Abstract:
Functional near infrared spectroscopy (fNIRS) is a method for monitoring cerebral hemodynamics with a wide range of clinical applications. fNIRS signals are contaminated with systemic physiological interferences from both the brain and superficial tissues, resulting in a poor estimation of the task related neuronal activation. In this study, we introduce an extended superficial signal regression (ESSR) method for cancelling physiology-based systemic interference in fNIRS signals. We apply and validate our method on the optically weighted BOLD signals, which are obtained by projecting the fMRI image onto optical measurement space by use of the optical forward problem. The performance of ESSR method in removing physiological artifacts is compared to i) a global signal regression (GSR) method and ii) a superficial signal regression (SSR) method. The retrieved signals from each method are compared with the neural signals that represent the "ground truth" brain activation cleaned from cerebral systemic fluctuations. We report significant improvements in the recovery of task induced neural activation with the ESSR method when compared to the other two methods with higher spatial localization, lower inter-trial variability, and higher contrast-to-noise (CNR) improvement. Our findings suggest that, during a cognitive task i) superficial scalp contribution to fNIRS signals varies significantly among different regions on the forehead and ii) using an average scalp measurement together with a local measure of superficial hemodynamics better accounts for the total systemic interference. We conclude that maximizing the overlap between the optical pathlength of superficial and deeper penetration measurements is of crucial importance for accurate recovery of the evoked hemodynamic response in fNIRS recordings.|Keywords : Hemodynamic response, systemic interference, functional near infrared spectroscopy, magnetic resonance imaging, physiological artifact removal.
