Causality as a unifying approach between activation and connectivity analysis of fMRI data

TL;DR

This paper proposes using causality analysis as a unified method to detect brain activations and connectivity in fMRI data, demonstrating improved localization of activations over traditional methods.

Contribution

It introduces causality analysis as a novel unified approach for both activation detection and connectivity analysis in fMRI data, showing superior localization accuracy.

Findings

Causality analysis outperforms GLM in activation localization.

Granger causality effectively detects stimulus influence on BOLD signals.

Unified causality approach simplifies brain activity analysis.

Abstract

This paper indicates causality as the tool that unifies the analysis of both activations and connectivity of brain areas, obtained with fMRI data. Causality analysis is commonly applied to study connectivity, so this work focuses on demonstrating that also the detection of activations can be handled with a causality analysis. We test our method on finger tapping data, in which GLM and Granger Causality approaches are compared in finding activations. Granger causality not only performs the task well, but indeed we obtained a better localization (i.e. precision) of activations. As a result we claim that causality must be the main tool to investigate activations, since it is a measure of "how much" the stimulus influences the BOLD signal, and since it unifies connectivity and activations analysis under the same area.