Advancing functional connectivity research from association to causation

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...

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Bibliographic Details
Published in:Nature neuroscience Vol. 22; no. 11; pp. 1751 - 1760
Main Authors: Reid, Andrew T, Headley, Drew B, Mill, Ravi D, Sanchez-Romero, Ruben, Uddin, Lucina Q, Marinazzo, Daniele, Lurie, Daniel J, Valdés-Sosa, Pedro A, Hanson, Stephen José, Biswal, Bharat B, Calhoun, Vince, Poldrack, Russell A, Cole, Michael W
Format: Journal Article
Language:English
Published: United States Nature Publishing Group 01.11.2019
Subjects:
Animals
Brain
Brain - physiology
Brain research
Causation
Cognition
Correlation analysis
Functional Neuroimaging - methods
Humans
Models, Neurological
Neural networks
Neural Pathways - physiology
Statistics
Validation Studies as Topic
ISSN:1097-6256, 1546-1726, 1546-1726
Online Access:Get full text
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Summary: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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ISSN:1097-6256
1546-1726
1546-1726
DOI:10.1038/s41593-019-0510-4