Early prediction of Alzheimers's disese using null longitudinal model-based classifiers

Incipient Alzheimer's Disease (AD) is characterized by a slow onset of clinical symptoms, with pathological brain changes starting several years earlier. Consequently, it is necessary to first understand and differentiate age-related changes in brain regions in the absence of disease, and then...

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Autores: Gavidia Bovadilla, Giovana Elizabeth, Kannan Izquierdo, Samir, Mataró Serrat, Maria, Perera Lluna, Alexandre
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/119504
Acesso em linha:https://hdl.handle.net/2445/119504
Access Level:acceso abierto
Palavra-chave:Malaltia d'Alzheimer
Imatges per ressonància magnètica
Líquid cefalorraquidi
Tests neuropsicològics
Alzheimer's disease
Magnetic resonance imaging
Cerebrospinal fluid
Neuropsychological tests
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spelling Early prediction of Alzheimers's disese using null longitudinal model-based classifiersGavidia Bovadilla, Giovana ElizabethKannan Izquierdo, SamirMataró Serrat, MariaPerera Lluna, AlexandreMalaltia d'AlzheimerImatges per ressonància magnèticaLíquid cefalorraquidiTests neuropsicològicsAlzheimer's diseaseMagnetic resonance imagingCerebrospinal fluidNeuropsychological testsIncipient Alzheimer's Disease (AD) is characterized by a slow onset of clinical symptoms, with pathological brain changes starting several years earlier. Consequently, it is necessary to first understand and differentiate age-related changes in brain regions in the absence of disease, and then to support early and accurate AD diagnosis. However, there is poor understanding of the initial stage of AD; seemingly healthy elderly brains lose matter in regions related to AD, but similar changes can also be found in non-demented subjects having mild cognitive impairment (MCI). By using a Linear Mixed Effects approach, we modelled the change of 166 Magnetic Resonance Imaging (MRI)-based biomarkers available at a 5-year follow up on healthy elderly control (HC, n = 46) subjects. We hypothesized that, by identifying their significant variant (vr) and quasi-variant (qvr) brain regions over time, it would be possible to obtain an age-based null model, which would characterize their normal atrophy and growth patterns as well as the correlation between these two regions. By using the null model on those subjects who had been clinically diagnosed as HC (n = 161), MCI (n = 209) and AD (n = 331), normal age-related changes were estimated and deviation scores (residuals) from the observed MRI-based biomarkers were computed. Subject classification, as well as the early prediction of conversion to MCI and AD, were addressed through residual-based Support Vector Machines (SVM) modelling. We found reductions in most cortical volumes and thicknesses (with evident gender differences) as well as in sub-cortical regions, including greater atrophy in the hippocampus. The average accuracies (ACC) recorded for men and women were: AD-HC: 94.11%, MCI-HC: 83.77% and MCI converted to AD (cAD)-MCI non-converter (sMCI): 76.72%. Likewise, as compared to standard clinical diagnosis methods, SVM classifiers predicted the conversion of cAD to be 1.9 years earlier for females (ACC:72.5%) and 1.4 years earlier for males (ACC:69.0%).Public Library of Science (PLoS)2018201820172018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion19 p.application/pdfhttps://hdl.handle.net/2445/119504Articles publicats en revistes (Psicologia Clínica i Psicobiologia)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/10.1371/journal.pone.0168011PLoS One, 2017, vol. 12, num. 1, p. e0168011https://doi.org/10.1371/journal.pone.0168011cc-by (c) Gavidia Bovadilla, Giovana Elizabeth et al., 2017http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1195042026-05-29T05:05:01Z
dc.title.none.fl_str_mv Early prediction of Alzheimers's disese using null longitudinal model-based classifiers
title Early prediction of Alzheimers's disese using null longitudinal model-based classifiers
spellingShingle Early prediction of Alzheimers's disese using null longitudinal model-based classifiers
Gavidia Bovadilla, Giovana Elizabeth
Malaltia d'Alzheimer
Imatges per ressonància magnètica
Líquid cefalorraquidi
Tests neuropsicològics
Alzheimer's disease
Magnetic resonance imaging
Cerebrospinal fluid
Neuropsychological tests
title_short Early prediction of Alzheimers's disese using null longitudinal model-based classifiers
title_full Early prediction of Alzheimers's disese using null longitudinal model-based classifiers
title_fullStr Early prediction of Alzheimers's disese using null longitudinal model-based classifiers
title_full_unstemmed Early prediction of Alzheimers's disese using null longitudinal model-based classifiers
title_sort Early prediction of Alzheimers's disese using null longitudinal model-based classifiers
dc.creator.none.fl_str_mv Gavidia Bovadilla, Giovana Elizabeth
Kannan Izquierdo, Samir
Mataró Serrat, Maria
Perera Lluna, Alexandre
author Gavidia Bovadilla, Giovana Elizabeth
author_facet Gavidia Bovadilla, Giovana Elizabeth
Kannan Izquierdo, Samir
Mataró Serrat, Maria
Perera Lluna, Alexandre
author_role author
author2 Kannan Izquierdo, Samir
Mataró Serrat, Maria
Perera Lluna, Alexandre
author2_role author
author
author
dc.subject.none.fl_str_mv Malaltia d'Alzheimer
Imatges per ressonància magnètica
Líquid cefalorraquidi
Tests neuropsicològics
Alzheimer's disease
Magnetic resonance imaging
Cerebrospinal fluid
Neuropsychological tests
topic Malaltia d'Alzheimer
Imatges per ressonància magnètica
Líquid cefalorraquidi
Tests neuropsicològics
Alzheimer's disease
Magnetic resonance imaging
Cerebrospinal fluid
Neuropsychological tests
description Incipient Alzheimer's Disease (AD) is characterized by a slow onset of clinical symptoms, with pathological brain changes starting several years earlier. Consequently, it is necessary to first understand and differentiate age-related changes in brain regions in the absence of disease, and then to support early and accurate AD diagnosis. However, there is poor understanding of the initial stage of AD; seemingly healthy elderly brains lose matter in regions related to AD, but similar changes can also be found in non-demented subjects having mild cognitive impairment (MCI). By using a Linear Mixed Effects approach, we modelled the change of 166 Magnetic Resonance Imaging (MRI)-based biomarkers available at a 5-year follow up on healthy elderly control (HC, n = 46) subjects. We hypothesized that, by identifying their significant variant (vr) and quasi-variant (qvr) brain regions over time, it would be possible to obtain an age-based null model, which would characterize their normal atrophy and growth patterns as well as the correlation between these two regions. By using the null model on those subjects who had been clinically diagnosed as HC (n = 161), MCI (n = 209) and AD (n = 331), normal age-related changes were estimated and deviation scores (residuals) from the observed MRI-based biomarkers were computed. Subject classification, as well as the early prediction of conversion to MCI and AD, were addressed through residual-based Support Vector Machines (SVM) modelling. We found reductions in most cortical volumes and thicknesses (with evident gender differences) as well as in sub-cortical regions, including greater atrophy in the hippocampus. The average accuracies (ACC) recorded for men and women were: AD-HC: 94.11%, MCI-HC: 83.77% and MCI converted to AD (cAD)-MCI non-converter (sMCI): 76.72%. Likewise, as compared to standard clinical diagnosis methods, SVM classifiers predicted the conversion of cAD to be 1.9 years earlier for females (ACC:72.5%) and 1.4 years earlier for males (ACC:69.0%).
publishDate 2017
dc.date.none.fl_str_mv 2017
2018
2018
2018
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/2445/119504
url https://hdl.handle.net/2445/119504
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/10.1371/journal.pone.0168011
PLoS One, 2017, vol. 12, num. 1, p. e0168011
https://doi.org/10.1371/journal.pone.0168011
dc.rights.none.fl_str_mv cc-by (c) Gavidia Bovadilla, Giovana Elizabeth et al., 2017
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Gavidia Bovadilla, Giovana Elizabeth et al., 2017
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 19 p.
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv Articles publicats en revistes (Psicologia Clínica i Psicobiologia)
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 15.812455

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