ArticleImaging neuroscience (Cambridge, Mass.)2025
The efficacy of resting-state fMRI denoising pipelines for motion correction and behavioural prediction.
Article in Imaging neuroscience (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Eigenvalue Signatures Reveal Residual Motion Effects Across Resting-State fMRI Denoising Strategies.bioRxiv : the preprint server for biology · 2026Article
- Using deep learning to identify brain networks mediating cognitive and motor impairments in alcohol use disorder.Translational psychiatry · 2026Article
- A new fMRI quality metric using multi-echo information: Theory, validation and implications.bioRxiv : the preprint server for biology · 2026Article
- An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies.Network neuroscience (Cambridge, Mass.) · 2026Article
- The motion sensitivity and predictive utility of different estimates of interregional functional coupling in resting-state functional MRI.Network neuroscience (Cambridge, Mass.) · 2026Article
- Functional, Structural, and AI-Based MRI Analysis: A Comprehensive Review of Recent Advances.Diagnostics (Basel, Switzerland) · 2025Review
- Transdiagnostic similarities and distinctions in brain networks associated with autistic social impairments: a prospective cohort study.Molecular autism · 2025Article
- Transdiagnostic similarities and distinctions in brain networks associated with ASD symptoms: A prospective cohort study.medRxiv : the preprint server for health sciences · 2025Article
- Geometric influences on the regional organization of the mammalian brain.bioRxiv : the preprint server for biology · 2025Article
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5 authors.
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Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) is a pivotal tool for mapping the functional organization of the brain and its relation to individual differences in behaviour. One challenge for the field is that rs-fMRI signals are contaminated by multiple sources of noise that can contaminate these rs-fMRI signals, affecting the reliability and validity of any derivative phenotypes and attenuating their correlations with behaviour. Here, we investigate the efficacy of different noise mitigation pipelines, including white matter and cerebrospinal fluid regression, independent component analysis (ICA)-based artefact removal, volume censoring, global signal regression (GSR), and diffuse cluster estimation and regression (DiCER), in simultaneously achieving two objectives: mitigating motion-related artefacts and augmenting brain-behaviour associations. Our analysis, which employed three distinct quality control metrics to evaluate motion influence and a kernel ridge regression for behavioural predictions of 81 different behavioural variables across two independent datasets, revealed that no single pipeline universally excels at achieving both objectives consistently across different cohorts. Pipelines combining ICA-FIX and GSR demonstrate a reasonable trade-off between motion reduction and behavioural prediction performance, but inter-pipeline variations in predictive performance are modest.
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