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

      research-article
      Author(s): a , b , * , c , d , e
      Publication date PMC-release:
      Journal: Neuroimage
      Publisher: Academic Press

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

          Highlights

          • •

            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.

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          Most cited references41

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          Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis.

          Peter Fransson (2005)
          Recent neuroimaging studies have lead to the proposal that rest is characterized by an organized, baseline level of activity, a default mode of brain function that is suspended during specific goal-oriented mental activity. Previous studies have shown that the primary function subserved by the default mode is that of an introspectively oriented, self-referential mode of mental activity. The default mode of brain function hypothesis is readdressed from the perspective of the presence of low-frequency blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal changes (0.012-0.1 Hz) in the resting brain. The results show that the brain during rest is not tonically active in a single mode of brain function. Rather, the findings presented here suggest that the brain recurrently toggles between an introspectively oriented mode (default mode) and a state-of-mind that tentatively might be interpreted as an extrospectively oriented mode that involves a readiness and alertness to changes in the external and internal environment.
          Article 0 comments Cited 407 times – based on 0 reviews     Review now
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            Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI.

            Rasmus Birn, Jason Diamond, Monica A Smith … (2006)
            Subtle changes in a subject's breathing rate or depth, which occur naturally during rest at low frequencies (<0.1 Hz), have been shown to be significantly correlated with fMRI signal changes throughout gray matter and near large vessels. The goal of this study was to investigate the impact of these low-frequency respiration variations on both task activation fMRI studies and resting-state functional connectivity analysis. Unlike MR signal changes correlated with the breathing motion ( approximately 0.3 Hz), BOLD signal changes correlated with across-breath variations in respiratory volume ( approximately 0.03 Hz) appear localized to blood vessels and regions with high blood volume, such as gray matter, similar to changes seen in response to a breath-hold challenge. In addition, the respiration-variation-induced signal changes were found to coincide with many of the areas identified as part of the 'default mode' network, a set of brain regions hypothesized to be more active at rest. Regions could therefore be classified as being part of a resting network based on their similar respiration-induced changes rather than their synchronized neuronal activity. Monitoring and removing these respiration variations led to a significant improvement in the identification of task-related activation and deactivation and only slight differences in regions correlated with the posterior cingulate at rest. Regressing out global signal changes or cueing the subject to breathe at a constant rate and depth resulted in an improved spatial overlap between deactivations and resting-state correlations among areas that showed deactivation.
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              Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies.

              Andreas Weissenbacher, Christian Kasess, Florian Gerstl … (2009)
              Resting-state data sets contain coherent fluctuations unrelated to neural processes originating from residual motion artefacts, respiration and cardiac action. Such confounding effects may introduce correlations and cause an overestimation of functional connectivity strengths. In this study we applied several multidimensional linear regression approaches to remove artificial coherencies and examined the impact of preprocessing on sensitivity and specificity of functional connectivity results in simulated data and resting-state data sets from 40 subjects. Furthermore, we aimed at clarifying possible causes of anticorrelations and test the hypothesis that anticorrelations are introduced via certain preprocessing approaches, with particular focus on the effects of regression against the global signal. Our results show that preprocessing in general greatly increased connection specificity, in particular correction for global signal fluctuations almost doubled connection specificity. However, widespread anticorrelated networks were only found when regression against the global signal was applied. Results in simulated data sets compared with result of human data strongly suggest that anticorrelations are indeed introduced by global signal regression and should therefore be interpreted very carefully. In addition, global signal regression may also reduce the sensitivity for detecting true correlations, i.e. increase the number of false negatives. Concluding from our results we suggest that is highly recommended to apply correction against realignment parameters, white matter and ventricular time courses, as well as the global signal to maximize the specificity of positive resting-state correlations.
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                Author and article information

                Contributors
                Kevin Murphy
                Michael D. Fox
                Journal
                Journal ID (nlm-ta): Neuroimage
                Journal ID (iso-abbrev): Neuroimage
                Title: Neuroimage
                Publisher: Academic Press
                ISSN (Print): 1053-8119
                ISSN (Electronic): 1095-9572
                Publication date PMC-release: 01 July 2017
                Publication date (Print): 01 July 2017
                Volume: 154
                Pages: 169-173
                Affiliations
                [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
                [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
                [e ]Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, United States
                Author notes
                [* ]Correspondence to: CUBRIC, Cardiff University, Maindy Road, CF24 4HQ, United Kingdom. murphyk2@ 123456cardiff.ac.uk
                Article
                Publisher ID: S1053-8119(16)30671-1
                DOI: 10.1016/j.neuroimage.2016.11.052
                PMC ID: 5489207
                PubMed ID: 27888059
                SO-VID: 3421561a-f364-41ec-8832-6de1a74c3545
                Copyright © © 2016 The Authors
                License:

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Date accepted : 21 November 2016
                Categories
                Subject: Article

                ScienceOpen disciplines: Neurosciences
                Data availability:
                ScienceOpen disciplines: Neurosciences

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