Structural brain network constrained neuroimaging marker identification for predicting cognitive functions

De Wang, Feiping Nie, Heng Huang, Jingwen Yan, Shannon L. Risacher, Andrew J. Saykin, Li Shen

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

4 Scopus citations

Abstract

Neuroimaging markers have been widely used to predict the cognitive functions relevant to the progression of Alzheimer's disease (AD). Most previous studies identify the imaging markers without considering the brain structural correlations between neuroimaging measures. However, many neuroimaging markers interrelate and work together to reveal the cognitive functions, such that these relevant markers should be selected together as the phenotypic markers. To solve this problem, in this paper, we propose a novel network constrained feature selection (NCFS) model to identify the neuroimaging markers guided by the structural brain network, which is constructed by the sparse representation method such that the interrelations between neuroimaging features are encoded into probabilities. Our new methods are evaluated by the MRI and AV45-PET data from ADNI-GO and ADNI-2 (Alzheimer's Disease Neuroimaging Initiative). In all cognitive function prediction tasks, our new NCFS method outperforms other state-of-the-art regression approaches. Meanwhile, we show that the new method can select the correlated imaging markers, which are ignored by the competing approaches.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging - 23rd International Conference, IPMI 2013, Proceedings
Pages536-547
Number of pages12
DOIs
StatePublished - Jul 12 2013
Event23rd International Conference on Information Processing in Medical Imaging, IPMI 2013 - Asilomar, CA, United States
Duration: Jun 28 2013Jul 3 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7917 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on Information Processing in Medical Imaging, IPMI 2013
CountryUnited States
CityAsilomar, CA
Period6/28/137/3/13

Keywords

  • Brain Network Based Feature Selection
  • Correlated Marker Selection
  • Imaging Genetics
  • Neuroimaging Marker Identification

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Wang, D., Nie, F., Huang, H., Yan, J., Risacher, S. L., Saykin, A. J., & Shen, L. (2013). Structural brain network constrained neuroimaging marker identification for predicting cognitive functions. In Information Processing in Medical Imaging - 23rd International Conference, IPMI 2013, Proceedings (pp. 536-547). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7917 LNCS). https://doi.org/10.1007/978-3-642-38868-2_45