Folded concave penalized learning of high-dimensional MRI data in Parkinson's disease

Background: Brain MRI is a promising technique for Parkinson’s disease (PD) biomarker development. Its analysis, however, is hindered by the high-dimensional nature of the data, particularly when the sample size is relatively small.

New Method: This study introduces a folded concave penalized machine learning scheme with spatial coupling fused penalty (fused FCP) to build biomarkers for PD directly from whole-brain voxel-wise MRI data. The penalized maximum likelihood estimation problem of the model is solved by local linear approximation. Results: The proposed approach is evaluated on synthetic and Parkinson’s Progression Marker Initiative (PPMI) data. It achieves good AUC scores, accuracy in classification, and biomarker identification with a relatively small sample size, and the results are robust for different tuning parameter choices. On the PPMI data, the proposed method discovers over 80 % of large regions of interest (ROIs) identified by the voxel-wise method, as well as potential new ROIs.

Comparison with Existing Methods: The fused FCP approach is compared with L1, fused-L1, and FCP method using three popular machine learning algorithms, logistic regression, support vector machine, and linear discriminant analysis, as well as the voxel-wise method, on both synthetic and PPMI datasets. The fused FCP method demonstrated better accuracy in separating PD from controls than L1 and fused-L1 methods, and similar performance when compared with FCP method. In addition, the fused FCP method showed better ROI identification.

Conclusions: The fused FCP method can be an effective approach for MRI biomarker discovery in PD and other studies using high dimensionality data/low sample sizes.

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Work Title Folded concave penalized learning of high-dimensional MRI data in Parkinson's disease
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Open Access
Creators
  1. Changcheng Li
  2. Xue Wang
  3. Guangwei Du
  4. Hairong Chen
  5. Gregory Brown
  6. Mechelle M. Lewis
  7. Tao Yao
  8. Runze Li
  9. Xuemei Huang
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Journal of Neuroscience Methods
Publication Date March 31, 2021
Publisher Identifier (DOI)
  1. https://doi.org/10.1016/j.jneumeth.2021.109157
Deposited November 15, 2021

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  • Created
  • Added 1-s2.0-S0165027021000923-main.pdf
  • Added Creator Changcheng Li
  • Added Creator Xue Wang
  • Added Creator Guangwei Du
  • Added Creator Hairong Chen
  • Added Creator Gregory Brown
  • Added Creator Mechelle M. Lewis
  • Added Creator Tao Yao
  • Added Creator Runze Li
  • Added Creator Xuemei Huang
  • Published
  • Updated
  • Updated