Brain parcellation driven by dynamic functional connectivity better capture intrinsic network dynamics.

ABSTRACT: Until now, dynamic functional connectivity (dFC) based on functional magnetic resonance imaging is typically estimated on a set of predefined regions of interest (ROIs) derived from an anatomical or static functional atlas which follows an implicit assumption of functional homogeneity within ROIs underlying temporal fluctuation of functional coupling, potentially leading to biases or underestimation of brain network dynamics. Here, we presented a novel computational method based on dynamic functional connectivity degree (dFCD) to derive meaningful brain parcellations that can capture functional homogeneous regions in temporal variance of functional connectivity. Several spatially distributed but functionally meaningful areas that are well consistent with known intrinsic connectivity networ

SUBMITTER: Fan L 

PROVIDER: S-EPMC7927310 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature