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Abstract
<p class="first" id="P1">Independent component analysis (ICA) and seed-based analyses
are widely used techniques
for studying intrinsic neuronal activity in task-based or resting scans. In this work,
we show there is a direct link between the two, and show that there are some important
differences between the two approaches in terms of what information they capture.
We developed an enhanced connectivity-matrix independent component analysis (cmICA)
for calculating whole brain voxel maps of functional connectivity, which reduces the
computational complexity of voxel-based connectivity analysis on performing many temporal
correlations. We also show there is a mathematical equivalency between parcellations
on voxel-to-voxel functional connectivity and simplified cmICA. Next, we used this
cost-efficient data-driven method to examine the resting state fMRI connectivity in
schizophrenia patients (SZ) and healthy controls (HC) on a whole brain scale and further
quantified the relationship between brain functional connectivity and cognitive performances
measured by the Measurement and Treatment Research to Improve Cognition in Schizophrenia
(MATRICS) battery. Current results suggest that SZ exhibit a wide-range abnormality,
primarily a decrease, in functional connectivity both between networks and within
different network hubs. Specific functional connectivity decreases were associated
with MATRICS performance deficits. In addition, we found that resting state functional
connectivity decreases was extensively associated with aging regardless of groups.
In contrast, there was no relationship between positive and negative symptoms in the
patients and functional connectivity. In sum, we have developed a novel mathematical
relationship between ICA and seed-based connectivity that reduces computational complexity,
which has broad applicability, and showed a specific application of this approach
to characterize connectivity changes associated with cognitive scores in SZ.
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