Significance: Despite its advantages in terms of safety, low cost, and portability, functional near-infrared spectroscopy applications can be challenging due to substantial signal contamination from hemodynamics in the extracerebral layer (ECL). Time-resolved near-infrared spectroscopy (tr NIRS) can improve sensitivity to brain activity but contamination from the ECL remains an issue. This study demonstrates how brain signal isolation can be further improved by applying regression analysis to tr data acquired at a single source-detector distance. Aim: To investigate if regression analysis can be applied to single-channel trNIRS data to further isolate the brain and reduce signal contamination from the ECL. Approach: Appropriate regressors for trNIRS were selected based on simulations, and performance was evaluated by applying the regression technique to oxygenation responses recording during hypercapnia and functional activation. Results: Compared to current methods of enhancing depth sensitivity for trNIRS (i.e., higher statistical moments and late gates), incorporating regression analysis using a signal sensitive to the ECL significantly improved the extraction of cerebral oxygenation signals. In addition, this study demonstrated that regression could be applied to trNIRS data from a single detector using the early arriving photons to capture hemodynamic changes in the ECL. Conclusion: Applying regression analysis to trNIRS metrics with different depth sensitivities improves the characterization of cerebral oxygenation signals.
Available at: http://works.bepress.com/keith-stlawrence/26/