Is Pearson's correlation coefficient enough for functional connectivity in fMRI?

ABSTRACT: Functional connectivity (FC) is commonly defined as the temporal coincidence of neurophysiological events, often quantified by the statistical dependency among signals from different brain regions and measured by Pearson's correlation coefficient in fMRI. However, Pearson's r captures only linear dependencies, potentially overlooking nonlinear interactions. Recently, Multiscale Graph Correlation (MGC) was introduced to measure statistical dependencies of both linear and nonlinear relationships across multiple scales, offering an "optimal scale" at which such dependencies can be inferred. In this study, we systematically compared FC measurements by Pearson's r and MGC across datasets, evaluating their reliability, sensitivity to data quantity, and ability to capture distinct experimental co

SUBMITTER: Jin H 

PROVIDER: S-EPMC12687289 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature