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 the k smallest 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.
|Title of host publication
|Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
|Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal
|Number of pages
|Published - 2016
|1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 2016 Oct 21 → 2016 Oct 21
|Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
|1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
|16/10/21 → 16/10/21
Bibliographical noteFunding Information:
Z. Huo and H. Huang—were supported in part by NSF IIS-1117965, IIS-1302675, IIS-1344152, DBI-1356628, and NIH AG049371. D. Shen was supported in part by NIH AG041721.
© Springer International Publishing AG 2016.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)