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

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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.

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There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.

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In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.

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Author and article information

Contributors

Natalia Z. Bielczyk:

ORCID: https://orcid.org/0000-0003-1604-9143

Sebo Uithol:

ORCID: https://orcid.org/0000-0002-7552-0364

Tim van Mourik:

ORCID: https://orcid.org/0000-0002-8155-8641

Paul Anderson:

ORCID: https://orcid.org/0000-0003-2716-6556

Jeffrey C. Glennon

Jan K. Buitelaar:

ORCID: https://orcid.org/0000-0001-8288-7757

Journal

Journal ID (nlm-ta): Netw Neurosci

Journal ID (iso-abbrev): Netw Neurosci

Journal ID (publisher-id): netn

Title: Network Neuroscience

Publisher: MIT Press (One Rogers Street, Cambridge, MA 02142-1209USAjournals-info@mit.edu )

ISSN (Electronic): 2472-1751

Publication date (Electronic): 2019

Publication date Collection: 2019

Volume: 3

Issue: 2

Pages: 237-273

Affiliations

[1]Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands

[2]Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands

[3]Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands

[4]Bernstein Centre for Computational Neuroscience, Charité Universitätsmedizin, Berlin, Germany

[5]Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands

[6]Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands

[7]Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands

[8]Faculty of Science, Radboud University Nijmegen, Nijmegen, the Netherlands

[9]Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands

[10]Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands

[11]Donders Institute for Brain, Cognition and Behavior, Nijmegen, the Netherlands

[12]Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands

Author notes

Competing Interests: The authors have declared that no competing interests exist.

* Corresponding Author: natalia.bielczyk@ 123456gmail.com

Handling Editor: Olaf Sporns

Author information

Natalia Z. Bielczyk https://orcid.org/0000-0003-1604-9143

Sebo Uithol https://orcid.org/0000-0002-7552-0364

Tim van Mourik https://orcid.org/0000-0002-8155-8641

Paul Anderson https://orcid.org/0000-0003-2716-6556

Jan K. Buitelaar https://orcid.org/0000-0001-8288-7757

Article

Publisher ID: netn_a_00062

DOI: 10.1162/netn_a_00062

PMC ID: 6370462

PubMed ID: 30793082

SO-VID: aae04f5b-8d5a-4b96-acf0-4650b7b79233

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/legalcode.

History

Date received : 13 March 2018

Date accepted : 08 June 2018