Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography

Recent studies combining difusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson's disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tract...

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Autores: Abós, Alexandra, Baggio, Hugo César, Segura i Fàbregas, Bàrbara, Campabadal Delgado, Anna, Uribe, Carme, Giraldo, Darly M., Pérez Soriano, Alexandra, Muñoz, Esteban, Compta, Yaroslau, Junqué i Plaja, Carme, 1955-, Martí Domènech, Ma. Josep
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/161243
Acceso en línea:https://hdl.handle.net/2445/161243
Access Level:acceso abierto
Palabra clave:Malalties neurodegeneratives
Imatges per ressonància magnètica
Xarxes neuronals (Neurobiologia)
Malaltia de Parkinson
Neurodegenerative Diseases
Magnetic resonance imaging
Neural networks (Neurobiology)
Parkinson's disease
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repository_id_str
spelling Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractographyAbós, AlexandraBaggio, Hugo CésarSegura i Fàbregas, BàrbaraCampabadal Delgado, AnnaUribe, CarmeGiraldo, Darly M.Pérez Soriano, AlexandraMuñoz, EstebanCompta, YaroslauJunqué i Plaja, Carme, 1955-Martí Domènech, Ma. JosepMalalties neurodegenerativesImatges per ressonància magnèticaXarxes neuronals (Neurobiologia)Malaltia de ParkinsonNeurodegenerative DiseasesMagnetic resonance imagingNeural networks (Neurobiology)Parkinson's diseaseRecent studies combining difusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson's disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classifcation performance of subcortical FA and MD was also evaluated to compare the discriminant ability between difusion tensor-derived metrics and NOS. Using difusion-weighted images acquired in a 3T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classifcation procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classifed. NOS features outperformed the discrimination performance obtained with FA and MD. Our fndings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than difusion tensor-derived metrics for the detection of MSA.Nature Publishing Group2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/161243Articles publicats en revistes (Medicina)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.1038/s41598-019-52829-8Scientific Reports, 2019, vol. 9, num. 1, p. 16488https://doi.org/10.1038/s41598-019-52829-8cc-by (c) Abós, Alexandra et al., 2019http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1612432026-05-27T06:46:51Z
dc.title.none.fl_str_mv Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography
title Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography
spellingShingle Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography
Abós, Alexandra
Malalties neurodegeneratives
Imatges per ressonància magnètica
Xarxes neuronals (Neurobiologia)
Malaltia de Parkinson
Neurodegenerative Diseases
Magnetic resonance imaging
Neural networks (Neurobiology)
Parkinson's disease
title_short Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography
title_full Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography
title_fullStr Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography
title_full_unstemmed Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography
title_sort Differentiation of multiple system atrophy from Parkinson's disease by structural connectivity derived from probabilistic tractography
dc.creator.none.fl_str_mv Abós, Alexandra
Baggio, Hugo César
Segura i Fàbregas, Bàrbara
Campabadal Delgado, Anna
Uribe, Carme
Giraldo, Darly M.
Pérez Soriano, Alexandra
Muñoz, Esteban
Compta, Yaroslau
Junqué i Plaja, Carme, 1955-
Martí Domènech, Ma. Josep
author Abós, Alexandra
author_facet Abós, Alexandra
Baggio, Hugo César
Segura i Fàbregas, Bàrbara
Campabadal Delgado, Anna
Uribe, Carme
Giraldo, Darly M.
Pérez Soriano, Alexandra
Muñoz, Esteban
Compta, Yaroslau
Junqué i Plaja, Carme, 1955-
Martí Domènech, Ma. Josep
author_role author
author2 Baggio, Hugo César
Segura i Fàbregas, Bàrbara
Campabadal Delgado, Anna
Uribe, Carme
Giraldo, Darly M.
Pérez Soriano, Alexandra
Muñoz, Esteban
Compta, Yaroslau
Junqué i Plaja, Carme, 1955-
Martí Domènech, Ma. Josep
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Malalties neurodegeneratives
Imatges per ressonància magnètica
Xarxes neuronals (Neurobiologia)
Malaltia de Parkinson
Neurodegenerative Diseases
Magnetic resonance imaging
Neural networks (Neurobiology)
Parkinson's disease
topic Malalties neurodegeneratives
Imatges per ressonància magnètica
Xarxes neuronals (Neurobiologia)
Malaltia de Parkinson
Neurodegenerative Diseases
Magnetic resonance imaging
Neural networks (Neurobiology)
Parkinson's disease
description Recent studies combining difusion tensor-derived metrics and machine learning have shown promising results in the discrimination of multiple system atrophy (MSA) and Parkinson's disease (PD) patients. This approach has not been tested using more complex methodologies such as probabilistic tractography. The aim of this work is assessing whether the strength of structural connectivity between subcortical structures, measured as the number of streamlines (NOS) derived from tractography, can be used to classify MSA and PD patients at the single-patient level. The classifcation performance of subcortical FA and MD was also evaluated to compare the discriminant ability between difusion tensor-derived metrics and NOS. Using difusion-weighted images acquired in a 3T MRI scanner and probabilistic tractography, we reconstructed the white matter tracts between 18 subcortical structures from a sample of 54 healthy controls, 31 MSA patients and 65 PD patients. NOS between subcortical structures were compared between groups and entered as features into a machine learning algorithm. Reduced NOS in MSA compared with controls and PD were found in connections between the putamen, pallidum, ventral diencephalon, thalamus, and cerebellum, in both right and left hemispheres. The classifcation procedure achieved an overall accuracy of 78%, with 71% of the MSA subjects and 86% of the PD patients correctly classifed. NOS features outperformed the discrimination performance obtained with FA and MD. Our fndings suggest that structural connectivity derived from tractography has the potential to correctly distinguish between MSA and PD patients. Furthermore, NOS measures obtained from tractography might be more useful than difusion tensor-derived metrics for the detection of MSA.
publishDate 2019
dc.date.none.fl_str_mv 2019
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/2445/161243
url https://hdl.handle.net/2445/161243
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/10.1038/s41598-019-52829-8
Scientific Reports, 2019, vol. 9, num. 1, p. 16488
https://doi.org/10.1038/s41598-019-52829-8
dc.rights.none.fl_str_mv cc-by (c) Abós, Alexandra et al., 2019
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Abós, Alexandra et al., 2019
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv Articles publicats en revistes (Medicina)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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