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Med Image Comput Comput Assist Interv. 2016 Oct;9900:326-334. doi: 10.1007/978-3-319-46720-7_38. Epub 2016 Oct 2.

Hyperbolic Space Sparse Coding with Its Application on Prediction of Alzheimer's Disease in Mild Cognitive Impairment.

Author information

1
School of Computing, Informatics, and Decision Systems Engineering, Arizona State Univ., Tempe, AZ.
2
Dept. of Psychiatry and Psychology, Mayo Clinic Arizona, Scottsdale, AZ.
3
Imaging Genetics Center, Institute for Neuroimaging and Informatics, Univ. of Southern California, Marina del Rey, CA.
4
Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ.
5
Dept. of Neurology, Mayo Clinic Arizona, Scottsdale, AZ.
6
Dept. of Computational Medicine and Bioinformatics, Univ. of Michigan, Ann Arbor, MI.

Abstract

Mild Cognitive Impairment (MCI) is a transitional stage between normal age-related cognitive decline and Alzheimer's disease (AD). Here we introduce a hyperbolic space sparse coding method to predict impending decline of MCI patients to dementia using surface measures of ventricular enlargement. First, we compute diffeomorphic mappings between ventricular surfaces using a canonical hyperbolic parameter space with consistent boundary conditions and surface tensor-based morphometry is computed to measure local surface deformations. Second, ring-shaped patches of TBM features are selected according to the geometric structure of the hyperbolic parameter space to initialize a dictionary. Sparse coding is then applied on the patch features to learn sparse codes and update the dictionary. Finally, we adopt max-pooling to reduce the feature dimensions and apply Adaboost to predict AD in MCI patients (N = 133) from the Alzheimer's Disease Neuroimaging Initiative baseline dataset. Our work achieved an accuracy rate of 96.7% and outperformed some other morphometry measures. The hyperbolic space sparse coding method may offer a more sensitive tool to study AD and its early symptom.

KEYWORDS:

Hyperbolic Parameter Space; Mild Cognitive Impairment; Ring-shaped Patches; Sparse Coding and Dictionary Learning

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