How language flows when movements don't: An automated analysis of spontaneous discourse in Parkinson's disease

Brain Lang. 2016 Nov;162:19-28. doi: 10.1016/j.bandl.2016.07.008. Epub 2016 Aug 5.


To assess the impact of Parkinson's disease (PD) on spontaneous discourse, we conducted computerized analyses of brief monologues produced by 51 patients and 50 controls. We explored differences in semantic fields (via latent semantic analysis), grammatical choices (using part-of-speech tagging), and word-level repetitions (with graph embedding tools). Although overall output was quantitatively similar between groups, patients relied less heavily on action-related concepts and used more subordinate structures. Also, a classification tool operating on grammatical patterns identified monologues as pertaining to patients or controls with 75% accuracy. Finally, while the incidence of dysfluent word repetitions was similar between groups, it allowed inferring the patients' level of motor impairment with 77% accuracy. Our results highlight the relevance of studying naturalistic discourse features to tap the integrity of neural (and, particularly, motor) networks, beyond the possibilities of standard token-level instruments.

Keywords: Grammatical features; Graph embedding; Latent semantic analysis; Parkinson’s disease; Part-of-speech tagging; Semantic fields; Spontaneous discourse; Word repetition.

MeSH terms

  • Case-Control Studies
  • Female
  • Humans
  • Male
  • Middle Aged
  • Motor Skills
  • Movement*
  • Nerve Net
  • Parkinson Disease / physiopathology*
  • Semantics
  • Speech / physiology*