Computational analysis of linguistic features in speech samples of first-episode bipolar disorder and psychosis.

Autor: Arslan B; Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey. Electronic address: ufukberat.arslan@ogr.deu.edu.tr., Kizilay E; Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey., Verim B; Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey., Demirlek C; Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey; Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA., Demir M; Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey., Cesim E; Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey., Eyuboglu MS; Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey., Ozbek SU; Department of Psychiatry, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey., Sut E; Department of Child and Adolescent Psychiatry, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey., Yalincetin B; Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey., Bora E; Department of Neurosciences, Health Sciences Institute, Dokuz Eylul University, Izmir, Turkey; Department of Psychiatry, Faculty of Medicine, Dokuz Eylul University, Izmir, Turkey; Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Carlton South, Victoria 3053, Australia.
Jazyk: angličtina
Zdroj: Journal of affective disorders [J Affect Disord] 2024 Oct 15; Vol. 363, pp. 340-347. Date of Electronic Publication: 2024 Jul 17.
DOI: 10.1016/j.jad.2024.07.102
Abstrakt: Background: In recent years, automated analyses using novel NLP methods have been used to investigate language abnormalities in schizophrenia. In contrast, only a few studies used automated language analyses in bipolar disorder. To our knowledge, no previous research compared automated language characteristics of first-episode psychosis (FEP) and bipolar disorder (FEBD) using NLP methods.
Methods: Our study included 53 FEP, 40 FEBD and 50 healthy control participants who are native Turkish speakers. Speech samples of the participants in the Thematic Apperception Test (TAT) underwent automated generic and part-of-speech analyses, as well as sentence-level semantic similarity analysis based on SBERT.
Results: Both FEBD and FEP were associated with the use of shorter sentences and increased sentence-level semantic similarity but less semantic alignment with the TAT pictures. FEP also demonstrated reduced verbosity and syntactic complexity. FEP differed from FEBD in reduced verbosity, decreased first-person singular pronouns, fewer conjunctions, increased semantic similarity as well as shorter sentence and word length. The mean classification accuracy was 82.45 % in FEP vs HC, 71.1 % in FEBD vs HC, and 73 % in FEP vs FEBD. After Bonferroni correction, the severity of negative symptoms in FEP was associated with reduced verbal output and increased 5th percentile of semantic similarity.
Limitations: The main limitation of this study was the cross-sectional nature.
Conclusion: Our findings demonstrate that both patient groups showed language abnormalities, which were more severe and widespread in FEP compared to FEBD. Our results suggest that NLP methods reveal transdiagnostic linguistic abnormalities in FEP and FEBD.
Competing Interests: Declaration of competing interest The authors have no conflicts of interest regarding subject of this manuscript.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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