New Multi-task Learning Model to Predict Alzheimer's Disease Cognitive Assessment.
As a neurodegenerative disorder, the Alzheimer's disease (AD) status can be characterized by the progressive impairment of memory and other cognitive functions. Thus, it is an important topic to use neuroimaging measures to predict cognitive performance and track the progression of AD. Many existing cognitive performance prediction methods employ the regression models to associate cognitive scores to neuroimaging measures, but these methods do not take into account the interconnected structures within imaging data and those among cognitive scores. To address this problem, we propose a novel multi-task learning model for minimizing theksmallest singular values to uncover the underlying low-rank common subspace and jointly analyze all the imaging and clinical data. The effectiveness of our method is demonstrated by the clearly improved prediction performances in all empirical AD cognitive scores prediction cases.
Computer Science and Engineering, University of Texas at Arlington, Arlington, USA.
Zhouyuan
Z
Huo
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA.
Dinggang
D
Shen
Computer Science and Engineering, University of Texas at Arlington, Arlington, USA.
Heng
H
Huang
D000465:Algorithms; D000544:Alzheimer Disease / Q000000981:diagnostic imaging*; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging*; D018450:Disease Progression; D006801:Humans; D059906:Neuroimaging / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity