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Title: Disentangling casual webs in the brain using functional magnetic resonance imaging: A review of current approaches
Author(s): Bielczyk, N.Z. ; Uithol, S. ; Mourik, T. van ; Anderson, P.M. ; Glennon, J.C. ; Buitelaar, J.K.
Publication year: 2019
Source: Network Neuroscience, vol. 3, iss. 2, (2019), pp. 237-273
ISSN: 2472-1751
DOI: https://doi.org/10.1162/netn_a_00062
Publication type: Article / Letter to editor

Please use this identifier to cite or link to this item : https://hdl.handle.net/2066/200833   https://hdl.handle.net/2066/200833

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Subject: 150 000 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
Organization: PI Group Statistical Imaging Neuroscience
SW OZ DCC SMN
PI Group MR Techniques in Brain Function
Cognitive Neuroscience
PI Group Memory and Emotion
Journal title:
Network Neuroscience
Volume: vol. 3
Issue: iss. 2
Page start: p. 237
Page end: p. 273
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.
Languages used: English (eng)

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