Scott W. Woods

Aim: Core schemas are considered stable traits in clinical high risk (CHR) research. However, unexamined fluctuation may conceal belief changes that precede psychosis emergence. We examined whether schemas possess considerable within-person variability in CHR, and whether that variability exceeds that of controls. Methods: In NAPLS-2, 411 CHR and 194 control participants completed the Brief Core Schema Scales (BCSS) at least three times across 24 months. Random-intercept models identified intraclass correlations in CHR. Bayesian censored location-scale models, correcting for floor scores and mean level differences, compared within-person SD between groups. Results: Within-person variance accounted for 41.3%–50.6% of BCSS variance in CHR. Latent within-person SD was greater in CHR on all four subscales, most for negative-self (ratio 1.71, 95% CrI [1.50, 1.94]). This variability was largely independent of shared improvement and psychotropic medication effects. Conclusions: Core schemas fluctuate substantially in CHR compared to controls, particularly in negative self-beliefs. Single baseline scores may understate schema–outcome associations.

Objective: Cognitive models of psychosis implicate maladaptive core schemas in the development and emergence of psychotic symptoms, but no study has tested temporal dynamics between schemas and psychotic symptoms in a large sample of clinical high-risk (CHR) youth. Given their sizable role in cognitive behavioral therapies, the first-line treatment for CHR youth, core schemas may be key to both symptom development and treatment in this population. Methods: Participants were 578 CHR individuals from the second North American Prodrome Longitudinal Study (NAPLS-2) assessed across five timepoints. Time-lagged mixed-effects models tested forward (schema→outcome) and reverse (outcome→schema) paths for each Brief Core Schema Scales subscale with both psychotic symptom and functioning changes, controlling for autoregressive effects, time-varying depression, and linear time. Results: Twenty of 36 schema–outcome pairs showed significant prospective associations. Negative-other schemas were prospectively associated with worsening across nearly all psychosis symptom and functioning domains. Suspiciousness exhibited bidirectional associations with all four schema subscales. Grandiose ideas were uniquely prospectively associated with positive-self and negative-other schemas. Positive schemas were prospectively associated with decreases in suspiciousness, negative symptoms, and improvements in functioning. Effects were largely independent of depressive symptoms and robust across sensitivity analyses, with partial attenuation of self-schema effects on suspiciousness. Conclusions: Core schemas were prospectively associated with psychosis symptom and functioning change in CHR youth, with symptom-domain specificity and bidirectionality. These findings extend cognitive models of psychosis into CHR syndromes and highlight schemas as potentially modifiable treatment targets to improve outcomes in this population. Highlights: Schema changes preceded shifts in functioning and psychosis symptoms among CHR youth Negative-other schemas preceded worsening across nearly all outcome domains Suspiciousness was the only outcome with bidirectional schema associations Positive schemas preceded fewer negative symptoms and better global functioning

Introduction: Suicidal ideation and behaviors (SIB) are common among individuals at clinical high risk for psychosis (CHR‐P), but their relationship with the overall clinical profile remains unclear. This study aimed to identify sociodemographic/clinical factors associated with SIB in CHR‐P individuals and to examine whether subthreshold psychotic symptom severity increases with suicidal risk. Methods: Data from the AMP SCZ initiative included 1443 CHR‐P participants aged 12–30 years. SIB were assessed using the Columbia Suicide Severity Rating Scale (C‐SSRS). Three Elastic Net regression models examined associations between suicidal ideation severity, intensity, and behaviors using 54 variables spanning sociodemographics, general psychopathology, subthreshold psychotic symptoms, and socio‐occupational functioning. Moreover, ANCOVA compared overall psychotic symptom severity across five groups defined by increasing severity on the C‐SSRS. Results: Sixty‐eight percent of participants reported lifetime suicidal ideation, and 26% reported previous suicidal behaviors. Model performance was modest but significant (cross‐validated ideation severity: R ² = 0.161, RMSE = 0.916; ideation intensity: R ² = 0.193, RMSE = 0.898; suicidal behavior: accuracy = 0.685; AUC = 0.675; p < 0.001). Across all models, concurrent depressive symptoms, present/past diagnosis of depression, female sex at birth, cannabis use, visual/gustatory abnormalities, guilt, and lower role functioning were the most consistent correlates of SIB. Ideation was associated with lower disorganized communication expression and lower negative symptoms, whereas behaviors were associated with schizotypal personality disorder. Other core CHR‐P symptoms, including Unusual Thoughts and Experiences, did not contribute to any model. ANCOVA ( p < 0.001, partial η ² = 0.041) indicated increasing overall subthreshold psychotic symptoms across the SIB spectrum. Conclusion: SIB in CHR‐P individuals reflects a multidimensional clinical profile primarily outside of core psychosis‐risk symptoms. Trial Registration: ClinicalTrials.gov identifier: NCT05905003

Precision psychiatry research has strived to characterize the neural circuitry underlying transdiagnostic symptoms with the goal of devising biologically informed diagnostic and treatment strategies. However, few clinically actionable findings have emerged from these initiatives. One potential complication in reaching this goal is that similar symptoms may arise from distinct patterns of neurobiological dysfunction. Therefore, models that can provide insight into where an individual’s disordered circuits are located are important. Here, we developed a data-driven approach to identify individualized patterns of disordered connectivity using a large ( n =317), transdiagnostic population with extensive fMRI data. Connectome-based predictive modeling identified functional connectivity networks associated with a broad array of psychiatric symptoms. Symptom networks were aggregated, and connections contributing most strongly to each individual’s symptoms were identified to construct personalized “disordered circuit models”. Within these models, many features were associated with specific diagnoses and patterns of comorbidity, despite the symptoms exhibiting no diagnostic specificity. This suggests that similar symptoms can emerge through diverse neurobiological mechanisms depending on an individual’s diagnostic profile. By characterizing patient-specific neural dysfunction, disordered circuit modeling offers a promising framework for advancing precision psychiatry by accounting for diverse mechanisms from which symptoms may arise.

