Combining Regional and Connectivity Metrics of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Individualized Prediction of Pain Sensitivity
Characterization and prediction of individual difference of pain sensitivity are of great importance in clinical practice. MRI techniques, such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), have been popularly used to predict an individual’s pain sensitivity, bu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Molecular Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnmol.2022.844146/full |
| _version_ | 1798045393718607872 |
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| author | Rushi Zou Rushi Zou Rushi Zou Linling Li Linling Li Linling Li Li Zhang Li Zhang Li Zhang Gan Huang Gan Huang Gan Huang Zhen Liang Zhen Liang Zhen Liang Lizu Xiao Zhiguo Zhang Zhiguo Zhang Zhiguo Zhang Zhiguo Zhang |
| author_facet | Rushi Zou Rushi Zou Rushi Zou Linling Li Linling Li Linling Li Li Zhang Li Zhang Li Zhang Gan Huang Gan Huang Gan Huang Zhen Liang Zhen Liang Zhen Liang Lizu Xiao Zhiguo Zhang Zhiguo Zhang Zhiguo Zhang Zhiguo Zhang |
| author_sort | Rushi Zou |
| collection | DOAJ |
| description | Characterization and prediction of individual difference of pain sensitivity are of great importance in clinical practice. MRI techniques, such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), have been popularly used to predict an individual’s pain sensitivity, but existing studies are limited by using one single imaging modality (fMRI or DTI) and/or using one type of metrics (regional or connectivity features). As a result, pain-relevant information in MRI has not been fully revealed and the associations among different imaging modalities and different features have not been fully explored for elucidating pain sensitivity. In this study, we investigated the predictive capability of multi-features (regional and connectivity metrics) of multimodal MRI (fMRI and DTI) in the prediction of pain sensitivity using data from 210 healthy subjects. We found that fusing fMRI-DTI and regional-connectivity features are capable of more accurately predicting an individual’s pain sensitivity than only using one type of feature or using one imaging modality. These results revealed rich information regarding individual pain sensitivity from the brain’s both structural and functional perspectives as well as from both regional and connectivity metrics. Hence, this study provided a more comprehensive characterization of the neural correlates of individual pain sensitivity, which holds a great potential for clinical pain management. |
| first_indexed | 2024-04-11T23:20:09Z |
| format | Article |
| id | doaj.art-982845699998473cbe878f6534e59e21 |
| institution | Directory Open Access Journal |
| issn | 1662-5099 |
| language | English |
| last_indexed | 2024-04-11T23:20:09Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Neuroscience |
| spelling | doaj.art-982845699998473cbe878f6534e59e212022-12-22T03:57:28ZengFrontiers Media S.A.Frontiers in Molecular Neuroscience1662-50992022-03-011510.3389/fnmol.2022.844146844146Combining Regional and Connectivity Metrics of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Individualized Prediction of Pain SensitivityRushi Zou0Rushi Zou1Rushi Zou2Linling Li3Linling Li4Linling Li5Li Zhang6Li Zhang7Li Zhang8Gan Huang9Gan Huang10Gan Huang11Zhen Liang12Zhen Liang13Zhen Liang14Lizu Xiao15Zhiguo Zhang16Zhiguo Zhang17Zhiguo Zhang18Zhiguo Zhang19School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, ChinaGuangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, ChinaMarshall Laboratory of Biomedical Engineering, Shenzhen, ChinaSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, ChinaGuangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, ChinaMarshall Laboratory of Biomedical Engineering, Shenzhen, ChinaSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, ChinaGuangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, ChinaMarshall Laboratory of Biomedical Engineering, Shenzhen, ChinaSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, ChinaGuangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, ChinaMarshall Laboratory of Biomedical Engineering, Shenzhen, ChinaSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, ChinaGuangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, ChinaMarshall Laboratory of Biomedical Engineering, Shenzhen, ChinaDepartment of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, The Affiliated Shenzhen Sixth Hospital of Guangdong Medical University, Shenzhen, ChinaSchool of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, ChinaGuangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, ChinaMarshall Laboratory of Biomedical Engineering, Shenzhen, ChinaPeng Cheng Laboratory, Shenzhen, ChinaCharacterization and prediction of individual difference of pain sensitivity are of great importance in clinical practice. MRI techniques, such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), have been popularly used to predict an individual’s pain sensitivity, but existing studies are limited by using one single imaging modality (fMRI or DTI) and/or using one type of metrics (regional or connectivity features). As a result, pain-relevant information in MRI has not been fully revealed and the associations among different imaging modalities and different features have not been fully explored for elucidating pain sensitivity. In this study, we investigated the predictive capability of multi-features (regional and connectivity metrics) of multimodal MRI (fMRI and DTI) in the prediction of pain sensitivity using data from 210 healthy subjects. We found that fusing fMRI-DTI and regional-connectivity features are capable of more accurately predicting an individual’s pain sensitivity than only using one type of feature or using one imaging modality. These results revealed rich information regarding individual pain sensitivity from the brain’s both structural and functional perspectives as well as from both regional and connectivity metrics. Hence, this study provided a more comprehensive characterization of the neural correlates of individual pain sensitivity, which holds a great potential for clinical pain management.https://www.frontiersin.org/articles/10.3389/fnmol.2022.844146/fullpain sensitivityfMRIDTIregional-connectivity featuresmachine learning |
| spellingShingle | Rushi Zou Rushi Zou Rushi Zou Linling Li Linling Li Linling Li Li Zhang Li Zhang Li Zhang Gan Huang Gan Huang Gan Huang Zhen Liang Zhen Liang Zhen Liang Lizu Xiao Zhiguo Zhang Zhiguo Zhang Zhiguo Zhang Zhiguo Zhang Combining Regional and Connectivity Metrics of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Individualized Prediction of Pain Sensitivity Frontiers in Molecular Neuroscience pain sensitivity fMRI DTI regional-connectivity features machine learning |
| title | Combining Regional and Connectivity Metrics of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Individualized Prediction of Pain Sensitivity |
| title_full | Combining Regional and Connectivity Metrics of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Individualized Prediction of Pain Sensitivity |
| title_fullStr | Combining Regional and Connectivity Metrics of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Individualized Prediction of Pain Sensitivity |
| title_full_unstemmed | Combining Regional and Connectivity Metrics of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Individualized Prediction of Pain Sensitivity |
| title_short | Combining Regional and Connectivity Metrics of Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging for Individualized Prediction of Pain Sensitivity |
| title_sort | combining regional and connectivity metrics of functional magnetic resonance imaging and diffusion tensor imaging for individualized prediction of pain sensitivity |
| topic | pain sensitivity fMRI DTI regional-connectivity features machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fnmol.2022.844146/full |
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