Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer’s Disease

Rui Li, Robert Perneczky, Igor Yakushev, Stefan Förster, Alexander Kurz, Alexander Drzezga, Stefan Kramer and Alzheimer’s Disease Neuroimaging Initiative

PLOS ONE, 2015, vol. 10, issue 4, 1-22

Abstract: We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer’s disease. In the work, the term “pattern” stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called “region of interest (ROI)”. In the literature, an ROI is often found by a given brain atlas that defines a number of brain regions, which corresponds to an anatomical approach. The present work introduces a semi-data-driven approach that is based on learning the characteristics of the given data, given some prior anatomical knowledge. A Gaussian Mixture Model (GMM) and model selection are combined to return a clustering of voxels that may serve for the definition of ROIs. Experiments on both an in-house dataset and data of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that the proposed approach arrives at a better diagnosis than a merely anatomical approach or conventional statistical hypothesis testing.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0122731

DOI: 10.1371/journal.pone.0122731

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