Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry
Abstract
Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method (cFSGL) with a novel MR‐based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 09, 2017
- Source ID
- 10.1002/brb3.733
Entities
People
- Jiayu Zhou
- Jie Shi
- Jieping Ye
- Natasha Leporé
- Niharika Gajawelli
- Sinchai Tsao
- Yalin Wang
Organizations
- Alzheimer's Disease Neuroimaging Initiative
- Arizona State University
- Canadian Institutes of Health Research
- Michigan State University
- National Institute of Biomedical Imaging and Bioengineering
- National Institute on Aging
- National Institutes of Health
- United States Department of Defense
- University of Michigan
- University of Southern California