Abstract
.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 . 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.
Highlights
Diffuse correlation spectroscopy (DCS) is an optical imaging method that measures blood flow non-invasively and continuously
We have found that the DCS signal change induced by neuronal cell dynamics is beyond the sensitivity of currently available DCS systems
We describe the use of Monte Carlo simulations to calculate the DCS signal variations induced by neuronal cell dynamics and hemodynamics
Summary
Diffuse correlation spectroscopy (DCS) is an optical imaging method that measures blood flow non-invasively and continuously. It quantifies a blood flow index by measuring the temporal autocorrelation function of the speckle intensity fluctuations of diffusive light remitted from tissue.[1,2,3,4] A change in tissue dynamics results in a change of the decay time of the temporal autocorrelation function. DCS can be utilized to detect tissue dynamics arising from neuronal activities. The variation of the decay time is often only attributed to a change in cerebral blood flow (CBF).[5,6] The peak of the CBF often occurs at a time delay of a few seconds with respect to the onset of neuronal activation, which is slow and not feasible for real-time monitoring of brain activation in applications such as brain–computer interfaces
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