Key result
A novel physiological de-noising method based on wavelet coherence analysis identified three main noise processes (respiration, Mayer waves, and local skin blood flow regulation) and significantly improved the sensitivity of fNIRS to cerebral signals.
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A novel physiological de-noising method using wavelet coherence analysis and concurrent peripheral physiology recordings improves the sensitivity of fNIRS to cerebral signals by removing noise from respiration, Mayer waves, and local skin blood flow.
Functional Near-Infrared Spectroscopy (fNIRS) is a promising method to study functional organization of the prefrontal cortex. However, in order to realize the high potential of fNIRS, effective discrimination between physiological noise originating from forehead skin haemodynamic and cerebral signals is required. Main sources of physiological noise are global and local blood flow regulation processes on multiple time scales. The goal of the present study was to identify the main physiological noise contributions in fNIRS forehead signals and to develop a method for physiological de-noising of fNIRS data. To achieve this goal we combined concurrent time-domain fNIRS and peripheral physiology recordings with wavelet coherence analysis (WCA). Depth selectivity was achieved by analyzing moments of photon time-of-flight distributions provided by time-domain fNIRS. Simultaneously, mean arterial blood pressure (MAP), heart rate (HR), and skin blood flow (SBF) on the forehead were recorded. WCA was employed to quantify the impact of physiological processes on fNIRS signals separately for different time scales. We identified three main processes contributing to physiological noise in fNIRS signals on the forehead. The first process with the period of about 3 s is induced by respiration. The second process is highly correlated with time lagged MAP and HR fluctuations with a period of about 10 s often referred as Mayer waves. The third process is local regulation of the facial SBF time locked to the task-evoked fNIRS signals. All processes affect oxygenated haemoglobin concentration more strongly than that of deoxygenated haemoglobin. Based on these results we developed a set of physiological regressors, which were used for physiological de-noising of fNIRS signals. Our results demonstrate that proposed de-noising method can significantly improve the sensitivity of fNIRS to cerebral signals.
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Kirlilna et al. (2013) studied Healthy (n=15). Physiological de-noising method using wavelet coherence analysis and GLM vs. Standard fNIRS analysis without physiological de-noising was evaluated on Identification of physiological noise components and improvement of fNIRS sensitivity to cerebral signals. A novel physiological de-noising method based on wavelet coherence analysis identified three main noise processes (respiration, Mayer waves, and local skin blood flow regulation) and significantly improved the sensitivity of fNIRS to cerebral signals.
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