BACKGROUND AND HYPOTHESES: Psychosis-risk inventories, like the Structured Interview for Psychosis-Risk Syndromes (SIPS), utilize symptom components and coalesce the information into a single-severity rating. These components include frequency, duration, in-the-moment conviction, retrospective insight, distress, and effect on social/role functioning. While combining components distills a great deal of important information into one practical symptom rating, this approach may mask important details of the greater clinical picture. STUDY DESIGN: Individuals at clinical high risk for psychosis (n = 115) were assessed with the SIPS Score Separable Components (SSSC) scale, created to accompany the SIPS positive items by dividing each item into the 7 components identified above. The latent structure of the SSSC was identified with an exploratory factor analysis (EFA). The factors were followed up with validation analyses including hypothesized cognitive, functioning, and symptom measures. Finally, clinical utility analyses were conducted to understand relationships between psychosis risk and common comorbidities. STUDY RESULTS: EFA revealed that the SSSC had 3 interpretable factors with the appropriate fit (rmsr = 0.018, TLI = 0.921): Conviction (in-the-moment conviction, retrospective insight), Distress-Impairment (distress, social/role functioning), and Frequency/Duration (frequency, duration). Conviction was minimally valid, Distress-Impairment had excellent validity, and Frequency/Duration was not related to any of the candidate validators. Conviction significantly predicted elevated psychosis risk. Distress-Impairment was related to common comorbid symptoms. Notably, the factors associated more strongly with clinical features than the traditional SIPS scores. CONCLUSIONS: The SSSC offers a supplemental approach to single-severity ratings, providing useful clinical insight, mechanistic understanding, and the potential for better capturing heterogeneity in this population.

Motor dysfunction in youth at clinical high risk (CHR) for psychosis is thought to reflect abnormal neurodevelopment within cortical-subcortical motor circuits and may be important for understanding clinical trajectories of CHR individuals. However, to date, our perspective of brain-behavior relationships has been informed solely by cross-sectional correlational studies linking behavior in the lab to brain structure or respective resting-state network connectivity. Here, we assess movement dysfunction from 2 perspectives: study 1 investigates the longitudinal progression of handwriting variability and positive symptoms in a sample of 91 CHR and healthy controls during a 12-month follow-up and study 2 involves a multiband functional magnetic resonance imaging task exploring the relationship between power grip force stability and motor network brain activation in a subset of participants. In study 1, we found that greater handwriting variability was a stable feature of CHR participants who experienced worse symptom progression. Study 2 results showed that CHR individuals had greater variability in their grip force and greater variability was related to decreased activation in the associative cortico-striatal network compared to controls. Motor variability may be a stable marker of vulnerability for psychosis risk and possible indicator of a vulnerable cortico-striatal brain network functioning in CHR participants, although the effects of antipsychotic medication should be considered.

There is accruing evidence of spontaneous dyskinesia in individuals with schizophrenia that is independent of medication exposure. Dyskinetic motor behavior is also present in individuals who are at high risk of schizophrenia and appears to have prognostic value for the development of psychosis. Nonetheless, it remains unclear whether dyskinesia is present in first-degree relatives of individuals with schizophrenia and thus associated with genetic liability for schizophrenia (i.e., an endophenotype), or whether the motor abnormality is a biomarker specific to the disease state spectrum. There is also limited information about links between dyskinesia and clinically relevant phenomena such as symptoms and cognition. Because dyskinesia marking genetic liability is likely to be subtle, we used sensitive instrument-based measurement of handwriting fluency to quantify dyskinesia in medicated individuals with schizophrenia or schizoaffective disorder, unaffected first-degree biological relatives of individuals with schizophrenia and schizoaffective disorder, and control participants. Results indicated that medicated individuals with schizophrenia or schizoaffective disorder exhibited more dyskinesia than both relatives and controls, with no difference between relatives and controls. Dyskinesia in individuals with schizophrenia or schizoaffective disorder was unrelated to current antipsychotic medication dosage, but associated with worse working memory function and greater positive formal thought disorder. These results provide evidence that dyskinesia is not associated with unexpressed genetic liability for schizophrenia.

Psychotic-like experiences (PLEs) may reflect elevated risk for serious mental illness, including psychosis. Although some studies report an association between PLEs and increased service utilization, there is evidence of unmet need among individuals with PLEs, with few studies exploring the relation between PLEs and intent to seek treatment. Characterizing factors that underlie intent to seek treatment in individuals not otherwise engaged in treatment may assist in determining the role of PLEs and future intentions, and help prioritize symptoms of greatest significance. Non-help-seeking participants ages 16-30 years (nanalysis = 2529) in a multi-site study completed online questionnaires of PLEs (PRIME with distress), depression (CESD), anxiety (STAI), and intention to seek mental health treatment. Associations between PLEs and intent to seek treatment were analyzed through multiple linear regressions. PRIME scores predicted intent to seek treatment, and item-level analyses suggested that this association was driven by items 12 (going crazy), 7 (wondering if people may hurt me), 5 (confused if things are real or imagination/dreams), and 1 (odd/unusual things going on). When accounting for the effects of anxiety and depression, PLE sum scores as well as individual experiences remained statistically significant, although effect sizes were negligible. Findings suggest that PLEs can play a role in identifying individuals who intend to seek mental health services and warrant further research in independent samples.

