Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean
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 chan...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-06082-1 |
| _version_ | 1818977027692167168 |
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| author | Zhengchen Cai Alexis Machado Rasheda Arman Chowdhury Amanda Spilkin Thomas Vincent Ümit Aydin Giovanni Pellegrino Jean-Marc Lina Christophe Grova |
| author_facet | Zhengchen Cai Alexis Machado Rasheda Arman Chowdhury Amanda Spilkin Thomas Vincent Ümit Aydin Giovanni Pellegrino Jean-Marc Lina Christophe Grova |
| author_sort | Zhengchen Cai |
| collection | DOAJ |
| description | 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 40 $$\text {cm}^2$$ 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. |
| first_indexed | 2024-12-20T16:21:13Z |
| format | Article |
| id | doaj.art-27c401a1f5084390be8bfa8d8b487aa5 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-20T16:21:13Z |
| publishDate | 2022-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-27c401a1f5084390be8bfa8d8b487aa52022-12-21T19:33:37ZengNature PortfolioScientific Reports2045-23222022-02-0112111810.1038/s41598-022-06082-1Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the meanZhengchen Cai0Alexis Machado1Rasheda Arman Chowdhury2Amanda Spilkin3Thomas Vincent4Ümit Aydin5Giovanni Pellegrino6Jean-Marc Lina7Christophe Grova8Department of Physics and PERFORM Centre, Concordia UniversityMultimodal Functional Imaging Lab, Biomedical Engineering Department, McGill UniversityMultimodal Functional Imaging Lab, Biomedical Engineering Department, McGill UniversityDepartment of Physics and PERFORM Centre, Concordia UniversityDepartment of Physics and PERFORM Centre, Concordia UniversityDepartment of Physics and PERFORM Centre, Concordia UniversityNeurology and Neurosurgery Department, Montreal Neurological Institute, McGill UniversityÉcole de technologie supérieure de l’Université du QuébecDepartment of Physics and PERFORM Centre, Concordia UniversityAbstract 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 $$\text {cm}^2$$ 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.https://doi.org/10.1038/s41598-022-06082-1 |
| spellingShingle | Zhengchen Cai Alexis Machado Rasheda Arman Chowdhury Amanda Spilkin Thomas Vincent Ümit Aydin Giovanni Pellegrino Jean-Marc Lina Christophe Grova Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-022-06082-1 |
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