Combining time-resolved near-infrared spectroscopy with regression analysis to improve the reconstruction of cerebral hemodynamic responses

摘要

Despite its advantages in terms of safety, low cost and portability, the reliability of functional near-infrared spectroscopy (fNIRS) is challenged by substantial signal contamination from hemodynamic changes in the extracerebral layer (ECL). The time-resolved (tr) variant of N1RS can improve the sensitivity to the brain by recording the distribution of times-of-flight (DTOF) of diffusely reflected photons that contain both time and intensity information. trNIRS data can be analyzed to obtain signals related to absorption changes at different depths within the medium; however, it can still be affected by ECL contamination. To further improve the isolation of the brain signal, this study adapted regression analysis, commonly used with short-channel functional NIRS, to trNIRS. Signals related to the early-arriving photons (0th moment, gates), selected based on sensitivity analysis, were used as the regressors, given their inherent sensitivity to superficial tissue. Performance of the regression was optimized using data from previously published studies that used trNIRS to measure oxygenation responses to hypercapnia caused by a rapid increase in end-tidal carbon dioxide pressure (P_(ET)CO_2). To assess the effect of the regression approach, correlations between reconstructed hemoglobin signals and modelled hemodynamic response function were calculated. The results confirmed that the regression approach successfully removed large residue signals observed in the oxyhemoglobin signals.