Multimodal neuroimaging predictors for cognitive performance using structured sparse learning

Jingwen Yan, Shannon L. Risacher, Sungeun Kim, Jacqueline C. Simon, Taiyong Li, Jing Wan, Hua Wang, Heng Huang, Andrew Saykin, Li Shen

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

4 Citations (Scopus)

Abstract

Regression models have been widely studied to investigate whether multimodal neuroimaging measures can be used as effective biomarkers for predicting cognitive outcomes in the study of Alzheimer's Disease (AD). Most existing models overlook the interrelated structures either within neuroimaging measures or between cognitive outcomes, and thus may have limited power to yield optimal solutions. To address this issue, we propose to incorporate an ℓ 2,1 norm and/or a group ℓ 2,1 norm (G 2,1 norm) in the regression models. Using ADNI-1 and ADNI-GO/2 data, we apply these models to examining the ability of structural MRI and AV-45 PET scans for predicting cognitive measures including ADAS and RAVLT scores. We focus our analyses on the participants with mild cognitive impairment (MCI), a prodromal stage of AD, in order to identify useful patterns for early detection. Compared with traditional linear and ridge regression methods, these new models not only demonstrate superior and more stable predictive performances, but also identify a small set of imaging markers that are biologically meaningful.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages1-17
Number of pages17
Volume7509 LNCS
DOIs
StatePublished - 2012
Event2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France
Duration: Oct 1 2012Oct 5 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7509 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period10/1/1210/5/12

Fingerprint

Neuroimaging
Predictors
Alzheimer's Disease
Norm
Regression Model
Ridge Regression
Biomarkers
Linear regression
Optimal Solution
Imaging
Model
Magnetic resonance imaging
Demonstrate
Learning
Imaging techniques

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Yan, J., Risacher, S. L., Kim, S., Simon, J. C., Li, T., Wan, J., ... Shen, L. (2012). Multimodal neuroimaging predictors for cognitive performance using structured sparse learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7509 LNCS, pp. 1-17). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7509 LNCS). https://doi.org/10.1007/978-3-642-33530-3_1

Multimodal neuroimaging predictors for cognitive performance using structured sparse learning. / Yan, Jingwen; Risacher, Shannon L.; Kim, Sungeun; Simon, Jacqueline C.; Li, Taiyong; Wan, Jing; Wang, Hua; Huang, Heng; Saykin, Andrew; Shen, Li.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7509 LNCS 2012. p. 1-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7509 LNCS).

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

Yan, J, Risacher, SL, Kim, S, Simon, JC, Li, T, Wan, J, Wang, H, Huang, H, Saykin, A & Shen, L 2012, Multimodal neuroimaging predictors for cognitive performance using structured sparse learning. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7509 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7509 LNCS, pp. 1-17, 2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012, Nice, France, 10/1/12. https://doi.org/10.1007/978-3-642-33530-3_1
Yan J, Risacher SL, Kim S, Simon JC, Li T, Wan J et al. Multimodal neuroimaging predictors for cognitive performance using structured sparse learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7509 LNCS. 2012. p. 1-17. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-33530-3_1
Yan, Jingwen ; Risacher, Shannon L. ; Kim, Sungeun ; Simon, Jacqueline C. ; Li, Taiyong ; Wan, Jing ; Wang, Hua ; Huang, Heng ; Saykin, Andrew ; Shen, Li. / Multimodal neuroimaging predictors for cognitive performance using structured sparse learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7509 LNCS 2012. pp. 1-17 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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