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Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score
(18)F‐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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7268066/ https://www.ncbi.nlm.nih.gov/pubmed/31507022 http://dx.doi.org/10.1002/hbm.24783 |
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author | Yee, Evangeline Popuri, Karteek Beg, Mirza Faisal |
author_facet | Yee, Evangeline Popuri, Karteek Beg, Mirza Faisal |
author_sort | Yee, Evangeline |
collection | PubMed |
description | (18)F‐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. |
format | Online Article Text |
id | pubmed-7268066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72680662020-06-12 Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score Yee, Evangeline Popuri, Karteek Beg, Mirza Faisal Hum Brain Mapp Research Articles (18)F‐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. John Wiley & Sons, Inc. 2019-09-10 /pmc/articles/PMC7268066/ /pubmed/31507022 http://dx.doi.org/10.1002/hbm.24783 Text en © 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Yee, Evangeline Popuri, Karteek Beg, Mirza Faisal Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score |
title | Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score |
title_full | Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score |
title_fullStr | Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score |
title_full_unstemmed | Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score |
title_short | Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score |
title_sort | quantifying brain metabolism from fdg‐pet images into a probability of alzheimer's dementia score |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7268066/ https://www.ncbi.nlm.nih.gov/pubmed/31507022 http://dx.doi.org/10.1002/hbm.24783 |
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