An open label pilot trial of low-dose lithium for young people at ultra-high risk for psychosis.

AIM: Lithium, even at low doses, appears to offer neuroprotection against a wide variety of insults. In this controlled pilot, we examined the safety (i.e., side-effect profile) of lithium in a sample of young people identified at ultra-high risk (UHR) for psychosis. The secondary aim was to explore whether lithium provided a signal of clinical efficacy in reducing transition to psychosis compared with treatment as usual (TAU). METHODS: Young people attending the PACE clinic at Orygen, Melbourne, were prescribed a fixed dose (450 mg) of lithium (n = 25) or received TAU (n = 78). The primary outcome examined side-effects, with transition to psychosis, functioning and measures of psychopathology assessed as secondary outcomes. RESULTS: Participants in both groups were functionally compromised (lithium group GAF = 56.6; monitoring group GAF = 56.9). Side-effect assessment indicated that lithium was well-tolerated. 64% (n = 16) of participants in the lithium group were lithium-adherent to week 12. Few cases transitioned to psychosis across the study period; lithium group 4% (n = 1); monitoring group 7.7% (n = 6). There was no difference in time to transition to psychosis between the groups. No group differences were observed in other functioning and symptom domains, although all outcomes improved over time. CONCLUSIONS: With a side-effect profile either comparable to, or better than UHR antipsychotic trials, lithium might be explored for further research with UHR young people. A definitive larger trial is needed to determine the efficacy of lithium in this cohort.

Nomenclature for psychosis risk in Japan: Survey results from high-risk individuals, caregivers, and mental health professionals.

BACKGROUND: Labeling terms for high-risk state for psychosis, such as 'ultra-high risk' (UHR), 'attenuated psychosis syndrome' (APS), and 'at-risk mental state' (ARMS), have been criticized for their potential to lead to stigma. Hence, mental health service users in Melbourne recently proposed new terms illustrating the at-risk concept ['pre-diagnosis stage' (PDS), 'potential of developing a mental illness' (PDMI), and 'disposition for developing a mental illness' (DDMI)]. We aimed at testing the suitability of these existing and new terms in the clinical settings of early psychiatric intervention in Japan. METHODS: At two centers of early intervention (Toyama and Tokyo), a questionnaire on the understanding and opinion of high-risk terminology was administered to 62 high-risk patients, 44 caregivers, and 64 clinicians. The questionnaire contained the existing and new terms, where the term ARMS was translated into two different Japanese terms ARMS-psychosis and ARMS-kokoro. Participants' opinion on the disclosure of high-risk status was also obtained. RESULTS: ARMS-kokoro was most preferred, least stigmatizing, and best explaining the patients' difficulties for all groups, while UHR and other terms including the Japanese word 'psychosis' (i.e., APS and ARMS-psychosis) were not preferred. New labeling terms were generally not well received. All groups preferred full disclosure of high-risk terms by the psychiatrist with or without the presence of family members. CONCLUSION: The term ARMS-kokoro was commonly accepted as a favorable labeling term for the high-risk state for psychosis in Japan. However, another translation ARMS-psychosis was considered stigmatizing, demonstrating the importance of appropriate translation of high-risk terminology into local languages.

PsyCog: A computerised mini battery for assessing cognition in psychosis.

Despite the functional impact of cognitive deficit in people with psychosis, objective cognitive assessment is not typically part of routine clinical care. This is partly due to the length of traditional assessments and the need for a highly trained administrator. Brief, automated computerised assessments could help to address this issue. We present data from an evaluation of PsyCog, a computerised, non-verbal, mini battery of cognitive tests. Healthy Control (HC) (N = 135), Clinical High Risk (CHR) (N = 233), and First Episode Psychosis (FEP) (N = 301) participants from a multi-centre prospective study were assessed at baseline, 6 months, and 12 months. PsyCog was used to assess cognitive performance at baseline and at up to two follow-up timepoints. Mean total testing time was 35.95 min (SD = 2.87). Relative to HCs, effect sizes of performance impairments were medium to large in FEP patients (composite score G = 1.21, subtest range = 0.52-0.88) and small to medium in CHR patients (composite score G = 0.59, subtest range = 0.18-0.49). Site effects were minimal, and test-retest reliability of the PsyCog composite was good (ICC = 0.82-0.89), though some practice effects and differences in data completion between groups were found. The present implementation of PsyCog shows it to be a useful tool for assessing cognitive function in people with psychosis. Computerised cognitive assessments have the potential to facilitate the evaluation of cognition in psychosis in both research and in clinical care, though caution should still be taken in terms of implementation and study design.

