in human frontal cortex yet did not show enrichment in rodent cortex. Since variation in CNTNAP2 is associated with both language endophenotypes (Alarcon et al., 2008), and autism spectrum disorder (Arking et al., 2008; Bakkaloglu et al., 2008), this specific pattern of expression in frontal cortex suggests an instrumental role for CNTNAP2 in cortical circuitry disrupted in autism, a human-specific disorder. The spatial overlap of non-coding RNAs with protein-coding mRNAs may also be playing a role in brain evolution. A significant number of non-coding RNAs are co-expressed with adjacent mRNAs specifically in the brain (Ponjavic et al., 2009). These spatiotemporal pairs denote another level of regulation in the brain that needs to be fully appreciated when attempting to dissect out the drivers of brain evolution

The next higher order of brain development that may have had a significant impact on brain evolution is the lateralization of the brain. While there is evidence for brain lateralization in other species (Bisazza et al., 1998), higher cognitive processes, for example language, which is typically left hemisphere lateralized in right-handers (in the majority; (Galaburda et al., 1978), show remarkable laterality in the human cerebral cortex. Thus, understanding the process of cerebral lateralization at the molecular level should provide insight into this feature likely adapted during evolution for novel cognitive functions in humans. Several studies have examined whether there are asymmetrically expressed genes in the hemispheres of the developing human brain (Johnson et al., 2009; Sun et al., 2005). While a number of candidate genes have been uncovered (Sun et al., 2005), it is likely that the process of cerebral lateralization is actually imparted at an earlier time point in development, via signals from the underlying mesoderm (i.e. first trimester) (Geschwind and Miller, 2001). In addition to gene expression, asymmetry has been examined using both neuroanatomical methods and neuroimaging techniques, such as MRI and diffusor tensor imaging (DTI), which can trace the pattern of white matter tracts in the brain. While both humans and chimpanzees have asymmetrical brains at the level of both grey and white matter (Cantalupo et al., 2009; Hopkins et al., 2008; Schenker et al., 2010), at least one study has uncovered a human-specific white matter pattern in the arcuate fasciculus, a tract important for language (Rilling et al., 2008). Thus, while the acquisition of an asymmetrical brain occurred far back in terms of evolutionary time, the further elaboration of an asymmetric cerebral cortex with new changes in gene expression on the human lineage may have been instrumental in the development of spoken language.


Using animal models to study language evolution

One of the most challenging phenotypes to model and study in terms of evolution is language (see Newbury and Monaco in this issue). While a number of organisms exhibit some form of vocal communication, human language is thought to be unique. One of the more salient human-specific features of language is the use of recursion, or embedding ideas within one another. This feature likely co-opted the evolving working memory system present in lower organisms to expound upon the new language function. Another human-specific language feature is the use of language to teach an extrinsic skill or to use language in the development of theory of mind (Penn et al., 2008; Premack, 2007).

Two basic genetic approaches can be taken: identification of common genetic variation and identification of rare, Mendelian mutations that alter human language. Few common variants related to language have been identified. Notable exceptions include variation in LRRTM1 (Francks et al., 2007) that has been related to brain asymmetry, and CNTNAP2, in which common variation contributes to language endophenotypes, such as non-word repetition in humans (Alarcon et al., 2008; Vernes et al., 2008). Remarkably, CNTNAP2 is regulated by the transcription factor FOXP2, one of the few genes associated with Mendelian forms of speech and language dysfunction in humans (Fisher et al., 2003; Vernes et al., 2008). These data provide the first glimmer of the emergence of a potential molecular pathway down stream of FOXP2 likely related to development of frontal-striatal circuits involved in language learning and function.

The uniquely human nature of language and evidence for accelerated evolution of FOXP2 on the human lineage seriously challenge the use of animal models for the study of speech and language evolution. However, the human brain was built on the foundation of its vertebrate ancestors’ nervous systems (Krubitzer, 2007; Krubitzer and Kaas, 2005), and there are many processes, such as vocal motor learning, that are shared in other organisms. In this regard, studies in non-human models have highlighted the conserved features of FOXP2. Both FoxP1 and FoxP2 show overlapping and highly parallel expression in cortical basal ganglia circuits involved in vocal motor learning in the song bird and human fetal brain (Teramitsu et al., 2004). Remarkably, lentiviral-mediated knockdown of Foxp2 in the zebra finch has demonstrated that FoxP2 is integral for learned song production (Haesler et al., 2007). Loss of function studies in mouse have demonstrated that Foxp2 is necessary for proper ultrasonic vocalizations in newborn mice (Shu et al., 2005). Further studies using conditional Foxp2 knockout mice should investigate whether Foxp2 is needed for socially-mediated adult vocalizations. Thus, because of the conservation of phenotypes from songbird to mouse to people, FoxP2 is not necessarily “language specific.” What is likely is that FoxP2 has an important role in sensory-motor integration, and in humans this has evolved to serve an important role in language (Teramitsu et al., 2004; White et al., 2006). FoxP2 stands as an example of the intricacies of attempting to study evolution, in particular human-specific phenotypes, in lower organisms. It can be very difficult to extrapolate these findings to human features. Nevertheless, this type of comparative phenotype-genotype studies will inform our understanding of the CNS in many species and ultimately the evolution of the human brain.


It is interesting to speculate that perhaps one untoward consequence of the highly evolved human brain has been the emergence of human-specific neurodegenerative and neuropsychiatric diseases (Crespi et al., 2007; Crespi, 2010; Dean, 2009; Preuss et al., 2004; Vernier et al., 2004). Combining an evolutionary perspective with an understanding of the pathology of these diseases should uncover the mechanisms that have led to the development of these diseases in the human lineage. Expression studies of disease-related genes in human brain have begun to elucidate human-specific patterns of gene expression in terms of both levels and localization. An example of this type of approach is again exemplified in the study of CNTNAP2, (Alarcon et al., 2008), which is enriched in frontal pole, an area of the brain critical for executive function (Abrahams et al., 2007). The results of human brain expression profiling studies thus far (Johnson et al., 2009) suggest that comparison of comprehensive gene expression maps in mature and developing human brain with mouse and non-human primates will provide enormous value.

One of the greatest challenges in studying brain evolution is the use of the most appropriate model systems. An issue with incorporating disease-relevant information is that many model organisms do not succumb to the same diseases that humans are susceptible to. For example, it has been exceedingly difficult to model neurodegeneration in mice. A recent study suggests that differences in gene expression networks and thus gene function may at least partially explain this observation, as genes such as PSEN1, which cause a dominant disease in humans, but not in mouse, show significant expression divergence (Miller et al., 2010). The absence of certain brain circuits in mice, including certain prefrontal regions, also challenges the development of mouse models of human neuropsychiatric diseases that involve these areas (Preuss, 2000).

Even more difficult to study than disease in humans is the evolution of positive attributes such as creativity or intelligence. Definitional issues certainly challenge this area of research. Nevertheless, one recent study used the power of comparative evolutionary genomics to identify genes under positive selection in the human genome (Bochdanovits et al., 2009). Association of these genes with intelligence was then conducted, and a polymorphism in ADRB2 was found to be significantly associated with cognitive ability. Further functional analyses of this gene needs to be carried out to place it within the context of brain development and/or neuronal signaling pathways.

Summary and Future Directions

Traditional comparative genomics are core tools for the study of evolution, as changes in the genome may allow one to determine causality with relation to specific phenotypic differences between species. However, one also needs to consider other forces affecting changes on the genome or phenotype level, such as environmental exposures and nutrition. Moreover, in the study of the brain, we need to be cognizant of cell- and circuit-specific factors, as well as emergent properties of human higher cognition and behavior, such as social and cultural buffers that could be acting in concert with changes on other levels (Varki et al., 2008).

Animal model organisms play a crucial role in neuroscience. However, one unfortunate trend has been a de-emphasis on studies of human brain. Advances in genomics and genetics and availability of human brain tissue, coupled with non-invasive methods for the study of brain function provide enormous opportunities. The critical challenge is to embrace the evolutionary differences between humans and model organisms, and use them as a platform for discovery, rather than pretending that they don’t exist. This will permit integration of the large amount of data produced in model organisms with an understanding of human brain function, so as to understand the true relevance of these data to humans. Comparative functional genomic investigations will provide a molecular basis for elucidation of brain circuit evolution, and the evolution of human cognition and behavior.

The technical advance of microarrays allowed for unprecedented access to differential gene expression in brain evolution. However, due to the biases and static nature of the information able to be queried using microarrays, this technology is limiting for cross-species comparisons. For comparative genomic purposes, NGS is rapidly making expression array issues of historic interest, but NGS still requires a draft genome for alignment. Therefore, as NGS becomes less expensive and computational resources improve, even individual labs will be able to conduct de novo sequencing of their unique model organism. The focus in evolutionary genomics needs to extend work in traditional experimental organisms such as mouse, rat, drosophila etc., into a deeper appreciation of the myriad of organisms with a CNS, especially those that might have unique brain and behavior functions. For example, recent deep sequencing of small RNAs in marine worms identified ancient microRNAs that may have been essential for the evolution of the CNS (Christodoulou et al., 2010).

As always, integration of multiple levels of genomic, regulatory, and RNA and protein expression, while challenging, is a must. Genome-wide transcription factor binding studies (ChIP-seq), sometimes combined with RNA-seq, are just beginning to be conducted (Kim et al., 2010; Visel et al., 2009). However, these and other studies are really only beginning to scratch the surface of what NGS can uncover, such as allele specific expression (Meaburn et al., 2010; Schalkwyk et al., 2010). Additionally, it will be useful to define molecular pathways based on functional genomic studies that identify gene sets expressed in either a tissue or cell-specific manner (Doyle et al., 2008), and use these unbiased pathways as a basis for studying genetic pathway associations in normal cognition and disease. Such studies will aid in broadening our acceptance of the role of particular genes in specific tissues.

Phenotype discovery, in general, is a huge area of need within the field of evolutionary genomics. Thus, the use of imaging data analysis to determine phenotypes has been essential for this progress. As imaging is one of the few windows into the brain we can utilize in living patients, it is critical that we harness this technology in combination with genomic data. Perhaps the greatest challenge for the evolutionary neuroscientist is to establish how to combine data from functional genomics studies with behavioral data, again, in particular imaging data, and genome-wide association study information. Such integration relies on being able to determine quantitative behaviorally relevant phenotypes. Recent work in the identification of “phenologs,” or phenotypes that arise from disruption of orthologous genes, is one approach to ascertain such data (McGary et al., 2010). Through this methodology, phenologs can be detected for diseases such as amyotrophic lateral sclerosis (ALS; human-yeast), autism (human-mouse), and intellectual disability (human-Arabidopsis). Combining this approach with other data from model systems will identify gene networks essential for normal human brain function that can be further explored in lower organisms.

The genomics revolution now permits us to perform key experiments that could rapidly advance our understanding of the evolution of higher cognition. But, one caveat to many of the evolution studies discussed above is the fundamental assumptions that are made in drawing the particular conclusion of each study. In particular, we need to be cautious about over-interpretation of any findings in the setting of uncertain models. Interspecies comparisons as far as human and mouse were important first steps, but some basic assumptions may not have been met and the positive selection of nervous system genes in humans, as initially suggested (Dorus et al., 2004), is not yet well established. For example, this and other studies need to consider the role of relaxation of constraint, or reducing the restrictions of amino acid changes, rather than only adaptive evolution in the primate brain. One hypothesis is that relaxation of constraint presented the occasion for adaptive evolution to occur. Furthermore, other fundamental characteristics such as mutation rates, and huge differences in population history, as well as gene choice and choice of arbitrary relaxed thresholds for determining protein evolution (Dorus et al. 2004) may hamper interpretation of these types of comparative studies. In fact, a number of studies have used other metrics in their analyses, and there seems to be little consensus in the field as to whether human brain genes have indeed undergone accelerated evolution or not (Bakewell et al., 2007; Clark et al., 2003; Nielsen et al., 2005; Shi et al., 2006). It is notable that studies that assess the genome in an unbiased manner, rather than picking only one arbitrary gene set, show no evidence for adaptive evolution in brain genes overall (e.g. (Nielsen et al., 2007; Shi et al., 2006)).

Another example is the pathbreaking study of a humanized Foxp2 mouse (Enard et al., 2009). In this case, are the assumptions about evolution made reasonable, and how can we know? What are the experiments that could even test this? We cannot obviously expect to generate a talking mouse and yet, there is no doubt that many experiments needed to more fully explore FoxP2 function can be done in mouse and other model organisms. The same issues are multiplied when we begin to study gene-gene interactions and molecular circuits. Rather than avoiding this complexity, it will be crucial to embrace the many levels of analyses and cross species comparisons that are necessary if we truly expect to advance to a new level of understanding of human brain evolution.


This work is supported by a grant from the NIMH (R37MH60233-06A1) to DHG. GK is supported by an A.P. Giannini Foundation Medical Research Fellowship, a NARSAD Young Investigator Award, and the NIMH (K99MH090238).


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  • Abellan A, Menuet A, Dehay C, Medina L, Retaux S. Differential expression of LIM-homeodomain factors in Cajal-Retzius cells of primates, rodents, and birds. Cereb Cortex. 2010a;20:1788–1798. [PubMed]
  • Abellan A, Vernier B, Retaux S, Medina L. Similarities and differences in the forebrain expression of Lhx1 and Lhx5 between chicken and mouse: Insights for understanding telencephalic development and evolution. J Comp Neurol. 2010b;518:3512–3528. [PubMed]
  • Abrahams BS, Tentler D, Perederiy JV, Oldham MC, Coppola G, Geschwind DH. Genome-wide analyses of human perisylvian cerebral cortical patterning. Proc Natl Acad Sci U S A. 2007;104:17849–17854. [PubMed]
  • Alarcon M, Abrahams BS, Stone JL, Duvall JA, Perederiy JV, Bomar JM, Sebat J, Wigler M, Martin CL, Ledbetter DH, et al. Linkage, association, and gene-expression analyses identify CNTNAP2 as an autism-susceptibility gene. Am J Hum Genet. 2008;82:150–159. [PubMed]
  • Allman JM, Tetreault NA, Hakeem AY, Manaye KF, Semendeferi K, Erwin JM, Park S, Goubert V, Hof PR. The von Economo neurons in frontoinsular and anterior cingulate cortex in great apes and humans. Brain Struct Funct. 2010;214:495–517. [PubMed]
  • Allman JM, Watson KK, Tetreault NA, Hakeem AY. Intuition and autism: a possible role for Von Economo neurons. Trends Cogn Sci. 2005;9:367–373. [PubMed]
  • Arking DE, Cutler DJ, Brune CW, Teslovich TM, West K, Ikeda M, Rea A, Guy M, Lin S, Cook EH, Chakravarti A. A common genetic variant in the neurexin superfamily member CNTNAP2 increases familial risk of autism. Am J Hum Genet. 2008;82:160–164. [PubMed]
  • Babbitt CC, Fedrigo O, Pfefferle AD, Boyle AP, Horvath JE, Furey TS, Wray GA. Both noncoding and protein-coding RNAs contribute to gene expression evolution in the primate brain. Genome Biol Evol. 2010;2:67–79. [PMC free article] [PubMed]
  • Bakewell MA, Shi P, Zhang J. More genes underwent positive selection in chimpanzee evolution than in human evolution. Proc Natl Acad Sci U S A. 2007;104:7489–7494. [PubMed]
  • Bakkaloglu B, O'Roak BJ, Louvi A, Gupta AR, Abelson JF, Morgan TM, Chawarska K, Klin A, Ercan-Sencicek AG, Stillman AA, et al. Molecular cytogenetic analysis and resequencing of contactin associated protein-like 2 in autism spectrum disorders. Am J Hum Genet. 2008;82:165–173. [PubMed]
  • Bilguvar K, Ozturk AK, Louvi A, Kwan KY, Choi M, Tatli B, Yalnizoglu D, Tuysuz B, Caglayan AO, Gokben S, et al. Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations. Nature. 2010;467:207–210. [PMC free article] [PubMed]
  • Bisazza A, Rogers LJ, Vallortigara G. The origins of cerebral asymmetry: a review of evidence of behavioural and brain lateralization in fishes, reptiles and amphibians. Neurosci Biobehav Rev. 1998;22:411–426. [PubMed]
  • Blekhman R, Marioni JC, Zumbo P, Stephens M, Gilad Y. Sex-specific and lineage-specific alternative splicing in primates. Genome Res. 2010;20:180–189. [PubMed]
  • Bochdanovits Z, Gosso FM, van den Berg L, Rizzu P, Polderman TJ, Pardo LM, Houlihan LM, Luciano M, Starr JM, Harris SE, et al. A Functional polymorphism under positive evolutionary selection in ADRB2 is associated with human intelligence with opposite effects in the young and the elderly. Behav Genet. 2009;39:15–23. [PubMed]
  • Bond J, Roberts E, Mochida GH, Hampshire DJ, Scott S, Askham JM, Springell K, Mahadevan M, Crow YJ, Markham AF, et al. ASPM is a major determinant of cerebral cortical size. Nat Genet. 2002;32:316–320. [PubMed]
  • Bonnefont J, Laforge T, Plastre O, Beck B, Sorce S, Dehay C, Krause KH. Primate-specific RFPL1 gene controls cell-cycle progression through cyclin B1/Cdc2 degradation. Cell Death Differ. 2010 [PMC free article] [PubMed]
  • Borrelli E, Nestler EJ, Allis CD, Sassone-Corsi P. Decoding the epigenetic language of neuronal plasticity. Neuron. 2008;60:961–974. [PMC free article] [PubMed]
  • Briggs AW, Good JM, Green RE, Krause J, Maricic T, Stenzel U, Lalueza-Fox C, Rudan P, Brajkovic D, Kucan Z, et al. Targeted retrieval and analysis of five Neandertal mtDNA genomes. Science. 2009;325:318–321. [PubMed]
  • Bruder CE, Yao S, Larson F, Camp JV, Tapp R, McBrayer A, Powers N, Granda WV, Jonsson CB. Transcriptome sequencing and development of an expression microarray platform for the domestic ferret. BMC Genomics. 2010;11:251. [PMC free article] [PubMed]
  • Brunelli S, Faiella A, Capra V, Nigro V, Simeone A, Cama A, Boncinelli E. Germline mutations in the homeobox gene EMX2 in patients with severe schizencephaly. Nat Genet. 1996;12:94–96. [PubMed]
  • Burbano HA, Hodges E, Green RE, Briggs AW, Krause J, Meyer M, Good JM, Maricic T, Johnson PL, Xuan Z, et al. Targeted investigation of the Neandertal genome by array-based sequence capture. Science. 2010;328:723–725. [PMC free article] [PubMed]
  • Butti C, Sherwood CC, Hakeem AY, Allman JM, Hof PR. Total number and volume of Von Economo neurons in the cerebral cortex of cetaceans. J Comp Neurol. 2009;515:243–259. [PubMed]
  • Byrne RW, Bates LA. Primate social cognition: uniquely primate, uniquely social, or just unique? Neuron. 2010;65:815–830. [PubMed]
  • Caceres M, Lachuer J, Zapala MA, Redmond JC, Kudo L, Geschwind DH, Lockhart DJ, Preuss TM, Barlow C. Elevated gene expression levels distinguish human from non-human primate brains. Proc Natl Acad Sci U S A. 2003;100:13030–13035. [PubMed]
  • Calarco JA, Superina S, O'Hanlon D, Gabut M, Raj B, Pan Q, Skalska U, Clarke L, Gelinas D, van der Kooy D, et al. Regulation of vertebrate nervous system alternative splicing and development by an SR-related protein. Cell. 2009;138:898–910. [PubMed]
  • Calarco JA, Xing Y, Caceres M, Calarco JP, Xiao X, Pan Q, Lee C, Preuss TM, Blencowe BJ. Global analysis of alternative splicing differences between humans and chimpanzees. Genes Dev. 2007;21:2963–2975. [PubMed]
  • Cantalupo C, Oliver J, Smith J, Nir T, Taglialatela JP, Hopkins WD. The chimpanzee brain shows human-like perisylvian asymmetries in white matter. Eur J Neurosci. 2009;30:431–438. [PMC free article] [PubMed]
  • Chenn A, Walsh CA. Regulation of cerebral cortical size by control of cell cycle exit in neural precursors. Science. 2002;297:365–369. [PubMed]
  • Christodoulou F, Raible F, Tomer R, Simakov O, Trachana K, Klaus S, Snyman H, Hannon GJ, Bork P, Arendt D. Ancient animal microRNAs and the evolution of tissue identity. Nature. 2010;463:1084–1088. [PMC free article] [PubMed]
  • Clark AG, Glanowski S, Nielsen R, Thomas PD, Kejariwal A, Todd MA, Tanenbaum DM, Civello D, Lu F, Murphy B, et al. Inferring nonneutral evolution from human-chimp-mouse orthologous gene trios. Science. 2003;302:1960–1963. [PubMed]
  • Consortium CSaA. Initial sequence of the chimpanzee genome and comparison with the human genome. Nature. 2005;437:69–87. [PubMed]
  • Craig AD. How do you feel--now? The anterior insula and human awareness. Nat Rev Neurosci. 2009;10:59–70. [PubMed]
  • Crespi B, Summers K, Dorus S. Adaptive evolution of genes underlying schizophrenia. Proc Biol Sci. 2007;274:2801–2810. [PMC free article] [PubMed]
  • Crespi BJ. The origins and evolution of genetic disease risk in modern humans. Ann N Y Acad Sci. 2010;1206:80–109. [PubMed]
  • Currat M, Excoffier L, Maddison W, Otto SP, Ray N, Whitlock MC, Yeaman S. Comment on “Ongoing adaptive evolution of ASPM, a brain size determinant in Homo sapiens” and “Microcephalin, a gene regulating brain size, continues to evolve adaptively in humans” Science. 2006;313:172. author reply 172. [PubMed]
  • Dean B. Is schizophrenia the price of human central nervous system complexity? Aust N Z J Psychiatry. 2009;43:13–24. [PubMed]
  • Deaner RO, Isler K, Burkart J, van Schaik C. Overall brain size, and not encephalization quotient, best predicts cognitive ability across non-human primates. Brain Behav Evol. 2007;70:115–124. [PubMed]
  • Dehay C, Kennedy H. Cell-cycle control and cortical development. Nat Rev Neurosci. 2007;8:438–450. [PubMed]
  • Dorus S, Vallender EJ, Evans PD, Anderson JR, Gilbert SL, Mahowald M, Wyckoff GJ, Malcom CM, Lahn BT. Accelerated evolution of nervous system genes in the origin of Homo sapiens. Cell. 2004;119:1027–1040. [PubMed]
  • Doyle JP, Dougherty JD, Heiman M, Schmidt EF, Stevens TR, Ma G, Bupp S, Shrestha P, Shah RD, Doughty ML, et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell. 2008;135:749–762. [PMC free article] [PubMed]
  • Dulac C. Brain function and chromatin plasticity. Nature. 2010;465:728–735. [PMC free article] [PubMed]
  • Emes RD, Pocklington AJ, Anderson CN, Bayes A, Collins MO, Vickers CA, Croning MD, Malik BR, Choudhary JS, Armstrong JD, Grant SG. Evolutionary expansion and anatomical specialization of synapse proteome complexity. Nat Neurosci. 2008;11:799–806. [PMC free article] [PubMed]
  • Enard W, Gehre S, Hammerschmidt K, Holter SM, Blass T, Somel M, Bruckner MK, Schreiweis C, Winter C, Sohr R, et al. A humanized version of Foxp2 affects cortico-basal ganglia circuits in mice. Cell. 2009;137:961–971. [PubMed]
  • Enard W, Khaitovich P, Klose J, Zollner S, Heissig F, Giavalisco P, Nieselt-Struwe K, Muchmore E, Varki A, Ravid R, et al. Intra- and interspecific variation in primate gene expression patterns. Science. 2002a;296:340–343. [PubMed]
  • Enard W, Przeworski M, Fisher SE, Lai CS, Wiebe V, Kitano T, Monaco AP, Paabo S. Molecular evolution of FOXP2, a gene involved in speech and language. Nature. 2002b;418:869–872. [PubMed]
  • Evans PD, Gilbert SL, Mekel-Bobrov N, Vallender EJ, Anderson JR, Vaez-Azizi LM, Tishkoff SA, Hudson RR, Lahn BT. Microcephalin, a gene regulating brain size, continues to evolve adaptively in humans. Science. 2005;309:1717–1720. [PubMed]
  • Faiella A, Brunelli S, Granata T, D'Incerti L, Cardini R, Lenti C, Battaglia G, Boncinelli E. A number of schizencephaly patients including 2 brothers are heterozygous for germline mutations in the homeobox gene EMX2. Eur J Hum Genet. 1997;5:186–190. [PubMed]
  • Fernandez E, Collins MO, Uren RT, Kopanitsa MV, Komiyama NH, Croning MD, Zografos L, Armstrong JD, Choudhary JS, Grant SG. Targeted tandem affinity purification of PSD-95 recovers core postsynaptic complexes and schizophrenia susceptibility proteins. Mol Syst Biol. 2009;5:269. [PMC free article] [PubMed]
  • Fietz SA, Kelava I, Vogt J, Wilsch-Brauninger M, Stenzel D, Fish JL, Corbeil D, Riehn A, Distler W, Nitsch R, Huttner WB. OSVZ progenitors of human and ferret neocortex are epithelial-like and expand by integrin signaling. Nat Neurosci. 2010;13:690–699. [PubMed]
  • Fisher SE, Lai CS, Monaco AP. Deciphering the genetic basis of speech and language disorders. Annu Rev Neurosci. 2003;26:57–80. [PubMed]
  • Fisher SE, Scharff C. FOXP2 as a molecular window into speech and language. Trends Genet. 2009;25:166–177. [PubMed]
  • Francks C, Maegawa S, Lauren J, Abrahams BS, Velayos-Baeza A, Medland SE, Colella S, Groszer M, McAuley EZ, Caffrey TM, et al. LRRTM1 on chromosome 2p12 is a maternally suppressed gene that is associated paternally with handedness and schizophrenia. Mol Psychiatry. 2007;12:1129–1139. 1057. [PMC free article] [PubMed]
  • Galaburda AM, LeMay M, Kemper TL, Geschwind N. Right-left asymmetrics in the brain. Science. 1978;199:852–856. [PubMed]
  • Geschwind DH, Konopka G. Neuroscience in the era of functional genomics and systems biology. Nature. 2009;461:908–915. [PMC free article] [PubMed]
  • Geschwind DH, Miller BL. Molecular approaches to cerebral laterality: development and neurodegeneration. Am J Med Genet. 2001;101:370–381. [PubMed]
  • Gherman A, Chen PE, Teslovich TM, Stankiewicz P, Withers M, Kashuk CS, Chakravarti A, Lupski JR, Cutler DJ, Katsanis N. Population bottlenecks as a potential major shaping force of human genome architecture. PLoS Genet. 2007;3:e119. [PubMed]
  • Goodman M, Sterner KN. Colloquium paper: phylogenomic evidence of adaptive evolution in the ancestry of humans. Proc Natl Acad Sci U S A 107. 2010 Suppl 2:8918–8923. [PubMed]
  • Goodman M, Sterner KN, Islam M, Uddin M, Sherwood CC, Hof PR, Hou ZC, Lipovich L, Jia H, Grossman LI, Wildman DE. Phylogenomic analyses reveal convergent patterns of adaptive evolution in elephant and human ancestries. Proc Natl Acad Sci U S A. 2009;106:20824–20829. [PubMed]
  • Graziano C, D'Elia AV, Mazzanti L, Moscano F, Guidelli Guidi S, Scarano E, Turchetti D, Franzoni E, Romeo G, Damante G, Seri M. A de novo nonsense mutation of PAX6 gene in a patient with aniridia, ataxia, and mental retardation. Am J Med Genet A. 2007;143A:1802–1805. [PubMed]
  • Green RE, Briggs AW, Krause J, Prufer K, Burbano HA, Siebauer M, Lachmann M, Paabo S. The Neandertal genome and ancient DNA authenticity. Embo J. 2009;28:2494–2502. [PubMed]
  • Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, Kircher M, Patterson N, Li H, Zhai W, Fritz MH, et al. A draft sequence of the Neandertal genome. Science. 2010;328:710–722. [PMC free article] [PubMed]
  • Grossman LI, Schmidt TR, Wildman DE, Goodman M. Molecular evolution of aerobic energy metabolism in primates. Mol Phylogenet Evol. 2001;18:26–36. [PubMed]
  • Haesler S, Rochefort C, Georgi B, Licznerski P, Osten P, Scharff C. Incomplete and inaccurate vocal imitation after knockdown of FoxP2 in songbird basal ganglia nucleus Area X. PLoS Biol. 2007;5:e321. [PubMed]
  • Hakeem AY, Sherwood CC, Bonar CJ, Butti C, Hof PR, Allman JM. Von Economo neurons in the elephant brain. Anat Rec (Hoboken) 2009;292:242–248. [PubMed]
  • Hansen DV, Lui JH, Parker PR, Kriegstein AR. Neurogenic radial glia in the outer subventricular zone of human neocortex. Nature. 2010;464:554–561. [PubMed]
  • Hopkins WD, Taglialatela JP, Meguerditchian A, Nir T, Schenker NM, Sherwood CC. Gray matter asymmetries in chimpanzees as revealed by voxel-based morphometry. Neuroimage. 2008;42:491–497. [PMC free article] [PubMed]
  • Irimia M, Rukov JL, Roy SW. Evolution of alternative splicing regulation: changes in predicted exonic splicing regulators are not associated with changes in alternative splicing levels in primates. PLoS One. 2009;4:e5800. [PMC free article] [PubMed]
  • Johnson MB, Kawasawa YI, Mason CE, Krsnik Z, Coppola G, Bogdanovic D, Geschwind DH, Mane SM, State MW, Sestan N. Functional and evolutionary insights into human brain development through global transcriptome analysis. Neuron. 2009;62:494–509. [PMC free article] [PubMed]
  • Khaitovich P, Enard W, Lachmann M, Paabo S. Evolution of primate gene expression. Nat Rev Genet. 2006a;7:693–702. [PubMed]
  • Khaitovich P, Hellmann I, Enard W, Nowick K, Leinweber M, Franz H, Weiss G, Lachmann M, Paabo S. Parallel patterns of evolution in the genomes and transcriptomes of humans and chimpanzees. Science. 2005;309:1850–1854. [PubMed]
  • Khaitovich P, Muetzel B, She X, Lachmann M, Hellmann I, Dietzsch J, Steigele S, Do HH, Weiss G, Enard W, et al. Regional patterns of gene expression in human and chimpanzee brains. Genome Res. 2004a;14:1462–1473. [PubMed]
  • Khaitovich P, Tang K, Franz H, Kelso J, Hellmann I, Enard W, Lachmann M, Paabo S. Positive selection on gene expression in the human brain. Curr Biol. 2006b;16:R356–R358. [PubMed]
  • Khaitovich P, Weiss G, Lachmann M, Hellmann I, Enard W, Muetzel B, Wirkner U, Ansorge W, Paabo S. A neutral model of transcriptome evolution. PLoS Biol. 2004b;2:E132. [PMC free article] [PubMed]
  • Kidd JM, Cooper GM, Donahue WF, Hayden HS, Sampas N, Graves T, Hansen N, Teague B, Alkan C, Antonacci F, et al. Mapping and sequencing of structural variation from eight human genomes. Nature. 2008;453:56–64. [PMC free article] [PubMed]
  • Kim TK, Hemberg M, Gray JM, Costa AM, Bear DM, Wu J, Harmin DA, Laptewicz M, Barbara-Haley K, Kuersten S, et al. Widespread transcription at neuronal activity-regulated enhancers. Nature. 2010;465:182–187. [PMC free article] [PubMed]
  • King MC, Wilson AC. Evolution at two levels in humans and chimpanzees. Science. 1975;188:107–116. [PubMed]
  • Konopka G, Bomar JM, Winden K, Coppola G, Jonsson ZO, Gao F, Peng S, Preuss TM, Wohlschlegel JA, Geschwind DH. Human-specific transcriptional regulation of CNS development genes by FOXP2. Nature. 2009;462:213–217. [PMC free article] [PubMed]
