Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data

Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learnin...

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Authors: Pérez Millan, Agnès, Contador Muñana, José Miguel, Juncà Parella, J., Bosch, B., Borrell, L., Tort Merino, Adrià, Falgàs, N., Borrego Écija, Sergi, Bargalló Alabart, Núria, Rami González, Lorena, Balasa, M., Lladó Plarrumaní, Albert, Sánchez Valle, Raquel, Sala Llonch, Roser
Format: article
Status:Published version
Publication Date:2023
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/207531
Online Access:https://hdl.handle.net/2445/207531
Access Level:Open access
Keyword:Malaltia d'Alzheimer
Aprenentatge automàtic
Alzheimer's disease
Machine learning
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spelling Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging dataPérez Millan, AgnèsContador Muñana, José MiguelJuncà Parella, J.Bosch, B.Borrell, L.Tort Merino, AdriàFalgàs, N.Borrego Écija, SergiBargalló Alabart, NúriaRami González, LorenaBalasa, M.Lladó Plarrumaní, AlbertSánchez Valle, RaquelSala Llonch, RoserMalaltia d'AlzheimerAprenentatge automàticAlzheimer's diseaseMachine learningAlzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.John Wiley and Sons Inc2024202420232024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion11 p.application/pdfhttps://hdl.handle.net/2445/207531Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1002/hbm.26205Human Brain Mapping, 2023, vol. 44, num. 6, p. 2234-2244https://doi.org/10.1002/hbm.26205cc by-nc (c) Pérez Millan, A. et al., 2023http://creativecommons.org/licenses/by-nc/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2075312026-05-29T05:05:01Z
dc.title.none.fl_str_mv Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data
title Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data
spellingShingle Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data
Pérez Millan, Agnès
Malaltia d'Alzheimer
Aprenentatge automàtic
Alzheimer's disease
Machine learning
title_short Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data
title_full Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data
title_fullStr Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data
title_full_unstemmed Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data
title_sort Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data
dc.creator.none.fl_str_mv Pérez Millan, Agnès
Contador Muñana, José Miguel
Juncà Parella, J.
Bosch, B.
Borrell, L.
Tort Merino, Adrià
Falgàs, N.
Borrego Écija, Sergi
Bargalló Alabart, Núria
Rami González, Lorena
Balasa, M.
Lladó Plarrumaní, Albert
Sánchez Valle, Raquel
Sala Llonch, Roser
author Pérez Millan, Agnès
author_facet Pérez Millan, Agnès
Contador Muñana, José Miguel
Juncà Parella, J.
Bosch, B.
Borrell, L.
Tort Merino, Adrià
Falgàs, N.
Borrego Écija, Sergi
Bargalló Alabart, Núria
Rami González, Lorena
Balasa, M.
Lladó Plarrumaní, Albert
Sánchez Valle, Raquel
Sala Llonch, Roser
author_role author
author2 Contador Muñana, José Miguel
Juncà Parella, J.
Bosch, B.
Borrell, L.
Tort Merino, Adrià
Falgàs, N.
Borrego Écija, Sergi
Bargalló Alabart, Núria
Rami González, Lorena
Balasa, M.
Lladó Plarrumaní, Albert
Sánchez Valle, Raquel
Sala Llonch, Roser
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Malaltia d'Alzheimer
Aprenentatge automàtic
Alzheimer's disease
Machine learning
topic Malaltia d'Alzheimer
Aprenentatge automàtic
Alzheimer's disease
Machine learning
description Alzheimer's disease (AD) and frontotemporal dementia (FTD) are common causes of dementia with partly overlapping, symptoms and brain signatures. There is a need to establish an accurate diagnosis and to obtain markers for disease tracking. We combined unsupervised and supervised machine learning to discriminate between AD and FTD using brain magnetic resonance imaging (MRI). We included baseline 3T-T1 MRI data from 339 subjects: 99 healthy controls (CTR), 153 AD and 87 FTD patients; and 2-year follow-up data from 114 subjects. We obtained subcortical gray matter volumes and cortical thickness measures using FreeSurfer. We used dimensionality reduction to obtain a single feature that was later used in a support vector machine for classification. Discrimination patterns were obtained with the contribution of each region to the single feature. Our algorithm differentiated CTR versus AD and CTR versus FTD at the cross-sectional level with 83.3% and 82.1% of accuracy. These increased up to 90.0% and 88.0% with longitudinal data. When we studied the classification between AD versus FTD we obtained an accuracy of 63.3% at the cross-sectional level and 75.0% for longitudinal data. The AD versus FTD versus CTR classification has reached an accuracy of 60.7%, and 71.3% for cross-sectional and longitudinal data respectively. Disease discrimination brain maps are in concordance with previous results obtained with classical approaches. By using a single feature, we were capable to classify CTR, AD, and FTD with good accuracy, considering the inherent overlap between diseases. Importantly, the algorithm can be used with cross-sectional and longitudinal data.© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/207531
url https://hdl.handle.net/2445/207531
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.1002/hbm.26205
Human Brain Mapping, 2023, vol. 44, num. 6, p. 2234-2244
https://doi.org/10.1002/hbm.26205
dc.rights.none.fl_str_mv cc by-nc (c) Pérez Millan, A. et al., 2023
http://creativecommons.org/licenses/by-nc/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc by-nc (c) Pérez Millan, A. et al., 2023
http://creativecommons.org/licenses/by-nc/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 11 p.
application/pdf
dc.publisher.none.fl_str_mv John Wiley and Sons Inc
publisher.none.fl_str_mv John Wiley and Sons Inc
dc.source.none.fl_str_mv Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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