Topological analyses of functional connectomics: A crucial role of global signal removal, brain parcellation, and null models.

ABSTRACT: Recently, functional connectome studies based on resting-state functional magnetic resonance imaging (R-fMRI) and graph theory have greatly advanced our understanding of the topological principles of healthy and diseased brains. However, how different strategies for R-fMRI data preprocessing and for connectome analyses jointly affect topological characterization and contrastive research of brain networks remains to be elucidated. Here, we used two R-fMRI data sets, a healthy young adult data set and an Alzheimer's disease (AD) patient data set, and up to 42 analysis strategies to comprehensively investigate the joint influence of three key factors (global signal regression, regional parcellation schemes, and null network models) on the topological analysis and contrastive research of whole

SUBMITTER: Chen X 

PROVIDER: S-EPMC6866637 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature