Hierarchical Genetic Organization of Human Cortical Surface Area

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The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute's human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.corticocortical connectivity | human transcriptome | association cortex | supragranular | brain evolution P atterns of gene expression in the cerebral cortex are generally conserved across species, reflecting strong constraints in the development and evolution of cortical architecture (1-6). Previous work examining transcriptional variation in nonhuman primates and rodents indicate that molecular similarities between cortical regions in the adult brain are best explained by spatial proximity (7, 8). Molecular variation often takes the form of graded expression along a principal axis, in many cases appearing as rostrocaudal gradients across the cortex (8). The strong tendency for transcriptional variation to follow spatial proximity in the adult cortex likely reflects both functional gradients, as well as their developmental origins in terms of physical and temporal adjacency during neurogenesis of cells destined for neighboring locations in the cortex (at least for cells derived from the ventricular proliferative pool) (7).Spatial proximity likely captures the major features governing how molecular profiles vary across the cortex in all species, including humans. However, the expansion of the cerebral cortex in primates...

Section: And 7)mentioning

confidence: 99%

Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

Krienen

Yeo

et al. 2016

201

203

“…For example, gene expression in the brain is modular; the transcriptomes of human brain regions are robustly organized into modules of coexpressed genes that reflect the underlying cellular composition of brain tissue (8), and the spatial topography of cortex is also strongly reflected in its genetic topography-the closer two cortical regions, the more similar their transcriptomes (9). Moreover, these genetic divisions corresponded largely to meaningful structural and functional divisions (10,11). This suggests a modular evolution of the brain, as functions that are adjusted in a modular manner require modular genetic bases (3).…”

mentioning

confidence: 99%

The modular and integrative functional architecture of the human brain

Bertolero

Yeo

D’Esposito

2015

474

478

Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules' processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an authortopic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network's modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules' functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in betweenmodule communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain's modular yet integrated implementation of cognitive functions. modularity | hubs | cognition | graph theory | network

“…Neural pathways change as our brains develop (2,3) and are altered in neurodegenerative conditions, including Alzheimer's disease (4). Our individual genetic makeup exerts a strong influence on the functional synchronization of brain regions (5) and the patterning of cortical structure (6,7), but particular genes that impact the brain's neural connectivity are still largely unknown.…”

mentioning

confidence: 99%

Genome-wide scan of healthy human connectome discoversSPON1gene variant influencing dementia severity

Jahanshad

Rajagopalan

Hou

et al. 2013

142

128

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, highangular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.diffusion tensor imaging | neuroimaging genetics | graph theory | HARDI tractography | multiple comparisons correction H uman brain anatomy involves a complex network of structural and functional pathways that connect anatomically distinct regions. These pathways can be visualized, on a gross anatomical scale, with diffusion imaging-based tractography (1). Neural pathways change as our brains develop (2, 3) and are altered in neurodegenerative conditions, including Alzheimer's disease (4). Our individual genetic makeup exerts a strong influence on the functional synchronization of brain regions (5) and the patterning of cortical structure (6, 7), but particular genes that impact the brain's neural connectivity are still largely unknown.To empower the search for genes that affect the brain's connectivity patterns, we first studied a large twin and family cohort consisting of 366 young adults (ages 20-29 y) from 223 families. We used anatomical brain MRI combined with high-angular resolution diffusion imaging (HARDI) at high magnetic field (4 T) to subdivide cortical regions into areas of known structure and function (8), while also mapping the white matter fiber pathways between them with high-resolution tractography. We defined connectivity maps...