Differential pathlength factor in continuous wave functional near-infrared spectroscopy: reducing hemoglobin’s cross talk in high-density recordings

Abstract

Functional near-infrared spectroscopy (fNIRS) estimates the functional oscillations of oxyhemoglobin and deoxyhemoglobin in the cortex through scalp-located multiwavelength recordings. Hemoglobin oscillations are inferred through temporal changes in continuous-wave (CW) light attenuation. However, because of the diffusive multilayered head tissue structures, the photon path is longer than the source-detector separation, complicating hemoglobin evaluation. This aspect is incorporated in the modified Beer-Lambert law where the source-detector distance is multiplied by the differential pathlength factor (DPF). Since DPF estimation requires photons' time-of-flight information, DPF is assumed a priori in CW-fNIRS. Importantly, errors in the DPF spectrum induce hemoglobin cross talk, which is detrimental for fNIRS. We propose to estimate subject-specific DPF spectral dependence relying on multidistance high-density measurements. The procedure estimates the effective attenuation coefficient (EAC), which is proportional to the geometric mean of absorption and reduced scattering. Since DPF depends on the scattering-to-absorption ratio, EAC limits the spectral dependence assumption to scattering. This approach was compared to a standard frequency-domain multidistance procedure. A good association between the two methods (r 2 ¼ 0.69) was obtained. This approach could estimate low-resolution maps of the DPF spectral dependence through large field of view, high-density systems, reducing hemoglobin cross talk, and increasing fNIRS sensitivity and specificity to brain activity without instrumentation modification.

Prediction of state anxiety by machine learning applied to photoplethysmography data

“…The signal pre-processing was performed in accordance with Perpetuini et al (2020) . Specifically, raw PPG signals were converted into optical densities (ODs) ( Chiarelli et al, 2019b ). The ODs were downsampled of a factor 10 (down to 100 Hz), then filtered with a zero-lag, 4th order, band-pass Butterworth digital filter (cut-off frequencies: 0.2 and 10 Hz).…”

Design of Smart Brain Oxygenation Monitoring System for Estimating Cardiovascular Disease Severity

“…where I o (λ) and I i (λ) denote the intensity of the penetrating and incident light corresponding to wavelength λ respectively, ε λ is the molar extinction coefficient corresponding to wavelength λ [15], C denotes the change of molar concentration of the absorber in medium, and L is the distance between the light source and the light detector. The parameter B(λ) is the pathlength factor corresponding to wavelength λ [16]- [19], which is a correction factor used for correcting L.…”

Fiberless, Multi-Channel fNIRS-EEG System Based on Silicon Photomultipliers: Towards Sensitive and Ecological Mapping of Brain Activity and Neurovascular Coupling

“…The extinction coefficients were extracted from Zijlstra et al [50] (ε O2Hb,735nm = 0.014 mm −1 , ε O2Hb,850nm = 0.025 mm −1 , ε HHb,735nm = 0.039 mm −1 , ε HHb,850nm = 0.019 mm −1 ). The DPFs were derived by Scholkmann and Wolf and Chiarelli et al (DPF 735nm = 6, DPF 850nm = 5.5) [51,52]. For each of the three tasks (motor, visual and Stroop tasks), both a grand-average and a general linear model (GLM) approach [53,54] were employed for visualization and statistical inference about brain activation.…”