Dynamic autoregressive neuromagnetic causality imaging (DANCI)

Authors:
Richard E. Frye;Meng Hung Wu;George Zouridakis
Affiliations:
Departments of Pediatrics and Neurology, University of Texas Health Science Center at Houston, Fannin, Houston, TX;Biomedical Imaging Laboratory, Department of Computer Science, University of Houston, Houston, TX;Biomedical Imaging Laboratory, Department of Computer Science, University of Houston, Houston, TX
Venue:
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
Year:
2007

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Abstract

This presentation provides a demonstration of how Granger causality (GC) can be applied to MEG data to visualize dynamic functional connectivity and causality between cortical regions on a millisecond time scale. GC is derived from autoregressive models and provides directionality information. We apply the GC technique to dynamic statistical parameter map source space to demonstrate that the dynamics of neural networks can visualized during a perceptual task. The results from this demonstration coincide with models of speech perception and suggest that Dynamic Autoregressive Neuromagnetic Causality Imaging (DANCI) can be used to investigate and verify theoretical neural network models of brain function.