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Computational modeling of tau pathology spread reveals patterns of regional vulnerability and the impact of a genetic risk factor
Neuropathological staging studies have suggested that tau pathology spreads through the brain in Alzheimer’s disease (AD) and other tauopathies, but it is unclear how neuroanatomical connections, spatial proximity, and regional vulnerability contribute. In this study, we seed tau pathology in the br...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189700/ https://www.ncbi.nlm.nih.gov/pubmed/34108219 http://dx.doi.org/10.1126/sciadv.abg6677 |
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author | Cornblath, Eli J. Li, Howard L. Changolkar, Lakshmi Zhang, Bin Brown, Hannah J. Gathagan, Ronald J. Olufemi, Modupe F. Trojanowski, John Q. Bassett, Danielle S. Lee, Virginia M. Y. Henderson, Michael X. |
author_facet | Cornblath, Eli J. Li, Howard L. Changolkar, Lakshmi Zhang, Bin Brown, Hannah J. Gathagan, Ronald J. Olufemi, Modupe F. Trojanowski, John Q. Bassett, Danielle S. Lee, Virginia M. Y. Henderson, Michael X. |
author_sort | Cornblath, Eli J. |
collection | PubMed |
description | Neuropathological staging studies have suggested that tau pathology spreads through the brain in Alzheimer’s disease (AD) and other tauopathies, but it is unclear how neuroanatomical connections, spatial proximity, and regional vulnerability contribute. In this study, we seed tau pathology in the brains of nontransgenic mice with AD tau and quantify pathology development over 9 months in 134 brain regions. Network modeling of pathology progression shows that diffusion through the connectome is the best predictor of tau pathology patterns. Further, deviations from pure neuroanatomical spread are used to estimate regional vulnerability to tau pathology and identify related gene expression patterns. Last, we show that pathology spread is altered in mice harboring a mutation in leucine-rich repeat kinase 2. While tau pathology spread is still constrained by anatomical connectivity in these mice, it spreads preferentially in a retrograde direction. This study provides a framework for understanding neuropathological progression in tauopathies. |
format | Online Article Text |
id | pubmed-8189700 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81897002021-06-22 Computational modeling of tau pathology spread reveals patterns of regional vulnerability and the impact of a genetic risk factor Cornblath, Eli J. Li, Howard L. Changolkar, Lakshmi Zhang, Bin Brown, Hannah J. Gathagan, Ronald J. Olufemi, Modupe F. Trojanowski, John Q. Bassett, Danielle S. Lee, Virginia M. Y. Henderson, Michael X. Sci Adv Research Articles Neuropathological staging studies have suggested that tau pathology spreads through the brain in Alzheimer’s disease (AD) and other tauopathies, but it is unclear how neuroanatomical connections, spatial proximity, and regional vulnerability contribute. In this study, we seed tau pathology in the brains of nontransgenic mice with AD tau and quantify pathology development over 9 months in 134 brain regions. Network modeling of pathology progression shows that diffusion through the connectome is the best predictor of tau pathology patterns. Further, deviations from pure neuroanatomical spread are used to estimate regional vulnerability to tau pathology and identify related gene expression patterns. Last, we show that pathology spread is altered in mice harboring a mutation in leucine-rich repeat kinase 2. While tau pathology spread is still constrained by anatomical connectivity in these mice, it spreads preferentially in a retrograde direction. This study provides a framework for understanding neuropathological progression in tauopathies. American Association for the Advancement of Science 2021-06-09 /pmc/articles/PMC8189700/ /pubmed/34108219 http://dx.doi.org/10.1126/sciadv.abg6677 Text en Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Research Articles Cornblath, Eli J. Li, Howard L. Changolkar, Lakshmi Zhang, Bin Brown, Hannah J. Gathagan, Ronald J. Olufemi, Modupe F. Trojanowski, John Q. Bassett, Danielle S. Lee, Virginia M. Y. Henderson, Michael X. Computational modeling of tau pathology spread reveals patterns of regional vulnerability and the impact of a genetic risk factor |
title | Computational modeling of tau pathology spread reveals patterns of regional vulnerability and the impact of a genetic risk factor |
title_full | Computational modeling of tau pathology spread reveals patterns of regional vulnerability and the impact of a genetic risk factor |
title_fullStr | Computational modeling of tau pathology spread reveals patterns of regional vulnerability and the impact of a genetic risk factor |
title_full_unstemmed | Computational modeling of tau pathology spread reveals patterns of regional vulnerability and the impact of a genetic risk factor |
title_short | Computational modeling of tau pathology spread reveals patterns of regional vulnerability and the impact of a genetic risk factor |
title_sort | computational modeling of tau pathology spread reveals patterns of regional vulnerability and the impact of a genetic risk factor |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8189700/ https://www.ncbi.nlm.nih.gov/pubmed/34108219 http://dx.doi.org/10.1126/sciadv.abg6677 |
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