A single composite index of semantic behavior tracks symptoms of psychosis over time
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- dc.contributor.author Palominos, Claudio
- dc.contributor.author Kyrdum, Maryia
- dc.contributor.author Nikzad, Amir H.
- dc.contributor.author Spilka, Michael J.
- dc.contributor.author Homan, Philipp
- dc.contributor.author Sommer, Iris E.
- dc.contributor.author Tang, Sunny X.
- dc.contributor.author Hinzen, Wolfram
- dc.date.accessioned 2025-11-17T15:41:12Z
- dc.date.available 2025-11-17T15:41:12Z
- dc.date.issued 2025
- dc.date.updated 2025-11-17T15:41:11Z
- dc.description.abstract 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.
- dc.description.sponsorship 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.
- dc.format.mimetype application/pdf
- dc.identifier.citation Palominos C, Kirdun M, Nikzad AH, Spilka MJ, Homan P, Sommer IE, Tang SX, Hinzen W. A single composite index of semantic behavior tracks symptoms of psychosis over time. Schizophr Res. 2025 May;279:116-127. DOI: 10.1016/j.schres.2025.03.038
- dc.identifier.doi https://dx.doi.org/10.1016/j.schres.2025.03.038
- dc.identifier.issn 0920-9964
- dc.identifier.uri http://hdl.handle.net/10230/71901
- dc.language.iso eng
- dc.publisher Elsevier
- dc.relation.ispartof Schizophrenia Research. 2025;279:116-127
- dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101094738
- dc.rights © 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/).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri https://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Schizophrenia
- dc.subject.keyword Large language models
- dc.subject.keyword Word embeddings
- dc.subject.keyword Semantics
- dc.subject.keyword Semantic space
- dc.title A single composite index of semantic behavior tracks symptoms of psychosis over time
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion