Abstract
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.
Highlights
Functional Near-Infrared Spectroscopy is a powerful tool to study functional organization of the prefrontal cortex (Scholkmann et al, 2013c)
We identified three main processes contributing to physiological noise in Functional Near-Infrared Spectroscopy (fNIRS) signals on the forehead
Based on results of the wavelet coherence analysis (WCA) we developed a physiological de-noising method for fNIRS signals based on General Linear Modeling (GLM) (Kiebel and Holmes, 2007) and auxiliary physiological regressors (Tachtsidis et al, 2010)
Summary
Functional Near-Infrared Spectroscopy (fNIRS) is a powerful tool to study functional organization of the prefrontal cortex (Scholkmann et al, 2013c). Due to absence of hair on the forehead, and relatively short distance between the forehead surface and the frontal cortex (Okamoto et al, 2004) this cortical area is well accessible by near-infrared light. Despite these beneficial biophysical circumstances physiological noise generally limits overall fNIRS sensitivity and specificity on the prefrontal cortex (Tachtsidis et al, 2009; Aletti et al, 2012; Gagnon et al, 2012; Kirilina et al, 2012). As a result fNIRS sensitivity to functional neuronal signals on the single subject level deteriorates and additional variance is added on the group level, due to inter-subject variability of the systemic and skin physiology
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