Disentangling casual webs in the brain using functional magnetic resonance imaging: A review of current approaches
Number of pages
SourceNetwork Neuroscience, 3, 2, (2019), pp. 237-273
Article / Letter to editor
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PI Group Statistical Imaging Neuroscience
SW OZ DCC SMN
PI Group MR Techniques in Brain Function
PI Group Memory & Emotion
Subject150 000 MR Techniques in Brain Function; 220 Statistical Imaging Neuroscience; Action, intention, and motor control; Radboudumc 13: Stress-related disorders DCMN: Donders Center for Medical Neuroscience; Radboudumc 7: Neurodevelopmental disorders DCMN: Donders Center for Medical Neuroscience
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.
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