Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches

Abstract

In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. 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 paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel’s Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.

Keywords

• Causal inference
• Effective connectivity
• Functional Magnetic Resonance Imaging
• Dynamic Causal Modeling
• Granger Causality
• Structural Equation Modeling
• Bayesian Nets
• Directed Acyclic Graphs
• Pairwise inference
• Large-scale brain networks