Clinical implementation of risk calculator models in the clinical high-risk for psychosis (CHR-P) population has been hindered by heterogeneous risk distributions across study cohorts which could be attributed to pre-ascertainment illness progression. To examine this, we tested whether the duration of attenuated psychotic symptom (APS) worsening prior to baseline moderated performance of the North American prodrome longitudinal study 2 (NAPLS2) risk calculator. We also examined whether rates of cortical thinning, another marker of illness progression, bolstered clinical prediction models.

Participants from both the NAPLS2 and NAPLS3 samples were classified as either ‘long’ or ‘short’ symptom duration based on time since APS increase prior to baseline. The NAPLS2 risk calculator model was applied to each of these groups. In a subset of NAPLS3 participants who completed follow-up magnetic resonance imaging scans, change in cortical thickness was combined with the individual risk score to predict conversion to psychosis.

The risk calculator models achieved similar performance across the combined NAPLS2/NAPLS3 sample [area under the curve (AUC) = 0.69], the long duration group (AUC = 0.71), and the short duration group (AUC = 0.71). The shorter duration group was younger and had higher baseline APS than the longer duration group. The addition of cortical thinning improved the prediction of conversion significantly for the short duration group (AUC = 0.84), with a moderate improvement in prediction for the longer duration group (AUC = 0.78).

These results suggest that early illness progression differs among CHR-P patients, is detectable with both clinical and neuroimaging measures, and could play an essential role in the prediction of clinical outcomes.

Despite significant advancements in healthcare technology, digital health solutions – especially those for serious mental illnesses – continue to fall short of their potential across both clinical practice and efficacy. The utility and impact of medicine, including digital medicine, hinges on relationships, trust, and engagement, particularly in the field of mental health. This paper details results from Phase 1 of a two-part study that seeks to engage people with schizophrenia, their family members, and clinicians in co-designing a digital mental health platform for use across different cultures and contexts in the United States and India.

Each site interviewed a mix of clinicians, patients, and their family members in focus groups (n = 20) of two to six participants. Open-ended questions and discussions inquired about their own smartphone use and, after a demonstration of the mindLAMP platform, specific feedback on the app's utility, design, and functionality.

Our results based on thematic analysis indicate three common themes: increased use and interest in technology during coronavirus disease 2019 (COVID-19), concerns over how data are used and shared, and a desire for concurrent human interaction to support app engagement.

People with schizophrenia, their family members, and clinicians are open to integrating technology into treatment to better understand their condition and help inform treatment. However, app engagement is dependent on technology that is complementary – not substitutive – of therapeutic care from a clinician.

Social cognition has not previously been assessed in treatment-naive patients with chronic schizophrenia, in patients over 60 years of age, or in patients with less than 5 years of schooling.

We revised a commonly used measure of social cognition, the Reading the Mind in the Eyes Test (RMET), by expanding the instructions, using both self-completion and interviewer-completion versions (for illiterate respondents), and classifying each test administration as ‘successfully completed’ or ‘incomplete’. The revised instrument (RMET-CV-R) was administered to 233 treatment-naive patients with chronic schizophrenia (UT), 154 treated controls with chronic schizophrenia (TC), and 259 healthy controls (HC) from rural communities in China.

In bivariate and multivariate analyses, successful completion rates and RMET-CV-R scores (percent correct judgments about emotion exhibited in 70 presented slides) were highest in HC, intermediate in TC, and lowest in UT (adjusted completion rates, 97.0, 72.4, and 49.9%, respectively; adjusted RMET-CV-R scores, 45.4, 38.5, and 34.6%, respectively; all p < 0.02). Stratified analyses by the method of administration (self-completed v. interviewer-completed) and by education and age (‘educated-younger’ v. ‘undereducated-older’) show the same relationship between groups (i.e. NC>TC>UT), though not all differences remain statistically significant.

We find poorer social cognition in treatment-naive than in treated patients with chronic schizophrenia. The discriminant validity of RMET-CV-R in undereducated, older patients demonstrates the feasibility of administering revised versions of RMET to patients who may otherwise be considered ineligible due to education or age by changing the method of test administration and carefully assessing respondents' ability to complete the task successfully.

