Dynamic Granger–Geweke causality modeling with application to interictal spike propagation

Authors

  • Fa-Hsuan Lin,

    Corresponding author
    1. Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan
    2. MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129
    • Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th Street, Rm. 2301, Charlestown, MA 02129 or Institute of Biomedical Engineering, National Taiwan University, 1, Sec. 4, Roosevelt Rd., Taipei 106, Taiwan
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  • Keiko Hara,

    1. MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129
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  • Victor Solo,

    1. MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129
    2. School of Electrical Engineering, University of New South Wales, Sydney, Australia
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  • Mark Vangel,

    1. MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129
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  • John W. Belliveau,

    1. MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129
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  • Steven M. Stufflebeam,

    1. MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129
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  • Matti S. Hämäläinen

    1. MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts 02129
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Abstract

A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using structural equation modeling (SEM) and, more recently, with Granger–Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger–Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger–Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger–Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested that the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain. Hum Brain Mapp, 2009. © 2009 Wiley-Liss, Inc.

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