Multivariate granger causality analysis of brain networks (bibtex)
by G. Deshpande, S. LaConte, S. Peltier, GA. James, X. Hu
Abstract:
This paper describes an approach combining multivariate Ganger causality analysis, temporal down-sampling of fMRI time series and graph theory to investigate causal brain networks. Multivariate granger causality utilizes the directed transfer function (DTF) which is better suited for modeling networks than bivariate granger causal methods. In addition to permitting the investigation of slowly varying processes such as fatigue, the coarse temporal scale of analysis removes the effect of the spatial variability of the hemodynamic response as a confounding factor. Finally, graph theoretic concepts provide a vehicle for characterizing the resulting network topology for effective interpretation of the results.
Reference:
abstract G. Deshpande, S. LaConte, S. Peltier, GA. James, X. Hu. Multivariate granger causality analysis of brain networks. In Proceedings 15th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Berlin, page 3184, 2007. [bibtex]
Bibtex Entry:
@inproceedings{Berlin3184,
   Author = {Deshpande, G. and LaConte, S. and Peltier, S. and James, GA. and Hu, X.},
   Title ={Multivariate granger causality analysis of brain networks},
   BookTitle = {Proceedings 15th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Berlin},
   Pages = {3184},
   Abstract = {This paper describes an approach combining multivariate Ganger causality analysis, temporal down-sampling of fMRI time series and graph theory to investigate causal brain networks. Multivariate granger causality utilizes the directed transfer function (DTF) which is better suited for modeling networks than bivariate granger causal methods. In addition to permitting the investigation of slowly varying processes such as fatigue, the coarse temporal scale of analysis removes the effect of the spatial variability of the hemodynamic response as a confounding factor. Finally, graph theoretic concepts provide a vehicle for characterizing the resulting network topology for effective interpretation of the results.},
 Keywords = {Berlin3184},
   Year = {2007} }
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