Classification of Alzheimer's disease using unsupervised diffusion component analysis

Dominique  Duncan; Thomas  Strohmer

doi:10.3934/mbe.2016033

Article Contents

2016, Volume 13, Issue 6: 1119-1130. Doi: 10.3934/mbe.2016033

This issue Previous Article Mathematical methods in systems biology Next Article Adjoint sensitivity analysis of a tumor growth model and its application to spatiotemporal radiotherapy optimization

Classification of Alzheimer's disease using unsupervised diffusion component analysis

Dominique Duncan^1, and
Thomas Strohmer^2,

1.
Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California
2.
Department of Mathematics, University of California, Davis

Received: October 2015

Revised: April 2016

Published: July 2016

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Abstract

The goal of this study is automated discrimination between early stage Alzheimer$'$s disease (AD) magnetic resonance imaging (MRI) and healthy MRI data. Unsupervised Diffusion Component Analysis, a novel approach based on the diffusion mapping framework, reduces data dimensionality and provides pattern recognition that can be used to distinguish AD brains from healthy brains. The new algorithm constructs coordinates as an extension of diffusion maps and generates efficient geometric representations of the complex structure of the MRI data. The key difference between our method and others used to classify and detect AD early in its course is our nonlinear and local network approach, which overcomes calibration differences among different scanners and centers collecting MRI data and solves the problem of individual variation in brain size and shape. In addition, our algorithm is completely automatic and unsupervised, which could potentially be a useful and practical tool for doctors to help identify AD patients.

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Mathematics Subject Classification: 68T10, 65F15, 92B99, 92C20, 00A69.

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