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  Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?

Hindriks, R., Adhikari, M., Murayama, Y., Ganzetti, M., Mantini, D., Logothetis, N., et al. (2016). Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage, 127, 242-256. doi:10.1016/j.neuroimage.2015.11.055.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-7A1E-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-7A1F-6
Genre: Journal Article

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Hindriks, R, Author
Adhikari, MH, Author
Murayama, Y1, 2, Author              
Ganzetti, M, Author
Mantini, D, Author
Logothetis, NK1, 2, Author              
Deco, G, Author
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: During the last several years, focus of research on resting-state functional magnetic resonance imaging (fMRI) shifted from the analysis of functional connectivity averaged over the duration of scanning sessions, to the analysis of changes of functional connectivity within sessions. Although several studies have reported the presence of dynamic functional connectivity (dFC), statistical assessment of the results is not always carried out in a sound way and in some studies is even omitted. In this study, we explain why appropriate statistical tests are needed to detect dFC, describe how they can be carried out, how to assess the performance of dFC measures, and illustrate the methodology using spontaneous blood-oxygen level-dependent (BOLD) fMRI recordings of macaque monkeys under general anesthesia human subjects under resting-state conditions. We mainly focus on sliding-window correlations since these are most widely used is assessing dFC, but also consider a recently proposed non-linear measure. The simulations and methodology however, are general and can be applied to any measure. The results are twofold. First, through simulations we show that in typical resting-state sessions of 10 minutes, it is almost impossible to detect dFC using sliding-window correlations. This prediction is validated by both the macaque and the human data: in none of the individual recording sessions, evidence for dFC was found. Second, detection power can be considerably increased by session- or subject-averaging of the measures. In doing so, we found that most of the functional connections are in fact dynamic. With this study, we hope to raise awareness of the statistical pitfalls in the assessment of dFC and how they can be avoided by using appropriate statistical methods.

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 Dates: 2016-02
 Publication Status: Published in print
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 Identifiers: DOI: 10.1016/j.neuroimage.2015.11.055
BibTex Citekey: HindriksAMGMLD2015
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Title: NeuroImage
Source Genre: Journal
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Pages: - Volume / Issue: 127 Sequence Number: - Start / End Page: 242 - 256 Identifier: -