Significance: Diffuse correlation spectroscopy (DCS) measures cerebral blood flow non-invasively. Variations in blood flow can be used to detect neuronal activities, but its peak has a latency of a few seconds, which is slow for real-time monitoring. Neuronal cells also deform during activation, which, in principle, can be utilized to detect neuronal activity on fast timescales (within 100 ms) using DCS. Aims: We aim to characterize DCS signal variation quantified as the change of the decay time of the speckle intensity autocorrelation function during neuronal activation on both fast (within 100 ms) and slow (100 ms to seconds) timescales. Approach: We extensively modeled the variations in the DCS signal that are expected to arise from neuronal activation using Monte Carlo simulations, including the impacts of neuronal cell motion, vessel wall dilation, and blood flow changes. Results: We found that neuronal cell motion induces a DCS signal variation of ∼10 − 5. We also estimated the contrast and number of channels required to detect hemodynamic signals at different time delays. Conclusions: From this extensive analysis, we do not expect to detect neuronal cell motion using DCS in the near future based on current technology trends. However, multi-channel DCS will be able to detect hemodynamic response with sub-second latency, which is interesting for brain–computer interfaces.

中文翻译：

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用弥散相关光谱测量神经元活动：理论研究

意义：弥散相关光谱 (DCS) 可无创测量脑血流量。血流的变化可用于检测神经元活动，但其峰值有几秒钟的延迟，这对于实时监测来说很慢。神经元细胞在激活过程中也会变形，原则上，这可用于使用 DCS 检测快速时间尺度（100 毫秒内）的神经元活动。目的：我们旨在表征 DCS 信号变化，量化为在快速（100 毫秒内）和慢速（100 毫秒到秒）时间尺度上的神经元激活过程中散斑强度自相关函数衰减时间的变化。方法：我们使用蒙特卡罗模拟广泛模拟了 DCS 信号的变化，这些变化预计由神经元激活引起，包括神经元细胞运动的影响，血管壁扩张，血流改变。结果：我们发现神经元细胞运动会导致 DCS 信号变化约为 10 - 5。我们还估计了在不同时间延迟下检测血流动力学信号所需的对比度和通道数量。结论：根据这项广泛的分析，我们不希望根据当前的技术趋势在不久的将来使用 DCS 检测神经元细胞运动。然而，多通道 DCS 将能够以亚秒级延迟检测血流动力学响应，这对于脑机接口来说很有趣。根据当前的技术趋势，我们不希望在不久的将来使用 DCS 检测神经元细胞运动。然而，多通道 DCS 将能够以亚秒级延迟检测血流动力学响应，这对于脑机接口来说很有趣。根据当前的技术趋势，我们不希望在不久的将来使用 DCS 检测神经元细胞运动。然而，多通道 DCS 将能够以亚秒级延迟检测血流动力学响应，这对于脑机接口来说很有趣。