title: Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression.
abstract: Traditional neuroimaging studies in Alzheimer's disease (AD) typically employ independent and pairwise analyses between multimodal data, which treat imaging biomarkers, cognitive measures, and disease status as isolated units. To enhance mechanistic understanding of AD, in this paper, we conduct a new study for identifying imaging biomarkers that are associated with both cognitive measures and AD. To achieve this goal, we propose a new sparse joint classification and regression method. The imaging biomarkers identified by our method are AD-sensitive and cognition-relevant and can help reveal complex relationships among brain structure, cognition and disease status. Using the imaging and cognition data from Alzheimer's Disease Neuroimaging Initiative database, the effectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status.
authors: Computer Science and Engineering, University of Texas at Arlington, TX, USA. huawangcs@gmail.com
authors: Hua
authors: H
authors: Wang
authors: Feiping
authors: F
authors: Nie
authors: Heng
authors: H
authors: Huang
authors: Shannon
authors: S
authors: Risacher
authors: Andrew J
authors: AJ
authors: Saykin
authors: Li
authors: L
authors: Shen
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000473:pathology*; D015415:Biomarkers / Q000378:metabolism*; D001931:Brain Mapping / Q000379:methods*; D003071:Cognition; D003072:Cognition Disorders / Q000175:diagnosis* / Q000473:pathology*; D003952:Diagnostic Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D012044:Regression Analysis
chemical_list: D015415:Biomarkers