Journal article
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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 628.9KB, Terms of use)
-
- Publisher copy:
- 10.1016/j.neuroimage.2019.04.046
Authors
- Publisher:
- Elsevier
- Journal:
- NeuroImage More from this journal
- Volume:
- 197
- Pages:
- 435-438
- Publication date:
- 2019-04-24
- Acceptance date:
- 2019-04-17
- DOI:
- EISSN:
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1095-9572
- ISSN:
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1053-8119
- Pmid:
-
31026516
- Language:
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English
- Keywords:
- Pubs id:
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pubs:996718
- UUID:
-
uuid:7cfc0aac-f477-4b41-a059-7fcf4bc15157
- Local pid:
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pubs:996718
- Source identifiers:
-
996718
- Deposit date:
-
2019-06-29
Terms of use
- Copyright holder:
- Elsevier
- Copyright date:
- 2019
- Rights statement:
- © 2019 Elsevier Inc. All rights reserved.
- Notes:
- This is the accepted manuscript version of the article. The final version is available from Elsevier at: https://doi.org/10.1016/j.neuroimage.2019.04.046
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