Multimodal neuroimaging predictors for cognitive performance using structured sparse learning

Authors:
Jingwen Yan;Shannon L. Risacher;Sungeun Kim;Jacqueline C. Simon;Taiyong Li;Jing Wan;Hua Wang;Heng Huang;Andrew J. Saykin;Li Shen
Affiliations:
Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA, School of Informatics, Indiana University, Indianapolis, IN;Radiology and Imaging Sciences, Indiana University School of Medicine, IN;Radiology and Imaging Sciences, Indiana University School of Medicine, IN;Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA, Biomedical Engineering and Mathematics, Rose-Hulman Inst. of Tech., IN;Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA, Economic Info. Eng., Southwestern Univ. of Finance & Economics, Chengdu, China;Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA, Computer and Information Science, Purdue University, Indianapolis, IN;Computer Science and Engineering, University of Texas at Arlington, TX;Computer Science and Engineering, University of Texas at Arlington, TX;Radiology and Imaging Sciences, Indiana University School of Medicine, IN;Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA, School of Informatics, Indiana University, Indianapolis, IN, USA, Computer and Information Science, Purdue Universit ...
Venue:
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
Year:
2012

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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 ℓ2,1 norm and/or a group ℓ2,1 norm (G2,1 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.