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Diffuse optical reconstructions of NIRS data using Maximum Entropy on the Mean

Zhengchen Cai, Alexis Machado, Rasheda Arman Chowdhury, Amanda Spilkin, Thomas Vincent, Ümit Aydin, Giovanni Pellegrino, Jean-Marc Lina, Christophe Grova
doi: https://doi.org/10.1101/2021.02.22.432263
Zhengchen Cai
aDepartment of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada
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  • For correspondence: zhengchen.cai@mail.concordia.ca
Alexis Machado
bNeurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
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Rasheda Arman Chowdhury
cCHU Sainte-Justine Research Centre, Montréal, Québec, Canada
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Amanda Spilkin
aDepartment of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada
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Thomas Vincent
dCentre de médecine préventive et d’activité physique, Montreal Heart Institute, Montreal Heart Institute, Montréal, Canada, Montréal, Québec, Canada
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Ümit Aydin
aDepartment of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada
eMRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, PO80, London, UK
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Giovanni Pellegrino
bNeurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
fSan Camillo Hospital IRCCS, Venice, Italy
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Jean-Marc Lina
gÉcole de technologie supérieure de l’Université du Québec, Canada
hCentre de Recherches Mathématiques, Université de Montréal, Québec, Canada
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Christophe Grova
aDepartment of Physics and PERFORM Centre, Concordia University, Montréal, Québec, Canada
bNeurology and Neurosurgery Department, Montreal Neurological Institute (MNI), McGill University, Montréal, Québec, Canada
hCentre de Recherches Mathématiques, Université de Montréal, Québec, Canada
iMultimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
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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.

Highlights

  • We introduced a new NIRS reconstruction method – 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 Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 23, 2021.
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Diffuse optical reconstructions of NIRS data using Maximum Entropy on the Mean
Zhengchen Cai, Alexis Machado, Rasheda Arman Chowdhury, Amanda Spilkin, Thomas Vincent, Ümit Aydin, Giovanni Pellegrino, Jean-Marc Lina, Christophe Grova
bioRxiv 2021.02.22.432263; doi: https://doi.org/10.1101/2021.02.22.432263
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Diffuse optical reconstructions of NIRS data using Maximum Entropy on the Mean
Zhengchen Cai, Alexis Machado, Rasheda Arman Chowdhury, Amanda Spilkin, Thomas Vincent, Ümit Aydin, Giovanni Pellegrino, Jean-Marc Lina, Christophe Grova
bioRxiv 2021.02.22.432263; doi: https://doi.org/10.1101/2021.02.22.432263

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