Estimating Dynamic Functional Brain Connectivity With a Sparse Hidden Markov Model.

ABSTRACT: Estimating dynamic functional network connectivity (dFNC) of the brain from functional magnetic resonance imaging (fMRI) data can reveal both spatial and temporal organization and can be applied to track the developmental trajectory of brain maturity as well as to study mental illness. Resting state fMRI (rs-fMRI) is regarded as a promising task since it reflects the spontaneous brain activity without an external stimulus. The sliding window method has been successfully used to extract dFNC but typically assumes a fixed window size. The hidden Markov model (HMM) based method is an alternative approach for estimating time-varying connectivity. In this paper, we propose a sparse HMM based on Gaussian HMM and Gaussian graphical model (GGM). In this model, the time-varying neural processes are

SUBMITTER: Zhang G 

PROVIDER: S-EPMC12948354 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature