title: 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 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.
authors.affiliation: Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA; Department of Biohealth, School of Informatics and Computing, Indiana University, Indianapolis, IN, USA.
authors.forename: Jingwen
authors.initials: J
authors.lastname: Yan
authors.affiliation: School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, China.
authors.forename: Taiyong
authors.initials: T
authors.lastname: Li
authors.affiliation: Department of Electrical Engineering and Computer Science, Colorado School of Mines, Golden, CO, USA.
authors.forename: Hua
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, TX, USA.
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.affiliation: Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA; Department of Computer and Information Science, Purdue University, Indianapolis, IN, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Wan
authors.affiliation: Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA.
authors.forename: Kwangsik
authors.initials: K
authors.lastname: Nho
authors.affiliation: Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA.
authors.forename: Sungeun
authors.initials: S
authors.lastname: Kim
authors.affiliation: Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA.
authors.forename: Shannon L
authors.initials: SL
authors.lastname: Risacher
authors.affiliation: Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Saykin
authors.affiliation: Department of Radiology and Imaging Sciences, School of Medicine, Indiana University, Indianapolis, IN, USA; Department of Biohealth, School of Informatics and Computing, Indiana University, Indianapolis, IN, USA; Department of Computer and Information Science, Purdue University, Indianapolis, IN, USA. Electronic address: shenli@iu.edu.
authors.forename: Li
authors.initials: L
authors.lastname: Shen
mesh_terms: D000544:Alzheimer Disease / Q000175:diagnosis* / Q000473:pathology / Q000523:psychology; D015415:Biomarkers / Q000378:metabolism; D002540:Cerebral Cortex / Q000473:pathology; D003071:Cognition; D015331:Cohort Studies; D003943:Diagnostic Techniques, Neurological; D005544:Forecasting; D006801:Humans; D007858:Learning; D059906:Neuroimaging; D012044:Regression Analysis
chemical_list: D015415:Biomarkers
keywords: Alzheimer's disease neuroimaging initiative (ADNI); Cognitive function prediction; Cortical thickness; Magnetic resonance imaging (MRI); Sparse learning