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

Avram J. Holmes(Massachusetts General Hospital), Marisa O. Hollinshead(Athinoula A. Martinos Center for Biomedical Imaging), Timothy M. O’Keefe(Harvard University Press), Victor I Petrov(Harvard University Press), Gabriele R Fariello(Massachusetts General Hospital), Lawrence L. Wald(Athinoula A. Martinos Center for Biomedical Imaging), Bruce Fischl(Athinoula A. Martinos Center for Biomedical Imaging), Bruce R. Rosen(Athinoula A. Martinos Center for Biomedical Imaging), R. W. Mair(Athinoula A. Martinos Center for Biomedical Imaging), Joshua L. Roffman(Massachusetts General Hospital), Jordan W. Smoller(Harvard University), Randy L. Buckner(Massachusetts General Hospital)
Scientific Data
July 6, 2015
Cited by 479Open Access
Full Text

Abstract

The goal of the Brain Genomics Superstruct Project (GSP) is to enable large-scale exploration of the links between brain function, behavior, and ultimately genetic variation. To provide the broader scientific community data to probe these associations, a repository of structural and functional magnetic resonance imaging (MRI) scans linked to genetic information was constructed from a sample of healthy individuals. The initial release, detailed in the present manuscript, encompasses quality screened cross-sectional data from 1,570 participants ages 18 to 35 years who were scanned with MRI and completed demographic and health questionnaires. Personality and cognitive measures were obtained on a subset of participants. Each dataset contains a T1-weighted structural MRI scan and either one (n=1,570) or two (n=1,139) resting state functional MRI scans. Test-retest reliability datasets are included from 69 participants scanned within six months of their initial visit. For the majority of participants self-report behavioral and cognitive measures are included (n=926 and n=892 respectively). Analyses of data quality, structure, function, personality, and cognition are presented to demonstrate the dataset's utility.


Related Papers

No related papers found

Powered by citation graph analysis