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Syntactic Network Analysis in Schizophrenia-Spectrum Disorders.

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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Psychiatry

Background:

  • Language anomalies are a key characteristic of schizophrenia-spectrum disorders (SSD).
  • Previous research has not fully explored syntactic network differences in SSD.
  • Understanding these linguistic patterns may offer new diagnostic avenues.

Purpose of the Study:

  • To investigate syntactic relation differences in speech between patients with SSD and healthy controls using network analysis.
  • To assess the relationship between syntactic network measures and sociodemographic factors, symptoms, and cognition.
  • To evaluate the diagnostic potential of quantifying syntactic network measures for SSD.

Main Methods:

  • Collected speech samples from 63 patients with SSD and 63 controls via semi-structured interviews.
  • Generated syntactic parse trees for each sentence to create input for network analysis.
  • Analyzed syntactic networks using 11 local and global measures, comparing groups with MANCOVA, controlling for age, sex, and education.

Main Results:

  • Significant differences in most syntactic network measures were found between patients with SSD and controls.
  • Sex significantly impacted syntactic measures, with anomalies most pronounced in women with SSD.
  • Syntactic measures correlated with negative symptoms and cognitive function; a classifier achieved 74% accuracy in distinguishing groups.

Conclusions:

  • Network analysis of syntactic relations reveals robust differences in SSD, especially in women.
  • Linguistic network analysis is a valid approach for studying SSD.
  • These findings suggest potential for automated language measures, including syntactic networks, as diagnostic markers for SSD.