Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies.
Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies.
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DOI：
10.1117/1.nph.7.3.035009
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发表时间：
2020-07
期刊：
影响因子：
5.3
通讯作者：
Huppert TJ
中科院分区：
文献类型：
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作者：
Santosa H;Zhai X;Fishburn F;Sparto PJ;Huppert TJ
意义：将任务诱发的大脑信号与背景生理噪声（例如心脏、呼吸和血压波动）隔离开来，对功能性近红外光谱（fNIRS）数据的分析提出了重大挑战。目的：定量比较几种从大脑活动中分离背景生理噪声的分析方法的性能，包括空间和时间过滤、回归、成分分析以及短间隔（SS）测量的使用。方法：使用实验记录的背景信号（屏气任务），通过添加不同级别的附加合成“大脑”响应来执行接收器操作特性模拟，以检查先前提出的几种分析方法的灵敏度和特异性。结果：我们发现使用 SS fNIRS 通道作为线性回归模型中不感兴趣的回归量是所检查的性能最佳的方法。此外，我们发现，尽管模型具有额外的自由度，但添加所有可用的 SS 数据（包括所有记录的通道和两种血红蛋白种类）仍可提高方法性能。当 SS 数据不可用时，我们发现使用单独的基线扫描进行主成分过滤是最佳选择。结论：使用多个 SS 测量作为不感兴趣的回归量，在稳健的、迭代预白化的通用线性模型中实现，具有测试的现有方法的最佳性能。
Significance: Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. Aim: The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared. Approach: Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic “brain” responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches. Results: We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative. Conclusions: The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods.