@article {Cai2021.02.22.432263, author = {Zhengchen Cai and Alexis Machado and Rasheda Arman Chowdhury and Amanda Spilkin and Thomas Vincent and {\"U}mit Aydin and Giovanni Pellegrino and Jean-Marc Lina and Christophe Grova}, title = {Diffuse optical reconstructions of NIRS data using Maximum Entropy on the Mean}, elocation-id = {2021.02.22.432263}, year = {2021}, doi = {10.1101/2021.02.22.432263}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Functional near-infrared spectroscopy (fNIRS) measures the hemoglobin concentration changes associated with neuronal activity. Diffuse optical tomography (DOT) consists in reconstructing the optical density changes measured from scalp channels to the near-infrared light attenuation 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 40cm2 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 fMRI main cluster, when compared to MNE.HighlightsWe introduced a new NIRS reconstruction method {\textendash} Maximum Entropy on the Mean.We implemented depth weighting strategy within the MEM framework.We improved the temporal accuracy of the original MEM reconstruction.Performances of MEM and MNE were evaluated with realistic simulations and real data.MEM provided more accurate and robust reconstructions than MNE.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2021/02/23/2021.02.22.432263.1}, eprint = {https://www.biorxiv.org/content/early/2021/02/23/2021.02.22.432263.1.full.pdf}, journal = {bioRxiv} }