Correcting physiological noise in whole-head functional near-infrared spectroscopy
Correcting physiological noise in whole-head functional near-infrared spectroscopy
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DOI：
10.1016/j.jneumeth.2021.109262
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发表时间：
2021-06
影响因子：
3
通讯作者：
Fan Zhang;Daniel Cheong;A. Khan;Yuxuan Chen;L. Ding;Han Yuan
中科院分区：
文献类型：
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作者：
Fan Zhang;Daniel Cheong;A. Khan;Yuxuan Chen;L. Ding;Han Yuan
背景功能近红外光谱技术（fNIRS）已越来越多地用于监测正常和疾病状态下的脑血流动力学。然而，fNIRS遭受其对表面活性和全身生理噪声的敏感性。本研究的目的是建立一个降噪方法fNIRS在一个全头montage.New MethodWe已经开发出一种自动去噪方法全头fNIRS。使用由109个长分离通道和8个短分离通道组成的高密度蒙太奇进行记录。辅助传感器还用于同时测量运动、呼吸和脉搏。该方法结合主成分分析和一般线性模型来识别和去除全局均匀的表面成分。我们的去噪方法进行了评估，从一组健康的人类受试者在视觉提示运动任务的实验数据，并进一步比较了最小的预处理方法和三个建立在文献中的去噪方法。定量指标，包括对比度噪声比，受试者内标准差和调整系数的determined.ResultsAfter降噪，fNIRS的全头部地形图显示局灶性激活同时在初级运动和视觉areas.Comparison with Existing MethodsAnalysis表明，我们的方法改进了四个建立的预处理方法在文献中. Conclusions一个自动，建立了有效的预处理流程，用于去除全头fNIRS记录中的生理噪声。我们的方法可以使fNIRS作为一个可靠的工具，在监测大规模的，网络级的大脑活动的临床用途。
BackgroundFunctional near-infrared spectroscopy (fNIRS) has been increasingly employed to monitor cerebral hemodynamics in normal and diseased conditions. However, fNIRS suffers from its susceptibility to superficial activity and systemic physiological noise. The objective of the study was to establish a noise reduction method for fNIRS in a whole-head montage.New MethodWe have developed an automated denoising method for whole-head fNIRS. A high-density montage consisting of 109 long-separation channels and 8 short-separation channels was used for recording. Auxiliary sensors were also used to measure motion, respiration and pulse simultaneously. The method incorporates principal component analysis and general linear model to identify and remove a globally uniform superficial component. Our denoising method was evaluated in experimental data acquired from a group of healthy human subjects during a visually cued motor task and further compared with a minimal preprocessing method and three established denoising methods in the literature. Quantitative metrics including contrast-to-noise ratio, within-subject standard deviation and adjusted coefficient of determination were evaluated.ResultsAfter denoising, whole-head topography of fNIRS revealed focal activations concurrently in the primary motor and visual areas.Comparison with Existing MethodsAnalysis showed that our method improves upon the four established preprocessing methods in the literature.ConclusionsAn automatic, effective and robust preprocessing pipeline was established for removing physiological noise in whole-head fNIRS recordings. Our method can enable fNIRS as a reliable tool in monitoring large-scale, network-level brain activities for clinical uses.