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The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders
We have previously argued from a theoretical basis that the standard practice of regression of the Global Signal from the fMRI time series in functional connectivity studies is ill advised, particularly when comparing groups of participants. Here, we demonstrate in resting-state data from participan...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3709423/ https://www.ncbi.nlm.nih.gov/pubmed/23874279 http://dx.doi.org/10.3389/fnhum.2013.00356 |
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author | Gotts, Stephen J. Saad, Ziad S. Jo, Hang Joon Wallace, Gregory L. Cox, Robert W. Martin, Alex |
author_facet | Gotts, Stephen J. Saad, Ziad S. Jo, Hang Joon Wallace, Gregory L. Cox, Robert W. Martin, Alex |
author_sort | Gotts, Stephen J. |
collection | PubMed |
description | We have previously argued from a theoretical basis that the standard practice of regression of the Global Signal from the fMRI time series in functional connectivity studies is ill advised, particularly when comparing groups of participants. Here, we demonstrate in resting-state data from participants with an Autism Spectrum Disorder and matched controls that these concerns are also well founded in real data. Using the prior theoretical work to formulate predictions, we show: (1) rather than simply altering the mean or range of correlation values amongst pairs of brain regions, Global Signal Regression systematically alters the rank ordering of values in addition to introducing negative values, (2) it leads to a reversal in the direction of group correlation differences relative to other preprocessing approaches, with a higher incidence of both long-range and local correlation differences that favor the Autism Spectrum Disorder group, (3) the strongest group differences under other preprocessing approaches are the ones most altered by Global Signal Regression, and (4) locations showing group differences no longer agree with those showing correlations with behavioral symptoms within the Autism Spectrum Disorder group. The correlation matrices of both participant groups under Global Signal Regression were well predicted by our previous mathematical analyses, demonstrating that there is nothing mysterious about these results. Finally, when independent physiological nuisance measures are lacking, we provide a simple alternative approach for assessing and lessening the influence of global correlations on group comparisons that replicates our previous findings. While this alternative performs less well for symptom correlations than our favored preprocessing approach that includes removal of independent physiological measures, it is preferable to the use of Global Signal Regression, which prevents unequivocal conclusions about the direction or location of group differences. |
format | Online Article Text |
id | pubmed-3709423 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-37094232013-07-19 The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders Gotts, Stephen J. Saad, Ziad S. Jo, Hang Joon Wallace, Gregory L. Cox, Robert W. Martin, Alex Front Hum Neurosci Neuroscience We have previously argued from a theoretical basis that the standard practice of regression of the Global Signal from the fMRI time series in functional connectivity studies is ill advised, particularly when comparing groups of participants. Here, we demonstrate in resting-state data from participants with an Autism Spectrum Disorder and matched controls that these concerns are also well founded in real data. Using the prior theoretical work to formulate predictions, we show: (1) rather than simply altering the mean or range of correlation values amongst pairs of brain regions, Global Signal Regression systematically alters the rank ordering of values in addition to introducing negative values, (2) it leads to a reversal in the direction of group correlation differences relative to other preprocessing approaches, with a higher incidence of both long-range and local correlation differences that favor the Autism Spectrum Disorder group, (3) the strongest group differences under other preprocessing approaches are the ones most altered by Global Signal Regression, and (4) locations showing group differences no longer agree with those showing correlations with behavioral symptoms within the Autism Spectrum Disorder group. The correlation matrices of both participant groups under Global Signal Regression were well predicted by our previous mathematical analyses, demonstrating that there is nothing mysterious about these results. Finally, when independent physiological nuisance measures are lacking, we provide a simple alternative approach for assessing and lessening the influence of global correlations on group comparisons that replicates our previous findings. While this alternative performs less well for symptom correlations than our favored preprocessing approach that includes removal of independent physiological measures, it is preferable to the use of Global Signal Regression, which prevents unequivocal conclusions about the direction or location of group differences. Frontiers Media S.A. 2013-07-12 /pmc/articles/PMC3709423/ /pubmed/23874279 http://dx.doi.org/10.3389/fnhum.2013.00356 Text en Copyright © 2013 Gotts, Saad, Jo, Wallace, Cox and Martin. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc. |
spellingShingle | Neuroscience Gotts, Stephen J. Saad, Ziad S. Jo, Hang Joon Wallace, Gregory L. Cox, Robert W. Martin, Alex The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders |
title | The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders |
title_full | The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders |
title_fullStr | The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders |
title_full_unstemmed | The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders |
title_short | The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders |
title_sort | perils of global signal regression for group comparisons: a case study of autism spectrum disorders |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3709423/ https://www.ncbi.nlm.nih.gov/pubmed/23874279 http://dx.doi.org/10.3389/fnhum.2013.00356 |
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