Parallel group independent component analysis for massive fMRI data sets

• Published in: PloS one Vol. 12; no. 3; p. e0173496
• Main Authors: Chen, Shaojie, Huang, Lei, Qiu, Huitong, Nebel, Mary Beth, Mostofsky, Stewart H., Pekar, James J., Lindquist, Martin A., Eloyan, Ani, Caffo, Brian S.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.03.2017; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Algorithms; Autism; Autism Spectrum Disorder - diagnostic imaging; Bioengineering; Biology and Life Sciences; Brain; Brain Mapping; Child; Computer and Information Sciences; Computer simulation; Data processing; Datasets; Functional magnetic resonance imaging; Health aspects; Humans; Image Processing, Computer-Assisted - methods; Independent component analysis; Magnetic Resonance Imaging; Medical imaging; Medicine and Health Sciences; Methods; Neuroimaging; Neurology; Neurosciences; Physical Sciences; Principal components analysis; Research and Analysis Methods; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0173496

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.