title: Parallel group independent component analysis for massive fMRI data sets.
abstract: Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America.
authors.forename: Shaojie
authors.identifier: http://orcid.org/0000-0002-3543-8964
authors.initials: S
authors.lastname: Chen
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America.
authors.forename: Lei
authors.initials: L
authors.lastname: Huang
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America.
authors.forename: Huitong
authors.initials: H
authors.lastname: Qiu
authors.affiliation: Kennedy Krieger Institute, Baltimore, United States of America.|Department of Neurology, Johns Hopkins University, Baltimore, United States of America.
authors.forename: Mary Beth
authors.initials: MB
authors.lastname: Nebel
authors.affiliation: School of Medicine, Johns Hopkins University, Baltimore, United States of America.|Kennedy Krieger Institute, Baltimore, United States of America.
authors.forename: Stewart H
authors.initials: SH
authors.lastname: Mostofsky
authors.affiliation: Kennedy Krieger Institute, Baltimore, United States of America.|Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, United States of America.
authors.forename: James J
authors.initials: JJ
authors.lastname: Pekar
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America.
authors.forename: Martin A
authors.initials: MA
authors.lastname: Lindquist
authors.affiliation: Department of Biostatistics, Brown University, Providence, Rhode Island, United States of America.
authors.forename: Ani
authors.initials: A
authors.lastname: Eloyan
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America.
authors.forename: Brian S
authors.initials: BS
authors.lastname: Caffo
mesh_terms: D000293:Adolescent; D000328:Adult; D000067877:Autism Spectrum Disorder / Q000000981:diagnostic imaging; D001931:Brain Mapping; D002648:Child; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D059906:Neuroimaging; D055815:Young Adult