Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power

被引：26

作者：

Glasser, Matthew F. ^{[1
,2
,3
]}

Coalson, Timothy S. ^{[1
]}

Bijsterbosch, Janine D. ^{[4
]}

Harrison, Samuel J. ^{[4
,5
,6
]}

Harms, Michael P. ^{[7
]}

Anticevic, Alan ^{[8
]}

Van Essen, David C. ^{[1
]}

Smith, Stephen M. ^{[4
]}

机构：

[1] Washington Univ, Med Sch, Dept Neurosci, St Louis, MO 63110 USA

[2] Washington Univ, Med Sch, Dept Radiol, St Louis, MO 63110 USA

[3] St Lukes Hosp, St Louis, MI 63017 USA

[4] Univ Oxford, Nuffield Dept Clin Neurosci, Wellcome Ctr Integrat Neuroimaging, Ctr Funct MRI Brain FMRIB,John Radcliffe Hosp, Headley Way, Oxford OX3 9DU, England

[5] Univ Zurich, Translat Neuromodeling Unit, Wilfriedstr 6, CH-8032 Zurich, Switzerland

[6] Swiss Fed Inst Technol, Wilfriedstr 6, CH-8032 Zurich, Switzerland

[7] Washington Univ, Sch Med, Dept Psychiat, St Louis, MO 63110 USA

[8] Yale Univ, Sch Med, Dept Psychiat, 300 George St, New Haven, CT 06511 USA

来源：

NEUROIMAGE | 2019年 / 197卷

基金：

英国惠康基金;

关键词：

FLUCTUATIONS; WAKEFULNESS; SIGNAL;

D O I：

10.1016/j.neuroimage.2019.04.046

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

We respond to a critique of our temporal Independent Components Analysis (ICA) method for separating global noise from global signal in fMRI data that focuses on the signal versus noise classification of several components. While we agree with several of Power's comments, we provide evidence and analysis to rebut his major criticisms and to reassure readers that temporal ICA remains a powerful and promising denoising approach.

引用

页码：435 / 438

页数：4

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