Introduction:Converging evidence suggests that hippocampal subregions subserve different functions, and are differentially affected by psychosis illness progression. Despite this fact, studies have not often studied subregions cross-sectionally across the psychosis spectrum. Furthermore, little is known about associations between subregion volumes and hippocampus-mediated cognition. Methods:A total of 222 participants (61 ultra high risk [UHR], 91 schizophrenia [SCZ], and 70 healthy volunteers) underwent a 3T MRI scan, as well as structured clinical interviews and a cognitive battery. Hippocampal subfield analysis was conducted with Freesurfer. We compared subregion volumes across groups, controlling for age, gender, and intracranial volume. We also examined associations in the UHR and SCZ groups between hippocampal subregion volumes and verbal learning, visual learning, and working memory. Results:We found a dose-dependent relationship such that the SCZ group showed significantly greater subfield volume reductions than the UHR group, which in turn showed significantly greater subfield volume reductions than the healthy volunteer group. We also found associations between subregion volume and cognitive performance in the visual memory, verbal memory, and working memory domains. Discussion:Our study examined hippocampal subregion volumes cross-sectionally in a large sample across the psychosis spectrum, as well as links with hippocampus-mediated cognitive function. Our findings suggest that hippocampal abnormalities emerge before first psychosis episode onset, and may be etiologically informative.

Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarkers has been significantly magnified, however, by recent advances in natural language processing (NLP) and machine learning (ML). Such methodologies allow for the rapid and objective measurement of language features, many of which are not easily recognized by human raters. Here we review the key findings on language production disturbance in psychosis. We also describe recent advances in the computational methods used to analyze language data, including methods for the automatic measurement of discourse coherence, syntactic complexity, poverty of content, referential coherence, and metaphorical language. Linguistic biomarkers of psychosis risk are now undergoing cross-validation, with attention to harmonization of methods. Future directions in extended CHR networks include studies of sources of variance, and combination with other promising biomarkers of psychosis risk, such as cognitive and sensory processing impairments likely to be related to language. Implications for the broader study of social communication, including reciprocal prosody, face expression and gesture, are discussed.

Background

Since its inception, research in the clinical high-risk (CHR) phase of psychosis has included identifying and exploring the impact of relevant socio-demographic factors. Employing a narrative review approach and highlighting work from the United States, sociocultural and contextual factors potentially affecting the screening, assessment, and service utilization of youth at CHR were reviewed from the current literature.

Results

Existing literature suggests that contextual factors impact the predictive performance of widely used psychosis-risk screening tools and may introduce systemic bias and challenges to differential diagnosis in clinical assessment. Factors reviewed include racialized identity, discrimination, neighborhood context, trauma, immigration status, gender identity, sexual orientation, and age. Furthermore, racialized identity and traumatic experiences appear related to symptom severity and service utilization among this population.

Conclusions

Collectively, a growing body of research from the United States and beyond suggests that considering context in psychosis-risk assessment can provide a more accurate appraisal of the nature of risk for psychosis, render more accurate results improving the field's prediction of conversion to psychosis, and enhance our understanding of psychosis-risk trajectories. More work is needed in the U.S. and across the globe to uncover how structural racism and systemic biases impact screening, assessment, treatment, and clinical and functional outcomes for those at CHR.

Background

Malhi et al. in this issue critique the clinical high risk (CHR) syndrome for psychosis.

Method

Response to points of critique.

Results

We agree that inconsistency in CHR nomenclature should be minimized. We respectfully disagree on other points. In our view: a) individuals with CHR and their families need help, using existing interventions, even though we do not yet fully understand disease mechanisms; b) substantial progress has been made in identification of biomarkers; c) symptoms used to identify CHR are specific to psychotic illnesses; d) CHR diagnosis is not "extremely difficult"; e) the pattern of progression, although heterogenous, is discernible; f) "psychosis-like symptoms" are common but are not used to identify CHR; and g) on the point described as 'the real risk,' CHR diagnosis does not frequently cause harmful stigma.

Discussion

Malhi et al.'s arguments do not fairly characterize progress in the CHR field nor efforts to minimize stigma. That said, much work remains in areas of consistent nomenclature, mechanisms of disease, dissecting heterogeneity, and biomarkers. With regard to what the authors term the "real risk" of stigma associated with a CHR "label," however, our view is that avoiding words like "risk" and "psychosis" reinforces the stigma that both they and we mean to oppose. Moreover, patients and their families benefit from being given a term that describes what is happening to them.

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.