Novel microstate method to identify fMRI networks and assess their dynamic temporal characteristics

Motivation: Implement a well-established method (microstate analysis) to characterize state and state-transitions from EEG to fMRI. Such an approach will provide information on dynamic network changes and could provide a novel way to assess brain function in health and disease. Goal(s): To provide a proof-of-concept that EEG-microstate analysis can be adapted to fMRI. Approach: Adapt EEG-microstate analysis to fMRI data and characterize dynamic brain network changes. Results: We demonstrate that this novel approach can identify distinct network states, their average duration, frequency of occurrence, total coverage of entire scan and transition probabilities between states. Impact: We provide proof-of-concept that commonly used EEG-microstate analysis can be adapted to characterize dynamic changes in brain network states in fMRI data. This novel analysis could provide novel insights in alterations of brain functionality in various disorders.