Cortical surface biomarkers for predicting cognitive outcomes using group l2,1 norm

Abstract Regression models have been widely studied to investigate the prediction power of neuroimaging measures as biomarkers for inferring cognitive outcomes in the Alzheimer's disease study. Most of these models ignore the interrelated structures either within neuroimaging measures or betwee...

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Bibliographic Details
Published in:Neurobiology of aging 2015-01, Vol.36, p.S185-S193
Main Authors: Yan, Jingwen, Li, Taiyong, Wang, Hua, Huang, Heng, Wan, Jing, Nho, Kwangsik, Kim, Sungeun, Risacher, Shannon L, Saykin, Andrew J, Shen, Li
Format: Article
Language:English
Subjects:
Alzheimer Disease - diagnosis
Alzheimer Disease - pathology
Alzheimer Disease - psychology
Alzheimer's disease neuroimaging initiative (ADNI)
Biomarkers - metabolism
Cerebral Cortex - pathology
Cognition
Cognitive function prediction
Cohort Studies
Cortical thickness
Diagnostic Techniques, Neurological
Forecasting
Humans
Internal Medicine
Learning
Magnetic resonance imaging (MRI)
Neuroimaging
Neurology
Regression Analysis
Sparse learning
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Summary:Abstract Regression models have been widely studied to investigate the prediction power of neuroimaging measures as biomarkers for inferring cognitive outcomes in the Alzheimer's disease study. Most of these models ignore 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 use a new sparse multitask learning model called Group-Sparse Multi-task Regression and Feature Selection (G-SMuRFS) and demonstrate its effectiveness by examining the predictive power of detailed cortical thickness measures toward 3 types of cognitive scores in a large cohort. G-SMuRFS proposes a group-level l2,1 -norm strategy to group relevant features together in an anatomically meaningful manner and use this prior knowledge to guide the learning process. This approach also takes into account the correlation among cognitive outcomes for building a more appropriate predictive model. Compared with traditional methods, G-SMuRFS not only demonstrates a superior performance but also identifies a small set of surface markers that are biologically meaningful.
ISSN:0197-4580
1558-1497
1558-1497
DOI:10.1016/j.neurobiolaging.2014.07.045