A single composite index of semantic behavior tracks symptoms of psychosis over time

Semantic variables automatically extracted from spontaneous speech characterize anomalous semantic associations generated by groups with schizophrenia spectrum disorders (SSD). However, with the use of different language models and numerous aspects of semantic associations that could be tracked, the...

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Detalles Bibliográficos
Autores: Palominos, Claudio, Kyrdum, Maryia, Nikzad, Amir H., Spilka, Michael J., Homan, Philipp, Sommer, Iris E., Tang, Sunny X., Hinzen, Wolfram
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
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:10230/71901
Acceso en línea:http://hdl.handle.net/10230/71901
http://dx.doi.org/10.1016/j.schres.2025.03.038
Access Level:acceso abierto
Palabra clave:Schizophrenia
Large language models
Word embeddings
Semantics
Semantic space
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spelling A single composite index of semantic behavior tracks symptoms of psychosis over timePalominos, ClaudioKyrdum, MaryiaNikzad, Amir H.Spilka, Michael J.Homan, PhilippSommer, Iris E.Tang, Sunny X.Hinzen, WolframSchizophreniaLarge language modelsWord embeddingsSemanticsSemantic spaceSemantic variables automatically extracted from spontaneous speech characterize anomalous semantic associations generated by groups with schizophrenia spectrum disorders (SSD). However, with the use of different language models and numerous aspects of semantic associations that could be tracked, the semantic space has become very high-dimensional, challenging both theoretical understanding and practical applications. This study aimed to summarize this space into a single composite semantic index and to test whether it can track diagnosis and symptom profiles over time at an individual level. The index was derived from a principal component analysis (PCA) yielding a linear combination of 117 semantic variables. It was tested in discourse samples of English speakers performing a picture description task, involving a total of 103 individuals with SSD and 36 healthy controls (HC) compared across four time points. Results showed that the index distinguished between SSD and HC groups, identified transitions from acute psychosis to remission and stabilization, predicted the sum of scores of the Thought, Language and Communication (TLC) index as well as subscores, capturing 65 % of the variance in the sum of TLC scores. These findings show that a single indicator meaningfully summarizes a shift in semantic associations in psychosis and tracks symptoms over time, while also pointing to variance unexplained, which is likely covered by other semantic and non-semantic factors.This work was supported by the European Union (GA 101080251 - TRUSTING). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the Agency. Neither the European Union nor the granting authority can be held responsible for them. Data collection for the LPoP sample was provided by Winterlight Labs, Inc. SXT is supported by the Brain and Behavior Research Foundation Young Investigator Grant and NIH K23 MH130750.Elsevier2025202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/71901http://dx.doi.org/10.1016/j.schres.2025.03.038http://hdl.handle.net/10230/71901reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésSchizophrenia Research. 2025;279:116-27info:eu-repo/grantAgreement/EC/HE/101094738© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/719012026-05-29T05:05:01Z
dc.title.none.fl_str_mv A single composite index of semantic behavior tracks symptoms of psychosis over time
title A single composite index of semantic behavior tracks symptoms of psychosis over time
spellingShingle A single composite index of semantic behavior tracks symptoms of psychosis over time
Palominos, Claudio
Schizophrenia
Large language models
Word embeddings
Semantics
Semantic space
title_short A single composite index of semantic behavior tracks symptoms of psychosis over time
title_full A single composite index of semantic behavior tracks symptoms of psychosis over time
title_fullStr A single composite index of semantic behavior tracks symptoms of psychosis over time
title_full_unstemmed A single composite index of semantic behavior tracks symptoms of psychosis over time
title_sort A single composite index of semantic behavior tracks symptoms of psychosis over time
dc.creator.none.fl_str_mv Palominos, Claudio
Kyrdum, Maryia
Nikzad, Amir H.
Spilka, Michael J.
Homan, Philipp
Sommer, Iris E.
Tang, Sunny X.
Hinzen, Wolfram
author Palominos, Claudio
author_facet Palominos, Claudio
Kyrdum, Maryia
Nikzad, Amir H.
Spilka, Michael J.
Homan, Philipp
Sommer, Iris E.
Tang, Sunny X.
Hinzen, Wolfram
author_role author
author2 Kyrdum, Maryia
Nikzad, Amir H.
Spilka, Michael J.
Homan, Philipp
Sommer, Iris E.
Tang, Sunny X.
Hinzen, Wolfram
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Schizophrenia
Large language models
Word embeddings
Semantics
Semantic space
topic Schizophrenia
Large language models
Word embeddings
Semantics
Semantic space
description Semantic variables automatically extracted from spontaneous speech characterize anomalous semantic associations generated by groups with schizophrenia spectrum disorders (SSD). However, with the use of different language models and numerous aspects of semantic associations that could be tracked, the semantic space has become very high-dimensional, challenging both theoretical understanding and practical applications. This study aimed to summarize this space into a single composite semantic index and to test whether it can track diagnosis and symptom profiles over time at an individual level. The index was derived from a principal component analysis (PCA) yielding a linear combination of 117 semantic variables. It was tested in discourse samples of English speakers performing a picture description task, involving a total of 103 individuals with SSD and 36 healthy controls (HC) compared across four time points. Results showed that the index distinguished between SSD and HC groups, identified transitions from acute psychosis to remission and stabilization, predicted the sum of scores of the Thought, Language and Communication (TLC) index as well as subscores, capturing 65 % of the variance in the sum of TLC scores. These findings show that a single indicator meaningfully summarizes a shift in semantic associations in psychosis and tracks symptoms over time, while also pointing to variance unexplained, which is likely covered by other semantic and non-semantic factors.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
2025
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 http://hdl.handle.net/10230/71901
http://dx.doi.org/10.1016/j.schres.2025.03.038
http://hdl.handle.net/10230/71901
url http://hdl.handle.net/10230/71901
http://dx.doi.org/10.1016/j.schres.2025.03.038
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Schizophrenia Research. 2025;279:116-27
info:eu-repo/grantAgreement/EC/HE/101094738
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv 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
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