Does short separation regression improve the localization of brain hemodynamic responses in NIRS measurements during tasks with differing autonomic responses?
Short separation regression is critical in NIRS data processing to remove superficial scalp hemodynamic fluctuations and accurately isolate brain activation signals.
Autonomic nervous system response is known to be highly task-dependent. The sensitivity of near-infrared spectroscopy (NIRS) measurements to superficial layers, particularly to the scalp, makes it highly susceptible to systemic physiological changes. Thus, one critical step in NIRS data processing is to remove the contribution of superficial layers to the NIRS signal and to obtain the actual brain response. This can be achieved using short separation channels that are sensitive only to the hemodynamics in the scalp. We investigated the contribution of hemodynamic fluctuations due to autonomous nervous system activation during various tasks. Our results provide clear demonstrations of the critical role of using short separation channels in NIRS measurements to disentangle differing autonomic responses from the brain activation signal of interest.
Yücel et al. (Fri,) studied this question.
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