Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models.

ABSTRACT: The study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inf

SUBMITTER: Shappell H 

PROVIDER: S-EPMC6504179 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature