Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures

Holmes, Avram J.; Hollinshead, Marisa O.; O’Keefe, Timothy M.; Петров, В. И.; Fariello, Gabriele R.; Wald, Lawrence L.; Fischl, Bruce; Rosen, Bruce R.; Mair, R. W.; Roffman, Joshua L.; Smoller, Jordan W.; Buckner, Randy L.

doi:10.1038/sdata.2015.31

Cited by 347 publications

(339 citation statements)

References 81 publications

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“…Consensus maps of 7 and 17 functional connectivity networks from resting-state data from 1,000 healthy young adults in the Brain Genomics Superstruct Project (71) were used in the present study. Participants provided written informed consent in accordance with guidelines set by institutional review boards of Harvard University or Partners Healthcare.…”

Section: Methodsmentioning

confidence: 99%

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

Krienen

Yeo

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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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...

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Section: Methodsmentioning

confidence: 99%

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

Krienen

Yeo

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

201

203

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“…Between October 2008 and March 2013 native English speaking young adults (ages 18 -35) with normal or corrected-to-normal vision were recruited from Harvard University, Massachusetts General Hospital, and the surrounding Boston communities (N ϭ 1234) through an ongoing large-scale study of brain imaging and genetics (Holmes et al, 2015). History of psychiatric illness and medication usage was assessed through a structured phone screen.…”

Section: Methodsmentioning

confidence: 99%

Individual Differences in Cognitive Control Circuit Anatomy Link Sensation Seeking, Impulsivity, and Substance Use

et al. 2016

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Individuals vary widely in their tendency to seek stimulation and act impulsively, early developing traits with genetic origins. Failures to regulate these behaviors increase risk for maladaptive outcomes including substance abuse. Here, we explored the neuroanatomical correlates of sensation seeking and impulsivity in healthy young adults. Our analyses revealed links between sensation seeking and reduced cortical thickness that were preferentially localized to regions implicated in cognitive control, including anterior cingulate and middle frontal gyrus (n ϭ 1015). These associations generalized to self-reported motor impulsivity, replicated in an independent group (n ϭ 219), and correlated with heightened alcohol, tobacco, and caffeine use. Critically, the relations between sensation seeking and brain structure were evident in participants without a historyofalcoholortobaccouse,suggestingthatobservedassociationswithanatomyarenotsolelyaconsequenceofsubstanceuse.Theseresults demonstrate that individual differences in the tendency to seek stimulation, act on impulse, and engage in substance use are correlated with the anatomical structure of cognitive control circuitry. Our findings suggest that, in healthy populations, covariation across these complex multidimensional behaviors may in part originate from a common underlying biology.

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“…Here, using rs-fMRI data collected by the Human Connectome Project (HCP) (17) and the Harvard/Massachusetts General Hospital (MGH) Brain Genomics Superstruct Project (GSP) (18,19), we demonstrate that (i) the relative contributions of intra-and intersubject variation to the total variation of rs-fMRI-derived functional connectivity measurements are dramatically different across space and strongly correlated with testretest reliability, (ii) the stable component of functional connectivity (within and between well-established large-scale functional brain networks) can be substantially heritable, and (iii) dissociation of intra-and intersubject variation can reveal genetic influences on the functional architecture of the human brain that are not detectable by conventional heritability analyses. Our results provide insights into the genetic basis of brain function and highlight the importance of accounting for the spatial pattern of measurement noise and transient signals when studying the heritability of functional connectivity measurements across brain regions.…”

Section: Significancementioning

confidence: 99%

Heritability analysis with repeat measurements and its application to resting-state functional connectivity

Holmes

Buckner

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing heritability analysis methods do not discriminate between stable effects (e.g., due to the subject's unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements-a prototypic data modality that exhibits variable levels of test-retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra-and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional heritability analyses.heritability | repeat measurements | resting-state fMRI | functional connectivity | test-retest reliability H eritability is defined as the proportion of phenotypic variation that can be explained by genetic variation among individuals in a population (1). It is a fundamental concept in population and statistical genetics, providing basic but important information about the genetic underpinnings of a trait. The heritability of a large number of human complex traits across a wide range of the phenotypic spectrum has been documented, using classical twin or pedigree designs (2-4), or statistical methodologies developed more recently that allow for the estimation of heritability in large samples of unrelated individuals (5-9).Statistical models for heritability analysis typically include an error term (residual) that absorbs the phenotypic variation that cannot be explained by genetics and common environment. The residual therefore accounts for unique (subject-specific) environmental effects, measurement error, and intrasubject fluctuation of the trait due to, for example, biological cycles such as the circadian rhythm. With one measurement for each subject, these factors cannot be dissociated and are typically modeled jointly as an independent and identically distributed random variable across subjects. However, this model imposes at least two problems on heritability analysis. First, measurement error has a ceiling effect on heritability estimates; it inflates the total phenotypic variation and downwardly biases heritability estimates. Second, comparing the heritability of traits that have different levels of measurement noise may be misleading.The issues can be partially resolved if repeat measurements of a trait are available. Assuming that multiple repeat measurements have been collected for e...

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Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures

Cited by 347 publications

References 81 publications

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

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

Individual Differences in Cognitive Control Circuit Anatomy Link Sensation Seeking, Impulsivity, and Substance Use

Heritability analysis with repeat measurements and its application to resting-state functional connectivity

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