The graph properties of psychotic speech differ between early and late onset, but not between acute and chronic psychosis.
Introdução Speech structure measured by graph analysis can help clinicians to quantify speech symptoms, and characterize the psychopathology of psychosis. One important feature observed by psychiatrists are the different aspects of early and late onset psychosis with regard to cognitive symptoms. Si...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | Brasil |
| Recursos: | Universidade Federal do Rio Grande do Norte (UFRN) |
| Repositorio: | Repositório Institucional da UFRN |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.ufrn.br:123456789/24053 |
| Acesso em linha: | https://repositorio.ufrn.br/jspui/handle/123456789/24053 |
| Access Level: | acceso abierto |
| Palavra-chave: | Bipolar disorder Schizophrenia Graph theory Speech Psychosis |
| Resumo: | Introdução Speech structure measured by graph analysis can help clinicians to quantify speech symptoms, and characterize the psychopathology of psychosis. One important feature observed by psychiatrists are the different aspects of early and late onset psychosis with regard to cognitive symptoms. Since larger cognitive deficits are expected in the population with early onset psychosis, early diagnosis is important to guide interventions able to mitigate a more severe evolution of the disease. Objetivos The aim of the present study is to identify the influence on different types of psychosis (schizophrenia or bipolar disorder) of different types of onset (early onset: psychosis before age 18, late onset: after age 18) and different disease durations (during first episode psychosis or chronic psychosis, after two years of disease). Métodos We interviewed 52 psychotic subjects, applying psychometric scales and asking for dream reports. Then we transcribed those reports, represented as wordgraphs (each word was represented as a node, and the temporal link between consecutive words was represented as an edge) and quantified 14 speech graph attributes (SGAs) after controlling for differences in word count. Resultados e Conclusões We performed a threeway ANOVA to predict the influence of age of onset (early or late), duration of disease (first episode psychosis or chronic) and diagnosis (schizophrenia and bipolar disorder), considering 3 different types of interaction (onset x duration, onset x diagnosis, duration x diagnosis), and correcting for 3 comparisons (α=0.0167). Diagnosis is related with differences in edges (p=0.0001), LCC (p=0.0002), LSC (p=0.0001), and ATD (p=0.0046); onset is related with differences in L1 (p=0.0091) and the interaction of diagnosis and onset is related to differences in ATD (p=0.0155). We also found a positive correlation between selfloops and age of onset (RHO=0.3992, p=0.0034), but no correlation was detected between standard psychometric measures and duration of disease or age of onset. The late onset group showed more selfloops than the early onset group (p=0.0308), and the late onset schizophrenia group showed a smaller ATD than the early onset schizophrenia group (p=0.0489). Conclusion: Selfloops measured on graphs of dream reports are better correlated with age of onset than standard psychometric measures, characterizing differences between early and late onset psychotic groups. We also found that early onset schizophrenia, but not bipolar psychosis, shows higher ATD than late onset schizophrenia. We did not find correlations between SGAs and duration of disease, or interactions between duration and onset or diagnosis to explain SGAs differences, pointing to the conclusion that SGAs must be a trace, more than a stage metric of psychotic speech. |
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