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Electroencephalogram (EEG) microstate analysis entails finding dynamics ofquasi-stable and generally recurrent discrete states in multichannel EEG timeseries data and relating properties of the estimated state-transition dynamicsto observables such as cognition and behavior. While microstate analysis hasbeen widely employed to analyze EEG data, its use remains less prevalent infunctional magnetic resonance imaging (fMRI) data, largely due to the slowertimescale of such data. In the present study, we extend various data clusteringmethods used in EEG microstate analysis to resting-state fMRI data from healthyhumans to extract their state-transition dynamics. We show that the quality ofclustering is on par with that for various microstate analyses of EEG data. Wethen develop a method for examining test-retest reliability of thediscrete-state transition dynamics between fMRI sessions and show that thewithin-participant test-retest reliability is higher than between-participanttest-retest reliability for different indices of state-transition dynamics,different networks, and different data sets. This result suggests thatstate-transition dynamics analysis of fMRI data could discriminate betweendifferent individuals and is a promising tool for performing fingerprintinganalysis of individuals.

State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis