A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Parallel group independent component analysis for massive fMRI data sets
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
doi:10.1371/journal.pone.0173496
pmid:28278208
pmcid:PMC5344430
fatcat:yd572ihc4zcajp6pdxy4rlnsra