Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia

Autores
Lopes Da Cunha, Pamela Johanna; Ruiz, Fabián; Ferrante, Franco Javier; Sterpin, Lucas Federico; Ibañez, Agustin Mariano; Slachevsky, Andrea; Matallana, Diana; Martínez, Ángela; Hesse Rizzi, Eugenia Fátima; García, Adolfo Martín
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer’s disease (AD) compromises the processing of entities manifested by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective, signaled by an increase of third-person references. Yet, no study has examined whether these patterns can be captured in spontaneous discourse via natural language processing tools (NLP). We asked persons with AD (n = 21), bvFTD (n = 21), and healthy controls (n = 21) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers via part-of-speech and morphological tagging. Inferential statistics and machine learning were used for group-level and subject-level discrimination. The above linguistic features were correlated with patients’ cognitive outcomes, captured through the Montreal Cognitive Assessment (MoCA). We found that, compared with HCs, AD (but not bvFTD) patients produced significantly fewer nouns, while bvFTD (but not AD) patients used significantly more third-person markers. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.76). No linguistic feature was significantly correlated with MoCA scores in either patient group. Taken together, we suggest that differential markers of AD and bvFTD can be automatically detected in spontaneous routine descriptions. By targeting specific features linked to each disorder’s cognitive profile, our approach favors interpretability for enhanced syndrome characterization, diagnosis, and monitoring.
Fil: Lopes Da Cunha, Pamela Johanna. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;
Fil: Ruiz, Fabián. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;
Fil: Ferrante, Franco Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;
Fil: Sterpin, Lucas Federico. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;
Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas; . Universidad Adolfo Ibañez; Chile. University of California; Estados Unidos. Trinity College; Irlanda
Fil: Slachevsky, Andrea. Universidad de Chile; Chile. Universidad del Desarrollo; Chile
Fil: Matallana, Diana. Pontificia Universidad Javeriana; Colombia. Hospital Universitario San Ignacio Bogota; Colombia. Hospital Universitario Santa Fe de Bogotá; Colombia
Fil: Martínez, Ángela. Universidad del Rosario; Colombia
Fil: Hesse Rizzi, Eugenia Fátima. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;
Fil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas; . Universidad Adolfo Ibañez; Chile. University of California; Estados Unidos. Universidad de Santiago de Chile; Chile
Materia
Alzheimer’s disease
Behavioral variant frontotemporal dementia
Natural language processing
Morphological tagging
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/282491
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementiaLopes Da Cunha, Pamela JohannaRuiz, FabiánFerrante, Franco JavierSterpin, Lucas FedericoIbañez, Agustin MarianoSlachevsky, AndreaMatallana, DianaMartínez, ÁngelaHesse Rizzi, Eugenia FátimaGarcía, Adolfo MartínAlzheimer’s diseaseBehavioral variant frontotemporal dementiaNatural language processingMorphological tagginghttps://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer’s disease (AD) compromises the processing of entities manifested by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective, signaled by an increase of third-person references. Yet, no study has examined whether these patterns can be captured in spontaneous discourse via natural language processing tools (NLP). We asked persons with AD (n = 21), bvFTD (n = 21), and healthy controls (n = 21) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers via part-of-speech and morphological tagging. Inferential statistics and machine learning were used for group-level and subject-level discrimination. The above linguistic features were correlated with patients’ cognitive outcomes, captured through the Montreal Cognitive Assessment (MoCA). We found that, compared with HCs, AD (but not bvFTD) patients produced significantly fewer nouns, while bvFTD (but not AD) patients used significantly more third-person markers. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.76). No linguistic feature was significantly correlated with MoCA scores in either patient group. Taken together, we suggest that differential markers of AD and bvFTD can be automatically detected in spontaneous routine descriptions. By targeting specific features linked to each disorder’s cognitive profile, our approach favors interpretability for enhanced syndrome characterization, diagnosis, and monitoring.Fil: Lopes Da Cunha, Pamela Johanna. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;Fil: Ruiz, Fabián. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;Fil: Ferrante, Franco Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;Fil: Sterpin, Lucas Federico. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas; . Universidad Adolfo Ibañez; Chile. University of California; Estados Unidos. Trinity College; IrlandaFil: Slachevsky, Andrea. Universidad de Chile; Chile. Universidad del Desarrollo; ChileFil: Matallana, Diana. Pontificia Universidad Javeriana; Colombia. Hospital Universitario San Ignacio Bogota; Colombia. Hospital Universitario Santa Fe de Bogotá; ColombiaFil: Martínez, Ángela. Universidad del Rosario; ColombiaFil: Hesse Rizzi, Eugenia Fátima. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;Fil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas; . Universidad Adolfo Ibañez; Chile. University of California; Estados Unidos. Universidad de Santiago de Chile; ChilePublic Library of Science2024-06info: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/11336/282491Lopes Da Cunha, Pamela Johanna; Ruiz, Fabián; Ferrante, Franco Javier; Sterpin, Lucas Federico; Ibañez, Agustin Mariano; et al.; Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia; Public Library of Science; Plos One; 19; 6; 6-2024; 1-191932-6203CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0304272info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0304272info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-03-11T12:02:09Zoai:ri.conicet.gov.ar:11336/282491instacron: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:34982026-03-11 12:02:10.041CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia
title Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia
spellingShingle Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia
Lopes Da Cunha, Pamela Johanna
Alzheimer’s disease
Behavioral variant frontotemporal dementia
