Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure
Alzheimer’s disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positi...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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American Journal Experts
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602128/ https://www.ncbi.nlm.nih.gov/pubmed/37886506 http://dx.doi.org/10.21203/rs.3.rs-3290598/v1 |
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author | Gebre, Robel K. Rial, Alexis Moscoso Raghavan, Sheelakumari Wiste, Heather J. Johnson Sparrman, Kohl L Heeman, Fiona Costoya-Sánchez, Alejandro Schwarz, Christopher G. Spychalla, Anthony J. Lowe, Val J. Graff-Radford, Jonathan Knopman, David S. Petersen, Ronald C. Schöll, Michael Jack, Clifford R. Vemuri, Prashanthi |
author_facet | Gebre, Robel K. Rial, Alexis Moscoso Raghavan, Sheelakumari Wiste, Heather J. Johnson Sparrman, Kohl L Heeman, Fiona Costoya-Sánchez, Alejandro Schwarz, Christopher G. Spychalla, Anthony J. Lowe, Val J. Graff-Radford, Jonathan Knopman, David S. Petersen, Ronald C. Schöll, Michael Jack, Clifford R. Vemuri, Prashanthi |
author_sort | Gebre, Robel K. |
collection | PubMed |
description | Alzheimer’s disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential. |
format | Online Article Text |
id | pubmed-10602128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-106021282023-10-27 Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure Gebre, Robel K. Rial, Alexis Moscoso Raghavan, Sheelakumari Wiste, Heather J. Johnson Sparrman, Kohl L Heeman, Fiona Costoya-Sánchez, Alejandro Schwarz, Christopher G. Spychalla, Anthony J. Lowe, Val J. Graff-Radford, Jonathan Knopman, David S. Petersen, Ronald C. Schöll, Michael Jack, Clifford R. Vemuri, Prashanthi Res Sq Article Alzheimer’s disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential. American Journal Experts 2023-10-18 /pmc/articles/PMC10602128/ /pubmed/37886506 http://dx.doi.org/10.21203/rs.3.rs-3290598/v1 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License (https://creativecommons.org/licenses/by/4.0/) https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Gebre, Robel K. Rial, Alexis Moscoso Raghavan, Sheelakumari Wiste, Heather J. Johnson Sparrman, Kohl L Heeman, Fiona Costoya-Sánchez, Alejandro Schwarz, Christopher G. Spychalla, Anthony J. Lowe, Val J. Graff-Radford, Jonathan Knopman, David S. Petersen, Ronald C. Schöll, Michael Jack, Clifford R. Vemuri, Prashanthi Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure |
title | Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure |
title_full | Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure |
title_fullStr | Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure |
title_full_unstemmed | Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure |
title_short | Advancing Tau-PET quantification in Alzheimer’s disease with machine learning: introducing THETA, a novel tau summary measure |
title_sort | advancing tau-pet quantification in alzheimer’s disease with machine learning: introducing theta, a novel tau summary measure |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602128/ https://www.ncbi.nlm.nih.gov/pubmed/37886506 http://dx.doi.org/10.21203/rs.3.rs-3290598/v1 |
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