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Abstract:
Recent years have witnessed the advent of increasingly large datasets of the human brain, both in terms of aggregated participants and/or the number of measurements taken per participant. These resources provide unparalleled chances to investigate the organization of human brain at both individual and population levels, spanning various spatial and temporal scales. Focusing mainly on human neuroimaging to simultaneously interrogate the structure and function of the living human brain, our chapter will first highlight recent “big” data initiatives that prioritized subject numbers during study design and aggregation. These include the Adolescent Brain Development Cohort (ABCD), the UK Biobank (UKBB), and the Enhancing Neuroimaging Genetics through Meta-Analyses (ENIGMA) consortium to study brain development, aging, as well as disease effects with neuroimaging, and that allow cross-referencing findings with genetic and behavioral information. We will also overview “deep” data initiatives on single individuals, including precision MRI datasets, such as the Midnight Scan Club (MSN), Human Connectome Project (HCP), and the MyConnectome project, which aggregated repeated and extended structural and functional human neuroimaging data. Richly phenotyped datasets have served as steppingstones to assess key principles of brain organization, structure-function relationships, and to differentiate state- from person-specific variations. We will also highlight several post-mortem data aggregation and sharing initiatives such as the BigBrain/HIBALL project and the Allen Human Brain Atlas (AHBA) that provide rich neural information in stereotaxic space, allowing for the contextualization of neuroimaging findings with 3D gene expression and histology. This rise in data resources is paralleled by the increased availability of toolboxes and analytics for multiscale feature integration and multivariate analyses, offering researchers new possibilities for data-driven discovery and theory-driven hypothesis testing. We will close the chapter by overviewing current challenges and next steps for a human neuroscience synergizing both big and deep datasets.
