Sparse Multi-Task Regression and Feature Selection to Identify Brain Imaging Predictors for Memory Performance

dc.contributor.authorWang, Hua
dc.contributor.authorNie, Feiping
dc.contributor.authorHuang, Heng
dc.contributor.authorRisacher, Shannon
dc.contributor.authorDing, Chris
dc.contributor.authorSaykin, Andrew J.
dc.contributor.authorShen, Li
dc.contributor.authorADNI
dc.contributor.departmentRadiology and Imaging Sciences, School of Medicine
dc.date.accessioned2025-03-28T12:57:37Z
dc.date.available2025-03-28T12:57:37Z
dc.date.issued2011
dc.description.abstractAlzheimer’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.
dc.eprint.versionAuthor's manuscript
dc.identifier.citationHua Wang et al., "Sparse multi-task regression and feature selection to identify brain imaging predictors for memory performance," 2011 International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 557-562, doi: 10.1109/ICCV.2011.6126288.
dc.identifier.urihttps://hdl.handle.net/1805/46635
dc.language.isoen_US
dc.publisherIEEE
dc.relation.isversionof10.1109/ICCV.2011.6126288
dc.relation.journal2011 International Conference on Computer Vision
dc.rightsPublisher Policy
dc.sourcePMC
dc.subjectMagnetic resonance imaging
dc.subjectParticle measurements
dc.subjectAtmospheric measurements
dc.subjectNeuroimaging
dc.subjectWeight measurement
dc.subjectPredictive models
dc.titleSparse Multi-Task Regression and Feature Selection to Identify Brain Imaging Predictors for Memory Performance
dc.typeArticle
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