Schizophrenia is a neurodevelopmental disorder involving clinical and genetic heterogeneity. Multiple recurrent copy number variants (CNVs) increase risk for schizophrenia spectrum disorders (SSDs). However, it is unclear how known risk CNVs and broader genome-wide CNVs influence clinical variability. Furthermore, whether biological annotation of CNV scores can improve power for patient stratification is unknown. This study therefore investigated the relationships between severe SSD-related phenotypes and varied CNV metrics, including CNV burden affecting genes involved in different aspects of neurodevelopment. This study of 617 individuals with SSDs examined associations of two severe phenotypes-childhood-onset psychosis and borderline intellectual functioning (IQ)-with 1) known risk CNVs, 2) genome-wide deletion burden scores, and 3) novel scores capturing deletion burden in 18 previously validated and mutually exclusive gene sets, representing distinct aspects of neurodevelopment. Associations with borderline IQ were assessed for replicability in 233 relatives of SSD patients, 581 control subjects, and 9,930 youths from the Adolescent Brain Cognitive Development (ABCD) Study. Known SSD-risk CNVs (odds ratio=7.07, 95% CI=1.60, 31.32) and neurodevelopmental disorder (NDD)-risk CNVs (odds ratio=4.56, 95% CI=1.48, 14.10) were associated with borderline IQ in SSDs. Furthermore, beyond effects of known NDD-risk CNVs, deletion of genes involved in regulating gene expression during fetal brain development was associated with borderline IQ across SSD cases and noncases (odds ratio=2.57, 95% CI=1.44, 4.60) and in the ABCD cohort (odds ratio=1.33, 95% CI=1.00, 1.76). Exploratory structural MRI-based analyses showed associations between fetal gene regulatory gene deletions and altered gray matter volume (b=0.09, 95% CI=0.004, 0.17) and cortical thickness (b=0.14, 95% CI=0.05, 0.24) across SSD cases and noncases. The study results confirm contributions of known risk CNVs to severe phenotypes in SSDs, implicate disrupted fetal brain development in poor cognition, and demonstrate the utility of a neurodevelopmental framework for identifying mechanisms underlying severe SSD-relevant phenotypes.

Background: Posttraumatic stress disorder (PTSD) is a common comorbid diagnosis in psychotic disorders. However, little is known about the effect of comorbid PTSD in youth at clinical high risk (CHR) for psychosis. Purpose: The purpose of this study was to investigate group differences in symptom severity, risk for psychotic conversion and functioning among youth at CHR for psychosis with and without PTSD. Methods: This longitudinal study utilized data from N = 693 individuals who met criteria for CHR as part of the North American Prodrome Longitudinal Study 3 (NAPLS‐3). Individuals who met criteria for PTSD were compared to those who did not meet criteria for PTSD on demographic, symptom and functional variables. Results: Individuals diagnosed with PTSD ( N = 54) reported more severe overall positive symptoms ( p = 0.008), negative symptoms ( p = 0.043) and general symptoms ( p = 0.008) compared to those without the diagnosis ( N = 639). These symptom differences were primarily accounted for by more grandiose symptoms ( p = 0.026), decreased experience of emotions ( p = 0.043), sleep disturbance ( p = 0.043), dysphoric mood ( p = 0.012) and impaired tolerance to stress ( p = 0.008) in those with PTSD. In Cox proportional hazards models using the full sample with censoring at last follow‐up, PTSD diagnosis was not associated with time to psychosis conversion. Exploratory analyses of trauma subtype within the PTSD group also did not identify any trauma category significantly associated with conversion risk. Timing of trauma exposure (early childhood, middle childhood, adolescence) was not significantly associated with conversion. Conclusions: Our findings suggest that a diagnosis of PTSD is associated with more severe overlapping symptoms in individuals meeting the criteria for CHR but not psychotic conversion. Our findings underscore the importance of recognizing and treating comorbid psychiatric conditions including PTSD in CHR populations as a means of reducing symptoms and improving outcomes.

We depart from the feed-forward approach of brain-behavior modeling, which identifies the functional brain connectivity networks associated with performance on external tests, and instead introduce a feedback approach that reveals the brain systems those external tests reflect. In fMRI data from n = 302 demographically and clinically diverse participants, we a priori define connectivity networks for six cognitive constructs and employ kernel ridge regression to quantify each network's contribution to test performance. This approach provides a ranking of test scores according to the predictive power of each cognitive network, revealing which tests probe which brain networks. It further identifies combinations of measures that optimally probe predefined brain systems and evaluates how specific subtests influence composite scores, revealing when subset inclusion reinforces or weakens specific brain circuit and composite score relationships. This work opens an avenue of research by providing a framework for the development of test instruments guided by quantitative brain metrics.