Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net

Authors:
Li Shen;Sungeun Kim;Yuan Qi;Mark Inlow;Shanker Swaminathan;Kwangsik Nho;Jing Wan;Shannon L. Risacher;Leslie M. Shaw;John Q. Trojanowski;Michael W. Weiner;Andrew J. Saykin
Affiliations:
Radiology and Imaging Sciences, Indiana University, IN;Radiology and Imaging Sciences, Indiana University, IN;Computer Science, Statistics and Biology, Purdue University, IN;Radiology and Imaging Sciences, Indiana University, IN and Mathematics, Rose-Hulman Institute of Technology, IN;Radiology and Imaging Sciences, Indiana University, IN;Radiology and Imaging Sciences, Indiana University, IN;Radiology and Imaging Sciences, Indiana University, IN;Radiology and Imaging Sciences, Indiana University, IN;Pathology and Laboratory Medicine, University of Pennsylvania, PA;Pathology and Laboratory Medicine, University of Pennsylvania, PA;Radiology, Medicine and Psychiatry, UC San Francisco, CA;Radiology and Imaging Sciences, Indiana University, IN
Venue:
MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
Year:
2011

Citing 2
Cited 0

Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Sparse bayesian learning for identifying imaging biomarkers in AD prediction

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 multimodal imaging and biomarker data has considerable potential for discovering biomarkers and enhancing mechanistic understanding of AD and MCI.