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Spatial Independent Component Analysis (ICA) decomposes the time by spacefunctional MRI (fMRI) matrix into a set of 1-D basis time courses and theirassociated 3-D spatial maps that are optimized for mutual independence. Whenapplied to resting state fMRI (rsfMRI), ICA produces several spatialindependent components (ICs) that seem to have biological relevance - theso-called resting state networks (RSNs). The ICA problem is well posed when thetrue data generating process follows a linear mixture of ICs model in terms ofthe identifiability of the mixing matrix. However, the contrast function usedfor promoting mutual independence in ICA is dependent on the finite amount ofobserved data and is potentially non-convex with multiple local minima. Hence,each run of ICA could produce potentially different IC estimates even for thesame data. One technique to deal with this run-to-run variability of ICA wasproposed by Yang et al. (2008) in their algorithm RAICAR which allows for theselection of only those ICs that have a high run-to-run reproducibility. Wepropose an enhancement to the original RAICAR algorithm that enables us toassign reproducibility p-values to each IC and allows for an objectiveassessment of both within subject and across subjects reproducibility. We callthe resulting algorithm RAICAR-N (N stands for null hypothesis test), and wehave applied it to publicly available human rsfMRI data (http://www.nitrc.org).Our reproducibility analyses indicated that many of the published RSNs inrsfMRI literature are highly reproducible. However, we found several other RSNsthat are highly reproducible but not frequently listed in the literature.

A simple and objective method for reproducible resting state network (RSN) detection in fMRI