Investigation of functional near-infrared spectroscopy signal quality and development of the hemodynamic phase correlation signal.
Investigation of functional near-infrared spectroscopy signal quality and development of the hemodynamic phase correlation signal.
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DOI：
10.1117/1.nph.9.2.025001
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发表时间：
2022-04
期刊：
影响因子：
5.3
通讯作者：
中科院分区：
文献类型：
--
作者：
在fNIRS领域，长期以来一直建议在分析和解释结果时使用氧合（）和脱氧（HHb）血红蛋白。尽管如此，许多fNIRS研究确实只关注。以前的工作表明，它本身容易受到系统干扰，结果可能主要反映了这一点，而不是功能激活。使用和HHb得出结论的研究使用不同的方法，可能导致研究之间的差异。和HHb的组合已被推荐为在分析中利用这两种信号的方法。我们介绍了血流动力学相位相关（HPC）信号的开发，以根据建议将联合收割机和HHb结合起来，从而在分析中利用这两种信号。我们使用合成和实验数据来评估用于fNIRS分析的HPC和电流信号的比较。使用从额叶上的16个通道获得的静息状态fNIRS数据形成约18个合成数据集。为了模拟用于块设计任务的fNIRS数据，我们将合成的任务相关血流动力学响应叠加到静息状态数据。该数据用于开发HPC-一般线性模型（GLM）框架。进行实验以研究每个信号在不同SNR下的性能，并研究假阳性对数据的影响。性能是基于每个信号的平均值跨渠道.在手指敲击任务期间从128名参与者在134个通道上记录的实验数据被用于研究多个信号[、HHb、HbT、HbD、基于相关性的信号改善（CBSI）和HPC]在真实的数据上的性能。根据其将激活定位于特定感兴趣区域的能力评价信号性能。从不同的SNR的结果表明，HPC信号具有最高的性能为高SNR。CBSI在中低信噪比下表现最好。接下来的分析评估了假阳性如何影响信号。评估假阳性影响的分析表明，HPC和CBSI信号反映了假阳性对和HHb的影响。对真实的实验数据的分析表明，HPC和HHb信号以最高的精度提供对初级运动皮层的定位。我们开发了一种新的血流动力学信号（HPC），有可能克服目前单独使用和HHb的局限性。我们的研究结果表明，HPC信号提供了与HHb相当的准确性，以定位功能激活，同时对假阳性更鲁棒。
There is a longstanding recommendation within the field of fNIRS to use oxygenated () and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus on only. Previous work has shown that on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using both and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination of and HHb has been recommended as a method to utilize both signals in analysis. We present the development of the hemodynamic phase correlation (HPC) signal to combine and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal’s mean -value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [, HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives on and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of using and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives.
影响因子：
3.3
作者：
Stephan F;Saalbach H;Rossi S
通讯作者：
Rossi S