Estimation of causal interactions between brain areas is necessary for elucidating large-scale functional brain networks underlying behavior and cognition. Granger causality analysis of time series data can quantitatively estimate directional information flow between brain regions. Here, we show that such estimates are significantly improved when the temporal sampling rate of functional magneti...

Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, C NPMR , Autoregressive modeling is replaced by Nonparametri...

Anatomical studies show the existence of corticomotor neuronal projections to the spinal cord before birth, but whether the primary motor cortex drives muscle activity in neonatal 'spontaneous' movements is unclear. To investigate this issue, we calculated corticomuscular coherence (CMC) and Granger causality in human neonates. CMC is widely used as an index of functional connectivity between t...

This paper demonstrates that linear regression models with an AR(1) error structure implicitly assume that yt does not Granger cause any of the exogenous variables in Xt. An indirect test of the common factor restrictions based on this Granger non-causality is proposed and shown to outperform existing tests. ∗Copyright 2004 by Anya McGuirk and Aris Spanos. All rights reserved.

BACKGROUND Advanced methods of signal analysis of the preictal and ictal activity dynamics characterizing absence epilepsy in humans with absences and in genetic animal models have revealed new and unknown electroencephalographic characteristics, that has led to new insights and theories. NEW METHOD Taking into account that some network associations can be considered as nonlinear, an adaptive...