Advancing functional connectivity research from association to causation
Document Type
Article
Publication Date
11-1-2019
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.
Identifier
85074221810 (Scopus)
Publication Title
Nature Neuroscience
External Full Text Location
https://doi.org/10.1038/s41593-019-0510-4
e-ISSN
15461726
ISSN
10976256
PubMed ID
31611705
First Page
1751
Last Page
1760
Issue
11
Volume
22
Grant
1539067
Fund Ref
National Science Foundation
Recommended Citation
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.; and Cole, Michael W., "Advancing functional connectivity research from association to causation" (2019). Faculty Publications. 7257.
https://digitalcommons.njit.edu/fac_pubs/7257
