Spatial Independent Component Analysis (ICA) decomposes the time by space
functional MRI (fMRI) matrix into a set of 1-D basis time courses and their
associated 3-D spatial maps that are optimized for mutual independence. When
applied to resting state fMRI (rsfMRI), ICA produces several spatial
independent components (ICs) that seem to have biological relevance - the
so-called resting state networks (RSNs). The ICA problem is well posed when the
true data generating process follows a linear mixture of ICs model in terms of
the identifiability of the mixing matrix. However, the contrast function used
for promoting mutual independence in ICA is dependent on the finite amount of
observed data and is potentially non-convex with multiple local minima. Hence,
each run of ICA could produce potentially different IC estimates even for the
same data. One technique to deal with this run-to-run variability of ICA was
proposed by Yang et al. (2008) in their algorithm RAICAR which allows for the
selection of only those ICs that have a high run-to-run reproducibility. We
propose an enhancement to the original RAICAR algorithm that enables us to
assign reproducibility p-values to each IC and allows for an objective
assessment of both within subject and across subjects reproducibility. We call
the resulting algorithm RAICAR-N (N stands for null hypothesis test), and we
have applied it to publicly available human rsfMRI data (http://www.nitrc.org).
Our reproducibility analyses indicated that many of the published RSNs in
rsfMRI literature are highly reproducible. However, we found several other RSNs
that are highly reproducible but not frequently listed in the literature.Comment: 54 pages, 13 figure