Abnormalities in nonverbal communication are a hallmark of schizophrenia. Results from studies using symptom rating scales suggest that these abnormalities are profound (i.e., 3-5 SDs) and occur across virtually every channel of vocal expression. Computerized acoustic analytic technologies, used to overcome practical and psychometric limitations with symptom rating scales, have found much more benign and isolated abnormalities. To better understand vocal deficits in schizophrenia and to advance acoustic analytic technologies for clinical and research applications, we examined archived speech samples from 5 separate studies, each using different speaking tasks (patient N = 309; control N = 117). We sought to: (a) use Principal Component Analysis (PCA) to identify independent vocal expression measures from a large set of variables, (b) quantify how patients with schizophrenia are abnormal with respect to these variables, (c) evaluate the impact of demographic and contextual factors (e.g., study site, speaking task), and (d) examine the relationship between clinically-rated psychiatric symptoms and vocal variables. PCA identified 7 independent markers of vocal expression. Most of these vocal variables varied considerably as a function of context and many were associated with demographic factors. After controlling for context and demographics, there were no meaningful differences in vocal expression between patients and controls. Within patients, vocal variables were associated with a range of psychiatric symptoms-though only pause length was significantly associated with clinically rated negative symptoms. The discussion centers on explaining the apparent discordance between clinical and computerized speech measures.

The latent structure of schizotypy and psychosis-spectrum symptoms remains poorly understood. Furthermore, molecular genetic substrates are poorly defined, largely due to the substantial resources required to collect rich phenotypic data across diverse populations. Sample sizes of phenotypic studies are often insufficient for advanced structural equation modeling approaches. In the last 50 years, efforts in both psychiatry and psychological science have moved toward (1) a dimensional model of psychopathology (eg, the current Hierarchical Taxonomy of Psychopathology [HiTOP] initiative), (2) an integration of methods and measures across traits and units of analysis (eg, the RDoC initiative), and (3) powerful, impactful study designs maximizing sample size to detect subtle genomic variation relating to complex traits (the Psychiatric Genomics Consortium [PGC]). These movements are important to the future study of the psychosis spectrum, and to resolving heterogeneity with respect to instrument and population. The International Consortium of Schizotypy Research is composed of over 40 laboratories in 12 countries, and to date, members have compiled a body of schizotypy- and psychosis-related phenotype data from more than 30000 individuals. It has become apparent that compiling data into a protected, relational database and crowdsourcing analytic and data science expertise will result in significant enhancement of current research on the structure and biological substrates of the psychosis spectrum. The authors present a data-sharing infrastructure similar to that of the PGC, and a resource-sharing infrastructure similar to that of HiTOP. This report details the rationale and benefits of the phenotypic data collective and presents an open invitation for participation.