Network-guided sparse learning for predicting cognitive outcomes from MRI measures

Jingwen Yan, Heng Huang, Shannon L. Risacher, Sungeun Kim, Mark Inlow, Jason H. Moore, Andrew J. Saykin, Li Shen

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

7 Scopus citations

Abstract

Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from MRI measures. In particular, sparse models have been proposed to identify the optimal imaging markers with high prediction power. However, the complex relationship among imaging markers are often overlooked or simplified in the existing methods. To address this issue, we present a new sparse learning method by introducing a novel network term to more flexibly model the relationship among imaging markers. The proposed algorithm is applied to the ADNI study for predicting cognitive outcomes using MRI scans. The effectiveness of our method is demonstrated by its improved prediction performance over several state-of-the-art competing methods and accurate identification of cognition-relevant imaging markers that are biologically meaningful.

Original languageEnglish (US)
Title of host publicationMultimodal Brain Image Analysis - Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Proceedings
Pages202-210
Number of pages9
DOIs
StatePublished - Sep 5 2013
Event3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: Sep 22 2013Sep 22 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8159 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Workshop on Multimodal Brain Image Analysis, MBIA 2013, Held in Conjunction with the 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period9/22/139/22/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yan, J., Huang, H., Risacher, S. L., Kim, S., Inlow, M., Moore, J. H., Saykin, A. J., & Shen, L. (2013). Network-guided sparse learning for predicting cognitive outcomes from MRI measures. In Multimodal Brain Image Analysis - Third International Workshop, MBIA 2013, Held in Conjunction with MICCAI 2013, Proceedings (pp. 202-210). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8159 LNCS). https://doi.org/10.1007/978-3-319-02126-3_20