Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry.

Journal: Brain and behavior
PMID: 28729939

Abstract

INTRODUCTION: 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.

Authors

  • Sinchai Tsao
    CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA.
  • Niharika Gajawelli
    CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA.
  • Jiayu Zhou
    Department of Computer Science and Engineering, Michigan State University, Michigan, USA.
  • Jie Shi
    Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Oilseeds processing, Ministry of Agriculture, Oil Crops and Lipids Process Technology National & Local Joint Engineering Laboratory, Hubei Key Laboratory of Lipid Chemistry and Nutrition, Wuhan 430062, China.
  • Jieping Ye
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Ml 48109.
  • Yalin Wang
    Comp.Sci.& Engin, Arizona State Univ, Arizona, USA.
  • Natasha Leporé
    CIBORG Children's Hospital Los Angeles and University of Southern California Los Angeles CA USA.

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