Division of Surgery, Oncology, Reproductive Biology and Anaesthetics (SORA), Imperial College, London, UK.
Phys Med Biol. 2010 Jul 7;55(13):3701-24. doi: 10.1088/0031-9155/55/13/009. Epub 2010 Jun 9.
Functional near infrared spectroscopy (fNIRS) is a rapidly developing neuroimaging modality for exploring cortical brain behaviour. Despite recent advances, the quality of fNIRS experimentation may be compromised in several ways: firstly, by altering the optical properties of the tissues encountered in the path of light; secondly, through adulteration of the recovered biological signals (noise) and finally, by modulating neural activity. Currently, there is no systematic way to guide the researcher regarding these factors when planning fNIRS studies. Conclusions extracted from fNIRS data will only be robust if appropriate methodology and analysis in accordance with the research question under investigation are employed. In order to address these issues and facilitate the quality control process, a taxonomy of factors influencing fNIRS data have been established. For each factor, a detailed description is provided and previous solutions are reviewed. Finally, a series of evidence-based recommendations are made with the aim of improving consistency and quality of fNIRS research.
功能近红外光谱（fNIRS）是一种快速发展的神经影像学方法，用于探索皮质脑行为。尽管最近取得了进展，但 fNIRS 实验的质量可能会受到多种方式的影响：首先，通过改变光路径中遇到的组织的光学特性；其次，通过恢复的生物信号（噪声）的掺杂，最后，通过调节神经活动。目前，在规划 fNIRS 研究时，没有系统的方法可以指导研究人员考虑这些因素。只有在根据研究问题采用适当的方法和分析的情况下，才能从 fNIRS 数据中得出可靠的结论。为了解决这些问题并促进质量控制过程，已经建立了影响 fNIRS 数据的因素分类法。对于每个因素，都提供了详细的描述，并回顾了以前的解决方案。最后，提出了一系列基于证据的建议，旨在提高 fNIRS 研究的一致性和质量。
