A neuropsychological approach to differentiating cannabis-induced and primary psychotic disorders.

AIM: Rates of cannabis use are elevated in early psychosis populations, rendering it difficult to determine if an episode of psychosis is related to cannabis use (e.g., cannabis-induced psychosis), or if substance use is co-occurring with a primary psychotic disorder (e.g., schizophrenia). Clinical presentations of these disorders are often indistinguishable, hindering assessment and treatment. Despite substantial research identifying cognitive deficits, eye movement abnormalities and speech impairment associated with primary psychotic disorders, these neuropsychological features have not been explored as targets for diagnostic differentiation in early psychosis. METHODS: Eighteen participants with cannabis-induced psychosis (Mage  = 21.9, SDage  = 4.25, 14 male) and 19 participants with primary psychosis (Mage  = 29.2, SDage  = 7.65, 17 male) were recruited from early intervention programs. Diagnoses were ascertained by primary treatment teams after a minimum of 6 months in the program. Participants completed tasks assessing cognitive performance, saccadic eye movements and speech. Clinical symptoms, trauma, substance use, premorbid functioning and illness insight were also assessed. RESULTS: Relative to individuals with primary psychosis, individuals with cannabis-induced psychosis demonstrated significantly better performance on the pro-saccade task, faster RT on pro- and anti-saccade tasks, better premorbid adjustment, and a higher degree of insight into their illness. There were no significant differences between groups on psychiatric symptoms, premorbid intellectual functioning, or problems related to cannabis use. CONCLUSIONS: In early stages of illness, reliance on traditional diagnostic tools or clinical interviews may be insufficient to distinguish between cannabis-induced and primary psychosis. Future research should continue to explore neuropsychological differences between these diagnoses to improve diagnostic accuracy.

Language Analytics for Assessment of Mental Health Status and Functional Competency.

BACKGROUND AND HYPOTHESIS: Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional language features to develop diagnostic and prognostic models, but less work has been done to use linguistic output to assess downstream functional outcomes, which is critically important for clinical care. In this work, we study the relationship between automated language composites and clinical variables that characterize mental health status and functional competency using predictive modeling. STUDY DESIGN: Conversational transcripts were collected from a social skills assessment of individuals with schizophrenia (n = 141), bipolar disorder (n = 140), and healthy controls (n = 22). A set of composite language features based on a theoretical framework of speech production were extracted from each transcript and predictive models were trained. The prediction targets included clinical variables for assessment of mental health status and social and functional competency. All models were validated on a held-out test sample not accessible to the model designer. STUDY RESULTS: Our models predicted the neurocognitive composite with Pearson correlation PCC = 0.674; PANSS-positive with PCC = 0.509; PANSS-negative with PCC = 0.767; social skills composite with PCC = 0.785; functional competency composite with PCC = 0.616. Language features related to volition, affect, semantic coherence, appropriateness of response, and lexical diversity were useful for prediction of clinical variables. CONCLUSIONS: Language samples provide useful information for the prediction of a variety of clinical variables that characterize mental health status and functional competency.

Attention bias modification in depression: A randomized trial using a novel, reward-based, eye-tracking approach.

BACKGROUND AND OBJECTIVES: Biased attention to negative information is a mechanism for risk and relapse in depression. Attentional bias modification (ABM) paradigms manipulate attention away from negative information to reduce this bias. ABM results have been mixed due to inconsistent methodologies and stimuli design. This randomized controlled trial used a novel approach to modifying attentional bias. METHODS: An eye tracker manipulated stimuli in response to participants' fixations to preferentially reward attention to positive stimuli by obscuring or enhancing image quality of negative and positive stimuli, respectively. Participants with major depressive disorder completed three 35-min sessions of active (n = 20) or sham (n = 20) ABM training. Attentional bias, memory for emotional words, and mood were assessed pre- and post-training. RESULTS: Training reduced negative attentional bias; relative to sham, active training participants focused significantly more on positive compared to negative stimuli in a free-viewing eye-tracker task (p = .038, ηp2 = 0.109) and, at trend, disengaged from sad information more quickly in a computerized task (p = .052, ηp2 = 0.096). Active training participants remembered more happy than sad words in an emotional word learning task, indicating a distal transfer of training to emotional memory (p = .036, ηp2 = 0.11). Training did not significantly affect mood in the one-week trial. LIMITATIONS: Future studies should build on this proof-of-principle study with larger sample sizes and more intensive treatment to explore which mechanisms of training may lead to improvements in mood. CONCLUSIONS: Attention biases in depression are modifiable through reward-based, eye-tracking training. These data suggest generalizability of training to other cognitive faculties - recall for affective information.

Patterns and perceptions of face-to-face and digital communication in the clinical high risk and early stages of psychosis.

Digital communication can mitigate some of the challenges inherent in face-to-face communication; however, it is unclear whether this communication format is preferred among youth with emerging psychosis. Therefore, we examined characteristics of face-to-face and digital communication in youth at clinical high risk for psychosis (CHR; n = 19) or in the first episode of psychosis (FEP; n = 57), as well as age-matched community comparisons (n = 51). Participants completed a 25-item self-report questionnaire to assess between- and within-group differences in the frequency of, satisfaction with, and barriers to face-to-face and digital communication. Compared to controls, both clinical groups endorsed a lower frequency of face-to-face and digital interactions across a range of communication partners. Controls reported higher satisfaction and fewer challenges with both communication formats than CHR and FEP groups. No between-group differences were identified among clinical participants in characteristics of face-to-face and digital interactions. Youth at clinical high risk for, or in the first episode of, psychosis exhibited similar communication patterns and perceptions that significantly diverged from community controls. These findings highlight that reductions in the quality and quantity of social interactions extend to digital contexts, and that both communication formats are relevant clinical targets in the high risk and early stages of psychosis.