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Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET

PURPOSE: Alzheimer’s disease (AD) studies revealed that abnormal deposition of tau spreads in a specific spatial pattern, namely Braak stage. However, Braak staging is based on post mortem brains, each of which represents the cross section of the tau trajectory in disease progression, and numerous s...

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Autores principales: Hong, Jimin, Kang, Seung Kwan, Alberts, Ian, Lu, Jiaying, Sznitman, Raphael, Lee, Jae Sung, Rominger, Axel, Choi, Hongyoon, Shi, Kuangyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250490/
https://www.ncbi.nlm.nih.gov/pubmed/35226120
http://dx.doi.org/10.1007/s00259-021-05662-z
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author Hong, Jimin
Kang, Seung Kwan
Alberts, Ian
Lu, Jiaying
Sznitman, Raphael
Lee, Jae Sung
Rominger, Axel
Choi, Hongyoon
Shi, Kuangyu
author_facet Hong, Jimin
Kang, Seung Kwan
Alberts, Ian
Lu, Jiaying
Sznitman, Raphael
Lee, Jae Sung
Rominger, Axel
Choi, Hongyoon
Shi, Kuangyu
author_sort Hong, Jimin
collection PubMed
description PURPOSE: Alzheimer’s disease (AD) studies revealed that abnormal deposition of tau spreads in a specific spatial pattern, namely Braak stage. However, Braak staging is based on post mortem brains, each of which represents the cross section of the tau trajectory in disease progression, and numerous studies were reported that do not conform to that model. This study thus aimed to identify the tau trajectory and quantify the tau progression in a data-driven approach with the continuous latent space learned by variational autoencoder (VAE). METHODS: A total of 1080 [(18)F]Flortaucipir brain positron emission tomography (PET) images were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. VAE was built to compress the hidden features from tau images in latent space. Hierarchical agglomerative clustering and minimum spanning tree (MST) were applied to organize the features and calibrate them to the tau progression, thus deriving pseudo-time. The image-level tau trajectory was inferred by continuously sampling across the calibrated latent features. We assessed the pseudo-time with regard to tau standardized uptake value ratio (SUVr) in AD-vulnerable regions, amyloid deposit, glucose metabolism, cognitive scores, and clinical diagnosis. RESULTS: We identified four clusters that plausibly capture certain stages of AD and organized the clusters in the latent space. The inferred tau trajectory agreed with the Braak staging. According to the derived pseudo-time, tau first deposits in the parahippocampal and amygdala, and then spreads to the fusiform, inferior temporal lobe, and posterior cingulate. Prior to the regional tau deposition, amyloid accumulates first. CONCLUSION: The spatiotemporal trajectory of tau progression inferred in this study was consistent with Braak staging. The profile of other biomarkers in disease progression agreed well with previous findings. We addressed that this approach additionally has the potential to quantify tau progression as a continuous variable by taking a whole-brain tau image into account. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-021-05662-z.
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spelling pubmed-92504902022-07-04 Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET Hong, Jimin Kang, Seung Kwan Alberts, Ian Lu, Jiaying Sznitman, Raphael Lee, Jae Sung Rominger, Axel Choi, Hongyoon Shi, Kuangyu Eur J Nucl Med Mol Imaging Original Article PURPOSE: Alzheimer’s disease (AD) studies revealed that abnormal deposition of tau spreads in a specific spatial pattern, namely Braak stage. However, Braak staging is based on post mortem brains, each of which represents the cross section of the tau trajectory in disease progression, and numerous studies were reported that do not conform to that model. This study thus aimed to identify the tau trajectory and quantify the tau progression in a data-driven approach with the continuous latent space learned by variational autoencoder (VAE). METHODS: A total of 1080 [(18)F]Flortaucipir brain positron emission tomography (PET) images were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. VAE was built to compress the hidden features from tau images in latent space. Hierarchical agglomerative clustering and minimum spanning tree (MST) were applied to organize the features and calibrate them to the tau progression, thus deriving pseudo-time. The image-level tau trajectory was inferred by continuously sampling across the calibrated latent features. We assessed the pseudo-time with regard to tau standardized uptake value ratio (SUVr) in AD-vulnerable regions, amyloid deposit, glucose metabolism, cognitive scores, and clinical diagnosis. RESULTS: We identified four clusters that plausibly capture certain stages of AD and organized the clusters in the latent space. The inferred tau trajectory agreed with the Braak staging. According to the derived pseudo-time, tau first deposits in the parahippocampal and amygdala, and then spreads to the fusiform, inferior temporal lobe, and posterior cingulate. Prior to the regional tau deposition, amyloid accumulates first. CONCLUSION: The spatiotemporal trajectory of tau progression inferred in this study was consistent with Braak staging. The profile of other biomarkers in disease progression agreed well with previous findings. We addressed that this approach additionally has the potential to quantify tau progression as a continuous variable by taking a whole-brain tau image into account. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00259-021-05662-z. Springer Berlin Heidelberg 2022-02-28 2022 /pmc/articles/PMC9250490/ /pubmed/35226120 http://dx.doi.org/10.1007/s00259-021-05662-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Hong, Jimin
Kang, Seung Kwan
Alberts, Ian
Lu, Jiaying
Sznitman, Raphael
Lee, Jae Sung
Rominger, Axel
Choi, Hongyoon
Shi, Kuangyu
Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET
title Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET
title_full Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET
title_fullStr Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET
title_full_unstemmed Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET
title_short Image-level trajectory inference of tau pathology using variational autoencoder for Flortaucipir PET
title_sort image-level trajectory inference of tau pathology using variational autoencoder for flortaucipir pet
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250490/
https://www.ncbi.nlm.nih.gov/pubmed/35226120
http://dx.doi.org/10.1007/s00259-021-05662-z
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