Support vector machine-driven Parkinson's disease identification: a 7-Tesla multidimensional structural MRI approach.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Chinese PLA General Hospital, Beijing, China.
- College of Medical Technology, Beijing Institute of Technology, Beijing, China.
- Department of Neurology, the Second Medical Center & National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China.
- Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
- Department of Radiology, Chinese PLA General Hospital, Beijing, China. [email protected].
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder diagnosed clinically by cardinal motor symptoms, with structural brain changes associated with its diverse motor and non-motor manifestations. This study integrated multidimensional 7-Tesla structural Magnetic Resonance Imaging (MRI) features (gray matter volume, cortical thickness, etc.) using Support Vector Machine (SVM) to distinguish 98 PD patients from 74 healthy controls. The SVM model achieved 0.80 accuracy (sensitivity: 100%, F1-score: 0.85) and identified key biomarkers. Partial Least Squares Regression (PLSR) revealed these features correlated significantly with motor symptoms (Movement Disorder Society-Unified Parkinson's Disease Rating Scale [MDS-UPDRS]-III, tremor, rigidity, bradykinesia, postural instability; P < 0.05) and non-motor symptoms (cognition, anxiety, depression, MDS-UPDRS-I; P < 0.05). The findings highlight the potential of 7-Tesla MRI and machine learning as diagnostic tools for PD, while also providing insights into its pathophysiology. This approach may aid in detection and understanding of PD's motor and non-motor manifestations.