Prediction of Memory Impairment with MRI Data: A Longitudinal Study of Alzheimer's Disease.
Alzheimer's Disease (AD), a severe type of neurodegenerative disorder with progressive impairment of learning and memory, has threatened the health of millions of people. How to recognize AD at early stage is crucial. Multiple models have been presented to predict cognitive impairments by means of neuroimaging data. However, traditional models did not employ the valuable longitudinal information along the progression of the disease. In this paper, we proposed a novel longitudinal feature learning model to simultaneously uncover the interrelations among different cognitive measures at different time points and utilize such interrelated structures to enhance the learning of associations between imaging features and prediction tasks. Moreover, we adopted Schattenp-norm to identify the interrelation structures existing in the low-rank subspace. Empirical results on the ADNI cohort demonstrated promising performance of our model.
Computer Science and Engineering, University of Texas at Arlington, Arlington, USA.
Xiaoqian
X
Wang
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; D018450:Disease Progression; D006801:Humans; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging; D008569:Memory Disorders / Q000000981:diagnostic imaging*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity