@article{a63bd7ec957149448d91f2b60a937ee8,
title = "Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power",
abstract = "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.",
author = "Glasser, {Matthew F.} and Coalson, {Timothy S.} and Bijsterbosch, {Janine D.} and Harrison, {Samuel J.} and Harms, {Michael P.} and Alan Anticevic and {Van Essen}, {David C.} and Smith, {Stephen M.}",
note = "Funding Information: Supported in part by the Human Connectome Project, WU-Minn-Ox Consortium ( 1U54MH091657 ) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research ; the McDonnell Center for Systems Neuroscience at Washington University ; and NIH F30 MH097312 (M.F.G.), RO1 MH-60974 (D.C.V.E.). Funding to SS, JB, SH gratefully acknowledged via Wellcome Trust strategic award 098369/Z/12/Z . SH was supported by the grant #2017-403 of the Strategic Focus Area “ Personalized Health and Related Technologies (PHRT) ” of the ETH Domain. Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",
year = "2019",
month = aug,
day = "15",
doi = "10.1016/j.neuroimage.2019.04.046",
language = "English",
volume = "197",
pages = "435--438",
journal = "NeuroImage",
issn = "1053-8119",
}