Year: 2017
Type: article
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.
Source: PLoS ONE
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,382
Awards: Statistical methods for large n and p problems, G179143872, Resource for Quantitative Functional FMRI, G472242964, G4961852482, Causal Inference for Neuroimaging, 2/2 - Anomalous Motor System Physiology in ADHD: Biomarker Validation and Modeling Domains of Function, G6681234998, G9000057464, Statistical Methods for Multilevel Multivariate Functional Studies
OpenAlex ID: W2593430479