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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMultimodal Brain Image Analysis - First International Workshop, MBIA 2011, Held in Conjunction with MICCAI 2011, Proceedings
Pages27-34
Number of pages8
DOIs
StatePublished - Oct 11 2011
Event1st International Workshop on Multimodal Brain Image Analysis, MBIA 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 18 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7012 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Workshop on Multimodal Brain Image Analysis, MBIA 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period9/18/119/18/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Shen, L., Kim, S., Qi, Y., Inlow, M., Swaminathan, S., Nho, K., Wan, J., Risacher, S. L., Shaw, L. M., Trojanowski, J. Q., Weiner, M. W., & Saykin, A. J. (2011). Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. In Multimodal Brain Image Analysis - First International Workshop, MBIA 2011, Held in Conjunction with MICCAI 2011, Proceedings (pp. 27-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7012 LNCS). https://doi.org/10.1007/978-3-642-24446-9_4