Antisaccade tasks can be used to index cognitive control processes, e.g. attention, behavioral inhibition, working memory, and goal maintenance in people with brain disorders. Though diagnoses of schizophrenia (SZ), schizoaffective (SAD), and bipolar I with psychosis (BDP) are typically considered to be distinct entities, previous work shows patterns of cognitive deficits differing in degree, rather than in kind, across these syndromes.

Large samples of individuals with psychotic disorders were recruited through the Bipolar-Schizophrenia Network on Intermediate Phenotypes 2 (B-SNIP2) study. Anti- and pro-saccade task performances were evaluated in 189 people with SZ, 185 people with SAD, 96 people with BDP, and 279 healthy comparison participants. Logistic functions were fitted to each group's antisaccade speed-performance tradeoff patterns.

Psychosis groups had higher antisaccade error rates than the healthy group, with SZ and SAD participants committing 2 times as many errors, and BDP participants committing 1.5 times as many errors. Latencies on correctly performed antisaccade trials in SZ and SAD were longer than in healthy participants, although error trial latencies were preserved. Parameters of speed-performance tradeoff functions indicated that compared to the healthy group, SZ and SAD groups had optimal performance characterized by more errors, as well as less benefit from prolonged response latencies. Prosaccade metrics did not differ between groups.

With basic prosaccade mechanisms intact, the higher speed-performance tradeoff cost for antisaccade performance in psychosis cases indicates a deficit that is specific to the higher-order cognitive aspects of saccade generation.

Antipsychotics are widely used for treating patients with psychosis, and target threshold psychotic symptoms. Individuals at clinical high risk (CHR) for psychosis are characterized by subthreshold psychotic symptoms. It is currently unclear who might benefit from antipsychotic treatment. Our objective was to apply a risk calculator (RC) to identify people that would benefit from antipsychotics.

Drawing on 400 CHR individuals recruited between 2011 and 2016, 208 individuals who received antipsychotic treatment were included. Clinical and cognitive variables were entered into an individualized RC for psychosis; personal risk was estimated and 4 risk components (negative symptoms-RC-NS, general function-RC-GF, cognitive performance-RC-CP, and positive symptoms-RC-PS) were constructed. The sample was further stratified according to the risk level. Higher risk was defined based on the estimated risk score (20% or higher).

In total, 208 CHR individuals received daily antipsychotic treatment of an olanzapine-equivalent dose of 8.7 mg with a mean administration duration of 58.4 weeks. Of these, 39 (18.8%) developed psychosis within 2 years. A new index of factors ratio (FR), which was derived from the ratio of RC-PS plus RC-GF to RC-NS plus RC-CP, was generated. In the higher-risk group, as FR increased, the conversion rate decreased. A small group (15%) of CHR individuals at higher-risk and an FR >1 benefitted from the antipsychotic treatment.

Through applying a personal risk assessment, the administration of antipsychotics should be limited to CHR individuals with predominantly positive symptoms and related function decline. A strict antipsychotic prescription strategy should be introduced to reduce inappropriate use.

Predicting and preventing relapse presents a crucial opportunity and first step to improve outcomes and reduce the care gap for persons living with schizophrenia. Using commercially available smartphones and smartwatches, technology now affords opportunities to capture real-time and longitudinal profiles of patients’ symptoms, cognition, physiology and social patterns. This novel data makes it possible to explore relationships between behaviours, physiology and symptoms, which may yield personalised relapse signals.

Smartphone Health Assessment for Relapse Prevention (SHARP), an international mental health research study supported by the Wellcome Trust, will inform the development of a scalable and sharable digital health solution to monitor personal risk of relapse. The resulting technology will be studied toward predicting and preventing relapse among individuals diagnosed with serious mental illness.

SHARP is a two-phase study with research sites in Boston, Massachusetts, and Bangalore and Bhopal, India. During phase 1, focus groups will be conducted at each study site to collect feedback on the design and features available on mindLAMP, a digital health platform. Individuals with serious mental illness will use mindLAMP for the duration of a year during phase 2.

The results of the research outlined in this protocol will guide the development of technology and digital tools to help address pervasive challenges in global mental health.

The digital tools developed as a result of this study, and participants’ experiences using them, may offer insight into opportunities to expand digital mental health resources and optimize their utilisation around the world.

The ability to manage emotions is an important social-cognitive domain impaired in schizophrenia and linked to functional outcome. The goal of our study was to examine the impact of cognitive enhancement therapy (CET) on the ability to manage emotions and brain functional connectivity in early-course schizophrenia.

Participants were randomly assigned to CET (n = 55) or an enriched supportive therapy (EST) control group (n = 45). The resting-state functional magnetic resonance imaging scans and measures of emotion management performances were collected at baseline, 9, and 18 months follow-up. The final sample consisted of 37 CET and 25 EST participants, including 19 CET and 12 EST participants with imaging data. Linear mixed-effects models investigated the impact of treatment on emotion management and functional connectivity from the amygdala to ventrolateral and dorsolateral prefrontal cortex (dlPFC).

The CET group showed significant improvement over time in emotion management compared to EST. Neither functional connectivity changes nor main group differences were observed following treatment. However, a significant between-group interaction showed that improved emotion management ability was associated with increased functional connectivity between the left amygdala and the left dlPFC in the CET group exclusively.

Our results replicate the previous work demonstrating that CET is effective at improving some aspects of social cognition in schizophrenia. We found evidence that improvement in emotion management may be associated with a change in amygdala-dlPFC connectivity. This fronto-limbic circuit may provide a mechanistic link between the biology of emotion management processes that can be enhanced in individuals with schizophrenia.

Symptoms of serious mental illness are multidimensional and often interact in complex ways. Generative models offer value in elucidating the underlying relationships that characterise these networks of symptoms.

In this paper we use generative models to find unique interactions of schizophrenia symptoms as experienced on a moment-by-moment basis.

Self-reported mood, anxiety and psychosis symptoms, self-reported measurements of sleep quality and social function, cognitive assessment, and smartphone touch screen data from two assessments modelled after the Trail Making A and B tests were collected with a digital phenotyping app for 47 patients in active treatment for schizophrenia over a 90-day period. Patients were retrospectively divided up into various non-exclusive subgroups based on measurements of depression, anxiety, sleep duration, cognition and psychosis symptoms taken in the clinic. Associated transition probabilities for the patient cohort and for the clinical subgroups were calculated using state transitions between adjacent 3-day timesteps of pairwise survey domains.

The three highest probabilities for associated transitions across all patients were anxiety-inducing mood (0.357, P < 0.001), psychosis-inducing mood (0.276, P < 0.001), and anxiety-inducing poor sleep (0.268, P < 0.001). These transition probabilities were compared against a validation set of 17 patients from a pilot study, and no significant differences were found. Unique symptom networks were found for clinical subgroups.

Using a generative model using digital phenotyping data, we show that certain symptoms of schizophrenia may play a role in elevating other schizophrenia symptoms in future timesteps. Symptom networks show that it is feasible to create clinically interpretable models that reflect the unique symptom interactions of psychosis-spectrum illness. These results offer a framework for researchers capturing temporal dynamics, for clinicians seeking to move towards preventative care, and for patients to better understand their lived experience.

Only 30% or fewer of individuals at clinical high risk (CHR) convert to full psychosis within 2 years. Efforts are thus underway to refine risk identification strategies to increase their predictive power. Our objective was to develop and validate the predictive accuracy and individualized risk components of a mobile app-based psychosis risk calculator (RC) in a CHR sample from the SHARP (ShangHai At Risk for Psychosis) program.

In total, 400 CHR individuals were identified by the Chinese version of the Structured Interview for Prodromal Syndromes. In the first phase of 300 CHR individuals, 196 subjects (65.3%) who completed neurocognitive assessments and had at least a 2-year follow-up assessment were included in the construction of an RC for psychosis. In the second phase of the SHARP sample of 100 subjects, 93 with data integrity were included to validate the performance of the SHARP-RC.

