Abstract
Functional near-infrared spectroscopy (fNIRS) measures the hemoglobin concentration changes associated with neuronal activity. Diffuse optical tomography (DOT) consists of reconstructing the optical density changes measured from scalp channels to the oxy-/deoxy-hemoglobin concentration changes within the cortical regions. In the present study, we adapted a nonlinear source localization method developed and validated in the context of Electro- and Magneto-Encephalography (EEG/MEG): the Maximum Entropy on the Mean (MEM), to solve the inverse problem of DOT reconstruction. We first introduced depth weighting strategy within the MEM framework for DOT reconstruction to avoid biasing the reconstruction results of DOT towards superficial regions. We also proposed a new initialization of the MEM model improving the temporal accuracy of the original MEM framework. To evaluate MEM performance and compare with widely used depth weighted Minimum Norm Estimate (MNE) inverse solution, we applied a realistic simulation scheme which contained 4000 simulations generated by 250 different seeds at different locations and 4 spatial extents ranging from 3 to 40text {cm}^2 along the cortical surface. Our results showed that overall MEM provided more accurate DOT reconstructions than MNE. Moreover, we found that MEM was remained particularly robust in low signal-to-noise ratio (SNR) conditions. The proposed method was further illustrated by comparing to functional Magnetic Resonance Imaging (fMRI) activation maps, on real data involving finger tapping tasks with two different montages. The results showed that MEM provided more accurate HbO and HbR reconstructions in spatial agreement with the main fMRI cluster, when compared to MNE.
Highlights
Functional near-infrared spectroscopy measures the hemoglobin concentration changes associated with neuronal activity
Based on those results reported in the Supplementary material S2 and Fig. S1, we decided to considered that most accurate Functional near-infrared spectroscopy (fNIRS) reconstructions were obtained when considering ω2 = 0.3 and 0.5 for depth weighted Minimum Norm Estimate (MNE)
We performed a detailed evaluation of depth-weighted MNE reconstruction and we proposed for the first time a depth weighting strategy within the Maximum Entropy on the Mean (MEM) framework, by introducing two parameters: ω1 acting on scaling the source covariance matrix, and ω2 tuning the initialization of the reference for MEM
Summary
Functional near-infrared spectroscopy (fNIRS) measures the hemoglobin concentration changes associated with neuronal activity. In continuous wave fNIRS, the conventional way to transform variations in optical density to HbO/HbR concentration changes at the level of each source-detector channel, is to apply the modified Beer Lambert Law (mBLL)[3] This model assumes homogeneous concentration changes within the detecting region, i.e., ignoring the partial volume effects which indicates the absorption of light within the illuminated regions varies locally. In order to handle these important quantification biases associated with sensor level based analysis, diffuse optical tomography (DOT) has been proposed to reconstruct, from sensor level measures of the optical density, the fluctuations of HbO/HbR concentrations within the b rain[6] This technique provides better spatial localization accuracy and resolution of the underlying hemodynamic responses[7,8], and avoids partial volume effect in classical mBLL, achieves better quantitative estimation of HbO/HbR concentration changes[4,5]. A non-linear method based on hierarchical Bayesian model for which inference is obtained through an iterative p rocess[29,30] has been proposed and applied on finger tapping experiments[11]
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