Disentangling casual webs in the brain using functional magnetic resonance imaging: A review of current approaches
Publication year
2019Number of pages
37 p.
Source
Network Neuroscience, 3, 2, (2019), pp. 237-273ISSN
Publication type
Article / Letter to editor
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Organization
PI Group Statistical Imaging Neuroscience
SW OZ DCC SMN
PI Group MR Techniques in Brain Function
Cognitive Neuroscience
PI Group Memory & Emotion
Journal title
Network Neuroscience
Volume
vol. 3
Issue
iss. 2
Languages used
English (eng)
Page start
p. 237
Page end
p. 273
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 NeuroscienceAbstract
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.
This item appears in the following Collection(s)
- Academic publications [238441]
- Donders Centre for Cognitive Neuroimaging [3824]
- Electronic publications [122508]
- Faculty of Medical Sciences [90373]
- Faculty of Social Sciences [29483]
- Open Access publications [97504]
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