Predicting Aging of Brain Metabolic Topography Using Variational Autoencoder

Predicting future brain topography can give insight into neural correlates of aging and neurodegeneration. Due to variability in the aging process, it has been challenging to precisely estimate brain topographical change according to aging. Here, we predict age-related brain metabolic change by gene...

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Vydané v:Frontiers in aging neuroscience Ročník 10; s. 212
Hlavní autori: Choi, Hongyoon, Kang, Hyejin, Lee, Dong Soo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland Frontiers Research Foundation 12.07.2018
Frontiers Media S.A
Predmet:
Age
Aging
Alzheimer's disease
Amygdala
APOE4
Apolipoprotein E4
Brain architecture
brain metabolism
Cognitive ability
deep generative model
FDG PET
Geriatrics
Medical imaging
Metabolism
Neurodegeneration
Neuroscience
Population
Population distribution
Positron emission tomography
Prediction models
Teaching methods
Topography
variational autoencoder
South Korea
brain metabolism
variational autoencoder
APOE4
FDG PET
deep generative model
ISSN:1663-4365, 1663-4365
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Shrnutí:Predicting future brain topography can give insight into neural correlates of aging and neurodegeneration. Due to variability in the aging process, it has been challenging to precisely estimate brain topographical change according to aging. Here, we predict age-related brain metabolic change by generating future brain F-Fluorodeoxyglucose PET. A cross-sectional PET dataset of cognitively normal subjects with different age was used to develop a generative model. The model generated PET images using age information and characteristic individual features. Predicted regional metabolic changes were correlated with the real changes obtained by follow-up data. This model was applied to produce a brain metabolism aging movie by generating PET at different ages. Normal population distribution of brain metabolic topography at each age was estimated as well. In addition, a generative model using APOE4 status as well as age as inputs revealed a significant effect of APOE4 status on age-related metabolic changes particularly in the calcarine, lingual cortex, hippocampus, and amygdala. It suggested APOE4 could be a factor affecting individual variability in age-related metabolic degeneration in normal elderly. This predictive model may not only be extended to understanding the cognitive aging process, but apply to the development of a preclinical biomarker for various brain disorders.
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Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
Reviewed by: Tonio Ball, Translational Neurotechnologie Labor, Albert-Ludwigs-Universität Freiburg, Germany; Nicola Amoroso, Università degli studi di Bari Aldo Moro, Italy
Edited by: Javier Ramírez, Universidad de Granada, Spain
ISSN:1663-4365
1663-4365
DOI:10.3389/fnagi.2018.00212