Dynamic brain states sequential modelling based on spontaneous brain activity of resting-state fMRI

Most dynamic functional connectivity analyses are performed using sliding window correlation. One problem is that a fixed sliding window with a predefined length selected ad hoc is used even though the temporal duration of the states is now known to vary. In order to address this challenge, we introduced a Gaussian Hidden Markov Model to model brain state transition with the time series of the fMRI data (in contrast to the method which models the functional connectivity states). This model allows us to detect the spatial patterns of states and the transition sequences of the states. In our study, we detected 9 reproducible brain states as combination of conventional resting state networks.

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