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Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean
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 withi...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831678/ https://www.ncbi.nlm.nih.gov/pubmed/35145148 http://dx.doi.org/10.1038/s41598-022-06082-1 |
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author | Cai, Zhengchen Machado, Alexis Chowdhury, Rasheda Arman Spilkin, Amanda Vincent, Thomas Aydin, Ümit Pellegrino, Giovanni Lina, Jean-Marc Grova, Christophe |
author_facet | Cai, Zhengchen Machado, Alexis Chowdhury, Rasheda Arman Spilkin, Amanda Vincent, Thomas Aydin, Ümit Pellegrino, Giovanni Lina, Jean-Marc Grova, Christophe |
author_sort | Cai, Zhengchen |
collection | PubMed |
description | 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 40[Formula: see text] 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. |
format | Online Article Text |
id | pubmed-8831678 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88316782022-02-14 Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean Cai, Zhengchen Machado, Alexis Chowdhury, Rasheda Arman Spilkin, Amanda Vincent, Thomas Aydin, Ümit Pellegrino, Giovanni Lina, Jean-Marc Grova, Christophe Sci Rep Article 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 40[Formula: see text] 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. Nature Publishing Group UK 2022-02-10 /pmc/articles/PMC8831678/ /pubmed/35145148 http://dx.doi.org/10.1038/s41598-022-06082-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cai, Zhengchen Machado, Alexis Chowdhury, Rasheda Arman Spilkin, Amanda Vincent, Thomas Aydin, Ümit Pellegrino, Giovanni Lina, Jean-Marc Grova, Christophe Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean |
title | Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean |
title_full | Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean |
title_fullStr | Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean |
title_full_unstemmed | Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean |
title_short | Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean |
title_sort | diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8831678/ https://www.ncbi.nlm.nih.gov/pubmed/35145148 http://dx.doi.org/10.1038/s41598-022-06082-1 |
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