S110. INTERESTING HAPPY THOUGHTS: STRUCTURAL, SEMANTIC AND EMOTIONAL ANALYSIS OF PSYCHOTIC SPEECH USING TIME-LIMITED POSITIVE IMAGE NARRATIVES

BACKGROUND: Speech and language analysis from free speech protocols has recently provided a discriminative signal, useful for early diagnosis of schizophrenia. Although different aspects of language (such as structural and semantic coherence) have been applied to different contexts using different data collection protocols, we need to standardize a safe and minimum-effort protocol that can reveal discriminative data, enabling large and remote dataset collection. Also, it is important to understand the correlations between semantic, structural and emotional analysis from the same dataset. In the past decade, we have developed a non-semantic structural analysis based on graph theory that was able to automatically discriminate speech samples from patients with schizophrenia diagnosis with more than 90% accuracy in chronic, first-episode patients, and in different languages. Moreover, we could verify correlations of structural attributes with negative symptoms, as well as with cognitive performances in patients and in typical children at regular school time. But the most predictive contents were a dream report (sometimes absent) or a negative image report (which could cause a psychological burden for some subjects). The current project aims to verify the accuracy to discriminate schizophrenia reports from 3 different positive image prompts, using a minimum of 30 seconds reports. Furthermore, we want to verify correlates between semantic, structural and emotional analysis. METHODS: We analyzed reports of 3 positive images from 31 subjects (10 matched controls and 21 at the first episode of psychosis - 11 with schizophrenia and 10 with bipolar disorder as a final diagnosis after 6 months of follow-up). We performed speech graph analysis to extract speech connectedness attributes. Next, we combined connectedness measures from the 3 prompts (after extracting collinear measures) to create a disorganization index (performing multilinear correlation with the PANSS negative subscale). We used this index as an input to a machine learning classifier to verify the accuracy to discriminate reports from the schizophrenia group. Finally, we studied the correlations between the connectedness-based disorganization index and minimum semantic coherence between consecutive sentences, and the emotional intensity measured by the proportion of emotional words. RESULTS: Speech connectedness of positive image reports was correlated with negative symptomatology severity measured by PANSS negative subscale (R2 = 0.73, p = 0.0160), and the disorganization index was able to discriminate the subjects diagnosed with schizophrenia disorder six months later with AUC = 0.82. Moreover, the disorganization index was negatively correlated with positive emotional intensity (Rho = -0.48, p = 0.0061), but not correlated with minimum semantic coherence (Rho = -0.06, p = 0.7442). Emotional intensity was not correlated with minimum semantic coherence (Rho = 0.17, p = 0.3458). DISCUSSION: This safe, short and standardized data collection protocol seems to be informative and reveals an interdependent relationship between different aspects of computational language analysis. With less than two minutes of oral speech data, we can accurately discriminate reports from the schizophrenia group at the first interview, and verify that the less connected the report, the fewer positive emotional words are used. Future directions point to the feasibility of automatic and remote access of a large and diverse population, allowing the upscaling of this type of assessment to big data.