Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

TL;DR

This review discusses eight methods for inferring causal relationships in brain networks using fMRI data, highlighting their limitations and suggesting future research directions.

Contribution

It provides a comprehensive overview of current causal inference techniques in fMRI research and offers recommendations for advancing the field.

Findings

Eight causal inference methods analyzed

Limitations identified for each method

Future research directions proposed

Abstract

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.