Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRI
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2022 |
| Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10451/51190 |
Resumo: | © 2021 The Authors. Human Brain Mappingpublished by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution-Non Commercial License. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRIDisease progressionFrontotemporal dementiaMagnetic resonance imagingUnsupervised machine learning© 2021 The Authors. Human Brain Mappingpublished by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution-Non Commercial License.Frontotemporal dementia in genetic forms is highly heterogeneous and begins many years to prior symptom onset, complicating disease understanding and treatment development. Unifying methods to stage the disease during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. Here we used the contrastive trajectory inference (cTI), an unsupervised machine learning algorithm that analyzes temporal patterns in high-dimensional large-scale population datasets to obtain individual scores of disease stage. We used cross-sectional MRI data (gray matter density, T1/T2 ratio as a proxy for myelin content, resting-state functional amplitude, gray matter fractional anisotropy, and mean diffusivity) from 383 gene carriers (269 presymptomatic and 115 symptomatic) and a control group of 253 noncarriers in the Genetic Frontotemporal Dementia Initiative. We compared the cTI-obtained disease scores to the estimated years to onset (age-mean age of onset in relatives), clinical, and neuropsychological test scores. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. Mean cTI scores were higher in the presymptomatic carriers than controls, indicating that the method may capture subtle pre-dementia cerebral changes, although this change was not replicated in a subset of subjects with complete data. This study provides a proof of concept that cTI can identify data-driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics.© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.WileyRepositório da Universidade de LisboaMcCarthy, JillianBorroni, BarbaraSanchez‐Valle, RaquelMoreno, FerminLaforce, RobertGraff, CarolineSynofzik, MatthisGalimberti, DanielaRowe, James B.Masellis, MarioTartaglia, Maria CarmelaFinger, ElizabethVandenberghe, RikDe Mendonça, AlexandreTagliavini, FabrizioSantana, IsabelButler, ChrisGerhard, AlexDanek, AdrianLevin, JohannesOtto, MarkusFrisoni, GiovanniGhidoni, RobertaSorbi, SandroJiskoot, Lize C.Seelaar, HarroSwieten, John C.Rohrer, Jonathan D.Iturria‐Medina, YasserDucharme, Simon2022-02-09T15:16:50Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/51190engHum Brain Mapp. 2022 Feb 31065-947110.1002/hbm.257271097-0193info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T16:55:44Zoai:repositorio.ul.pt:10451/51190Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:02:31.722521Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRI |
| title |
Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRI |
| spellingShingle |
Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRI McCarthy, Jillian Disease progression Frontotemporal dementia Magnetic resonance imaging Unsupervised machine learning |
| title_short |
Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRI |
| title_full |
Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRI |
| title_fullStr |
Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRI |
| title_full_unstemmed |
Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRI |
| title_sort |
Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRI |
| author |
McCarthy, Jillian |
| author_facet |
McCarthy, Jillian Borroni, Barbara Sanchez‐Valle, Raquel Moreno, Fermin Laforce, Robert Graff, Caroline Synofzik, Matthis Galimberti, Daniela Rowe, James B. Masellis, Mario Tartaglia, Maria Carmela Finger, Elizabeth Vandenberghe, Rik De Mendonça, Alexandre Tagliavini, Fabrizio Santana, Isabel Butler, Chris Gerhard, Alex Danek, Adrian Levin, Johannes Otto, Markus Frisoni, Giovanni Ghidoni, Roberta Sorbi, Sandro Jiskoot, Lize C. Seelaar, Harro Swieten, John C. Rohrer, Jonathan D. Iturria‐Medina, Yasser Ducharme, Simon |
| author_role |
author |
| author2 |
Borroni, Barbara Sanchez‐Valle, Raquel Moreno, Fermin Laforce, Robert Graff, Caroline Synofzik, Matthis Galimberti, Daniela Rowe, James B. Masellis, Mario Tartaglia, Maria Carmela Finger, Elizabeth Vandenberghe, Rik De Mendonça, Alexandre Tagliavini, Fabrizio Santana, Isabel Butler, Chris Gerhard, Alex Danek, Adrian Levin, Johannes Otto, Markus Frisoni, Giovanni Ghidoni, Roberta Sorbi, Sandro Jiskoot, Lize C. Seelaar, Harro Swieten, John C. Rohrer, Jonathan D. Iturria‐Medina, Yasser Ducharme, Simon |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
| dc.contributor.author.fl_str_mv |
McCarthy, Jillian Borroni, Barbara Sanchez‐Valle, Raquel Moreno, Fermin Laforce, Robert Graff, Caroline Synofzik, Matthis Galimberti, Daniela Rowe, James B. Masellis, Mario Tartaglia, Maria Carmela Finger, Elizabeth Vandenberghe, Rik De Mendonça, Alexandre Tagliavini, Fabrizio Santana, Isabel Butler, Chris Gerhard, Alex Danek, Adrian Levin, Johannes Otto, Markus Frisoni, Giovanni Ghidoni, Roberta Sorbi, Sandro Jiskoot, Lize C. Seelaar, Harro Swieten, John C. Rohrer, Jonathan D. Iturria‐Medina, Yasser Ducharme, Simon |
| dc.subject.por.fl_str_mv |
Disease progression Frontotemporal dementia Magnetic resonance imaging Unsupervised machine learning |
| topic |
Disease progression Frontotemporal dementia Magnetic resonance imaging Unsupervised machine learning |
| description |
© 2021 The Authors. Human Brain Mappingpublished by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution-Non Commercial License. |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022-02-09T15:16:50Z 2022 2022-01-01T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/51190 |
| url |
http://hdl.handle.net/10451/51190 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
Hum Brain Mapp. 2022 Feb 3 1065-9471 10.1002/hbm.25727 1097-0193 |
| dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Wiley |
| publisher.none.fl_str_mv |
Wiley |
| dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
| instacron_str |
RCAAP |
| institution |
RCAAP |
| reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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|
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1799134575058747392 |