  • Krause J, Fu Q, Good JM, Viola B, Shunkov MV, Derevianko AP, Paabo S. The complete mitochondrial DNA genome of an unknown hominin from southern Siberia. Nature. 2010;464:894–897. [PubMed]
  • Krause J, Lalueza-Fox C, Orlando L, Enard W, Green RE, Burbano HA, Hublin JJ, Hanni C, Fortea J, de la, Rasilla M, et al. The derived FOXP2 variant of modern humans was shared with Neandertals. Curr Biol. 2007;17:1908–1912. [PubMed]
  • Kriegstein A, Noctor S, Martinez-Cerdeno V. Patterns of neural stem and progenitor cell division may underlie evolutionary cortical expansion. Nat Rev Neurosci. 2006;7:883–890. [PubMed]
  • Krubitzer L. The magnificent compromise: cortical field evolution in mammals. Neuron. 2007;56:201–208. [PubMed]
  • Krubitzer L, Kaas J. The evolution of the neocortex in mammals: how is phenotypic diversity generated? Curr Opin Neurobiol. 2005;15:444–453. [PubMed]
  • Lai CS, Fisher SE, Hurst JA, Vargha-Khadem F, Monaco AP. A forkhead-domain gene is mutated in a severe speech and language disorder. Nature. 2001;413:519–523. [PubMed]
  • Lalueza-Fox C, Rompler H, Caramelli D, Staubert C, Catalano G, Hughes D, Rohland N, Pilli E, Longo L, Condemi S, et al. A melanocortin 1 receptor allele suggests varying pigmentation among Neanderthals. Science. 2007;318:1453–1455. [PubMed]
  • Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921. [PubMed]
  • Lari M, Rizzi E, Milani L, Corti G, Balsamo C, Vai S, Catalano G, Pilli E, Longo L, Condemi S, et al. The microcephalin ancestral allele in a Neanderthal individual. PLoS One. 2010;5:e10648. [PMC free article] [PubMed]
  • Larkin DM, Pape G, Donthu R, Auvil L, Welge M, Lewin HA. Breakpoint regions and homologous synteny blocks in chromosomes have different evolutionary histories. Genome Res. 2009;19:770–777. [PubMed]
  • Licatalosi DD, Mele A, Fak JJ, Ule J, Kayikci M, Chi SW, Clark TA, Schweitzer AC, Blume JE, Wang X, et al. HITS-CLIP yields genome-wide insights into brain alternative RNA processing. Nature. 2008;456:464–469. [PMC free article] [PubMed]
  • Lin L, Shen S, Jiang P, Sato S, Davidson BL, Xing Y. Evolution of alternative splicing in primate brain transcriptomes. Hum Mol Genet. 2010;19:2958–2973. [PMC free article] [PubMed]
  • Lukaszewicz A, Savatier P, Cortay V, Giroud P, Huissoud C, Berland M, Kennedy H, Dehay C. G1 phase regulation, area-specific cell cycle control, and cytoarchitectonics in the primate cortex. Neuron. 2005;47:353–364. [PMC free article] [PubMed]
  • MacDonald RB, Debiais-Thibaud M, Martin K, Poitras L, Tay BH, Venkatesh B, Ekker M. Functional conservation of a forebrain enhancer from the elephant shark (Callorhinchus milii ) in zebrafish and mice. BMC Evol Biol. 2010;10:157. [PMC free article] [PubMed]
  • Marin-Padilla M. Cajal-Retzius cells and the development of the neocortex. Trends Neurosci. 1998;21:64–71. [PubMed]
  • McGary KL, Park TJ, Woods JO, Cha HJ, Wallingford JB, Marcotte EM. Systematic discovery of nonobvious human disease models through orthologous phenotypes. Proc Natl Acad Sci U S A. 2010;107:6544–6549. [PubMed]
  • Meaburn EL, Schalkwyk LC, Mill J. Allele-specific methylation in the human genome Implications for genetic studies of complex disease. Epigenetics. 2010;5 [PMC free article] [PubMed]
  • Mekel-Bobrov N, Gilbert SL, Evans PD, Vallender EJ, Anderson JR, Hudson RR, Tishkoff SA, Lahn BT. Ongoing adaptive evolution of ASPM, a brain size determinant in Homo sapiens. Science. 2005;309:1720–1722. [PubMed]
  • Miller JA, Horvath S, Geschwind DH. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci U S A. 2010;107:12698–12703. [PubMed]
  • Miller JA, Oldham MC, Geschwind DH. A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J Neurosci. 2008;28:1410–1420. [PMC free article] [PubMed]
  • Murphy WJ, Larkin DM, Everts-van der Wind A, Bourque G, Tesler G, Auvil L, Beever JE, Chowdhary BP, Galibert F, Gatzke L, et al. Dynamics of mammalian chromosome evolution inferred from multispecies comparative maps. Science. 2005;309:613–617. [PubMed]
  • Nielsen KB, Kruhoffer M, Holm IE, Jorgensen AL, Nielsen AL. 1Identification of genes differentially expressed in the embryonic pig cerebral cortex before and after appearance of gyration. BMC Res Notes. 2010;3:127. [PMC free article] [PubMed]
  • Nielsen R, Bustamante C, Clark AG, Glanowski S, Sackton TB, Hubisz MJ, Fledel-Alon A, Tanenbaum DM, Civello D, White TJ, et al. A scan for positively selected genes in the genomes of humans and chimpanzees. PLoS Biol. 2005;3:e170. [PubMed]
  • Nielsen R, Hellmann I, Hubisz M, Bustamante C, Clark AG. Recent and ongoing selection in the human genome. Nat Rev Genet. 2007;8:857–868. [PMC free article] [PubMed]
  • Nimchinsky EA, Gilissen E, Allman JM, Perl DP, Erwin JM, Hof PR. A neuronal morphologic type unique to humans and great apes. Proc Natl Acad Sci U S A. 1999;96:5268–5273. [PubMed]
  • Nowick K, Gernat T, Almaas E, Stubbs L. Differences in human and chimpanzee gene expression patterns define an evolving network of transcription factors in brain. Proc Natl Acad Sci U S A. 2009;106:22358–22363. [PubMed]
  • Oldham MC, Geschwind DH. Gene expression in the evolution of the human brain. In: Squire LR, editor. Encyclopedia of Neuroscience. Oxford: Academic Press; 2009. pp. 597–603.
  • Oldham MC, Horvath S, Geschwind DH. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci U S A. 2006;103:17973–17978. [PubMed]
  • Penn DC, Holyoak KJ, Povinelli DJ. Darwin's mistake: explaining the discontinuity between human and nonhuman minds. Behav Brain Sci. 2008;31:109–130. discussion 130–178. [PubMed]
  • Pennacchio LA, Ahituv N, Moses AM, Prabhakar S, Nobrega MA, Shoukry M, Minovitsky S, Dubchak I, Holt A, Lewis KD, et al. In vivo enhancer analysis of human conserved non-coding sequences. Nature. 2006;444:499–502. [PubMed]
  • Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, Veyrieras JB, Stephens M, Gilad Y, Pritchard JK. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010;464:768–772. [PMC free article] [PubMed]
  • Pilaz LJ, Patti D, Marcy G, Ollier E, Pfister S, Douglas RJ, Betizeau M, Gautier E, Cortay V, Doerflinger N, et al. Forced G1-phase reduction alters mode of division, neuron number, and laminar phenotype in the cerebral cortex. Proc Natl Acad Sci U S A. 2009;106:21924–21929. [PubMed]
  • Pollard KS, Salama SR, King B, Kern AD, Dreszer T, Katzman S, Siepel A, Pedersen JS, Bejerano G, Baertsch R, et al. Forces shaping the fastest evolving regions in the human genome. PLoS Genet. 2006a;2:e168. [PubMed]
  • Pollard KS, Salama SR, Lambert N, Lambot MA, Coppens S, Pedersen JS, Katzman S, King B, Onodera C, Siepel A, et al. An RNA gene expressed during cortical development evolved rapidly in humans. Nature. 2006b;443:167–172. [PubMed]
  • Ponjavic J, Oliver PL, Lunter G, Ponting CP. Genomic and transcriptional co-localization of protein-coding and long non-coding RNA pairs in the developing brain. PLoS Genet. 2009;5:e1000617. [PMC free article] [PubMed]
  • Povinelli DJ, Rulf AB, Landau KR, Bierschwale DT. Self-recognition in chimpanzees (Pan troglodytes): distribution, ontogeny, and patterns of emergence. J Comp Psychol. 1993;107:347–372. [PubMed]
  • Prabhakar S, Noonan JP, Paabo S, Rubin EM. Accelerated evolution of conserved noncoding sequences in humans. Science. 2006;314:786. [PubMed]
  • Prabhakar S, Visel A, Akiyama JA, Shoukry M, Lewis KD, Holt A, Plajzer-Frick I, Morrison H, Fitzpatrick DR, Afzal V, et al. Human-specific gain of function in a developmental enhancer. Science. 2008;321:1346–1350. [PMC free article] [PubMed]
  • Premack D. Human and animal cognition: continuity and discontinuity. Proc Natl Acad Sci U S A. 2007;104:13861–13867. [PubMed]
  • Preuss TM. Taking the measure of diversity: comparative alternatives to the model-animal paradigm in cortical neuroscience. Brain Behav Evol. 2000;55:287–299. [PubMed]
  • Preuss TM, Caceres M, Oldham MC, Geschwind DH. Human brain evolution: insights from microarrays. Nat Rev Genet. 2004;5:850–860. [PubMed]
  • Rakic P. A small step for the cell, a giant leap for mankind: a hypothesis of neocortical expansion during evolution. Trends Neurosci. 1995;18:383–388. [PubMed]
  • Rilling JK, Glasser MF, Preuss TM, Ma X, Zhao T, Hu X, Behrens TE. The evolution of the arcuate fasciculus revealed with comparative DTI. Nat Neurosci. 2008;11:426–428. [PubMed]
  • Rosso L, Marques AC, Reichert AS, Kaessmann H. Mitochondrial targeting adaptation of the hominoid-specific glutamate dehydrogenase driven by positive Darwinian selection. PLoS Genet. 2008;4:e1000150. [PMC free article] [PubMed]
  • Roth G, Dicke U. Evolution of the brain and intelligence. Trends Cogn Sci. 2005;9:250–257. [PubMed]
  • Rushton JP, Ankney CD. Whole brain size and general mental ability: a review. Int J Neurosci. 2009;119:691–731. [PMC free article] [PubMed]
  • Santos M, Uppal N, Butti C, Wicinski B, Schmeidler J, Giannakopoulos P, Heinsein H, Schmitz C, Hof PR. Von Economo neurons in autism: A stereologic study of the frontoinsular cortex in children. Brain Res. 2010 [PubMed]
  • Schalkwyk LC, Meaburn EL, Smith R, Dempster EL, Jeffries AR, Davies MN, Plomin R, Mill J. Allelic skewing of DNA methylation is widespread across the genome. Am J Hum Genet. 2010;86:196–212. [PubMed]
  • Schenker NM, Hopkins WD, Spocter MA, Garrison AR, Stimpson CD, Erwin JM, Hof PR, Sherwood CC. Broca's area homologue in chimpanzees (Pan troglodytes): probabilistic mapping, asymmetry, and comparison to humans. Cereb Cortex. 2010;20:730–742. [PMC free article] [PubMed]
  • Seeley WW, Carlin DA, Allman JM, Macedo MN, Bush C, Miller BL, Dearmond SJ. Early frontotemporal dementia targets neurons unique to apes and humans. Ann Neurol. 2006;60:660–667. [PubMed]
  • Semendeferi K, Lu A, Schenker N, Damasio H. Humans and great apes share a large frontal cortex. Nat Neurosci. 2002;5:272–276. [PubMed]
  • Sharma A, Singh P. Detection of transgenerational spermatogenic inheritance of adult male acquired CNS gene expression characteristics using a Drosophila systems model. PLoS One. 2009;4:e5763. [PMC free article] [PubMed]
  • Shi P, Bakewell MA, Zhang J. Did brain-specific genes evolve faster in humans than in chimpanzees? Trends Genet. 2006;22:608–613. [PubMed]
  • Shu W, Cho JY, Jiang Y, Zhang M, Weisz D, Elder GA, Schmeidler J, De Gasperi R, Sosa MA, Rabidou D, et al. Altered ultrasonic vocalization in mice with a disruption in the Foxp2 gene. Proc Natl Acad Sci U S A. 2005;102:9643–9648. [PubMed]
  • Silver DL, Watkins-Chow DE, Schreck KC, Pierfelice TJ, Larson DM, Burnetti AJ, Liaw HJ, Myung K, Walsh CA, Gaiano N, Pavan WJ. The exon junction complex component Magoh controls brain size by regulating neural stem cell division. Nat Neurosci. 2010;13:551–558. [PMC free article] [PubMed]
