Advancing functional connectivity research from association to causation

Andrew T. Reid, Drew B. Headley, Ravi D. Mill, Ruben Sanchez-Romero, Lucina Q. Uddin, Daniele Marinazzo, Daniel J. Lurie, Pedro A. Valdés-Sosa, Stephen José Hanson, Bharat B. Biswal, Vince Calhoun, Russell A. Poldrack, Michael W. Cole

Research output: Contribution to journalReview article

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.

Original languageEnglish (US)
Pages (from-to)1751-1760
Number of pages10
JournalNature neuroscience
Volume22
Issue number11
DOIs
StatePublished - Nov 1 2019
Externally publishedYes

Fingerprint

Causality
Brain
Research
Practice Guidelines
Cognition

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Reid, A. T., Headley, D. B., Mill, R. D., Sanchez-Romero, R., Uddin, L. Q., Marinazzo, D., ... Cole, M. W. (2019). Advancing functional connectivity research from association to causation. Nature neuroscience, 22(11), 1751-1760. https://doi.org/10.1038/s41593-019-0510-4

Advancing functional connectivity research from association to causation. / 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.

In: Nature neuroscience, Vol. 22, No. 11, 01.11.2019, p. 1751-1760.

Research output: Contribution to journalReview article

Reid, AT, Headley, DB, Mill, RD, Sanchez-Romero, R, Uddin, LQ, Marinazzo, D, Lurie, DJ, Valdés-Sosa, PA, Hanson, SJ, Biswal, BB, Calhoun, V, Poldrack, RA & Cole, MW 2019, 'Advancing functional connectivity research from association to causation', Nature neuroscience, vol. 22, no. 11, pp. 1751-1760. https://doi.org/10.1038/s41593-019-0510-4
Reid AT, Headley DB, Mill RD, Sanchez-Romero R, Uddin LQ, Marinazzo D et al. Advancing functional connectivity research from association to causation. Nature neuroscience. 2019 Nov 1;22(11):1751-1760. https://doi.org/10.1038/s41593-019-0510-4
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. / Advancing functional connectivity research from association to causation. In: Nature neuroscience. 2019 ; Vol. 22, No. 11. pp. 1751-1760.
@article{d2c7b8eb939842939d43924d90b85ffd,
title = "Advancing functional connectivity research from association to causation",
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.",
author = "Reid, {Andrew T.} and Headley, {Drew B.} and Mill, {Ravi D.} and Ruben Sanchez-Romero and Uddin, {Lucina Q.} and Daniele Marinazzo and Lurie, {Daniel J.} and Vald{\'e}s-Sosa, {Pedro A.} and Hanson, {Stephen Jos{\'e}} and Biswal, {Bharat B.} and Vince Calhoun and Poldrack, {Russell A.} and Cole, {Michael W.}",
year = "2019",
month = "11",
day = "1",
doi = "10.1038/s41593-019-0510-4",
language = "English (US)",
volume = "22",
pages = "1751--1760",
journal = "Nature Neuroscience",
issn = "1097-6256",
publisher = "Nature Publishing Group",
number = "11",

}

TY - JOUR

T1 - Advancing functional connectivity research from association to causation

AU - Reid, Andrew T.

AU - Headley, Drew B.

AU - Mill, Ravi D.

AU - Sanchez-Romero, Ruben

AU - Uddin, Lucina Q.

AU - Marinazzo, Daniele

AU - Lurie, Daniel J.

AU - Valdés-Sosa, Pedro A.

AU - Hanson, Stephen José

AU - Biswal, Bharat B.

AU - Calhoun, Vince

AU - Poldrack, Russell A.

AU - Cole, Michael W.

PY - 2019/11/1

Y1 - 2019/11/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85074221810&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85074221810&partnerID=8YFLogxK

U2 - 10.1038/s41593-019-0510-4

DO - 10.1038/s41593-019-0510-4

M3 - Review article

C2 - 31611705

AN - SCOPUS:85074221810

VL - 22

SP - 1751

EP - 1760

JO - Nature Neuroscience

JF - Nature Neuroscience

SN - 1097-6256

IS - 11

ER -