The efficacy of resting-state fMRI denoising pipelines for motion correction and behavioural prediction.

Imaging neuroscience (Cambridge, Mass.) Pub Date : 2025-08-07 eCollection Date: 2025-01-01 DOI:10.1162/IMAG.a.97
Kane Pavlovich, James Pang, Alexander Holmes, Toby Constable, Alex Fornito
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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.

静息状态fMRI去噪管道对运动校正和行为预测的有效性。
静息状态功能磁共振成像(rs-fMRI)是绘制大脑功能组织及其与个体行为差异关系的关键工具。该领域面临的一个挑战是,rs-fMRI信号受到多种噪声源的污染,这些噪声源会污染这些rs-fMRI信号,影响任何衍生表型的可靠性和有效性,并减弱它们与行为的相关性。在这里,我们研究了不同的降噪管道的有效性,包括白质和脑脊液回归、基于独立分量分析(ICA)的伪影去除、体积滤波、全局信号回归(GSR)和弥散聚类估计和回归(DiCER),同时实现两个目标:减轻运动相关的伪影和增强脑行为关联。我们的分析采用了三种不同的质量控制指标来评估运动影响,并对两个独立数据集的81种不同行为变量的行为预测采用核脊回归,结果表明,没有一个单一的管道能够在不同的队列中一致地实现这两个目标。结合ICA-FIX和GSR的管道在运动减少和行为预测性能之间表现出合理的权衡,但预测性能的管道间差异不大。
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