  • Somel M, Franz H, Yan Z, Lorenc A, Guo S, Giger T, Kelso J, Nickel B, Dannemann M, Bahn S, et al. Transcriptional neoteny in the human brain. Proc Natl Acad Sci U S A. 2009;106:5743–5748. [PubMed]
  • Somel M, Guo S, Fu N, Yan Z, Hu HY, Xu Y, Yuan Y, Ning Z, Hu Y, Menzel C, et al. MicroRNA, mRNA, and protein expression link development and aging in human and macaque brain. Genome Res. 2010 [PubMed]
  • Soriano E, Del Rio JA. The cells of cajal-retzius: still a mystery one century after. Neuron. 2005;46:389–394. [PubMed]
  • Sun T, Patoine C, Abu-Khalil A, Visvader J, Sum E, Cherry TJ, Orkin SH, Geschwind DH, Walsh CA. Early asymmetry of gene transcription in embryonic human left and right cerebral cortex. Science. 2005;308:1794–1798. [PMC free article] [PubMed]
  • Teramitsu I, Kudo LC, London SE, Geschwind DH, White SA. Parallel FoxP1 and FoxP2 expression in songbird and human brain predicts functional interaction. J Neurosci. 2004;24:3152–3163. [PubMed]
  • Ticho BH, Hilchie-Schmidt C, Egel RT, Traboulsi EI, Howarth RJ, Robinson D. Ocular findings in Gillespie-like syndrome: association with a new PAX6 mutation. Ophthalmic Genet. 2006;27:145–149. [PubMed]
  • Timpson N, Heron J, Smith GD, Enard W. Comment on papers by Evans et al. and Mekel-Bobrov et al. on Evidence for Positive Selection of MCPH1 and ASPM. Science. 2007;317:1036. author reply 1036. [PubMed]
  • Ule J, Ule A, Spencer J, Williams A, Hu JS, Cline M, Wang H, Clark T, Fraser C, Ruggiu M, et al. Nova regulates brain-specific splicing to shape the synapse. Nat Genet. 2005;37:844–852. [PubMed]
  • Varki A, Geschwind DH, Eichler EE. Explaining human uniqueness: genome interactions with environment, behaviour and culture. Nat Rev Genet. 2008;9:749–763. [PMC free article] [PubMed]
  • Venkatesh B, Kirkness EF, Loh YH, Halpern AL, Lee AP, Johnson J, Dandona N, Viswanathan LD, Tay A, Venter JC, et al. Survey sequencing and comparative analysis of the elephant shark (Callorhinchus milii) genome. PLoS Biol. 2007;5:e101. [PubMed]
  • Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, et al. The sequence of the human genome. Science. 2001;291:1304–1351. [PubMed]
  • Vernes SC, Newbury DF, Abrahams BS, Winchester L, Nicod J, Groszer M, Alarcon M, Oliver PL, Davies KE, Geschwind DH, et al. A functional genetic link between distinct developmental language disorders. N Engl J Med. 2008;359:2337–2345. [PMC free article] [PubMed]
  • Vernier P, Moret F, Callier S, Snapyan M, Wersinger C, Sidhu A. The degeneration of dopamine neurons in Parkinson's disease: insights from embryology and evolution of the mesostriatocortical system. Ann N Y Acad Sci. 2004;1035:231–249. [PubMed]
  • Visel A, Blow MJ, Li Z, Zhang T, Akiyama JA, Holt A, Plajzer-Frick I, Shoukry M, Wright C, Chen F, et al. ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature. 2009;457:854–858. [PMC free article] [PubMed]
  • von Economo C. Eine neue Art Spezialzellen des Lobus cinguli und Lobus insulae. Zschr ges Neurol Psychiat. 1926;100:706–712.
  • Wall JD, Kim SK. Inconsistencies in Neanderthal genomic DNA sequences. PLoS Genet. 2007;3:1862–1866. [PubMed]
  • Waterston RH, Lindblad-Toh K, Birney E, Rogers J, Abril JF, Agarwal P, Agarwala R, Ainscough R, Alexandersson M, An P, et al. Initial sequencing and comparative analysis of the mouse genome. Nature. 2002;420:520–562. [PubMed]
  • Wetterbom A, Ameur A, Feuk L, Gyllensten U, Cavelier L. Identification of novel exons and transcribed regions by chimpanzee transcriptome sequencing. Genome Biol. 2010;11:R78. [PMC free article] [PubMed]
  • White SA, Fisher SE, Geschwind DH, Scharff C, Holy TE. Singing mice, songbirds, and more: models for FOXP2 function and dysfunction in human speech and language. J Neurosci. 2006;26:10376–10379. [PMC free article] [PubMed]
  • Woods RP, Freimer NB, De Young JA, Fears SC, Sicotte NL, Service SK, Valentino DJ, Toga AW, Mazziotta JC. Normal variants of Microcephalin and ASPM do not account for brain size variability. Hum Mol Genet. 2006;15:2025–2029. [PubMed]
  • Xu AG, He L, Li Z, Xu Y, Li M, Fu X, Yan Z, Yuan Y, Menzel C, Li N, et al. Intergenic and repeat transcription in human, chimpanzee and macaque brains measured by RNA-Seq. PLoS Comput Biol. 2010;6:e1000843. [PMC free article] [PubMed]
  • Xu X, Lee J, Stern DF. Microcephalin is a DNA damage response protein involved in regulation of CHK1 and BRCA1. J Biol Chem. 2004;279:34091–34094. [PubMed]
  • Yu F, Hill RS, Schaffner SF, Sabeti PC, Wang ET, Mignault AA, Ferland RJ, Moyzis RK, Walsh CA, Reich D. Comment on “Ongoing adaptive evolution of ASPM, a brain size determinant in Homo sapiens” Science. 2007;316:370. [PubMed]
  • Zecevic N, Rakic P. Development of layer I neurons in the primate cerebral cortex. J Neurosci. 2001;21:5607–5619. [PubMed]
  • Zhang C, Frias MA, Mele A, Ruggiu M, Eom T, Marney CB, Wang H, Licatalosi DD, Fak JJ, Darnell RB. Integrative modeling defines the Nova splicing-regulatory network and its combinatorial controls. Science. 2010;329:439–443. [PMC free article] [PubMed]
  • Zhang C, Zhang Z, Castle J, Sun S, Johnson J, Krainer AR, Zhang MQ. Defining the regulatory network of the tissue-specific splicing factors Fox-1 and Fox-2. Genes Dev. 2008;22:2550–2563. [PubMed]
  • Zurner M, Schoch S. The mouse and human Liprin-alpha family of scaffolding proteins: genomic organization, expression profiling and regulation by alternative splicing. Genomics. 2009;93:243–253. [PubMed]