A Dynamical Systems View of Psychiatric Disorders-Theory: A Review.

Importance: Psychiatric disorders may come and go with symptoms changing over a lifetime. This suggests the need for a paradigm shift in diagnosis and treatment. Here we present a fresh look inspired by dynamical systems theory. This theory is used widely to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. Observations: In the dynamical systems view, we propose the healthy state has a basin of attraction representing its resilience, while disorders are alternative attractors in which the system can become trapped. Rather than an immutable trait, resilience in this approach is a dynamical property. Recent work has demonstrated the universality of generic dynamical indicators of resilience that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforests and tipping elements of the climate system. Other dynamical systems tools are used in ecology and climate science to infer causality from time series. Moreover, experiences in ecological restoration confirm the theoretical prediction that under some conditions, short interventions may invoke long-term success when they flip the system into an alternative basin of attraction. All this implies practical applications for psychiatry, as are discussed in part 2 of this article. Conclusions and Relevance: Work in the field of dynamical systems points to novel ways of inferring causality and quantifying resilience from time series. Those approaches have now been tried and tested in a range of complex systems. The same tools may help monitoring and managing resilience of the healthy state as well as psychiatric disorders.

A Dynamical Systems View of Psychiatric Disorders-Practical Implications: A Review.

Importance: Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry. Observations: Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions. Conclusions and Relevance: Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.

Staging 2.0: refining transdiagnostic clinical staging frameworks to enhance reliability and utility for youth mental health.

Globally, 75% of depressive, bipolar, and psychotic disorders emerge by age 25 years. However, these disorders are often preceded by non-specific symptoms or attenuated clinical syndromes. Difficulties in determining optimal treatment interventions for these emerging mental disorders, and uncertainties about accounting for co-occurring psychopathology and illness trajectories, have led many youth mental health services to adopt transdiagnostic clinical staging frameworks. In this Health Policy paper, an international working group highlights ongoing challenges in applying transdiagnostic staging frameworks in clinical research and practice, and proposes refinements to the transdiagnostic model to enhance its reliability, consistent recording, and clinical utility. We introduce the concept of within-stage heterogeneity and describe the advantages of defining stage in terms of clinical psychopathology and stage modifiers. Using examples from medicine, we discuss the utility of categorising stage modifiers into factors associated with progression (ie, potential predictors of stage transition) and extension (ie, factors associated with the current presentation that add complexity to treatment selection). Lastly, we suggest how it is possible to revise the currently used transdiagnostic staging approach to incorporate these key concepts, and how the revised framework could be applied in clinical and research practice.

A naturalistic cohort study of first-episode schizophrenia spectrum disorder: A description of the early phase of illness in the PSYSCAN cohort.

BACKGROUND: We examined the course of illness over a 12-month period in a large, international multi-center cohort of people with a first-episode schizophrenia spectrum disorder (FES) in a naturalistic, prospective study (PSYSCAN). METHOD: Patients with a first episode of schizophrenia, schizoaffective disorder (depressive type) or schizophreniform disorder were recruited at 16 institutions in Europe, Israel and Australia. Participants (N = 304) received clinical treatment as usual throughout the study. RESULTS: The mean age of the cohort was 24.3 years (SD = 5.6), and 67 % were male. At baseline, participants presented with a range of intensities of psychotic symptoms, 80 % were taking antipsychotic medication, 68 % were receiving psychological treatment, with 46.5 % in symptomatic remission. The mean duration of untreated psychosis was 6.2 months (SD = 17.0). After one year, 67 % were in symptomatic remission and 61 % were in functional remission, but 31 % had been readmitted to hospital at some time after baseline. In the cohort as a whole, depressive symptoms remained stable over the follow-up period. In patients with a current depressive episode at baseline, depressive symptoms slightly improved. Alcohol, tobacco and cannabis were the most commonly used substances, with daily users of cannabis ranging between 9 and 11 % throughout the follow-up period. CONCLUSIONS: This study provides valuable insight into the early course of a broad range of clinical and functional aspects of illness in FES patients in routine clinical practice.

Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis.

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.

Non-psychotic Outcomes in Young People at Ultra-High Risk of Developing a Psychotic Disorder: A Long-Term Follow-up Study.

BACKGROUND: The majority of individuals at ultra-high risk (UHR) for psychosis do not transition to a full threshold psychotic disorder. It is therefore important to understand their longer-term clinical and functional outcomes, particularly given the high prevalence of comorbid mental disorders in this population at baseline. AIMS: This study investigated the prevalence of non-psychotic disorders in the UHR population at entry and long-term follow-up and their association with functional outcomes. Persistence of UHR status was also investigated. STUDY DESIGN: The sample comprised 102 UHR young people from the Personal Assessment and Crisis Evaluation (PACE) Clinic who had not transitioned to psychosis by long-term follow-up (mean = 8.8 years, range = 6.8-12.1 years since baseline). RESULTS: Eighty-eight percent of participants at baseline were diagnosed with at least one mental disorder, the majority of which were mood disorders (78%), anxiety disorders (35%), and substance use disorders (SUDs) (18%). This pattern of disorder prevalence continued at follow-up, though prevalence was reduced, with 52% not meeting criteria for current non-psychotic mental disorder. However, 35% of participants developed a new non-psychotic mental disorder by follow-up. Presence of a continuous non-psychotic mental disorder was associated with poorer functional outcomes at follow-up. 28% of participants still met UHR criteria at follow-up. CONCLUSIONS: The study adds to the evidence base that a substantial proportion of UHR individuals who do not transition to psychosis experience persistent attenuated psychotic symptoms and persistent and incident non-psychotic disorders over the long term. Long-term treatment and re-entry into services is indicated.

Psychosis superspectrum I: Nosology, etiology, and lifespan development.

This review describes the Hierarchical Taxonomy of Psychopathology (HiTOP) model of psychosis-related psychopathology, the psychosis superspectrum. The HiTOP psychosis superspectrum was developed to address shortcomings of traditional diagnoses for psychotic disorders and related conditions including low reliability, arbitrary boundaries between psychopathology and normality, high symptom co-occurrence, and heterogeneity within diagnostic categories. The psychosis superspectrum is a transdiagnostic dimensional model comprising two spectra-psychoticism and detachment-which are in turn broken down into fourteen narrow components, and two auxiliary domains-cognition and functional impairment. The structure of the spectra and their components are shown to parallel the genetic structure of psychosis and related traits. Psychoticism and detachment have distinct patterns of association with urbanicity, migrant and ethnic minority status, childhood adversity, and cannabis use. The superspectrum also provides a useful model for describing the emergence and course of psychosis, as components of the superspectrum are relatively stable over time. Changes in psychoticism predict the onset of psychosis-related psychopathology, whereas changes in detachment and cognition define later course. Implications of the superspectrum for genetic, socio-environmental, and longitudinal research are discussed. A companion review focuses on neurobiology, treatment response, and clinical utility of the superspectrum, and future research directions.

The temporal association between social isolation, distress, and psychotic experiences in individuals at clinical high-risk for psychosis.

BACKGROUND: Psychotic experiences (PEs) and social isolation (SI) seem related during early stages of psychosis, but the temporal dynamics between the two are not clear. Literature so far suggests a self-perpetuating cycle wherein momentary increases in PEs lead to social withdrawal, which, subsequently, triggers PEs at a next point in time, especially when SI is associated with increased distress. The current study investigated the daily-life temporal associations between SI and PEs, as well as the role of SI-related and general affective distress in individuals at clinical high risk (CHR) for psychosis. METHODS: We used experience sampling methodology in a sample of 137 CHR participants. We analyzed the association between SI, PEs, and distress using time-lagged linear mixed-effects models. RESULTS: SI did not predict next-moment fluctuations in PEs, or vice versa. Furthermore, although SI-related distress was not predictive of subsequent PEs, general affective distress during SI was a robust predictor of next-moment PEs. CONCLUSIONS: Our results suggest that SI and PEs are not directly related on a moment-to-moment level, but a negative emotional state when alone does contribute to the risk of PEs. These findings highlight the role of affective wellbeing during early-stage psychosis development.

