Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches
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...
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The MIT Press
2019-02-01
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| Series: | Network Neuroscience |
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| Online Access: | https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00062 |
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doaj-3f5ec86e15134ec38d53ef0aa106e7cf2020-11-25T01:00:53ZengThe MIT PressNetwork Neuroscience2472-17512019-02-013223727310.1162/netn_a_00062netn_a_00062Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approachesNatalia Z. Bielczyk0Sebo Uithol1Tim van Mourik2Paul Anderson3Jeffrey C. Glennon4Jan K. Buitelaar5Donders Institute for Brain, Cognition and Behavior, Nijmegen, the NetherlandsDonders Institute for Brain, Cognition and Behavior, Nijmegen, the NetherlandsDonders Institute for Brain, Cognition and Behavior, Nijmegen, the NetherlandsDonders Institute for Brain, Cognition and Behavior, Nijmegen, the NetherlandsDonders Institute for Brain, Cognition and Behavior, Nijmegen, the NetherlandsDonders Institute for Brain, Cognition and Behavior, Nijmegen, the NetherlandsIn 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.https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00062Causal inferenceEffective connectivityFunctional Magnetic Resonance ImagingDynamic Causal ModelingGranger CausalityStructural Equation ModelingBayesian NetsDirected Acyclic GraphsPairwise inferenceLarge-scale brain networks |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Natalia Z. Bielczyk Sebo Uithol Tim van Mourik Paul Anderson Jeffrey C. Glennon Jan K. Buitelaar |
| spellingShingle |
Natalia Z. Bielczyk Sebo Uithol Tim van Mourik Paul Anderson Jeffrey C. Glennon Jan K. Buitelaar Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches Network Neuroscience 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 |
| author_facet |
Natalia Z. Bielczyk Sebo Uithol Tim van Mourik Paul Anderson Jeffrey C. Glennon Jan K. Buitelaar |
| author_sort |
Natalia Z. Bielczyk |
| title |
Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches |
| title_short |
Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches |
| title_full |
Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches |
| title_fullStr |
Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches |
| title_full_unstemmed |
Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches |
| title_sort |
disentangling causal webs in the brain using functional magnetic resonance imaging: a review of current approaches |
| publisher |
The MIT Press |
| series |
Network Neuroscience |
| issn |
2472-1751 |
| publishDate |
2019-02-01 |
| description |
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. |
| topic |
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 |
| url |
https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00062 |
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