Abstract
The most common pipelines for studying time-varying network connectivity in resting state functional magnetic resonance imaging (rs-fMRI) operate at the whole brain level, capturing a small discrete set of “states” that best represent time-resolved joint measures of connectivity over all network pairs in the brain. This whole-brain hidden Markov model (HMM) approach “uniformizes” the dynamics over what is typically more than 1000 pairs of networks, forcing each time-resolved high-dimensional observation into its best-matched high-dimensional state. While straightforward and convenient, this HMM simplification obscures functional and temporal nonstationarities that could reveal systematic, informative features of resting state brain dynamics at a more granular scale. We introduce a framework for studying functionally localized dynamics that intrinsically embeds them within a whole-brain HMM frame of reference. The approach is validated in a large rs-fMRI schizophrenia study where it identifies group differences in localized patterns of entropy and dynamics that help explain consistently observed differences between schizophrenia patients and controls in occupancy of whole-brain dFNC states more mechanistically.
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