Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis
Abstract The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between patients with multiple sclerosis (MS) and those with neuromyelitis optic spectrum disorders (NMOSD), highlighting the need for improved diagnostic effi...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84508-8 |
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| author | Xiao Liang Qingwen Zeng Yanyan Zhu Yao Wang Ting He Lin Wu Muhua Huang Fuqing Zhou |
| author_facet | Xiao Liang Qingwen Zeng Yanyan Zhu Yao Wang Ting He Lin Wu Muhua Huang Fuqing Zhou |
| author_sort | Xiao Liang |
| collection | DOAJ |
| description | Abstract The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between patients with multiple sclerosis (MS) and those with neuromyelitis optic spectrum disorders (NMOSD), highlighting the need for improved diagnostic efficacy. In this study, multilevel functional metrics including resting state functional connectivity, amplitude of low frequency fluctuation (ALFF), and regional homogeneity (ReHo) were calculated and extracted from 116 regions of interest in the anatomical automatic labeling atlas. Subsequently, classifiers were developed using different combinations of these selected features to distinguish between MS and NMOSD. Compared to models based on individual MRI features, support vector machine (SVM) and logistic regression (LR) models that integrated multilevel functional features such as RSFC, ALFF, and ReHo demonstrated the highest levels of performance on the testing cohorts (SVM, AUC = 0.857; LR, AUC = 0.929). Adding structural features of gray matter volume (GMV) data did not notably improve the classification performance of the machine learning models using multilevel rs-fMRI features. Notably, similar trends were observed across different brain templates, with models based on RSFC, ALFF, and ReHo yielding optimal performance. These findings suggest that utilizing multilevel fMRI features can effectively differentiate between MS and NMOSD patients. |
| format | Article |
| id | doaj-art-4d7b3c006af24549b9a6557127f6e308 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4d7b3c006af24549b9a6557127f6e3082025-01-19T12:22:20ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-84508-8Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysisXiao Liang0Qingwen Zeng1Yanyan Zhu2Yao Wang3Ting He4Lin Wu5Muhua Huang6Fuqing Zhou7Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityDepartment of General Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityJiangxi Province Medical Imaging Research InstituteDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityJiangxi Province Medical Imaging Research InstituteDepartment of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang UniversityAbstract The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between patients with multiple sclerosis (MS) and those with neuromyelitis optic spectrum disorders (NMOSD), highlighting the need for improved diagnostic efficacy. In this study, multilevel functional metrics including resting state functional connectivity, amplitude of low frequency fluctuation (ALFF), and regional homogeneity (ReHo) were calculated and extracted from 116 regions of interest in the anatomical automatic labeling atlas. Subsequently, classifiers were developed using different combinations of these selected features to distinguish between MS and NMOSD. Compared to models based on individual MRI features, support vector machine (SVM) and logistic regression (LR) models that integrated multilevel functional features such as RSFC, ALFF, and ReHo demonstrated the highest levels of performance on the testing cohorts (SVM, AUC = 0.857; LR, AUC = 0.929). Adding structural features of gray matter volume (GMV) data did not notably improve the classification performance of the machine learning models using multilevel rs-fMRI features. Notably, similar trends were observed across different brain templates, with models based on RSFC, ALFF, and ReHo yielding optimal performance. These findings suggest that utilizing multilevel fMRI features can effectively differentiate between MS and NMOSD patients.https://doi.org/10.1038/s41598-024-84508-8Multiple sclerosisNeuromyelitis optic spectrum disordersResting state functional magnetic resonance imagingMachine learning |
| spellingShingle | Xiao Liang Qingwen Zeng Yanyan Zhu Yao Wang Ting He Lin Wu Muhua Huang Fuqing Zhou Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis Scientific Reports Multiple sclerosis Neuromyelitis optic spectrum disorders Resting state functional magnetic resonance imaging Machine learning |
| title | Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis |
| title_full | Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis |
| title_fullStr | Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis |
| title_full_unstemmed | Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis |
| title_short | Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis |
| title_sort | differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fmri features a machine learning analysis |
| topic | Multiple sclerosis Neuromyelitis optic spectrum disorders Resting state functional magnetic resonance imaging Machine learning |
| url | https://doi.org/10.1038/s41598-024-84508-8 |
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