Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imaging
IntroductionIn the context of functional magnetic resonance imaging (fMRI), carbon dioxide (CO2) is a well-known vasodilator that has been widely used to monitor and interrogate vascular physiology. Moreover, spontaneous fluctuations in end-tidal carbon dioxide (PETCO2) reflects changes in arterial...
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Neuroimaging |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnimg.2023.1119539/full |
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| author | Vismay Agrawal Xiaole Z. Zhong Xiaole Z. Zhong J. Jean Chen J. Jean Chen J. Jean Chen |
| author_facet | Vismay Agrawal Xiaole Z. Zhong Xiaole Z. Zhong J. Jean Chen J. Jean Chen J. Jean Chen |
| author_sort | Vismay Agrawal |
| collection | DOAJ |
| description | IntroductionIn the context of functional magnetic resonance imaging (fMRI), carbon dioxide (CO2) is a well-known vasodilator that has been widely used to monitor and interrogate vascular physiology. Moreover, spontaneous fluctuations in end-tidal carbon dioxide (PETCO2) reflects changes in arterial CO2 and has been demonstrated as the largest physiological noise source for denoising the low-frequency range of the resting-state fMRI (rs-fMRI) signal. However, the majority of rs-fMRI studies do not involve CO2 recordings, and most often only heart rate and respiration are recorded. While the intrinsic link between these latter metrics and CO2 led to suggested possible analytical models, they have not been widely applied.MethodsIn this proof-of-concept study, we propose a deep-learning (DL) approach to reconstruct CO2 and PETCO2 data from respiration waveforms in the resting state.ResultsWe demonstrate that the one-to-one mapping between respiration and CO2 recordings can be well predicted using fully convolutional networks (FCNs), achieving a Pearson correlation coefficient (r) of 0.946 ± 0.056 with the ground truth CO2. Moreover, dynamic PETCO2 can be successfully derived from the predicted CO2, achieving r of 0.512 ± 0.269 with the ground truth. Importantly, the FCN-based methods outperform previously proposed analytical methods. In addition, we provide guidelines for quality assurance of respiration recordings for the purposes of CO2 prediction.DiscussionOur results demonstrate that dynamic CO2 can be obtained from respiration-volume using neural networks, complementing the still few reports in DL of physiological fMRI signals, and paving the way for further research in DL based bio-signal processing. |
| first_indexed | 2024-04-10T10:00:03Z |
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| institution | Directory Open Access Journal |
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| language | English |
| last_indexed | 2024-04-10T10:00:03Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroimaging |
| spelling | doaj.art-6ac8603b7c3f499a8e4d50973ef861c62023-02-16T09:20:52ZengFrontiers Media S.A.Frontiers in Neuroimaging2813-11932023-02-01210.3389/fnimg.2023.11195391119539Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imagingVismay Agrawal0Xiaole Z. Zhong1Xiaole Z. Zhong2J. Jean Chen3J. Jean Chen4J. Jean Chen5Baycrest Centre for Geriatric Care, Rotman Research Institute, Toronto, ON, CanadaBaycrest Centre for Geriatric Care, Rotman Research Institute, Toronto, ON, CanadaDepartment of Medical Biophysics, University of Toronto, Toronto, ON, CanadaBaycrest Centre for Geriatric Care, Rotman Research Institute, Toronto, ON, CanadaDepartment of Medical Biophysics, University of Toronto, Toronto, ON, CanadaDepartment of Biomedical Engineering, University of Toronto, Toronto, ON, CanadaIntroductionIn the context of functional magnetic resonance imaging (fMRI), carbon dioxide (CO2) is a well-known vasodilator that has been widely used to monitor and interrogate vascular physiology. Moreover, spontaneous fluctuations in end-tidal carbon dioxide (PETCO2) reflects changes in arterial CO2 and has been demonstrated as the largest physiological noise source for denoising the low-frequency range of the resting-state fMRI (rs-fMRI) signal. However, the majority of rs-fMRI studies do not involve CO2 recordings, and most often only heart rate and respiration are recorded. While the intrinsic link between these latter metrics and CO2 led to suggested possible analytical models, they have not been widely applied.MethodsIn this proof-of-concept study, we propose a deep-learning (DL) approach to reconstruct CO2 and PETCO2 data from respiration waveforms in the resting state.ResultsWe demonstrate that the one-to-one mapping between respiration and CO2 recordings can be well predicted using fully convolutional networks (FCNs), achieving a Pearson correlation coefficient (r) of 0.946 ± 0.056 with the ground truth CO2. Moreover, dynamic PETCO2 can be successfully derived from the predicted CO2, achieving r of 0.512 ± 0.269 with the ground truth. Importantly, the FCN-based methods outperform previously proposed analytical methods. In addition, we provide guidelines for quality assurance of respiration recordings for the purposes of CO2 prediction.DiscussionOur results demonstrate that dynamic CO2 can be obtained from respiration-volume using neural networks, complementing the still few reports in DL of physiological fMRI signals, and paving the way for further research in DL based bio-signal processing.https://www.frontiersin.org/articles/10.3389/fnimg.2023.1119539/fulldeep learningfully convoluted neural networkcarbon dioxiderespiratory variabilityfunctional MRIphysiological signal analysis |
| spellingShingle | Vismay Agrawal Xiaole Z. Zhong Xiaole Z. Zhong J. Jean Chen J. Jean Chen J. Jean Chen Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imaging Frontiers in Neuroimaging deep learning fully convoluted neural network carbon dioxide respiratory variability functional MRI physiological signal analysis |
| title | Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imaging |
| title_full | Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imaging |
| title_fullStr | Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imaging |
| title_full_unstemmed | Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imaging |
| title_short | Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imaging |
| title_sort | generating dynamic carbon dioxide traces from respiration belt recordings feasibility using neural networks and application in functional magnetic resonance imaging |
| topic | deep learning fully convoluted neural network carbon dioxide respiratory variability functional MRI physiological signal analysis |
| url | https://www.frontiersin.org/articles/10.3389/fnimg.2023.1119539/full |
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