Translating fMRI to fNIRS

Blood oxygenation level dependent (BOLD) functional magnetic resonance (fMRI) imaging and functional near infrared spectroscopy (fNIRS) have been widely used to investigate hemodynamic responses to functional stimulation in the human brain. In the present study, we propose a time dependent multi layered Monte Carlo (TD-MCML) simulation approach for mapping brain and scalp tissue absorbance attained from BOLD fMRI to fNIRS optical density (OD) changes as an initial step to i) facilitate the solution of finding an optimal NIRS probe geometry for monitoring brain hemodynamics and ii) eliminate non-brain tissue contamination in fNIRS signals. We demonstrate a consistency between OD changes obtained from real-time NIRS data and the OD changes attained via embedding scalp and gray matter BOLD signals as attenuation change into the TD-MCML simulation algorithm. Our findings will form a basis for predicting the superficial contribution in fNIRS signals and will assist the development and validation of techniques for separation of functional brain signals from fluctuations in superficial and systemic perfusion.