Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance

Hua Wang, Feiping Nie, Heng Huang, Shannon Risacher, Chris Ding, Andrew Saykin, Li Shen

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

75 Citations (Scopus)

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions, which makes regression analysis a suitable model to study whether neuroimaging measures can help predict memory performance and track the progression of AD. Existing memory performance prediction methods via regression, however, do not take into account either the interconnected structures within imaging data or those among memory scores, which inevitably restricts their predictive capabilities. To bridge this gap, we propose a novel Sparse Multi-tAsk Regression and feaTure selection (SMART) method to jointly analyze all the imaging and clinical data under a single regression framework and with shared underlying sparse representations. Two convex regularizations are combined and used in the model to enable sparsity as well as facilitate multi-task learning. The effectiveness of the proposed method is demonstrated by both clearly improved prediction performances in all empirical test cases and a compact set of selected RAVLT-relevant MRI predictors that accord with prior studies.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Computer Vision
Pages557-562
Number of pages6
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Computer Vision, ICCV 2011 - Barcelona, Spain
Duration: Nov 6 2011Nov 13 2011

Other

Other2011 IEEE International Conference on Computer Vision, ICCV 2011
CountrySpain
CityBarcelona
Period11/6/1111/13/11

Fingerprint

Feature extraction
Brain
Imaging techniques
Data storage equipment
Neuroimaging
Regression analysis
Magnetic resonance imaging

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Wang, H., Nie, F., Huang, H., Risacher, S., Ding, C., Saykin, A., & Shen, L. (2011). Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. In Proceedings of the IEEE International Conference on Computer Vision (pp. 557-562). [6126288] https://doi.org/10.1109/ICCV.2011.6126288

Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. / Wang, Hua; Nie, Feiping; Huang, Heng; Risacher, Shannon; Ding, Chris; Saykin, Andrew; Shen, Li.

Proceedings of the IEEE International Conference on Computer Vision. 2011. p. 557-562 6126288.

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

Wang, H, Nie, F, Huang, H, Risacher, S, Ding, C, Saykin, A & Shen, L 2011, Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. in Proceedings of the IEEE International Conference on Computer Vision., 6126288, pp. 557-562, 2011 IEEE International Conference on Computer Vision, ICCV 2011, Barcelona, Spain, 11/6/11. https://doi.org/10.1109/ICCV.2011.6126288
Wang H, Nie F, Huang H, Risacher S, Ding C, Saykin A et al. Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. In Proceedings of the IEEE International Conference on Computer Vision. 2011. p. 557-562. 6126288 https://doi.org/10.1109/ICCV.2011.6126288
Wang, Hua ; Nie, Feiping ; Huang, Heng ; Risacher, Shannon ; Ding, Chris ; Saykin, Andrew ; Shen, Li. / Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance. Proceedings of the IEEE International Conference on Computer Vision. 2011. pp. 557-562
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