Open AccessMultivariate Granger causality analysis of fMRI data
Author(s) -
Deshpande Gopikrishna,
LaConte Stephan,
James George Andrew,
Peltier Scott,
Hu Xiaoping
Publication year - 2009
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20606
Subject(s) - granger causality , multivariate statistics , causality (physics) , artificial intelligence , computer science , multivariate analysis , cluster analysis , confounding , psychology , mathematics , machine learning , statistics , physics , quantum mechanics
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.