Speech graph analysis in obsessive-compulsive disorder: The relevance of dream reports

Abstract

Obsessive-compulsive disorder (OCD) is a distressing disorder characterized by the presence of intrusive thoughts, images or urges (obsessions) and/or behavioral efforts to reduce the anxiety (compulsions). OCD lifetime prevalence varies between 1% and 3% in the general population and there are no reliable markers that support the diagnosis. In order to fill this gap, Computational Psychiatry employs multiple types of quantitative analyses to improve the understanding, diagnosis, prediction, and treatment of mental illnesses including OCD. One of these computational tools is speech graphs analysis. A graph represents a network of nodes connected by edges: in non-semantic speech graphs, nodes correspond to words and edges correspond to the directed link between consecutive words. Using non-semantic speech graphs, we compared free speech samples from OCD patients and healthy controls (HC), to test whether speech graphs analysis can grasp structural differences in speech between these groups. To this end, 39 OCD patients and 37 HC were interviewed and recorded during six types of speech reports: yesterday, dream, old memory, positive image, negative image and neutral image. Also, the Obsessive-Compulsive Inventory-Revised (OCI-R) and the Yale Brown Obsessive-Compulsive Scale (Y-BOCS) were used to assess symptom severity. The graph-theoretical structural analysis of dream reports showed that OCD patients have significantly smaller lexical diversity, lower speech connectedness and a higher recurrence of words in comparison with HC. The other five report types failed to show differences between the groups, adding to the notion that dream reports are especially informative of speech structure in different psychiatric states. Further investigation is necessary to completely assess the potential of this tool in OCD.