Integrating genetic, neuropsychological and neuroimaging data to model early-onset obsessive compulsive disorder severity

We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete inform...

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Autores: Mas Herrero, Sergi, Gassó Astorga, Patricia, Morer Liñán, Astrid, Calvo, Anna, Bargalló Alabart, Núria, Lafuente, Amàlia, 1952-2022, Lázaro García, Luisa
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución: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:2445/104559
Acceso en línea:https://hdl.handle.net/2445/104559
Access Level:acceso abierto
Palabra clave:Neurosi obsessiva
Neuropsicologia
Genètica humana
Ressonància magnètica
Diagnòstic per la imatge
Farmacogenètica
Obsessive-compulsive disorder
Neuropsychology
Human genetics
Magnetic resonance
Diagnostic imaging
Pharmacogenetics
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spelling Integrating genetic, neuropsychological and neuroimaging data to model early-onset obsessive compulsive disorder severityMas Herrero, SergiGassó Astorga, PatriciaMorer Liñán, AstridCalvo, AnnaBargalló Alabart, NúriaLafuente, Amàlia, 1952-2022Lázaro García, LuisaNeurosi obsessivaNeuropsicologiaGenètica humanaRessonància magnèticaDiagnòstic per la imatgeFarmacogenèticaObsessive-compulsive disorderNeuropsychologyHuman geneticsMagnetic resonanceDiagnostic imagingPharmacogeneticsWe propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the train- ing set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our under- standing of the neurobiological basis of the disorder.Public Library of Science (PLoS)2016201620162016info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion13 p.application/pdfhttps://hdl.handle.net/2445/104559Articles publicats en revistes (Fonaments Clínics)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.0153846PLoS One, 2016, vol. 11, num. 4, p. e0153846https://doi.org/10.1371/journal.pone.0153846cc-by (c) Mas Herrero et al., 2016http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:recercat.cat:2445/1045592026-05-29T05:05:01Z
dc.title.none.fl_str_mv Integrating genetic, neuropsychological and neuroimaging data to model early-onset obsessive compulsive disorder severity
title Integrating genetic, neuropsychological and neuroimaging data to model early-onset obsessive compulsive disorder severity
spellingShingle Integrating genetic, neuropsychological and neuroimaging data to model early-onset obsessive compulsive disorder severity
Mas Herrero, Sergi
Neurosi obsessiva
Neuropsicologia
Genètica humana
Ressonància magnètica
Diagnòstic per la imatge
Farmacogenètica
Obsessive-compulsive disorder
Neuropsychology
Human genetics
Magnetic resonance
Diagnostic imaging
Pharmacogenetics
title_short Integrating genetic, neuropsychological and neuroimaging data to model early-onset obsessive compulsive disorder severity
title_full Integrating genetic, neuropsychological and neuroimaging data to model early-onset obsessive compulsive disorder severity
title_fullStr Integrating genetic, neuropsychological and neuroimaging data to model early-onset obsessive compulsive disorder severity
title_full_unstemmed Integrating genetic, neuropsychological and neuroimaging data to model early-onset obsessive compulsive disorder severity
title_sort Integrating genetic, neuropsychological and neuroimaging data to model early-onset obsessive compulsive disorder severity
dc.creator.none.fl_str_mv Mas Herrero, Sergi
Gassó Astorga, Patricia
Morer Liñán, Astrid
Calvo, Anna
Bargalló Alabart, Núria
Lafuente, Amàlia, 1952-2022
Lázaro García, Luisa
author Mas Herrero, Sergi
author_facet Mas Herrero, Sergi
Gassó Astorga, Patricia
Morer Liñán, Astrid
Calvo, Anna
Bargalló Alabart, Núria
Lafuente, Amàlia, 1952-2022
Lázaro García, Luisa
author_role author
author2 Gassó Astorga, Patricia
Morer Liñán, Astrid
Calvo, Anna
Bargalló Alabart, Núria
Lafuente, Amàlia, 1952-2022
Lázaro García, Luisa
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Neurosi obsessiva
Neuropsicologia
Genètica humana
Ressonància magnètica
Diagnòstic per la imatge
Farmacogenètica
Obsessive-compulsive disorder
Neuropsychology
Human genetics
Magnetic resonance
Diagnostic imaging
Pharmacogenetics
topic Neurosi obsessiva
Neuropsicologia
Genètica humana
Ressonància magnètica
Diagnòstic per la imatge
Farmacogenètica
Obsessive-compulsive disorder
Neuropsychology
Human genetics
Magnetic resonance
Diagnostic imaging
Pharmacogenetics
description We propose an integrative approach that combines structural magnetic resonance imaging data (MRI), diffusion tensor imaging data (DTI), neuropsychological data, and genetic data to predict early-onset obsessive compulsive disorder (OCD) severity. From a cohort of 87 patients, 56 with complete information were used in the present analysis. First, we performed a multivariate genetic association analysis of OCD severity with 266 genetic polymorphisms. This association analysis was used to select and prioritize the SNPs that would be included in the model. Second, we split the sample into a training set (N = 38) and a validation set (N = 18). Third, entropy-based measures of information gain were used for feature selection with the training subset. Fourth, the selected features were fed into two supervised methods of class prediction based on machine learning, using the leave-one-out procedure with the train- ing set. Finally, the resulting model was validated with the validation set. Nine variables were used for the creation of the OCD severity predictor, including six genetic polymorphisms and three variables from the neuropsychological data. The developed model classified child and adolescent patients with OCD by disease severity with an accuracy of 0.90 in the testing set and 0.70 in the validation sample. Above its clinical applicability, the combination of particular neuropsychological, neuroimaging, and genetic characteristics could enhance our under- standing of the neurobiological basis of the disorder.
publishDate 2016
dc.date.none.fl_str_mv 2016
2016
2016
2016
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/2445/104559
url https://hdl.handle.net/2445/104559
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.0153846
PLoS One, 2016, vol. 11, num. 4, p. e0153846
https://doi.org/10.1371/journal.pone.0153846
dc.rights.none.fl_str_mv cc-by (c) Mas Herrero et al., 2016
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Mas Herrero et al., 2016
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 13 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 (Fonaments Clínics)
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 15,81155

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