The SHARP-RC showed good discrimination of subsequent transition to psychosis with an AUC of 0.78 (p < 0.001). The individualized risk generated by the SHARP-RC provided a solid estimation of conversion in the independent validation sample, with an AUC of 0.80 (p = 0.003). A risk estimate of 20% or higher had excellent sensitivity (84%) and moderate specificity (63%) for the prediction of psychosis. The relative contribution of individual risk components can be simultaneously generated. The mobile app-based SHARP-RC was developed as a convenient tool for individualized psychosis risk appraisal.

The SHARP-RC provides a practical tool not only for assessing the probability that an individual at CHR will develop full psychosis, but also personal risk components that might be targeted in early intervention.

Neurological Examination Abnormalities (NES) are quantified by measuring subtle, partially localizable (cerebello-thalamo-prefrontal cortical circuit) and heritable neurological signs comprising sensory integration, motor coordination and complex motor sequencing that are associated with first-episode psychosis (FEP). A few studies have evaluated NES longitudinally and as a predictor for diagnostic and response classification, but these studies have been confounded, underpowered and divergent. We examined (1) baseline and longitudinal NES differences between diagnostic and year 1 response groups; (2) if NES predicts diagnostic and response groups and (3) relationships between clinical variables and NES measures in antipsychotic-naïve FEP.

NES and clinical measures were obtained for FEP-schizophrenia (FEP-SZ, n = 232), FEP non-schizophrenia (FEP-NSZ, n = 117) and healthy controls (HC, n = 204). Response groups with >25% improvement in average year 1 positive and negative symptomatology scores were classified as responsive (n = 97) and <25% improvement as non-responsive (n = 95). Analysis of covariance, NES trajectory analysis and logistic regression models assessed diagnostic and response group differences. Baseline and longitudinal NES relationships with clinical variables were performed with Spearman correlations. Data were adjusted for age, sex, race, socioeconomic status and handedness.

Cognitive perceptual (COGPER) score was better than repetitive motor (REPMOT) at differentiating FEP-SZ from FEP-NSZ and distinguishing responders from non-responders. We identified significant group-specific associations between COGPER and worse GAF, positive and negative symptomatology and some of these findings persisted at 1-year assessment.

NES are an easy to administer, bedside-elicited, endophenotypic measure and could be a cost-effective clinical tool in antipsychotic-naïve FEP.

Autism Spectrum Disorder (ASD) and schizophrenia are neurodevelopmental disorders which share substantial overlap in cognitive deficits during adulthood. However, treatment evaluation in ASD and treatment comparisons across ASD and schizophrenia are limited by a dearth of empirical work establishing the validity of a standard cognitive battery across ASD and schizophrenia. Promisingly, the MATRICS Consensus Cognitive Battery (MCCB) has been validated in schizophrenia and encompasses cognitive domains that are impacted in ASD. Thus, this study aimed to establish MCCB's generalizability from schizophrenia to ASD.

Community-residing adults with schizophrenia (N = 100) and ASD (N = 113) underwent MCCB assessment. Using multigroup confirmatory factor analysis, MCCB's transdiagnostic validity was evaluated by examining whether schizophrenia and ASD demonstrate the same configuration, magnitude, and directionality of relationships within and among measures and their underlying cognitive domains.

Across schizophrenia and ASD, the same subsets of MCCB measures inform three cognitive domains: processing speed, attention/working memory, and learning. Except for group means in category fluency, continuous performance, and spatial span, both groups show vastly comparable factor structures and characteristics.

To our knowledge, this study is the first to establish the validity of a standard cognitive battery in adults with ASD and furthermore the first to establish a cognitive battery's comparability across ASD and schizophrenia. Cognitive domain scores can be compared across new samples using weighted sums of MCCB scores resulting from this study. These findings highlight MCCB's applicability to ASD and support its utility for standardizing treatment evaluation of cognitive outcomes across the autism-schizophrenia spectrum.