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Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power
Matthew F Glasser1, Timothy S Coalson2, Janine D Bijsterbosch3
1Department of Neuroscience, Washington University Medical School, Saint Louis, MI, 63110, USA; Department of Radiology, Washington University Medical School, Saint Louis, MI, 63110, USA; St. Luke's Hospital, Saint Louis, MI, 63017, USA.
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.
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