Neuroimaging mechanisms of acupuncture on functional reorganization for post-stroke motor improvement: a machine learning-based functional magnetic resonance imaging study
ObjectiveMotor recovery is crucial in stroke rehabilitation, and acupuncture can influence recovery. Neuroimaging and machine learning approaches provide new research directions to explore the brain functional reorganization and acupuncture mechanisms after stroke. We applied machine learning to pre...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1143239/full |
| _version_ | 1797823699675512832 |
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| author | Mengxin Lu Zhongming Du Jiping Zhao Lan Jiang Ruoyi Liu Muzhao Zhang Tianjiao Xu Jingpei Wei Wei Wang Lingling Xu Haijiao Guo Chen Chen Xin Yu Zhongjian Tan Jiliang Fang Yihuai Zou |
| author_facet | Mengxin Lu Zhongming Du Jiping Zhao Lan Jiang Ruoyi Liu Muzhao Zhang Tianjiao Xu Jingpei Wei Wei Wang Lingling Xu Haijiao Guo Chen Chen Xin Yu Zhongjian Tan Jiliang Fang Yihuai Zou |
| author_sort | Mengxin Lu |
| collection | DOAJ |
| description | ObjectiveMotor recovery is crucial in stroke rehabilitation, and acupuncture can influence recovery. Neuroimaging and machine learning approaches provide new research directions to explore the brain functional reorganization and acupuncture mechanisms after stroke. We applied machine learning to predict the classification of the minimal clinically important differences (MCID) for motor improvement and identify the neuroimaging features, in order to explore brain functional reorganization and acupuncture mechanisms for motor recovery after stroke.MethodsIn this study, 49 patients with unilateral motor pathway injury (basal ganglia and/or corona radiata) after ischemic stroke were included and evaluated the motor function by Fugl–Meyer Assessment scores (FMA) at baseline and at 2-week follow-up sessions. Patients were divided by the difference between the twice FMA scores into one group showing minimal clinically important difference (MCID group, n = 28) and the other group with no minimal clinically important difference (N-MCID, n = 21). Machine learning was performed by PRoNTo software to predict the classification of the patients and identify the feature brain regions of interest (ROIs). In addition, a matched group of healthy controls (HC, n = 26) was enrolled. Patients and HC underwent magnetic resonance imaging examination in the resting state and in the acupuncture state (acupuncture at the Yanglingquan point on one side) to compare the differences in brain functional connectivity (FC) and acupuncture effects.ResultsThrough machine learning, we obtained a balance accuracy rate of 75.51% and eight feature ROIs. Compared to HC, we found that the stroke patients with lower FC between these feature ROIs with other brain regions, while patients in the MCID group exhibited a wider range of lower FC. When acupuncture was applied to Yanglingquan (GB 34), the abnormal FC of patients was decreased, with different targets of effects in different groups.ConclusionFeature ROIs identified by machine learning can predict the classification of stroke patients with different motor improvements, and the FC between these ROIs with other brain regions is decreased. Acupuncture can modulate the bilateral cerebral hemispheres to restore abnormal FC via different targets, thereby promoting motor recovery after stroke.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=37359, ChiCTR1900022220. |
| first_indexed | 2024-03-13T10:27:52Z |
| format | Article |
| id | doaj.art-5a65c58a4ed146c6a5f3a8f9eabeabc9 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-13T10:27:52Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-5a65c58a4ed146c6a5f3a8f9eabeabc92023-05-19T04:50:55ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-05-011710.3389/fnins.2023.11432391143239Neuroimaging mechanisms of acupuncture on functional reorganization for post-stroke motor improvement: a machine learning-based functional magnetic resonance imaging studyMengxin Lu0Zhongming Du1Jiping Zhao2Lan Jiang3Ruoyi Liu4Muzhao Zhang5Tianjiao Xu6Jingpei Wei7Wei Wang8Lingling Xu9Haijiao Guo10Chen Chen11Xin Yu12Zhongjian Tan13Jiliang Fang14Yihuai Zou15Department of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Acupuncture, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Acupuncture, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Chinese Medicine, Peking Union Medical College Hospital, Beijing, ChinaDepartment of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Radiology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaDepartment of Radiology, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaDepartment of Neurology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, ChinaObjectiveMotor recovery is crucial in stroke rehabilitation, and acupuncture can influence recovery. Neuroimaging and machine learning approaches provide new research directions to explore the brain functional reorganization and acupuncture mechanisms after stroke. We applied machine learning to predict the classification of the minimal clinically important differences (MCID) for motor improvement and identify the neuroimaging features, in order to explore brain functional reorganization and acupuncture mechanisms for motor recovery after stroke.MethodsIn this study, 49 patients with unilateral motor pathway injury (basal ganglia and/or corona radiata) after ischemic stroke were included and evaluated the motor function by Fugl–Meyer Assessment scores (FMA) at baseline and at 2-week follow-up sessions. Patients were divided by the difference between the twice FMA scores into one group showing minimal clinically important difference (MCID group, n = 28) and the other group with no minimal clinically important difference (N-MCID, n = 21). Machine learning was performed by PRoNTo software to predict the classification of the patients and identify the feature brain regions of interest (ROIs). In addition, a matched group of healthy controls (HC, n = 26) was enrolled. Patients and HC underwent magnetic resonance imaging examination in the resting state and in the acupuncture state (acupuncture at the Yanglingquan point on one side) to compare the differences in brain functional connectivity (FC) and acupuncture effects.ResultsThrough machine learning, we obtained a balance accuracy rate of 75.51% and eight feature ROIs. Compared to HC, we found that the stroke patients with lower FC between these feature ROIs with other brain regions, while patients in the MCID group exhibited a wider range of lower FC. When acupuncture was applied to Yanglingquan (GB 34), the abnormal FC of patients was decreased, with different targets of effects in different groups.ConclusionFeature ROIs identified by machine learning can predict the classification of stroke patients with different motor improvements, and the FC between these ROIs with other brain regions is decreased. Acupuncture can modulate the bilateral cerebral hemispheres to restore abnormal FC via different targets, thereby promoting motor recovery after stroke.Clinical trial registrationhttps://www.chictr.org.cn/showproj.html?proj=37359, ChiCTR1900022220.https://www.frontiersin.org/articles/10.3389/fnins.2023.1143239/fullstrokemotor recoveryminimal clinically important difference (MCID)acupuncturemachine learningfMRI |
| spellingShingle | Mengxin Lu Zhongming Du Jiping Zhao Lan Jiang Ruoyi Liu Muzhao Zhang Tianjiao Xu Jingpei Wei Wei Wang Lingling Xu Haijiao Guo Chen Chen Xin Yu Zhongjian Tan Jiliang Fang Yihuai Zou Neuroimaging mechanisms of acupuncture on functional reorganization for post-stroke motor improvement: a machine learning-based functional magnetic resonance imaging study Frontiers in Neuroscience stroke motor recovery minimal clinically important difference (MCID) acupuncture machine learning fMRI |
| title | Neuroimaging mechanisms of acupuncture on functional reorganization for post-stroke motor improvement: a machine learning-based functional magnetic resonance imaging study |
| title_full | Neuroimaging mechanisms of acupuncture on functional reorganization for post-stroke motor improvement: a machine learning-based functional magnetic resonance imaging study |
| title_fullStr | Neuroimaging mechanisms of acupuncture on functional reorganization for post-stroke motor improvement: a machine learning-based functional magnetic resonance imaging study |
| title_full_unstemmed | Neuroimaging mechanisms of acupuncture on functional reorganization for post-stroke motor improvement: a machine learning-based functional magnetic resonance imaging study |
| title_short | Neuroimaging mechanisms of acupuncture on functional reorganization for post-stroke motor improvement: a machine learning-based functional magnetic resonance imaging study |
| title_sort | neuroimaging mechanisms of acupuncture on functional reorganization for post stroke motor improvement a machine learning based functional magnetic resonance imaging study |
| topic | stroke motor recovery minimal clinically important difference (MCID) acupuncture machine learning fMRI |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1143239/full |
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