# Integrative analysis of multiomic datasets for discovery of molecular underpinnings of large-scale human brain networks > **NIH NIH RF1** · VANDERBILT UNIVERSITY · 2021 · $1,091,985 ## Abstract SUMMARY Brain-mapping initiatives are acquiring increasingly large and comprehensive neuroimaging and multiomic— e.g. genomic and transcriptomic—datasets. Existing analyses of such data in human neuroscience tend to search for links between cognition, behavior or disease on the one hand, and properties of genomes, transcrip- tomes or brain morphology and connectivity on the other. Such valuable analyses have steadily advanced our knowledge of human brain function. But they have also left a critical gap in our understanding of how this func- tion arises from the interplay of brain evolution, development and organization. The present proposal will help fill this gap by integrating several large and disparately acquired neuroimaging and multiomic datasets. It will do so by combining the increasing availability of rich data, with modern statistical methods, and with complementary expertise of its investigators in network neuroscience, computational biol- ogy, human evolution, data harmonization, and cognitive developmental and aging neuroscience. The proposal will link heritable expression to brain-network phenotypes across several key brain regions for thousands of genes and in thousands of individuals. It will do so by adopting and applying models of heritable gene expression (trained on transcriptomic data acquired by the Gene Tissue Expression Project, and allied projects) to neuroimaging genomic data acquired by the Human Connectome Project and the UK Biobank. The proposal will then distinguish between adaptive and non-adaptive brain-network phenotypes. It will do so by quantifying the natural selection of these phenotypes in recent human evolution, using ancient DNA from archaic hominins, to test for natural-selection pressures on genes associated with brain-network variation. The proposal will finally delineate the relationship between heritable gene expression and network phenotypes in typical and atypical development and aging. It will do so by imputing heritable gene expression from large neuroimaging genomic datasets acquired by the Adolescent Brain Cognitive Development Study, the Cam- bridge Centre for Ageing and Neuroscience, and the Alzheimer's Disease Neuroimaging Initiative. It will link the variation in this expression to the variation of brain-network phenotypes in development and aging, and will delineate gene-expression brain-network signatures of psychosis-spectrum symptoms or cognitive impairment. Collectively, the proposal integrates the evolution, development, and organization of large-scale brain net- works. Specifically it links, for the first time, gene expression and brain-network phenotypes across several re- gions in many individuals, and in this way opens a new direction in neuroimaging genomics. The proposal ad- vances discovery neuroscience through analyses that enhance existing genomic, transcriptomic and neuroim- aging data. Finally, through dissemination of all software and results created as part of these analyse... ## Key facts - **NIH application ID:** 10361057 - **Project number:** 1RF1MH125933-01A1 - **Recipient organization:** VANDERBILT UNIVERSITY - **Principal Investigator:** Mikail Rubinov - **Activity code:** RF1 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $1,091,985 - **Award type:** 1 - **Project period:** 2021-09-17 → 2025-09-16 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10361057 ## Citation > US National Institutes of Health, RePORTER application 10361057, Integrative analysis of multiomic datasets for discovery of molecular underpinnings of large-scale human brain networks (1RF1MH125933-01A1). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10361057. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*