Adaptive filtering and random variables coefficient for analyzing functional magnetic resonance imaging data

Functional magnetic resonance imaging (fMRI) is used to study brain functional connectivity (FC) after filtering the physiological noise (PN). Herein, we employ: adaptive filtering for removing nonstationary PN; random variables (RV) coefficient for FC analysis. Comparisons with standard techniques were performed by quantifying PN filtering and FC in neural vs. non-neural regions. As a result, adaptive filtering plus RV coefficient showed a greater suppression of PN and higher connectivity in neural regions, representing a novel effective approach to analyze fMRI data. © 2013 World Scientific Publishing Company.