Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score

Abstract

18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) enables in‐vivo capture of the topographic metabolism patterns in the brain. These images have shown great promise in revealing the altered metabolism patterns in Alzheimer's disease (AD). The AD pathology is progressive, and leads to structural and functional alterations that lie on a continuum. There is a need to quantify the altered metabolism patterns that exist on a continuum into a simple measure. This work proposes a 3D convolutional neural network with residual connections that generates a probability score useful for interpreting the FDG‐PET images along the continuum of AD. This network is trained and tested on images of stable normal control and stable Dementia of the Alzheimer's type (sDAT) subjects, achieving an AUC of 0.976 via repeated fivefold cross‐validation. An independent test set consisting of images in between the two extreme ends of the DAT spectrum is used to further test the generalization performance of the network. Classification performance of 0.811 AUC is achieved in the task of predicting conversion of mild cognitive impairment to DAT for conversion time of 0–3 years. The saliency and class activation maps, which highlight the regions of the brain that are most important to the classification task, implicate many known regions affected by DAT including the posterior cingulate cortex, precuneus, and hippocampus.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 10, 2019
Source ID
10.1002/hbm.24783

Entities

People

  • Evangeline Yee
  • Karteek Popuri
  • Mirza Faisal Beg
  • The Alzheimer’s Disease Neuroimaging Initiative*

Organizations

  • Alzheimer's Disease Neuroimaging Initiative
  • Canadian Institutes of Health Research
  • Compute Canada
  • Foundation for the National Institutes of Health
  • National Institute of Biomedical Imaging and Bioengineering
  • National Institute on Aging
  • National Institutes of Health
  • Simon Fraser University
  • United States Department of Defense

Tags

Readers

  • Neural Network Machine Learning.
  • Neuroscience
  • Psychometric Testing or Psychological Assessment.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks