Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders

Authors
Gotts, Stephen J.Saad, Ziad S.Jo, Hang JoonWallace, GregoryCox, Robert W.Martin, Alex
Issue Date
Jul-2013
Publisher
FRONTIERS RESEARCH FOUNDATION
Keywords
functional connectivity; typically developing; artifact; resting-state fMRI; GCOR; global correlation
Citation
FRONTIERS IN HUMAN NEUROSCIENCE, v.7, pp.1 - 20
Indexed
SCIE
SCOPUS
Journal Title
FRONTIERS IN HUMAN NEUROSCIENCE
Volume
7
Start Page
1
End Page
20
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/162338
DOI
10.3389/fnhum.2013.00356
ISSN
16625161
Abstract
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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
서울 의과대학 > 서울 생리학교실 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Jo, Hang Joon photo

Jo, Hang Joon
COLLEGE OF MEDICINE (DEPARTMENT OF PHYSIOLOGY)
Read more

Altmetrics

Total Views & Downloads

BROWSE