Folded concave penalized learning in identifying multimodal MRI marker for Parkinson's disease.
Brain MRI holds promise to gauge different aspects of Parkinson's disease (PD)-related pathological changes. Its analysis, however, is hindered by the high-dimensional nature of the data.



This study introduces folded concave penalized (FCP) sparse logistic regression to identify biomarkers for PD from a large number of potential factors. The proposed statistical procedures target the challenges of high-dimensionality with limited data samples acquired. The maximization problem associated with the sparse logistic regression model is solved by local linear approximation. The proposed procedures then are applied to the empirical analysis of multimodal MRI data.



From 45 features, the proposed approach identified 15 MRI markers and the UPSIT, which are known to be clinically relevant to PD. By combining the MRI and clinical markers, we can enhance substantially the specificity and sensitivity of the model, as indicated by the ROC curves.



We compare the folded concave penalized learning scheme with both the Lasso penalized scheme and the principle component analysis-based feature selection (PCA) in the Parkinson's biomarker identification problem that takes into account both the clinical features and MRI markers. The folded concave penalty method demonstrates a substantially better clinical potential than both the Lasso and PCA in terms of specificity and sensitivity.



For the first time, we applied the FCP learning method to MRI biomarker discovery in PD. The proposed approach successfully identified MRI markers that are clinically relevant. Combining these biomarkers with clinical features can substantially enhance performance.
Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, United States.
Hongcheng
H
Liu
Departments of Neurology, Milton S. Hershey College of Medicine, The Pennsylvania State University, Hershey, PA 17033, United States.
Guangwei
G
Du
Departments of Biochemistry and Molecular Biology, Milton S. Hershey College of Medicine, The Pennsylvania State University, Hershey, PA 17033, United States.
Lijun
L
Zhang
Departments of Neurology, Milton S. Hershey College of Medicine, The Pennsylvania State University, Hershey, PA 17033, United States; Departments of Pharmacology, Milton S. Hershey College of Medicine, The Pennsylvania State University, Hershey, PA 17033, United States.
Mechelle M
MM
Lewis
Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, United States.
Xue
X
Wang
Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, United States. Electronic address: taoyao@psu.edu.
Tao
T
Yao
Department of Statistics, The Pennsylvania State University, University Park, PA 16802, United States. Electronic address: rzli@psu.edu.
Runze
R
Li
Departments of Neurology, Milton S. Hershey College of Medicine, The Pennsylvania State University, Hershey, PA 17033, United States; Departments of Pharmacology, Milton S. Hershey College of Medicine, The Pennsylvania State University, Hershey, PA 17033, United States; Department of Radiology, Milton S. Hershey College of Medicine, The Pennsylvania State University, Hershey, PA 17033, United States; Department of Neurosurgery, Milton S. Hershey College of Medicine, The Pennsylvania State University, Hershey, PA 17033, United States; Department of Kinesiology, Milton S. Hershey College of Medicine, The Pennsylvania State University, Hershey, PA 17033, United States. Electronic address: Xuemei@psu.edu.
Xuemei
X
Huang
D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D056324:Diffusion Tensor Imaging / Q000379:methods; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D016015:Logistic Models; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods*; D010300:Parkinson Disease / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods; D012680:Sensitivity and Specificity
Biomarker discovery; Diffusion tensor imaging; Magnetic resonance imaging; Parkinson’s disease; Penalized learning; R2*