Journal article
Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power.
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Glasser MF
Department 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. Electronic address: glasserm@wustl.edu.
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Coalson TS
Department of Neuroscience, Washington University Medical School, Saint Louis, MI, 63110, USA.
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Bijsterbosch JD
Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
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Harrison SJ
Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK; Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Wilfriedstrasse 6, 8032, Zurich, Switzerland.
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Harms MP
Department of Psychiatry, Washington University Medical School, Saint Louis, MO, USA.
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Anticevic A
Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, 06511, USA.
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Van Essen DC
Department of Neuroscience, Washington University Medical School, Saint Louis, MI, 63110, USA.
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Smith SM
Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
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English
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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Language
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Open access status
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green
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Identifiers
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Persistent URL
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https://sonar.ch/global/documents/288859
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