Theatre improvisation training to promote social cognition: A novel recovery-oriented intervention for youths at clinical risk for psychosis.

AIM: Few interventions address social cognition or functioning in individuals at clinical risk (CR) for psychosis. Theatre Improvisation Training to Promote Social Cognition (TIPS) is a manualized intervention based on drama therapy. We aim to describe TIPS, evaluate feasibility and acceptability, and present a preliminary investigation of outcomes in a quasi-experimental design. METHODS: Thirty-six CR participants (15-25 years) were ascertained from the Philadelphia Neurodevelopmental Cohort. Twenty-six completed the TIPS protocol: 18 weekly 2-hour group sessions led by a theatre director and actor-assistant. Participants engaged in collaborative acting and improvisation exercises. Baseline and follow-up assessments included the Clinical Assessment Interview for Negative Symptoms (CAINS), Structured Interview for Prodromal Syndromes, Global Assessment of Functioning (GAF) and Penn Computerized Neurocognitive Battery (CNB), which includes social cognitive tests. Acceptability was assessed using focus groups. Preliminary outcomes were compared to CR controls who were not enrolled in the study but completed follow-up assessments using the same methods. RESULTS: There were no significant differences in baseline demographics, psychosis symptoms, or cognition between those who did and did not complete the protocol. Overall, TIPS was considered feasible and acceptable among CR. Preliminary outcomes suggest that TIPS may be effective in improving positive and negative psychosis-spectrum symptoms and GAF, but not performance on facial emotion processing. CONCLUSIONS: TIPS is a promising and acceptable intervention that may improve symptoms and functioning in CR while providing a framework for participants to develop more empowered and confident ways of relating to others. Larger randomized controlled trials investigating TIPS efficacy are warranted.

Quantitative assessment of structural image quality.

Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.