How much do time-domain functional near-infrared spectroscopy (fNIRS) moments improve estimation of brain activity over traditional fNIRS?
How much do time-domain functional near-infrared spectroscopy (fNIRS) moments improve estimation of brain activity over traditional fNIRS?
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时域功能近红外光谱（fNIRS）矩比传统的fNIRS矩在多大程度上改善了对大脑活动的估计？
DOI：
10.1117/1.nph.10.1.013504
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发表时间：
2023-01
期刊：
影响因子：
5.3
通讯作者：
中科院分区：
文献类型：
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作者：
电子学的进步使得紧凑、高通道数时域功能近红外光谱 (TD-fNIRS) 系统的最新发展成为可能。由于高阶时间矩的深度选择性，时间矩分析被提议用于提高大脑敏感性。我们提出了一种结合 TD 矩数据和辅助生理测量（例如短分离通道）的通用线性模型 (GLM)，以提高 HRF 的恢复。我们将先前报道的多距离 TD 力矩技术与连续波 (CW) fNIRS 血流动力学响应函数 (HRF) 恢复常用技术（即块平均和 CW GLM）的性能进行了比较。此外，我们还将多距离 TD 矩技术与 TD 矩 GLM 进行了比较。我们使用已知的合成 HRF 增强了静息 TD-fNIRS 矩数据（六名受试者）。然后，我们采用块平均和 GLM 技术以及专为 CW 和 TD 设计的“短间隔回归”来恢复 HRF。我们计算了均方根误差 (RMSE) 以及恢复的 HRF 与地面实况的相关性。我们通过配对 t 检验比较了等效 CW 和 TD 技术的性能。我们发现，平均而言，与 CW GLM 相比，TD 矩 HRF 恢复将 HbO 和 HbR 的相关性分别提高了 98% 和 48%。 TD GLM 相对于 TD 矩的相关性改善为 12% (HbO) 和 27% (HbR)。与 CW GLM 相比，TD 时刻的 RMSE 降低了 56% 和 52%（HbO 和 HbR）。我们发现，与 TD 矩相比，TD GLM 的 RMSE 没有统计学上的显着改善。适当协方差缩放的 TD 矩技术在 RMSE 和合成 HRF 恢复的相关性方面均优于其 CW 等效技术。此外，我们提出的基于矩的 TD GLM 优于常规 TD 矩分析，同时允许结合来自头皮的混杂生理信号的辅助测量。
Advances in electronics have allowed the recent development of compact, high channel count time domain functional near-infrared spectroscopy (TD-fNIRS) systems. Temporal moment analysis has been proposed for increased brain sensitivity due to the depth selectivity of higher order temporal moments. We propose a general linear model (GLM) incorporating TD moment data and auxiliary physiological measurements, such as short separation channels, to improve the recovery of the HRF. We compare the performance of previously reported multi-distance TD moment techniques to commonly used techniques for continuous wave (CW) fNIRS hemodynamic response function (HRF) recovery, namely block averaging and CW GLM. Additionally, we compare the multi-distance TD moment technique to TD moment GLM. We augmented resting TD-fNIRS moment data (six subjects) with known synthetic HRFs. We then employed block averaging and GLM techniques with “short-separation regression” designed both for CW and TD to recover the HRFs. We calculated the root mean square error (RMSE) and the correlation of the recovered HRF to the ground truth. We compared the performance of equivalent CW and TD techniques with paired t-tests. We found that, on average, TD moment HRF recovery improves correlations by 98% and 48% for HbO and HbR respectively, over CW GLM. The improvement on the correlation for TD GLM over TD moment is 12% (HbO) and 27% (HbR). RMSE decreases 56% and 52% (HbO and HbR) for TD moment compared to CW GLM. We found no statistically significant improvement in the RMSE for TD GLM compared to TD moment. Properly covariance-scaled TD moment techniques outperform their CW equivalents in both RMSE and correlation in the recovery of the synthetic HRFs. Furthermore, our proposed TD GLM based on moments outperforms regular TD moment analysis, while allowing the incorporation of auxiliary measurements of the confounding physiological signals from the scalp.