Parallel group independent component analysis for massive fMRI data sets

Shaojie Chen, Lei Huang, Huitong Qiu, Mary Beth Nebel, Stewart H. Mostofsky, James J. Pekar, Martin A. Lindquist, Ani Eloyan, Brian S. Caffo, Satoru Hayasaka
2017 PLoS ONE  
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
more » ... 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. Conceptualization : BC AE ML JP. Data curation: MB SM JP. Formal analysis: SC LH HQ AE. Funding acquisition: BC. Investigation: MB SM JP AE. Parallel group independent component analysis for massive fMRI data sets PLOS ONE |
doi:10.1371/journal.pone.0173496 pmid:28278208 pmcid:PMC5344430 fatcat:yd572ihc4zcajp6pdxy4rlnsra