biostudies-literatureJahanshad NRoyal Netherlands Academy of Arts and SciencesNICHD NIH HHSNIBIB NIH HHSDutch Technology Foundation STWNIA NIH HHSNIMH NIH HHSBiobanking and Biomolecular Resources Research InfrastructureNational Institutes of HealthMunicipality of RotterdamEuropean CommissionAustralian National Health and Medical Research CouncilNational Institute on AgingResearch Institute for Diseases in the ElderlyEuropean Community‘s Seventh Framework ProgrammeDutch Research Council (NWO)Netherlands Organisation of Scientific Research NWO InvestmentsNational Institute of Biomedical Imaging and BioengineeringMinistry of Economic AffairsHersenstichting NederlandZonMwNetherlands Genomics Initiative (NGI)/Netherlands Organisation for Scientific ResearchMinistry for Health, Welfare, and SportsMinistry of Education, Culture, and ScienceErasmus Medical Center and Erasmus UniversityU.S. Department of DefenseNational Institute of Child Health and Human Development1508-1514https://www.ebi.ac.uk/biostudies/studies/S-EPMC9067093biostudies-literatureUnknown16(5)Genome-wide association studies (GWAS) link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses (e.g., tensor-based morphometryIEEE/ACM transactions on computational biology and bioinformaticsMulti-Site Meta-Analysis of Morphometry.PMC9067093916.13.054R01 MH117601496682911-03-012U54 EB020403W81XWH-12-2-0012R01 AG059874nr. 175.010.2005.011050-060-810U01 AG024904602805 (Aggressotype)R01HD050735602450 (IMAGEMEND)R01 HD050735014-93-015; RIDE21009064Vernooij MWFaskowitz JDesrivieres SMartin NGSoares JCFranke BMcMahon KLKroll JLHibar DPNiessen WJIkram MAIng AWright MJJahanshad NRoshchupkin GTustison NJGutman BAVasquez AAde Zubicaray GIZwiers MPHansell NKAdams HHHThompson PMSchumann GMwangi BfalseMulti-Site Meta-Analysis of Morphometry.Genome-wide association studies (GWAS) link full genome data to a handful of traits. However, in neuroimaging studies, there is an almost unlimited number of traits that can be extracted for full image-wide big data analyses. Large populations are needed to achieve the necessary power to detect statistically significant effects, emphasizing the need to pool data across multiple studies. Neuroimaging consortia, e.g., ENIGMA and CHARGE, are now analyzing MRI data from over 30,000 individuals. Distributed processing protocols extract harmonized features at each site, and pool together only the cohort statistics using meta analysis to avoid data sharing. To date, such MRI projects have focused on single measures such as hippocampal volume, yet voxelwise analyses (e.g., tensor-based morphometry2019-01-01T00:00:00Z2019 Sep-Oct2025-04-18T12:12:09.25Z2025-04-06T21:49:36.306ZS-EPMC90670933113536610.1109/tcbb.2019.291490510.1109/TCBB.2019.2914905