Goffi Federica, Bianchi Anna Maria, Schiena Giandomenico, Brambilla Paolo, Maggioni Eleonora
Department of Electronics Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Hum Brain Mapp. 2025 May;46(7):e70080. doi: 10.1002/hbm.70080.
Despite the increasing use of resting-state functional magnetic resonance imaging (rs-fMRI) data for studying the spontaneous functional interactions within the brain, the achievement of robust results is often hampered by insufficient data quality and by poor knowledge of the most effective denoising methods. The present study aims to define an appropriate denoising strategy for rs-fMRI data by proposing a robust framework for the quantitative and comprehensive comparison of the performance of multiple pipelines made available by the newly proposed HALFpipe software. This will ultimately contribute to standardizing rs-fMRI preprocessing and denoising steps. Fifty-three participants took part in the study by undergoing a rs-fMRI session. Synthetic rs-fMRI data from one subject were also generated. Nine different denoising pipelines were applied in parallel to the minimally preprocessed fMRI data. The comparison was conducted by computing previously proposed and novel metrics that quantify the degree of artifact removal, signal enhancement, and resting-state network identifiability. A summary performance index, accounting for both noise removal and information preservation, was proposed. The results confirm the performance heterogeneity of different denoising pipelines across the different quality metrics. In both real and synthetic data, the summary performance index favored the denoising strategy including the regression of mean signals from white matter and cerebrospinal fluid brain areas and global signal. This pipeline resulted in the best compromise between artifact removal and preservation of the information on resting-state networks. Our study provided useful methodological tools and key information on the effectiveness of multiple denoising strategies for rs-fMRI data. Besides providing a robust comparison approach that could be adapted to other fMRI studies, a suitable denoising pipeline for rs-fMRI data was identified, which could be used to improve the reproducibility of rs-fMRI findings.
尽管静息态功能磁共振成像（rs-fMRI）数据在研究大脑内自发功能相互作用方面的应用日益广泛，但数据质量不足以及对最有效去噪方法了解有限，常常阻碍了稳健结果的获得。本研究旨在通过提出一个稳健的框架，对新提出的HALFpipe软件提供的多个处理流程的性能进行定量和全面比较，从而为rs-fMRI数据定义一种合适的去噪策略。这最终将有助于使rs-fMRI预处理和去噪步骤标准化。五十三名参与者通过进行一次rs-fMRI扫描参与了该研究。还生成了来自一名受试者的合成rs-fMRI数据。九个不同的去噪处理流程被并行应用于经过最小预处理的fMRI数据。通过计算先前提出的和新的指标进行比较，这些指标量化了伪影去除程度、信号增强以及静息态网络可识别性。提出了一个综合性能指标，该指标兼顾了噪声去除和信息保留。结果证实了不同去噪处理流程在不同质量指标上的性能异质性。在真实数据和合成数据中，综合性能指标都有利于包括对白质和脑脊液脑区平均信号以及全局信号进行回归的去噪策略。这个处理流程在伪影去除和静息态网络信息保留之间实现了最佳平衡。我们的研究提供了有用的方法学工具以及关于rs-fMRI数据多种去噪策略有效性的关键信息。除了提供一种可适用于其他fMRI研究的稳健比较方法外，还确定了一个适用于rs-fMRI数据的去噪处理流程，可用于提高rs-fMRI研究结果的可重复性。
