Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.neuroimage.2008.02.020 | NeuroImage |
Keywords | Field | DocType |
granger causality,information flow,wavelet transform,data model,high resolution,synthetic data,network model,multivariate data,mathematics,autoregressive model,local field potential | Pairwise comparison,Autoregressive model,Data mining,Data modeling,Information flow (information theory),Computer science,Granger causality,Nonparametric statistics,Synthetic data,Wavelet transform | Journal |
Volume | Issue | ISSN |
41 | 2 | 1053-8119 |
Citations | PageRank | References |
60 | 3.06 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mukeshwar Dhamala | 1 | 81 | 5.82 |
| Govindan Rangarajan | 2 | 111 | 11.23 |
| Mingzhou Ding | 3 | 701 | 114.88 |