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Lin F, Zou X, Su J, Wan L, Wu S, Xu H, Zeng Y, Li Y, Chen X, Cai G, Ye Q, Cai G. Cortical thickness and white matter microstructure predict freezing of gait development in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:16. [PMID: 38195780 PMCID: PMC10776850 DOI: 10.1038/s41531-024-00629-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 12/29/2023] [Indexed: 01/11/2024] Open
Abstract
The clinical applications of the association of cortical thickness and white matter fiber with freezing of gait (FoG) are limited in patients with Parkinson's disease (PD). In this retrospective study, using white matter fiber from diffusion-weighted imaging and cortical thickness from structural-weighted imaging of magnetic resonance imaging, we investigated whether a machine learning-based model can help assess the risk of FoG at the individual level in patients with PD. Data from the Parkinson's Disease Progression Marker Initiative database were used as the discovery cohort, whereas those from the Fujian Medical University Union Hospital Parkinson's Disease database were used as the external validation cohort. Clinical variables, white matter fiber, and cortical thickness were selected by random forest regression. The selected features were used to train the support vector machine(SVM) learning models. The median area under the receiver operating characteristic curve (AUC) was calculated. Model performance was validated using the external validation cohort. In the discovery cohort, 25 patients with PD were defined as FoG converters (15 men, mean age 62.1 years), whereas 60 were defined as FoG nonconverters (38 men, mean age 58.5 years). In the external validation cohort, 18 patients with PD were defined as FoG converters (8 men, mean age 66.9 years), whereas 37 were defined as FoG nonconverters (21 men, mean age 65.1 years). In the discovery cohort, the model trained with clinical variables, cortical thickness, and white matter fiber exhibited better performance (AUC, 0.67-0.88). More importantly, SVM-radial kernel models trained using random over-sampling examples, incorporating white matter fiber, cortical thickness, and clinical variables exhibited better performance (AUC, 0.88). This model trained using the above mentioned features was successfully validated in an external validation cohort (AUC, 0.91). Furthermore, the following minimal feature sets that were used: fractional anisotropy value and mean diffusivity value for right thalamic radiation, age at baseline, and cortical thickness for left precentral gyrus and right dorsal posterior cingulate gyrus. Therefore, machine learning-based models using white matter fiber and cortical thickness can help predict the risk of FoG conversion at the individual level in patients with PD, with improved performance when combined with clinical variables.
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Affiliation(s)
- Fabin Lin
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, 350001, China
- Department of Neurosurgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xinyang Zou
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, 350001, China
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, 350001, Fujian, China
| | - Jiaqi Su
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, 350001, China
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350001, China
| | - Lijun Wan
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, 350001, China
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350001, China
| | - Shenglong Wu
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, 350001, China
- School of Basic Medical Sciences, Fujian Medical University, Fuzhou, 350001, China
| | - Haoling Xu
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, 350001, China
| | - Yuqi Zeng
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, 350001, China
| | - Yongjie Li
- College of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Xiaochun Chen
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, 350001, China
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, 350001, China
| | - Guofa Cai
- College of Information Engineering, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China.
| | - Qinyong Ye
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, 350001, China.
| | - Guoen Cai
- Department of Neurology, Center for Cognitive Neurology, Institute of Clinical Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
- Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
- Fujian Key Laboratory of Molecular Neurology, Fujian Medical University, Fuzhou, 350001, China.
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