Syntactic complexity of spoken language in the diagnosis of schizophrenia: A probabilistic Bayes network model

Angelica M Silva; Roberto Limongi; Michael MacKinley; Sabrina D Ford; Maria Francisca Alonso-Sánchez; Lena Palaniyappan

doi:10.1016/j.schres.2022.06.011

Syntactic complexity of spoken language in the diagnosis of schizophrenia: A probabilistic Bayes network model

Schizophr Res. 2023 Sep:259:88-96. doi: 10.1016/j.schres.2022.06.011. Epub 2022 Jun 22.

Authors

Angelica M Silva¹, Roberto Limongi², Michael MacKinley³, Sabrina D Ford⁴, Maria Francisca Alonso-Sánchez⁵, Lena Palaniyappan⁶

Affiliations

¹ Robarts Research Institute, Western University, London, Ontario, Canada. Electronic address: asilva43@uwo.ca.
² Robarts Research Institute, Western University, London, Ontario, Canada; Department of Psychology, Western University, London, Canada; Faculty of Human and Social Sciences, Wilfred Laurier University, Brantford, Ontario, Canada.
³ Robarts Research Institute, Western University, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada.
⁴ Lawson Health Research Institute, London, Ontario, Canada.
⁵ Robarts Research Institute, Western University, London, Ontario, Canada.
⁶ Robarts Research Institute, Western University, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada; Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Canada; Department of Medical Biophysics, Western University, London, Canada.

PMID: 35752547
DOI: 10.1016/j.schres.2022.06.011

Abstract

In the clinical linguistics of schizophrenia, syntactic complexity has received much attention. In this study, we address whether syntactic complexity deteriorates within the six months following the first episode of psychosis in those who develop a diagnosis of schizophrenia. We collected data from a cohort of twenty-six first-episode psychosis and 12 healthy control subjects using the Thought and Language Index interview in response to three pictures from the Thematic Apperception Test at first assessment and after six months (the time of consensus diagnosis). An automated labeling (part-of-speech tagging) for specific syntactic elements calculated large and granular syntactic complexity indices with a focus on clause complexity as a particular case from this spoken language data. Probabilistic reasoning leveraging the conditional independence properties of Bayes networks revealed that consensus diagnosis of schizophrenia predicted a decrease in nominal subjects per clause among individuals with first episode psychosis. From the entire sample, we estimate a 95.4 % probability that a 50 % decrease in mean nominal subjects per clause after six months is explained by the presence of first episode psychosis. Among those with psychosis, a 30 % decrease in this clause-complexity index after six months of experiencing the first episode predicted with 95 % probability a consensus diagnosis of schizophrenia, representing a conditional relationship between a longitudinal decrease in syntactic complexity and a diagnosis of schizophrenia. We conclude that an early drift towards linguistic disorganization/impoverishment of clause complexity-at the granular level of nominal subject per clause-is a distinctive feature of schizophrenia that decreases longitudinally, thus differentiating schizophrenia from other psychotic illnesses with shared phenomenology.

Keywords: Early intervention; Longitudinal; Progressive; Speech; Thought disorder.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
Humans
Language
Linguistics
Psychotic Disorders* / complications
Psychotic Disorders* / diagnosis
Schizophrenia* / complications
Schizophrenia* / diagnosis

Abstract

Publication types

MeSH terms

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