A GICA-TVGL framework to study sex differences in resting state fMRI dynamic connectivity.

ABSTRACT:

Background

Functional magnetic resonance imaging (fMRI) has been implemented widely to study brain connectivity. In particular, time-varying connectivity analysis has emerged as an important measure to uncover essential knowledge within the network. On the other hand, independent component analysis (ICA) has served as a powerful tool to preprocess fMRI data before performing network analysis. Together, they may lead to novel findings.

Methods

We propose a new framework (GICA-TVGL) that combines group ICA (GICA) with time-varying graphical LASSO (TVGL) to improve the power of analyzing functional connectivity (FNC) changes, which is then applied for neuro-developmental study. To investigate the performance of our proposed approach, we apply it to capture dynamic FNC using bot

SUBMITTER: Cai B 

PROVIDER: S-EPMC10187053 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature