A whitening approach for Transfer Entropy permits the application to narrow-band signals

Abstract

Transfer Entropy, a generalisation of Granger Causality, promises to measure "information transfer" from a source to a target signal by ignoring self-predictability of a target signal when quantifying the source-target relationship. A simple example for signals with such selfpredictability are narrowband signals. These are both thought to be intrinsically generated by the brain as well as commonly dealt with in analyses of brain signals, where band-pass filters are used to separate responses from noise. However, the use of Transfer Entropy is usually discouraged in such cases. We simulate simplistic examples where we confirm the failure of classic implementations of Transfer Entropy when applied to narrow-band signals, as made evident by a flawed recovery of effect sizes and interaction delays. We propose an alternative approach based on a whitening of the input signals before computing a bivariate measure of directional time-lagged dependency. This approach solves the problems found in the simple simulated systems. Finally, we explore the behaviour of our measure when applied to delta and theta response components in Magnetoencephalography (MEG) responses to continuous speech. The small effects that our measure attributes to a directed interaction from the stimulus to the neuronal responses are stronger in the theta than in the delta band. This suggests that the delta band reflects a more predictive coupling, while the theta band is stronger involved in bottom-up, reactive processing. Taken together, we hope to increase the interest in directed perspectives on frequency-specific dependencies.

NLGC: Network Localized Granger Causality with Application to MEG Directional Functional Connectivity Analysis

“…Third, unlike most existing connectivity analysis methods that require heavy trial averaging to mitigate spurious detection, NLGC exhibits robustness to model mismatch and low SNR conditions, even where few trials are available. Finally, thanks the plug-and-play nature of the NLGC building blocks, it can be modified for inferring other network-level characterizations, such as cortical transfer entropy (Daube et al, 2022). To ease reproducibility, we have made a python implementation of NLGC publicly available on Github (Soleimani and Das, 2022).…”