Brain entropy changes in classical trigeminal neuralgia
BackgroundClassical trigeminal neuralgia (CTN) is a common and severe chronic neuropathic facial pain disorder. The pathological mechanisms of CTN are not fully understood. Recent studies have shown that resting-state functional magnetic resonance imaging (rs-fMRI) could provide insights into the fu...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2023.1273336/full |
| _version_ | 1797501106846171136 |
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| author | Xiang Liu Xiuhong Ge Xue Tang Haiqi Ye Lei Pan Xiaofen Zhu Hanjun Hu Zhongxiang Ding Luoyu Wang |
| author_facet | Xiang Liu Xiuhong Ge Xue Tang Haiqi Ye Lei Pan Xiaofen Zhu Hanjun Hu Zhongxiang Ding Luoyu Wang |
| author_sort | Xiang Liu |
| collection | DOAJ |
| description | BackgroundClassical trigeminal neuralgia (CTN) is a common and severe chronic neuropathic facial pain disorder. The pathological mechanisms of CTN are not fully understood. Recent studies have shown that resting-state functional magnetic resonance imaging (rs-fMRI) could provide insights into the functional changes of CTN patients and the complexity of neural processes. However, the precise spatial pattern of complexity changes in CTN patients is still unclear. This study is designed to explore the spatial distribution of complexity alterations in CTN patients using brain entropy (BEN).MethodsA total of 85 CTN patients and 79 age- and sex-matched healthy controls (HCs) were enrolled in this study. All participants underwent rs-fMRI and neuropsychological evaluations. BEN changes were analyzed to observe the spatial distribution of CTN patient complexity, as well as the relationship between these changes and clinical variables. Sixteen different machine learning methods were employed to classify the CTN patients from the HCs, and the best-performing method was selected.ResultsCompared with HCs, CTN patients exhibited increased BEN in the thalamus and brainstem, and decreased BEN in the inferior semilunar lobule. Further analyses revealed a low positive correlation between the average BEN values of the thalamus and neuropsychological assessments. Among the 16 machine learning methods, the Conditional Mutual Information Maximization-Random Forest (CMIM-RF) method yielded the highest area under the curve (AUC) of 0.801.ConclusionsOur study demonstrated that BEN changes in the thalamus and pons and inferior semilunar lobule were associated with CTN and machine learning methods could effectively classify CTN patients and HCs based on BEN changes. Our findings may provide new insights into the neuropathological mechanisms of CTN and have implications for the diagnosis and treatment of CTN. |
| first_indexed | 2024-03-10T03:13:34Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-03-10T03:13:34Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-604c81823a3d4748b54b602185770f872023-11-23T10:19:22ZengFrontiers Media S.A.Frontiers in Neurology1664-22952023-11-011410.3389/fneur.2023.12733361273336Brain entropy changes in classical trigeminal neuralgiaXiang Liu0Xiuhong Ge1Xue Tang2Haiqi Ye3Lei Pan4Xiaofen Zhu5Hanjun Hu6Zhongxiang Ding7Luoyu Wang8Department of Radiology, Hangzhou First People's Hospital, Hangzhou, Zhejiang, ChinaDepartment of Radiology, Hangzhou First People's Hospital, Hangzhou, Zhejiang, ChinaSchool of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, ChinaDepartment of Radiology, Hangzhou First People's Hospital, Hangzhou, Zhejiang, ChinaDepartment of Radiology, Hangzhou First People's Hospital, Hangzhou, Zhejiang, ChinaDepartment of Radiology, Hangzhou First People's Hospital, Hangzhou, Zhejiang, ChinaThe Fourth Clinical College, Zhejiang Chinses Medical University, Hangzhou, Zhejiang, ChinaDepartment of Radiology, Hangzhou First People's Hospital, Hangzhou, Zhejiang, ChinaDepartment of Radiology, Hangzhou First People's Hospital, Hangzhou, Zhejiang, ChinaBackgroundClassical trigeminal neuralgia (CTN) is a common and severe chronic neuropathic facial pain disorder. The pathological mechanisms of CTN are not fully understood. Recent studies have shown that resting-state functional magnetic resonance imaging (rs-fMRI) could provide insights into the functional changes of CTN patients and the complexity of neural processes. However, the precise spatial pattern of complexity changes in CTN patients is still unclear. This study is designed to explore the spatial distribution of complexity alterations in CTN patients using brain entropy (BEN).MethodsA total of 85 CTN patients and 79 age- and sex-matched healthy controls (HCs) were enrolled in this study. All participants underwent rs-fMRI and neuropsychological evaluations. BEN changes were analyzed to observe the spatial distribution of CTN patient complexity, as well as the relationship between these changes and clinical variables. Sixteen different machine learning methods were employed to classify the CTN patients from the HCs, and the best-performing method was selected.ResultsCompared with HCs, CTN patients exhibited increased BEN in the thalamus and brainstem, and decreased BEN in the inferior semilunar lobule. Further analyses revealed a low positive correlation between the average BEN values of the thalamus and neuropsychological assessments. Among the 16 machine learning methods, the Conditional Mutual Information Maximization-Random Forest (CMIM-RF) method yielded the highest area under the curve (AUC) of 0.801.ConclusionsOur study demonstrated that BEN changes in the thalamus and pons and inferior semilunar lobule were associated with CTN and machine learning methods could effectively classify CTN patients and HCs based on BEN changes. Our findings may provide new insights into the neuropathological mechanisms of CTN and have implications for the diagnosis and treatment of CTN.https://www.frontiersin.org/articles/10.3389/fneur.2023.1273336/fullclassical trigeminal neuralgiaresting-state functional magnetic resonance imagingbrain entropymachine learningcross-validation |
| spellingShingle | Xiang Liu Xiuhong Ge Xue Tang Haiqi Ye Lei Pan Xiaofen Zhu Hanjun Hu Zhongxiang Ding Luoyu Wang Brain entropy changes in classical trigeminal neuralgia Frontiers in Neurology classical trigeminal neuralgia resting-state functional magnetic resonance imaging brain entropy machine learning cross-validation |
| title | Brain entropy changes in classical trigeminal neuralgia |
| title_full | Brain entropy changes in classical trigeminal neuralgia |
| title_fullStr | Brain entropy changes in classical trigeminal neuralgia |
| title_full_unstemmed | Brain entropy changes in classical trigeminal neuralgia |
| title_short | Brain entropy changes in classical trigeminal neuralgia |
| title_sort | brain entropy changes in classical trigeminal neuralgia |
| topic | classical trigeminal neuralgia resting-state functional magnetic resonance imaging brain entropy machine learning cross-validation |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2023.1273336/full |
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