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Benchmarking fMRI Denoising Pipelines.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Benchmarking fMRI Denoising Pipelines.
Συγγραφείς: Zhai T; Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, USA., Gu H; Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, USA., Holton A; Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, USA., Chang E; Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, USA., Frederick BB; Department of Psychiatry, Harvard University Medical School, Boston, Massachusetts, USA., Ross TJ; Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, USA., Yang Y; Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, USA., Janes AC; Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland, USA.
Πηγή: Human brain mapping [Hum Brain Mapp] 2026 Jul; Vol. 47 (10), pp. e70561.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley Country of Publication: United States NLM ID: 9419065 Publication Model: Print Cited Medium: Internet ISSN: 1097-0193 (Electronic) Linking ISSN: 10659471 NLM ISO Abbreviation: Hum Brain Mapp Subsets: MEDLINE
Imprint Name(s): Publication: New York : Wiley
Original Publication: New York : Wiley-Liss, c1993-
Ιατρικοί όροι (MeSH): Magnetic Resonance Imaging*/methods , Magnetic Resonance Imaging*/standards , Brain*/physiology , Brain*/diagnostic imaging , Image Processing, Computer-Assisted*/methods , Image Processing, Computer-Assisted*/standards , Brain Mapping*/methods , Brain Mapping*/standards, Humans ; Signal-To-Noise Ratio ; Benchmarking ; Artifacts
Περίληψη: Functional magnetic resonance imaging (fMRI) is a powerful tool for probing neuronal activity in vivo, but fMRI data are inherently noisy. To mitigate this, a wide range of denoising strategies have been developed, including volume censoring, anatomical component-based noise correction (aCompCor), ICA-based methods (e.g., AROMA, FIX), and multi-echo approaches (e.g., ME-ICA, tedana). These techniques are often applied in different combinations and have been predominantly evaluated on single-echo resting-state fMRI data-typically without incorporating more recent methodological advances known to improve modeling, such as order-independent "1-step regression", modeling temporal autocorrelation (pre-whitening), and temporal shifting of physiological nuisance regressors. To fill this gap, we used a framework that incorporates these methods and benchmarked a range of denoising pipelines across task and resting-state, single- and multi-band, and single- and multi-echo fMRI datasets, using different combinations of standard denoising confounds. Pipeline performance was evaluated using temporal signal-to-noise ratio (tSNR) and percentage remaining degrees-of-freedom (DoF), effectiveness of motion correction, and effectiveness of signal preservation. While pipelines only using ICA were insufficient, those that incorporated physiological nuisance regressors performed well. Additional improvements were observed when temporally shifted physiological regressors were accounted for. Based on these results, we provide recommendations for selecting denoising pipelines and emphasize the need for continued benchmarking as new methods are developed or applied in novel contexts.
(© 2026 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)
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Grant Information: ZIA DA000641 United States ImNIH Intramural NIH HHS
Contributed Indexing: Keywords: 1‐step regression; fMRI denoising pipeline; multi‐echo; pre‐whitening; time‐shifted physiological confounds
Entry Date(s): Date Created: 20260701 Date Completed: 20260701 Latest Revision: 20260726
Update Code: 20260726
PubMed Central ID: PMC13320824
DOI: 10.1002/hbm.70561
PMID: 42383403
Βάση Δεδομένων: MEDLINE
Περιγραφή
ISSN:1097-0193
DOI:10.1002/hbm.70561