Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power.
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ABSTRACT:
SUBMITTER: Glasser MF
PROVIDER: S-EPMC6591096 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
Publications
Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power.
Glasser Matthew F MF Coalson Timothy S TS Bijsterbosch Janine D JD Harrison Samuel J SJ Harms Michael P MP Anticevic Alan A Van Essen David C DC Smith Stephen M SM
NeuroImage 20190424
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. ...[more]
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