Modelling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models.

ABSTRACT: Recent studies have proposed that one can summarize brain activity into dynamics among a relatively small number of hidden states and that such an approach is a promising tool for revealing brain function. Hidden Markov models (HMMs) are a prevalent approach to inferring such neural dynamics among discrete brain states. However, the impact of assuming Markovian structure in neural time series data has not been sufficiently examined. Here, to address this situation and examine the performance of the HMM, we compare the model with the Gaussian mixture model (GMM), which is with no temporal regularization and thus a statistically simpler model than the HMM, by applying both models to synthetic time series generated from empirical resting-state functional magnetic resonance imaging (fMRI) data

SUBMITTER: Ezaki T 

PROVIDER: S-EPMC9291560 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature