A Novel Hidden Markov Approach to Studying Dynamic Functional Connectivity States in Human Neuroimaging.

ABSTRACT: Introduction: Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns. Methods: We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profil

SUBMITTER: Hussain S 

PROVIDER: S-EPMC10079241 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature