Towards a consensus regarding global signal regression for resting state functional connectivity MRI

The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the processing options, few have generated as much controversy as global signal regression and the subsequent observation of negative correlations (anti-...

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Bibliographic Details
Published in	NeuroImage (Orlando, Fla.) Vol. 154; pp. 169 - 173
Main Authors	Murphy, Kevin, Fox, Michael D.
Format	Journal Article
Language	English
Published	United States Elsevier Inc 01.07.2017 Elsevier Limited Academic Press
Subjects	Blood Caffeine Carbon dioxide Connectome - methods Consensus Data processing Functional magnetic resonance imaging Humans Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging - methods Neural networks Time series
Online Access	Get full text
ISSN	1053-8119 1095-9572
DOI	10.1016/j.neuroimage.2016.11.052

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Abstract	The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the processing options, few have generated as much controversy as global signal regression and the subsequent observation of negative correlations (anti-correlations). This debate has motivated new processing strategies and advancement in the field, but has also generated significant confusion and contradictory guidelines. In this article, we work towards a consensus regarding global signal regression. We highlight several points of agreement including the fact that there is not a single “right” way to process resting state data that reveals the “true” nature of the brain. Although further work is needed, different processing approaches likely reveal complementary insights about the brain's functional organisation. •Global signal regression is a controversial resting state fMRI pre-processing option.•The debate about GSR has generated significant confusion and contradictory guidelines.•Here, we present our consensus statement on the use of GSR in resting state analyses.•There is no “right” way to pre-process that reveals the “true” nature of the brain.•Different processing approaches reveal complimentary insights about brain function.
AbstractList	The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the processing options, few have generated as much controversy as global signal regression and the subsequent observation of negative correlations (anti-correlations). This debate has motivated new processing strategies and advancement in the field, but has also generated significant confusion and contradictory guidelines. In this article, we work towards a consensus regarding global signal regression. We highlight several points of agreement including the fact that there is not a single “right” way to process resting state data that reveals the “true” nature of the brain. Although further work is needed, different processing approaches likely reveal complementary insights about the brain's functional organisation. • Global signal regression is a controversial resting state fMRI pre-processing option. • The debate about GSR has generated significant confusion and contradictory guidelines. • Here, we present our consensus statement on the use of GSR in resting state analyses. • There is no “right” way to pre-process that reveals the “true” nature of the brain. • Different processing approaches reveal complimentary insights about brain function. The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the processing options, few have generated as much controversy as global signal regression and the subsequent observation of negative correlations (anti-correlations). This debate has motivated new processing strategies and advancement in the field, but has also generated significant confusion and contradictory guidelines. In this article, we work towards a consensus regarding global signal regression. We highlight several points of agreement including the fact that there is not a single “right” way to process resting state data that reveals the “true” nature of the brain. Although further work is needed, different processing approaches likely reveal complementary insights about the brain's functional organisation. The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the processing options, few have generated as much controversy as global signal regression and the subsequent observation of negative correlations (anti-correlations). This debate has motivated new processing strategies and advancement in the field, but has also generated significant confusion and contradictory guidelines. In this article, we work towards a consensus regarding global signal regression. We highlight several points of agreement including the fact that there is not a single “right” way to process resting state data that reveals the “true” nature of the brain. Although further work is needed, different processing approaches likely reveal complementary insights about the brain's functional organisation. •Global signal regression is a controversial resting state fMRI pre-processing option.•The debate about GSR has generated significant confusion and contradictory guidelines.•Here, we present our consensus statement on the use of GSR in resting state analyses.•There is no “right” way to pre-process that reveals the “true” nature of the brain.•Different processing approaches reveal complimentary insights about brain function.
Author	Fox, Michael D. Murphy, Kevin
AuthorAffiliation	e Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, United States c Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States d Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States a Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom b Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, CF24 4HQ, United Kingdom
AuthorAffiliation_xml	– name: d Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States – name: c Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States – name: a Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom – name: b Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, CF24 4HQ, United Kingdom – name: e Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, United States
Author_xml	– sequence: 1 givenname: Kevin surname: Murphy fullname: Murphy, Kevin email: murphyk2@cardiff.ac.uk organization: Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, CF24 4HQ, United Kingdom – sequence: 2 givenname: Michael D. surname: Fox fullname: Fox, Michael D. email: foxmdphd@gmail.com organization: Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27888059$$D View this record in MEDLINE/PubMed
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ContentType	Journal Article
Copyright	2016 The Authors Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved. Copyright Elsevier Limited Jul 1, 2017 2016 The Authors 2016
Copyright_xml	– notice: 2016 The Authors – notice: Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved. – notice: Copyright Elsevier Limited Jul 1, 2017 – notice: 2016 The Authors 2016
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Snippet	The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the...
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SubjectTerms	Blood Caffeine Carbon dioxide Connectome - methods Consensus Data processing Functional magnetic resonance imaging Humans Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging - methods Neural networks Time series
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Title	Towards a consensus regarding global signal regression for resting state functional connectivity MRI
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