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

  • 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
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7012)

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

Keywords

Magnetic Resonance Imaging Data High Predictive Power Lower Measurement Level Good Prediction Rate Structural Magnetic Resonance Imaging Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Li Shen
    • 1
  • Sungeun Kim
    • 1
  • Yuan Qi
    • 2
  • Mark Inlow
    • 1
    • 3
  • Shanker Swaminathan
    • 1
  • Kwangsik Nho
    • 1
  • Jing Wan
    • 1
  • Shannon L. Risacher
    • 1
  • Leslie M. Shaw
    • 4
  • John Q. Trojanowski
    • 4
  • Michael W. Weiner
    • 5
  • Andrew J. Saykin
    • 1
  1. 1.Radiology and Imaging SciencesIndiana UniversityUSA
  2. 2.Computer Science, Statistics and BiologyPurdue UniversityUSA
  3. 3.Mathematics, Rose-Hulman Institute of TechnologyUSA
  4. 4.Pathology and Laboratory MedicineUniversity of PennsylvaniaUSA
  5. 5.Radiology, Medicine and PsychiatryUC San FranciscoUSA

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