Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes

Autores
Ibanez Barassi, Agustin Mariano; Fittipaldi, Sol; Trujillo, Catalina; Jaramillo, Tania; Torres, Alejandra; Cardona, Juan F.; Rivera, Rodrigo; Slachevsky, Andrea; Garciá, Adolfo; Bertoux, Maxime; Baez, Sandra
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Background: Social cognition is critically compromised across neurodegenerative diseases, including the behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and Parkinson's disease (PD). However, no previous study has used social cognition and other cognitive tasks to predict diagnoses of these conditions, let alone reporting the brain correlates of prediction outcomes. Objective: We performed a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors. Methods: Multiple group discriminant function analyses (MDAs) and ROC analyses of social cognition (facial emotional recognition, theory of mind), CS, and EF were implemented in 223 participants (bvFTD, AD, PD, controls). Gray matter volume and functional connectivity correlates of top discriminant scores were investigated. Results: Although all patient groups revealed deficits in social cognition, CS, and EF, our classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3%, social cognition), AD versus PD (98.6%, social cognition+CS), and bvFTD versus AD (71.7%, social cognition+CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions. Conclusion: Standardized validated measures of social cognition, in combination with CS, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses.
Fil: Ibanez Barassi, Agustin Mariano. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; Chile. Universidad de Dublin; Irlanda. University of California; Estados Unidos
Fil: Fittipaldi, Sol. Universidad Nacional de Córdoba; Argentina. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Trujillo, Catalina. Universidad del Valle; Colombia
Fil: Jaramillo, Tania. Universidad del Valle; Colombia
Fil: Torres, Alejandra. Universidad del Valle; Colombia
Fil: Cardona, Juan F.. Universidad del Valle; Colombia
Fil: Rivera, Rodrigo. Universidad de Chile; Chile
Fil: Slachevsky, Andrea. Universidad de Chile; Chile
Fil: Garciá, Adolfo. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Santiago de Chile; Chile. University of California; Estados Unidos
Fil: Bertoux, Maxime. Universidad Adolfo Ibañez; Chile
Fil: Baez, Sandra. Universidad de los Andes; Colombia
Materia
CLASSIFICATION
DEMENTIA
DIAGNOSIS
NEURODEGENERATIVE DISEASES
SOCIAL COGNITION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/166974
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/166974
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive ProcessesIbanez Barassi, Agustin MarianoFittipaldi, SolTrujillo, CatalinaJaramillo, TaniaTorres, AlejandraCardona, Juan F.Rivera, RodrigoSlachevsky, AndreaGarciá, AdolfoBertoux, MaximeBaez, SandraCLASSIFICATIONDEMENTIADIAGNOSISNEURODEGENERATIVE DISEASESSOCIAL COGNITIONhttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3Background: Social cognition is critically compromised across neurodegenerative diseases, including the behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and Parkinson's disease (PD). However, no previous study has used social cognition and other cognitive tasks to predict diagnoses of these conditions, let alone reporting the brain correlates of prediction outcomes. Objective: We performed a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors. Methods: Multiple group discriminant function analyses (MDAs) and ROC analyses of social cognition (facial emotional recognition, theory of mind), CS, and EF were implemented in 223 participants (bvFTD, AD, PD, controls). Gray matter volume and functional connectivity correlates of top discriminant scores were investigated. Results: Although all patient groups revealed deficits in social cognition, CS, and EF, our classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3%, social cognition), AD versus PD (98.6%, social cognition+CS), and bvFTD versus AD (71.7%, social cognition+CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions. Conclusion: Standardized validated measures of social cognition, in combination with CS, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses.Fil: Ibanez Barassi, Agustin Mariano. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; Chile. Universidad de Dublin; Irlanda. University of California; Estados UnidosFil: Fittipaldi, Sol. Universidad Nacional de Córdoba; Argentina. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Trujillo, Catalina. Universidad del Valle; ColombiaFil: Jaramillo, Tania. Universidad del Valle; ColombiaFil: Torres, Alejandra. Universidad del Valle; ColombiaFil: Cardona, Juan F.. Universidad del Valle; ColombiaFil: Rivera, Rodrigo. Universidad de Chile; ChileFil: Slachevsky, Andrea. Universidad de Chile; ChileFil: Garciá, Adolfo. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Santiago de Chile; Chile. University of California; Estados UnidosFil: Bertoux, Maxime. Universidad Adolfo Ibañez; ChileFil: Baez, Sandra. Universidad de los Andes; ColombiaIOS Press2021-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/166974Ibanez Barassi, Agustin Mariano; Fittipaldi, Sol; Trujillo, Catalina; Jaramillo, Tania; Torres, Alejandra; et al.; Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes; IOS Press; Journal of Alzheimer's Disease; 83; 1; 8-2021; 227-2481387-2877CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3233/JAD-210163info:eu-repo/semantics/altIdentifier/url/https://content.iospress.com/articles/journal-of-alzheimers-disease/jad210163info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:56:54Zoai:ri.conicet.gov.ar:11336/166974instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:56:54.839CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes
title Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes
spellingShingle Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes
Ibanez Barassi, Agustin Mariano
CLASSIFICATION
DEMENTIA
DIAGNOSIS
NEURODEGENERATIVE DISEASES
SOCIAL COGNITION
title_short Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes
title_full Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes
title_fullStr Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes
title_full_unstemmed Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes
title_sort Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes
dc.creator.none.fl_str_mv Ibanez Barassi, Agustin Mariano
Fittipaldi, Sol
Trujillo, Catalina
Jaramillo, Tania
Torres, Alejandra
Cardona, Juan F.
Rivera, Rodrigo
Slachevsky, Andrea
Garciá, Adolfo
Bertoux, Maxime
Baez, Sandra
author Ibanez Barassi, Agustin Mariano
author_facet Ibanez Barassi, Agustin Mariano
Fittipaldi, Sol
Trujillo, Catalina
Jaramillo, Tania
Torres, Alejandra
Cardona, Juan F.
Rivera, Rodrigo
Slachevsky, Andrea
Garciá, Adolfo
Bertoux, Maxime
Baez, Sandra
author_role author
author2 Fittipaldi, Sol
Trujillo, Catalina
Jaramillo, Tania
Torres, Alejandra
Cardona, Juan F.
Rivera, Rodrigo
Slachevsky, Andrea
Garciá, Adolfo
Bertoux, Maxime
Baez, Sandra
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv CLASSIFICATION
DEMENTIA
DIAGNOSIS
NEURODEGENERATIVE DISEASES
SOCIAL COGNITION
topic CLASSIFICATION
DEMENTIA
DIAGNOSIS
NEURODEGENERATIVE DISEASES
SOCIAL COGNITION
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Background: Social cognition is critically compromised across neurodegenerative diseases, including the behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and Parkinson's disease (PD). However, no previous study has used social cognition and other cognitive tasks to predict diagnoses of these conditions, let alone reporting the brain correlates of prediction outcomes. Objective: We performed a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors. Methods: Multiple group discriminant function analyses (MDAs) and ROC analyses of social cognition (facial emotional recognition, theory of mind), CS, and EF were implemented in 223 participants (bvFTD, AD, PD, controls). Gray matter volume and functional connectivity correlates of top discriminant scores were investigated. Results: Although all patient groups revealed deficits in social cognition, CS, and EF, our classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3%, social cognition), AD versus PD (98.6%, social cognition+CS), and bvFTD versus AD (71.7%, social cognition+CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions. Conclusion: Standardized validated measures of social cognition, in combination with CS, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses.
Fil: Ibanez Barassi, Agustin Mariano. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Adolfo Ibañez; Chile. Universidad de Dublin; Irlanda. University of California; Estados Unidos
Fil: Fittipaldi, Sol. Universidad Nacional de Córdoba; Argentina. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Trujillo, Catalina. Universidad del Valle; Colombia
Fil: Jaramillo, Tania. Universidad del Valle; Colombia
Fil: Torres, Alejandra. Universidad del Valle; Colombia
Fil: Cardona, Juan F.. Universidad del Valle; Colombia
Fil: Rivera, Rodrigo. Universidad de Chile; Chile
Fil: Slachevsky, Andrea. Universidad de Chile; Chile
Fil: Garciá, Adolfo. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Santiago de Chile; Chile. University of California; Estados Unidos
Fil: Bertoux, Maxime. Universidad Adolfo Ibañez; Chile
Fil: Baez, Sandra. Universidad de los Andes; Colombia
description Background: Social cognition is critically compromised across neurodegenerative diseases, including the behavioral variant frontotemporal dementia (bvFTD), Alzheimer's disease (AD), and Parkinson's disease (PD). However, no previous study has used social cognition and other cognitive tasks to predict diagnoses of these conditions, let alone reporting the brain correlates of prediction outcomes. Objective: We performed a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors. Methods: Multiple group discriminant function analyses (MDAs) and ROC analyses of social cognition (facial emotional recognition, theory of mind), CS, and EF were implemented in 223 participants (bvFTD, AD, PD, controls). Gray matter volume and functional connectivity correlates of top discriminant scores were investigated. Results: Although all patient groups revealed deficits in social cognition, CS, and EF, our classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3%, social cognition), AD versus PD (98.6%, social cognition+CS), and bvFTD versus AD (71.7%, social cognition+CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions. Conclusion: Standardized validated measures of social cognition, in combination with CS, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses.
publishDate 2021
dc.date.none.fl_str_mv 2021-08
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/166974
Ibanez Barassi, Agustin Mariano; Fittipaldi, Sol; Trujillo, Catalina; Jaramillo, Tania; Torres, Alejandra; et al.; Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes; IOS Press; Journal of Alzheimer's Disease; 83; 1; 8-2021; 227-248
1387-2877
CONICET Digital
CONICET
url http://hdl.handle.net/11336/166974
identifier_str_mv Ibanez Barassi, Agustin Mariano; Fittipaldi, Sol; Trujillo, Catalina; Jaramillo, Tania; Torres, Alejandra; et al.; Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes; IOS Press; Journal of Alzheimer's Disease; 83; 1; 8-2021; 227-248
1387-2877
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3233/JAD-210163
info:eu-repo/semantics/altIdentifier/url/https://content.iospress.com/articles/journal-of-alzheimers-disease/jad210163
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv IOS Press
publisher.none.fl_str_mv IOS Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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