Nonlinear functional network connectivity in resting functional magnetic resonance imaging data.

ABSTRACT: In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. This resting-state fMRI data included 151 schizophrenia patients and 163 age- and gender-matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modul

SUBMITTER: Motlaghian SM 

PROVIDER: S-EPMC9491296 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature