{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Nieto-Castanon A"],"funding":["National Institute of Neurological Disorders and Stroke","NIDCD NIH HHS","NIMH NIH HHS","National Institute on Deafness and Other Communication Disorders","NINDS NIH HHS","National Institute of Mental Health"],"pagination":["e1010634"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9707802"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(11)"],"pubmed_abstract":["Current functional Magnetic Resonance Imaging technology is able to resolve billions of individual functional connections characterizing the human connectome. Classical statistical inferential procedures attempting to make valid inferences across this many measures from a reduced set of observations and from a limited number of subjects can be severely underpowered for any but the largest effect sizes. This manuscript discusses fc-MVPA (functional connectivity Multivariate Pattern Analysis), a novel method using multivariate pattern analysis techniques in the context of brain-wide connectome inferences. The theory behind fc-MVPA is presented, and several of its key concepts are illustrated through examples from a publicly available resting state dataset, including an analysis of gender dif"],"journal":["PLoS computational biology"],"pubmed_title":["Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)."],"pmcid":["PMC9707802"],"funding_grant_id":["R01 DC002852","U01 NS117836","R01 DC016270","R01 DC007683","U01 MH108168"],"pubmed_authors":["Nieto-Castanon A"],"additional_accession":[]},"is_claimable":false,"name":"Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA).","description":"Current functional Magnetic Resonance Imaging technology is able to resolve billions of individual functional connections characterizing the human connectome. Classical statistical inferential procedures attempting to make valid inferences across this many measures from a reduced set of observations and from a limited number of subjects can be severely underpowered for any but the largest effect sizes. This manuscript discusses fc-MVPA (functional connectivity Multivariate Pattern Analysis), a novel method using multivariate pattern analysis techniques in the context of brain-wide connectome inferences. The theory behind fc-MVPA is presented, and several of its key concepts are illustrated through examples from a publicly available resting state dataset, including an analysis of gender dif","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2026-05-28T02:50:24.193Z","creation":"2025-04-06T16:33:58.165Z"},"accession":"S-EPMC9707802","cross_references":{"pubmed":["36378714"],"doi":["10.1371/journal.pcbi.1010634"]}}