Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression

Hua Wang, Feiping Nie, Heng Huang, Shannon Risacher, Andrew J. Saykin, Li Shen

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

53 Scopus citations

Abstract

Traditional neuroimaging studies in (AD) typically employ independent and pairwise analyses between multimodal data, which treat imaging biomarkers, cognitive measures, and disease status as isolated units. To enhance mechanistic understanding of AD, in this paper, we conduct a new study for identifying imaging biomarkers that are associated with both cognitive measures and AD. To achieve this goal, we propose a new sparse joint classification and regression method. The imaging biomarkers identified by our method are AD-sensitive and cognition-relevant and can help reveal complex relationships among brain structure, cognition and disease status. Using the imaging and cognition data from Alzheimer's Disease Neuroimaging Initiative , database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
Pages115-123
Number of pages9
EditionPART 3
DOIs
StatePublished - Oct 11 2011
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 22 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6893 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period9/18/119/22/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Wang, H., Nie, F., Huang, H., Risacher, S., Saykin, A. J., & Shen, L. (2011). Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings (PART 3 ed., pp. 115-123). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6893 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-23626-6_15