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

PLoS One. 2015 Apr 28;10(4):e0122731. doi: 10.1371/journal.pone.0122731. eCollection 2014.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Alzheimer Disease / diagnostic imaging*
  • Alzheimer Disease / pathology*
  • Brain / diagnostic imaging
  • Brain / pathology
  • Female
  • Fluorodeoxyglucose F18 / pharmacokinetics*
  • Humans
  • Male
  • Models, Theoretical
  • Normal Distribution
  • Positron-Emission Tomography / methods*
  • Radiopharmaceuticals / pharmacokinetics*
  • Sensitivity and Specificity

Substances

  • Radiopharmaceuticals
  • Fluorodeoxyglucose F18