Neurofeedback training using real‐time functional magnetic resonance imaging (rtfMRI‐NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non‐invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI‐NF studies. We found: (a) that less than a third of the studies reported implementing standard real‐time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI‐NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI‐NF studies: (a) report implementation of a set of standard real‐time fMRI denoising steps according to a proposed COBIDAS‐style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community‐informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open‐source rtfMRI‐NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github.com/jsheunis/quality‐and‐denoising‐in‐rtfmri‐nf.

中文翻译：

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实时功能磁共振成像神经反馈的质量和去噪：方法回顾。


使用实时功能磁共振成像（rtfMRI-NF）的神经反馈训练允许受试者自愿控制局部和分布式大脑活动。尽管其功效和临床意义尚待确定，但作为神经精神和神经认知障碍的一种有前途的非侵入性治疗选择，它引起了越来越多的兴趣。在这项工作中，我们首次对可用于提高神经反馈信号质量的采集、处理和质量控制方法进行了广泛的回顾。此外，我们还调查了最近发表的 128 项 rtfMRI-NF 研究中的去噪和质量控制实践状况。我们发现：(a) 不到三分之一的研究报告实施了标准实时 fMRI 去噪步骤，(b) 方法报告方面还有很大的改进空间，(c) 需要进行方法学研究来量化和比较贡献神经反馈信号质量的去噪步骤。 rtfMRI-NF 研究的进展取决于方法和结果的可重复性。值得注意的是，需要系统地努力建立证据来理清影响神经反馈效应的各种机制。为此，我们建议未来的 rtfMRI-NF 研究：（a）根据提议的 COBIDAS 式清单（https://osf.io/kjwhf/）报告一组标准实时 fMRI 去噪步骤的实施情​​况， (b) 通过计算和报告社区知情的质量指标并应用离线控制检查来确保神经反馈信号的质量，以及 (c) 努力采用方法和数据共享以及支持开源 rtfMRI-NF 形式的透明原则软件。 可重复性的代码和数据，以及探索研究数据的交互式环境，可以在 https://github.com/jsheunis/quality‐and‐denoising‐in‐rtfmri‐nf 上访问。