Literature DB >> 28213118

The global signal in fMRI: Nuisance or Information?

Thomas T Liu1, Alican Nalci2, Maryam Falahpour3.   

Abstract

The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) studies. However, there is considerable controversy over its use because of the potential bias that can be introduced when it is applied to the analysis of both task-related and resting-state fMRI studies. In this paper we take a closer look at the global signal, examining in detail the various sources that can contribute to the signal. For the most part, the global signal has been treated as a nuisance term, but there is growing evidence that it may also contain valuable information. We also examine the various ways that the global signal has been used in the analysis of fMRI data, including global signal regression, global signal subtraction, and global signal normalization. Furthermore, we describe new ways for understanding the effects of global signal regression and its relation to the other approaches.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  General linear model; Global signal; Motion; Physiological noise; Vigilance; fMRI

Mesh:

Year:  2017        PMID: 28213118      PMCID: PMC5406229          DOI: 10.1016/j.neuroimage.2017.02.036

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  93 in total

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3.  Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression.

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4.  The Effects of Global Signal Regression on Estimates of Resting-State Blood Oxygen-Level-Dependent Functional Magnetic Resonance Imaging and Electroencephalogram Vigilance Correlations.

Authors:  Maryam Falahpour; Alican Nalci; Thomas T Liu
Journal:  Brain Connect       Date:  2018-12

5.  Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal.

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6.  Rectified Gaussian Scale Mixtures and the Sparse Non-Negative Least Squares Problem.

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7.  All roads lead to the default-mode network-global source of DMN abnormalities in major depressive disorder.

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8.  Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms.

Authors:  Hengyi Cao; Sarah C McEwen; Jennifer K Forsyth; Dylan G Gee; Carrie E Bearden; Jean Addington; Bradley Goodyear; Kristin S Cadenhead; Heline Mirzakhanian; Barbara A Cornblatt; Ricardo E Carrión; Daniel H Mathalon; Thomas H McGlashan; Diana O Perkins; Aysenil Belger; Larry J Seidman; Heidi Thermenos; Ming T Tsuang; Theo G M van Erp; Elaine F Walker; Stephan Hamann; Alan Anticevic; Scott W Woods; Tyrone D Cannon
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9.  Global fluctuations of cerebral blood flow indicate a global brain network independent of systemic factors.

Authors:  Li Zhao; David C Alsop; John A Detre; Weiying Dai
Journal:  J Cereb Blood Flow Metab       Date:  2017-08-17       Impact factor: 6.200

10.  Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data.

Authors:  Matthew F Glasser; Timothy S Coalson; Janine D Bijsterbosch; Samuel J Harrison; Michael P Harms; Alan Anticevic; David C Van Essen; Stephen M Smith
Journal:  Neuroimage       Date:  2018-08-02       Impact factor: 6.556

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