Analyzing Information Flow in Brain Networks with Nonparametric Granger Causality

Overview

Journal Neuroimage

Specialty Radiology

Date 2008 Apr 9

PMID 18394927

Citations 129

Authors

Mukeshwar Dhamala

Govindan Rangarajan

Mingzhou Ding

Affiliations

Soon will be listed here.

Abstract

Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.

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