Objective. Blood-oxygenated-level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) is a widely used non-invasive tool for mapping brain function and connectivity. However, the BOLD signal is highly affected by non-neuronal contributions arising from head motion, physiological noise and scanner artefacts. Therefore, it is necessary to recover the signal of interest from the other noise-related fluctuations to obtain reliable functional connectivity (FC) results. Several pre-processing pipelines have been developed, mainly based on nuisance regression and independent component analysis (ICA). The aim of this work was to investigate the impact of seven widely used denoising methods on both resting-state and task fMRI. Approach. Task fMRI can provide some ground truth given that the task administered has well established brain activations. The resulting cleaned data were compared using a wide range of measures: motion evaluation and data quality, resting-state networks and task activations, FC. Main results. Improved signal quality and reduced motion artefacts were obtained with all advanced pipelines, compared to the minimally pre-processed data. Larger variability was observed in the case of brain activation and FC estimates, with ICA-based pipelines generally achieving more reliable and accurate results. Significance. This work provides an evidence-based reference for investigators to choose the most appropriate method for their study and data.

客观。基于血氧水平依赖 (BOLD) 的功能性磁共振成像 (fMRI) 是一种广泛使用的非侵入性工具，用于绘制大脑功能和连通性。然而，BOLD 信号受到头部运动、生理噪声和扫描仪伪影引起的非神经元贡献的高度影响。因此，有必要从其他与噪声相关的波动中恢复感兴趣的信号，以获得可靠的功能连接（FC）结果。已经开发了几个预处理管道，主要基于滋扰回归和独立成分分析（ICA）。这项工作的目的是调查七种广泛使用的去噪方法对静息状态和任务 fMRI 的影响。方法. 鉴于所执行的任务具有良好的大脑激活，任务 fMRI 可以提供一些基本事实。使用广泛的措施比较产生的清洁数据：运动评估和数据质量、静息状态网络和任务激活、FC。主要结果。与最低限度的预处理数据相比，所有先进的管道都获得了改进的信号质量和减少的运动伪影。在脑激活和 FC 估计的情况下观察到更大的变异性，基于 ICA 的管道通常可以获得更可靠和准确的结果。意义。这项工作为研究人员选择最适合他们的研究和数据的方法提供了循证参考。