Automated text-level semantic markers of Alzheimer's disease

Autores
Sanz, Camila; Carrillo, Facundo; Slachevsky, Andrea; Forno, Gonzalo; Gorno Tempini, María L.; Villagra, Roque; Ibañez, Agustin Mariano; Tagliazucchi, Enzo Rodolfo; García, Adolfo Martín
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
INTRODUCTION: Automated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer?s disease (AD). Yet, most research is undermined by low interpretability and specificity. METHODS: Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate AD dementia (ADD) patients from healthy controls (HCs) based on automated measures of domains typically affected in AD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson?s disease (PD) patients. RESULTS: Relative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly classified between ADD patients and HCs (AUC = 0.8), yielding near-chance classification between PD patients and HCs (AUC = 0.65). DISCUSSION: Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.
Fil: Sanz, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Carrillo, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Slachevsky, Andrea. Universidad de Chile; Chile
Fil: Forno, Gonzalo. Universidad de Chile; Chile. Universidad de Barcelona; España
Fil: Gorno Tempini, María L.. University of California; Estados Unidos
Fil: Villagra, Roque. Universidad de Chile; Chile
Fil: Ibañez, Agustin Mariano. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: García, Adolfo Martín. Universidad de San Andrés; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
ALZHEIMER'S DISEASE DEMENTIA
AUTOMATED SPEECH ANALYSIS
SEMANTIC GRANULARITY
SEMANTIC VARIABILITY
PARKINSON'S DISEASE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/161127
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/161127
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Automated text-level semantic markers of Alzheimer's diseaseSanz, CamilaCarrillo, FacundoSlachevsky, AndreaForno, GonzaloGorno Tempini, María L.Villagra, RoqueIbañez, Agustin MarianoTagliazucchi, Enzo RodolfoGarcía, Adolfo MartínALZHEIMER'S DISEASE DEMENTIAAUTOMATED SPEECH ANALYSISSEMANTIC GRANULARITYSEMANTIC VARIABILITYPARKINSON'S DISEASEhttps://purl.org/becyt/ford/6.2https://purl.org/becyt/ford/6https://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5INTRODUCTION: Automated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer?s disease (AD). Yet, most research is undermined by low interpretability and specificity. METHODS: Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate AD dementia (ADD) patients from healthy controls (HCs) based on automated measures of domains typically affected in AD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson?s disease (PD) patients. RESULTS: Relative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly classified between ADD patients and HCs (AUC = 0.8), yielding near-chance classification between PD patients and HCs (AUC = 0.65). DISCUSSION: Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.Fil: Sanz, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Carrillo, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Slachevsky, Andrea. Universidad de Chile; ChileFil: Forno, Gonzalo. Universidad de Chile; Chile. Universidad de Barcelona; EspañaFil: Gorno Tempini, María L.. University of California; Estados UnidosFil: Villagra, Roque. Universidad de Chile; ChileFil: Ibañez, Agustin Mariano. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: García, Adolfo Martín. Universidad de San Andrés; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaWiley2022-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/161127Sanz, Camila; Carrillo, Facundo; Slachevsky, Andrea; Forno, Gonzalo; Gorno Tempini, María L.; et al.; Automated text-level semantic markers of Alzheimer's disease; Wiley; Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring; 14; 1; 1-2022; 1-102352-8729CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/dad2.12276info:eu-repo/semantics/altIdentifier/doi/10.1002/dad2.12276info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:06:42Zoai:ri.conicet.gov.ar:11336/161127instacron: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 10:06:42.818CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automated text-level semantic markers of Alzheimer's disease
title Automated text-level semantic markers of Alzheimer's disease
spellingShingle Automated text-level semantic markers of Alzheimer's disease
Sanz, Camila
ALZHEIMER'S DISEASE DEMENTIA
AUTOMATED SPEECH ANALYSIS
SEMANTIC GRANULARITY
SEMANTIC VARIABILITY
PARKINSON'S DISEASE
title_short Automated text-level semantic markers of Alzheimer's disease
title_full Automated text-level semantic markers of Alzheimer's disease
title_fullStr Automated text-level semantic markers of Alzheimer's disease
title_full_unstemmed Automated text-level semantic markers of Alzheimer's disease
title_sort Automated text-level semantic markers of Alzheimer's disease
dc.creator.none.fl_str_mv Sanz, Camila
Carrillo, Facundo
Slachevsky, Andrea
Forno, Gonzalo
Gorno Tempini, María L.
Villagra, Roque
Ibañez, Agustin Mariano
Tagliazucchi, Enzo Rodolfo
García, Adolfo Martín
author Sanz, Camila
author_facet Sanz, Camila
Carrillo, Facundo
Slachevsky, Andrea
Forno, Gonzalo
Gorno Tempini, María L.
Villagra, Roque
Ibañez, Agustin Mariano
Tagliazucchi, Enzo Rodolfo
García, Adolfo Martín
author_role author
author2 Carrillo, Facundo
Slachevsky, Andrea
Forno, Gonzalo
Gorno Tempini, María L.
Villagra, Roque
Ibañez, Agustin Mariano
Tagliazucchi, Enzo Rodolfo
García, Adolfo Martín
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv ALZHEIMER'S DISEASE DEMENTIA
AUTOMATED SPEECH ANALYSIS
SEMANTIC GRANULARITY
SEMANTIC VARIABILITY
PARKINSON'S DISEASE
topic ALZHEIMER'S DISEASE DEMENTIA
AUTOMATED SPEECH ANALYSIS
SEMANTIC GRANULARITY
SEMANTIC VARIABILITY
PARKINSON'S DISEASE
purl_subject.fl_str_mv https://purl.org/becyt/ford/6.2
https://purl.org/becyt/ford/6
https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv INTRODUCTION: Automated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer?s disease (AD). Yet, most research is undermined by low interpretability and specificity. METHODS: Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate AD dementia (ADD) patients from healthy controls (HCs) based on automated measures of domains typically affected in AD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson?s disease (PD) patients. RESULTS: Relative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly classified between ADD patients and HCs (AUC = 0.8), yielding near-chance classification between PD patients and HCs (AUC = 0.65). DISCUSSION: Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.
Fil: Sanz, Camila. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Carrillo, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Slachevsky, Andrea. Universidad de Chile; Chile
Fil: Forno, Gonzalo. Universidad de Chile; Chile. Universidad de Barcelona; España
Fil: Gorno Tempini, María L.. University of California; Estados Unidos
Fil: Villagra, Roque. Universidad de Chile; Chile
Fil: Ibañez, Agustin Mariano. Universidad Adolfo Ibañez; Chile. Universidad de San Andrés; Argentina. University of California; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: García, Adolfo Martín. Universidad de San Andrés; Argentina. Universidad Nacional de Cuyo. Facultad de Educación Elemental y Especial; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description INTRODUCTION: Automated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer?s disease (AD). Yet, most research is undermined by low interpretability and specificity. METHODS: Combining statistical and machine learning analyses of natural speech data, we aimed to discriminate AD dementia (ADD) patients from healthy controls (HCs) based on automated measures of domains typically affected in AD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson?s disease (PD) patients. RESULTS: Relative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly classified between ADD patients and HCs (AUC = 0.8), yielding near-chance classification between PD patients and HCs (AUC = 0.65). DISCUSSION: Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.
publishDate 2022
dc.date.none.fl_str_mv 2022-01
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/11336/161127
Sanz, Camila; Carrillo, Facundo; Slachevsky, Andrea; Forno, Gonzalo; Gorno Tempini, María L.; et al.; Automated text-level semantic markers of Alzheimer's disease; Wiley; Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring; 14; 1; 1-2022; 1-10
2352-8729
CONICET Digital
CONICET
url http://hdl.handle.net/11336/161127
identifier_str_mv Sanz, Camila; Carrillo, Facundo; Slachevsky, Andrea; Forno, Gonzalo; Gorno Tempini, María L.; et al.; Automated text-level semantic markers of Alzheimer's disease; Wiley; Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring; 14; 1; 1-2022; 1-10
2352-8729
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/dad2.12276
info:eu-repo/semantics/altIdentifier/doi/10.1002/dad2.12276
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
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 13.070432

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