MRI-based differentiation of Parkinson's disease by cerebellar gray matter volume
Background: The underlying mechanism of Parkinson's disease (PD) is associated with the neurodegeneration of the dopaminergic neurons, and the cerebellum plays a significant role together in non-motor and motor functions in PD progression. Morphological changes in the cerebellum can greatly imp...
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| Language: | English |
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Elsevier
2025-04-01
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| Series: | SLAS Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2472630325000184 |
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| author | Dacong Zhao Jiang Guo Guanghua Lu Rui Jiang Chao Tian Xu Liang |
| author_facet | Dacong Zhao Jiang Guo Guanghua Lu Rui Jiang Chao Tian Xu Liang |
| author_sort | Dacong Zhao |
| collection | DOAJ |
| description | Background: The underlying mechanism of Parkinson's disease (PD) is associated with the neurodegeneration of the dopaminergic neurons, and the cerebellum plays a significant role together in non-motor and motor functions in PD progression. Morphological changes in the cerebellum can greatly impact patients' clinical symptoms, especially motor control symptoms, and may also help distinguish patients from healthy subjects. This study aimed to explore the potential of cerebellar gray matter volume, related to motor control function, as a neuroimaging biomarker to classify patients with PD and healthy controls (HC) by using voxel-based morphometric (VBM) measurements and support vector machine (SVM) methods based on independent component analysis (ICA). Methods: Cerebellar gray matter volume was measured using VBM in patients with PD (n = 27) and HC (n = 16) from the Neurocon dataset. ICA analysis was performed on the gray matter volume of all subregions, resulting in 7 independent components. These independent components were then utilized for correlation analysis with clinical scales and trained as input features for the SVM model. PD patients (n = 20) and HC (n = 20) from the TaoWu dataset were used as test data to validate our SVM model. Results: Among patients with PD, 3 out of the 7 independent components showed a significant correlation with clinical scales. The SVM model achieved an accuracy of 86 % in classifying PD patients and HC, with a sensitivity of 72.2 %, specificity of 88 %, and F1 Score of 76.5 %. The accuracy of the SVM model verification analysis using the TaoWu dataset was 70 %, with a sensitivity of 62.5 %, a specificity of 100 %, and the F1 Score was 76.9 %. Conclusions: The results suggest that abnormal cerebellar gray matter volume, which is highly correlated with motor control function in Parkinson's patients, may serve as a valuable neuroimaging biomarker capable of distinguishing Parkinson's patients from healthy individuals. We observed that the combination of the ICA method and the SVM method produced an improved classification model. This model may function as an early warning tool that enables clinicians to conduct preliminary identification and intervention for patients with PD. |
| format | Article |
| id | doaj-art-ec8ab3433ff548a28edf5d41cb4bddf4 |
| institution | DOAJ |
| issn | 2472-6303 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SLAS Technology |
| spelling | doaj-art-ec8ab3433ff548a28edf5d41cb4bddf42025-08-20T02:56:06ZengElsevierSLAS Technology2472-63032025-04-013110026010.1016/j.slast.2025.100260MRI-based differentiation of Parkinson's disease by cerebellar gray matter volumeDacong Zhao0Jiang Guo1Guanghua Lu2Rui Jiang3Chao Tian4Xu Liang5Medical Imaging Center, Dazhou Integrated TCM & Western Medicine Hospital, Dazhou 635000, PR ChinaMedical Imaging Center, Dazhou Integrated TCM & Western Medicine Hospital, Dazhou 635000, PR ChinaMedical Imaging Center, Dazhou Integrated TCM & Western Medicine Hospital, Dazhou 635000, PR ChinaDepartment of Radiology, The General Hospital of Western Theater Command, Chengdu 610083, PR ChinaDepartment of Radiology, The General Hospital of Western Theater Command, Chengdu 610083, PR ChinaDepartment of Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China; Corresponding author.Background: The underlying mechanism of Parkinson's disease (PD) is associated with the neurodegeneration of the dopaminergic neurons, and the cerebellum plays a significant role together in non-motor and motor functions in PD progression. Morphological changes in the cerebellum can greatly impact patients' clinical symptoms, especially motor control symptoms, and may also help distinguish patients from healthy subjects. This study aimed to explore the potential of cerebellar gray matter volume, related to motor control function, as a neuroimaging biomarker to classify patients with PD and healthy controls (HC) by using voxel-based morphometric (VBM) measurements and support vector machine (SVM) methods based on independent component analysis (ICA). Methods: Cerebellar gray matter volume was measured using VBM in patients with PD (n = 27) and HC (n = 16) from the Neurocon dataset. ICA analysis was performed on the gray matter volume of all subregions, resulting in 7 independent components. These independent components were then utilized for correlation analysis with clinical scales and trained as input features for the SVM model. PD patients (n = 20) and HC (n = 20) from the TaoWu dataset were used as test data to validate our SVM model. Results: Among patients with PD, 3 out of the 7 independent components showed a significant correlation with clinical scales. The SVM model achieved an accuracy of 86 % in classifying PD patients and HC, with a sensitivity of 72.2 %, specificity of 88 %, and F1 Score of 76.5 %. The accuracy of the SVM model verification analysis using the TaoWu dataset was 70 %, with a sensitivity of 62.5 %, a specificity of 100 %, and the F1 Score was 76.9 %. Conclusions: The results suggest that abnormal cerebellar gray matter volume, which is highly correlated with motor control function in Parkinson's patients, may serve as a valuable neuroimaging biomarker capable of distinguishing Parkinson's patients from healthy individuals. We observed that the combination of the ICA method and the SVM method produced an improved classification model. This model may function as an early warning tool that enables clinicians to conduct preliminary identification and intervention for patients with PD.http://www.sciencedirect.com/science/article/pii/S2472630325000184Parkinson's DiseaseCerebellumVoxel-based MorphometryIndependent Component AnalysisSupport Vector Machine |
| spellingShingle | Dacong Zhao Jiang Guo Guanghua Lu Rui Jiang Chao Tian Xu Liang MRI-based differentiation of Parkinson's disease by cerebellar gray matter volume SLAS Technology Parkinson's Disease Cerebellum Voxel-based Morphometry Independent Component Analysis Support Vector Machine |
| title | MRI-based differentiation of Parkinson's disease by cerebellar gray matter volume |
| title_full | MRI-based differentiation of Parkinson's disease by cerebellar gray matter volume |
| title_fullStr | MRI-based differentiation of Parkinson's disease by cerebellar gray matter volume |
| title_full_unstemmed | MRI-based differentiation of Parkinson's disease by cerebellar gray matter volume |
| title_short | MRI-based differentiation of Parkinson's disease by cerebellar gray matter volume |
| title_sort | mri based differentiation of parkinson s disease by cerebellar gray matter volume |
| topic | Parkinson's Disease Cerebellum Voxel-based Morphometry Independent Component Analysis Support Vector Machine |
| url | http://www.sciencedirect.com/science/article/pii/S2472630325000184 |
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