title: APPLYING SPARSE CODING TO SURFACE MULTIVARIATE TENSOR-BASED MORPHOMETRY TO PREDICT FUTURE COGNITIVE DECLINE.
abstract: Alzheimer's disease (AD) is a progressive brain disease. Accurate diagnosis of AD and its prodromal stage, mild cognitive impairment, is crucial for clinical trial design. There is also growing interests in identifying brain imaging biomarkers that help evaluate AD risk presymptomatically. Here, we applied a recently developed multivariate tensor-based morphometry (mTBM) method to extract features from hippocampal surfaces, derived from anatomical brain MRI. For such surface-based features, the feature dimension is usually much larger than the number of subjects. We used dictionary learning and sparse coding to effectively reduce the feature dimensions. With the new features, an Adaboost classifier was employed for binary group classification. In tests on publicly available data from the Alzheimers Disease Neuroimaging Initiative, the new framework outperformed several standard imaging measures in classifying different stages of AD. The new approach combines the efficiency of sparse coding with the sensitivity of surface mTBM, and boosts classification performance.
authors: School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ.
authors: Jie
authors: J
authors: Zhang
authors: Dept. of Psychiatry and Psychology, Mayo Clinic Arizona, Scottsdale, AZ.
authors: Cynthia
authors: C
authors: Stonnington
authors: School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ.
authors: Qingyang
authors: Q
authors: Li
authors: School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ.
authors: Jie
authors: J
authors: Shi
authors: Banner Alzheimer's Institute, Phoenix, AZ.
authors: Robert J
authors: RJ
authors: Bauer
authors: Imaging Genetics Center, Institute for Neuroimaging and Informatics, Univ.of Southern California, Marina del Rey, CA.
authors: Boris A
authors: BA
authors: Gutman
authors: Banner Alzheimer's Institute, Phoenix, AZ.
authors: Kewei
authors: K
authors: Chen
authors: Banner Alzheimer's Institute, Phoenix, AZ.
authors: Eric M
authors: EM
authors: Reiman
authors: Imaging Genetics Center, Institute for Neuroimaging and Informatics, Univ.of Southern California, Marina del Rey, CA.
authors: Paul M
authors: PM
authors: Thompson
authors: Dept. of Computational Medicine and Bioinformatics, Univ. of Michigan, Ann Arbor, MI.
authors: Jieping
authors: J
authors: Ye
authors: School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ.
authors: Yalin
authors: Y
authors: Wang
keywords: Alzheimer’s disease; dictionary learning and sparse coding; multivariate tensor-based morphometry