Format

Send to

Choose Destination
Neuroimage. 2008 Jun;41(2):354-62. doi: 10.1016/j.neuroimage.2008.02.020. Epub 2008 Feb 25.

Analyzing information flow in brain networks with nonparametric Granger causality.

Author information

1
Department of Physics and Astronomy, Brains and Behavior Program, Center for Behavioral Neuroscience, Georgia State University, Atlanta, GA 30303, USA. mdhamala@gsu.edu

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.

PMID:
18394927
PMCID:
PMC2685256
DOI:
10.1016/j.neuroimage.2008.02.020
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center