Optical tomography reconstruction algorithm based on the radiative transfer equation considering refractive index: Part 2. Inverse model

Design of NIRS Probe Based on Computational Model to Find Out the Optimal Location for Non-Invasive Brain Stimulation

The paper presents a computational model to analyse the electric field distribution on the cerebral cortex during high definition transcranial direct current stimulation (HD-tDCS) technique. The current research aims to improve the focality in term of magnitude of electric field (norm [E]) and magnitude of current density (norm [J]) in the gyri and sulci of white matter. The proposed computational model is used to predict the magnitude of current density and magnitude of electric field distribution generated across the target region of cerebral cortex for specific small size 1 × 1 cm² multi-electrode HD-tDCS configurations. The current works aims at optimizing the number of electrodes and current density for multielectrode HD-tDCS configuration and weak current intensity is obtained by calculating surface area and penetration depth of target region of cerebral cortex. In terms of surface area and penetration depth 4 × 1 HD-tDCS and 2 mA weak dc current configuration has been selected. The optimized 4 × 1 HD-tDCS configuration is placed on target location of the brain surface and the changes in the magnitude of current density and magnitude of electric field distribution is calculated at the different locations on brain surface including scalp surface, skull surface gray matter and white matter surface. The variation in magnitude electric field distribution is seen in the cerebrospinal fluid (CSF), gray and white matter surface of target cerebral cortex. Based on the insights received from the variation in the magnitude of current density and magnitude of electric field distribution, the design of an appropriate NIRS probe has been proposed to aid in non-invasive brain stimulation. Designed NIRS probe is based on distance of separation between source and photodetector to cover the affected area with 4 × 1 HD-tDCS technique and measurement sensitivity distribution at gray matter surface of cerebral cortex. The estimated percentage of pixel area of measurement sensitivity distribution is 17.094%, which confirm to cover the 7.9384% distributed pixel area in term of calculated magnitude of current density affected with 4 × 1 HD-tDCS configuration.

Application of the sequential quadratic programming algorithm for reconstructing the distribution of optical parameters based on the time-domain radiative transfer equation

Sequential quadratic programming (SQP) is used as an optimization algorithm to reconstruct the optical parameters based on the time-domain radiative transfer equation (TD-RTE). Numerous time-resolved measurement signals are obtained using the TD-RTE as forward model. For a high computational efficiency, the gradient of objective function is calculated using an adjoint equation technique. SQP algorithm is employed to solve the inverse problem and the regularization term based on the generalized Gaussian Markov random field (GGMRF) model is used to overcome the ill-posed problem. Simulated results show that the proposed reconstruction scheme performs efficiently and accurately.

Correspondence of electroencephalography and near-infrared spectroscopy sensitivities to the cerebral cortex using a high-density layout

This study investigates the correspondence of the cortical sensitivity of electroencephalography (EEG) and near-infrared spectroscopy (NIRS). EEG forward model sensitivity to the cerebral cortex was calculated for 329 EEG electrodes following the 10-5 EEG positioning system using a segmented structural magnetic resonance imaging scan of a human subject. NIRS forward model sensitivity was calculated for the same subject using 156 NIRS source-detector pairs selected from 32 source and 32 detector optodes positioned on the scalp using a subset of the 10-5 EEG positioning system. Sensitivity correlations between colocalized NIRS source-detector pair groups and EEG channels yielded R = 0.46 ± 0.08. Groups of NIRS source-detector pairs with maximum correlations to EEG electrode sensitivities are tabulated. The mean correlation between the point spread functions for EEG and NIRS regions of interest (ROI) was R = 0.43 ± 0.07. Spherical ROIs with radii of 26 mm yielded the maximum correlation between EEG and NIRS averaged across all cortical mesh nodes. These sensitivity correlations between EEG and NIRS should be taken into account when designing multimodal studies of neurovascular coupling and when using NIRS as a statistical prior for EEG source localization.