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Advancing functional connectivity research from association to causation

Nature Neuroscience volume 22, pages 1751–1760 (2019)Cite this article

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Abstract

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series—functional connectivity (FC) methods—are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods (‘effective connectivity’) is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures.

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Fig. 1: Ontological levels relevant to mechanistic interpretation of FC, defining the pathway from neural mechanisms (neural level) to imaging measurements (observational level) to inferences about target theoretical properties (inferential level).
Fig. 2: The conceptual structure of the FC framework.
Fig. 3: A systematic approach to validate mechanistic interpretations of FC measures.

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Acknowledgements

The authors acknowledge the following support: National Institutes of Health grant R01MH107549 to L.Q.U.; National Institutes of Health grants R01MH109520 and R01AG055556 to M.W.C.; National Institutes of Health grants P20GM103472, R01EB020407, and NSF National Science Foundation grant 1539067 to V.C. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. School of Psychology, University of Nottingham, Nottingham, UK

    Andrew T. Reid

  2. Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, USA

    Drew B. Headley, Ravi D. Mill, Ruben Sanchez-Romero & Michael W. Cole

  3. Department of Psychology, University of Miami, Coral Gables, FL, USA

    Lucina Q. Uddin

  4. Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA

    Lucina Q. Uddin

  5. Department of Data Analysis, Ghent University, Ghent, Belgium

    Daniele Marinazzo

  6. Department of Psychology, University of California, Berkeley, Berkeley, CA, USA

    Daniel J. Lurie

  7. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China

    Pedro A. Valdés-Sosa

  8. Cuban Neuroscience Center, La Habana, Cuba

    Pedro A. Valdés-Sosa

  9. RUBIC & Department of Psychology, Rutgers University, Newark, NJ, USA

    Stephen José Hanson

  10. Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA

    Bharat B. Biswal

  11. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], Atlanta, GA, USA

    Vince Calhoun

  12. Department of Psychology, Stanford University, Stanford, CA, USA

    Russell A. Poldrack

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Reid, A.T., Headley, D.B., Mill, R.D. et al. Advancing functional connectivity research from association to causation. Nat Neurosci 22, 1751–1760 (2019). https://doi.org/10.1038/s41593-019-0510-4

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