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 Saykin

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

33 Citations (Scopus)

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
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages27-34
Number of pages8
Volume7012 LNCS
DOIs
StatePublished - 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)03029743
ISSN (Electronic)16113349

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

Fingerprint

Elastic Net
Neuroimaging
Proteomics
Biomarkers
Magnetic resonance imaging
Feature extraction
Feature Selection
Imaging techniques
Imaging
Support vector machines
Logistic Regression
Logistics
Sparsity
Disorder
Support Vector Machine
Plasma
Plasmas
Classify
Optimise
Prediction

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Shen, L., Kim, S., Qi, Y., Inlow, M., Swaminathan, S., Nho, K., ... Saykin, A. (2011). Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7012 LNCS, 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

Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. / Shen, Li; Kim, Sungeun; Qi, Yuan; Inlow, Mark; Swaminathan, Shanker; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Shaw, Leslie M.; Trojanowski, John Q.; Weiner, Michael W.; Saykin, Andrew.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7012 LNCS 2011. p. 27-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7012 LNCS).

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

Shen, L, Kim, S, Qi, Y, Inlow, M, Swaminathan, S, Nho, K, Wan, J, Risacher, SL, Shaw, LM, Trojanowski, JQ, Weiner, MW & Saykin, A 2011, Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7012 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7012 LNCS, pp. 27-34, 1st 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, 9/18/11. https://doi.org/10.1007/978-3-642-24446-9_4
Shen L, Kim S, Qi Y, Inlow M, Swaminathan S, Nho K et al. Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7012 LNCS. 2011. p. 27-34. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-24446-9_4
Shen, Li ; Kim, Sungeun ; Qi, Yuan ; Inlow, Mark ; Swaminathan, Shanker ; Nho, Kwangsik ; Wan, Jing ; Risacher, Shannon L. ; Shaw, Leslie M. ; Trojanowski, John Q. ; Weiner, Michael W. ; Saykin, Andrew. / Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7012 LNCS 2011. pp. 27-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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