Multivariate Granger causality analysis of fMRI data

TLDR

The study combines multivariate Granger causality, temporal down‑sampling of fMRI, and graph theory to investigate causal brain networks and their dynamics. The authors applied directed transfer function multivariate Granger causality to integrated epoch responses from a hand‑gripping fatigue experiment, modeling early, middle, and late periods and using graph‑theoretic clustering and eccentricity to interpret the resulting networks. The analysis shows that motor fatigue causes a temporal disconnection in the related neural network. Published in Human Brain Mapping, 2009; © 2008 Wiley‑Liss, Inc.

Abstract

Abstract This article describes the combination of multivariate Granger causality analysis, temporal down‐sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch‐to‐epoch changes in a hand‐gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated epoch response as the input, allowing us to account for the effects of several relevant regions simultaneously. Integrated responses were used in lieu of originally sampled time points to remove the effect of the spatially varying hemodynamic response as a confounding factor; using integrated responses did not affect our ability to capture its slowly varying affects of fatigue. We separately modeled the early, middle, and late periods in the fatigue. We adopted graph theoretic concepts of clustering and eccentricity to facilitate the interpretation of the resultant complex networks. Our results reveal the temporal evolution of the network and demonstrate that motor fatigue leads to a disconnection in the related neural network. Hum Brain Mapp, 2009. © 2008 Wiley‐Liss, Inc.