Natural language processing
Morphological tagging
title_short Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia
title_full Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia
title_fullStr Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia
title_full_unstemmed Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia
title_sort Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia
dc.creator.none.fl_str_mv Lopes Da Cunha, Pamela Johanna
Ruiz, Fabián
Ferrante, Franco Javier
Sterpin, Lucas Federico
Ibañez, Agustin Mariano
Slachevsky, Andrea
Matallana, Diana
Martínez, Ángela
Hesse Rizzi, Eugenia Fátima
García, Adolfo Martín
author Lopes Da Cunha, Pamela Johanna
author_facet Lopes Da Cunha, Pamela Johanna
Ruiz, Fabián
Ferrante, Franco Javier
Sterpin, Lucas Federico
Ibañez, Agustin Mariano
Slachevsky, Andrea
Matallana, Diana
Martínez, Ángela
Hesse Rizzi, Eugenia Fátima
García, Adolfo Martín
author_role author
author2 Ruiz, Fabián
Ferrante, Franco Javier
Sterpin, Lucas Federico
Ibañez, Agustin Mariano
Slachevsky, Andrea
Matallana, Diana
Martínez, Ángela
Hesse Rizzi, Eugenia Fátima
García, Adolfo Martín
author2_role author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Alzheimer’s disease
Behavioral variant frontotemporal dementia
Natural language processing
Morphological tagging
topic Alzheimer’s disease
Behavioral variant frontotemporal dementia
Natural language processing
Morphological tagging
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.2
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer’s disease (AD) compromises the processing of entities manifested by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective, signaled by an increase of third-person references. Yet, no study has examined whether these patterns can be captured in spontaneous discourse via natural language processing tools (NLP). We asked persons with AD (n = 21), bvFTD (n = 21), and healthy controls (n = 21) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers via part-of-speech and morphological tagging. Inferential statistics and machine learning were used for group-level and subject-level discrimination. The above linguistic features were correlated with patients’ cognitive outcomes, captured through the Montreal Cognitive Assessment (MoCA). We found that, compared with HCs, AD (but not bvFTD) patients produced significantly fewer nouns, while bvFTD (but not AD) patients used significantly more third-person markers. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.76). No linguistic feature was significantly correlated with MoCA scores in either patient group. Taken together, we suggest that differential markers of AD and bvFTD can be automatically detected in spontaneous routine descriptions. By targeting specific features linked to each disorder’s cognitive profile, our approach favors interpretability for enhanced syndrome characterization, diagnosis, and monitoring.
Fil: Lopes Da Cunha, Pamela Johanna. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;
Fil: Ruiz, Fabián. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;
Fil: Ferrante, Franco Javier. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;
Fil: Sterpin, Lucas Federico. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;
Fil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas; . Universidad Adolfo Ibañez; Chile. University of California; Estados Unidos. Trinity College; Irlanda
Fil: Slachevsky, Andrea. Universidad de Chile; Chile. Universidad del Desarrollo; Chile
Fil: Matallana, Diana. Pontificia Universidad Javeriana; Colombia. Hospital Universitario San Ignacio Bogota; Colombia. Hospital Universitario Santa Fe de Bogotá; Colombia
Fil: Martínez, Ángela. Universidad del Rosario; Colombia
Fil: Hesse Rizzi, Eugenia Fátima. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas;
Fil: García, Adolfo Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Rectorado. Centro de Neurociencias Cognitivas; . Universidad Adolfo Ibañez; Chile. University of California; Estados Unidos. Universidad de Santiago de Chile; Chile
description Dementia can disrupt how people experience and describe events as well as their own role in them. Alzheimer’s disease (AD) compromises the processing of entities manifested by nouns, while behavioral variant frontotemporal dementia (bvFTD) entails a depersonalized perspective, signaled by an increase of third-person references. Yet, no study has examined whether these patterns can be captured in spontaneous discourse via natural language processing tools (NLP). We asked persons with AD (n = 21), bvFTD (n = 21), and healthy controls (n = 21) to narrate a typical day of their lives and calculated the proportion of nouns, verbs, and first- or third-person markers via part-of-speech and morphological tagging. Inferential statistics and machine learning were used for group-level and subject-level discrimination. The above linguistic features were correlated with patients’ cognitive outcomes, captured through the Montreal Cognitive Assessment (MoCA). We found that, compared with HCs, AD (but not bvFTD) patients produced significantly fewer nouns, while bvFTD (but not AD) patients used significantly more third-person markers. Machine learning analyses showed that these features identified individuals with AD and bvFTD (AUC = 0.76). No linguistic feature was significantly correlated with MoCA scores in either patient group. Taken together, we suggest that differential markers of AD and bvFTD can be automatically detected in spontaneous routine descriptions. By targeting specific features linked to each disorder’s cognitive profile, our approach favors interpretability for enhanced syndrome characterization, diagnosis, and monitoring.
publishDate 2024
dc.date.none.fl_str_mv 2024-06
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/11336/282491
Lopes Da Cunha, Pamela Johanna; Ruiz, Fabián; Ferrante, Franco Javier; Sterpin, Lucas Federico; Ibañez, Agustin Mariano; et al.; Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia; Public Library of Science; Plos One; 19; 6; 6-2024; 1-19
1932-6203
CONICET Digital
CONICET
url http://hdl.handle.net/11336/282491
identifier_str_mv Lopes Da Cunha, Pamela Johanna; Ruiz, Fabián; Ferrante, Franco Javier; Sterpin, Lucas Federico; Ibañez, Agustin Mariano; et al.; Automated free speech analysis reveals distinct markers of Alzheimer’s and frontotemporal dementia; Public Library of Science; Plos One; 19; 6; 6-2024; 1-19
1932-6203
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0304272
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science
publisher.none.fl_str_mv Public Library of Science
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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