Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net

By Li Shen, Sungeun Kim, Yuan (Alan) Qi, M. Inlow, S. Swaminathan, Kwangsik Nho, Jing Wan, Shannon L. Risacher, Leslie M. Shaw, John Q. Trojanowski and 2 more. 2011

60 citations recorded by the source corpus.

Multi-modal neuroimaging and biomarker data provide exciting opportunities to enhance our understanding of phenotypic characteristics associated with complex disorders. This study focuses on integrative analysis of structural MRI data and proteomic data from an RBM panel to examine their predictive power and identify relevant biomarkers in a large MCI/AD cohort. MRI data included volume and thickness measures of 98 regions estimated by FreeSurfer. RBM data included 146 proteomic analytes extracted from plasma and serum. A sparse learning model, elastic net logistic regression, was proposed to classify AD and MCI, and select disease-relevant biomarkers. A linear support vector machine coupled with feature selection was employed for comparison. Combining RBM and MRI data yielded improved prediction rates: HC vs AD (91.9%), HC vs MCI (90.5%) and MCI vs AD (86.5%). Elastic net identified a small set of meaningful imaging and proteomic biomarkers. The elastic net has great power to optimize the sparsity of feature selection while maintaining high predictive power. Its application to multi-modal imaging and biomarker data has considerable potential for discovering biomarkers and enhancing mechanistic understanding of AD and MCI.

Subfields: Molecular Biology

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In Molecular Biology

1. Weidong (Tom) Cai
2. D. Feng
3. Michael J. Fulham
4. Siqi Liu
5. Ender Konukoglu
6. Ben Glocker

Citations in the corpus

• Interval Coded Scoring: a toolbox for interpretable scoring systems (2018, background): “Similarly, (Shen et al., 2011) used it to identify neuroimaging and proteomic biomarkers for Alzheimer’s disease.”
• Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects (2014, unknown): “, 2010) for the multi-voxel pattern analyses of functional neuroimaging data as well as for AD related studies using structural MRI data (Ye et al., 2012; Casanova et al., 2011a,b, 2012; Shen et al., 2011; Janoušová et al., 2012).”
• Multi-Modal Neuroimaging Feature Learning for Multi-Class Diagnosis of Alzheimer’s Disease (2014, methodology): “We perform Elastic Net [9], [41], [42] only on the training samples to select the discriminative subset of the feature parameters.”
• A Review of Feature Reduction Techniques in Neuroimaging (2013, methodology): “Previous applications of feature reduction using Elastic Net in neuroimaging machine learning tasks include: AD classification (Rao et al. 2011; Shen et al. 2011; Wan et al. 2011) and treatment response predictions in ADHD (Marquand et al.”