Transdiagnostic risk identification: A validation study of the Clinical High At Risk Mental State (CHARMS) criteria.

A set of clinical criteria, the Clinical High At-Risk Mental State (CHARMS) criteria, have been developed to identify symptomatic young people who are at-risk of disorder progression. The current study aimed to validate the CHARMS criteria by testing whether they prospectively identify individuals at-risk of progressing from attenuated symptomatology to a first episode of serious mental disorder, namely first episode psychosis, first episode mania, severe major depression, and borderline personality disorder. 121 young people completed clinical evaluations at baseline, 6- and 12-month follow-up. The Kaplan-Meier method was used to assess transition rates. Cox regression and LASSO were used to examine baseline clinical predictors of transition. Linear mixed effects modelling was used to examine symptom severity. 28 % of CHARMS+ individuals transitioned to a Stage 2 disorder by 12-month follow-up. The CHARMS+ group had more severe symptoms at follow-up than the CHARMS- group. 96 % of Stage 2 transitions were initially to severe depression. Meeting criteria for multiple CHARMS subgroups was associated with higher transition risk: meeting one at-risk group = 24 %; meeting two at-risk groups = 17 %, meeting three at-risk groups = 55 %, meeting four at-risk groups = 50 %. The strongest baseline predictor of transition was severity of depressive symptoms. The CHARMS criteria identified a group of individuals at-risk of imminent onset of severe mental disorder, particularly severe depression. Larger scale studies and longer follow-up periods are required to validate and extend these findings.

Proteomic Biomarkers for the Prediction of Transition to Psychosis in Individuals at Clinical High Risk: A Multi-cohort Model Development Study.

Psychosis risk prediction is one of the leading challenges in psychiatry. Previous investigations have suggested that plasma proteomic data may be useful in accurately predicting transition to psychosis in individuals at clinical high risk (CHR). We hypothesized that an a priori-specified proteomic prediction model would have strong predictive accuracy for psychosis risk and aimed to replicate longitudinal associations between plasma proteins and transition to psychosis. This study used plasma samples from participants in 3 CHR cohorts: the North American Prodrome Longitudinal Studies 2 and 3, and the NEURAPRO randomized control trial (total n = 754). Plasma proteomic data were quantified using mass spectrometry. The primary outcome was transition to psychosis over the study follow-up period. Logistic regression models were internally validated, and optimism-corrected performance metrics derived with a bootstrap procedure. In the overall sample of CHR participants (age: 18.5, SD: 3.9; 51.9% male), 20.4% (n = 154) developed psychosis within 4.4 years. The a priori-specified model showed poor risk-prediction accuracy for the development of psychosis (C-statistic: 0.51 [95% CI: 0.50, 0.59], calibration slope: 0.45). At a group level, Complement C8B, C4B, C5, and leucine-rich α-2 glycoprotein 1 (LRG1) were associated with transition to psychosis but did not surpass correction for multiple comparisons. This study did not confirm the findings from a previous proteomic prediction model of transition from CHR to psychosis. Certain complement proteins may be weakly associated with transition at a group level. Previous findings, derived from small samples, should be interpreted with caution.

The self, neuroscience and psychosis study: Testing a neurophenomenological model of the onset of psychosis.

AIM: Basic self disturbance is a putative core vulnerability marker of schizophrenia spectrum disorders. The primary aims of the Self, Neuroscience and Psychosis (SNAP) study are to: (1) empirically test a previously described neurophenomenological self-disturbance model of psychosis by examining the relationship between specific clinical, neurocognitive, and neurophysiological variables in UHR patients, and (2) develop a prediction model using these neurophenomenological disturbances for persistence or deterioration of UHR symptoms at 12-month follow-up. METHODS: SNAP is a longitudinal observational study. Participants include 400 UHR individuals, 100 clinical controls with no attenuated psychotic symptoms, and 50 healthy controls. All participants complete baseline clinical and neurocognitive assessments and electroencephalography. The UHR sample are followed up for a total of 24 months, with clinical assessment completed every 6 months. RESULTS: This paper presents the protocol of the SNAP study, including background rationale, aims and hypotheses, design, and assessment procedures. CONCLUSIONS: The SNAP study will test whether neurophenomenological disturbances associated with basic self-disturbance predict persistence or intensification of UHR symptomatology over a 2-year follow up period, and how specific these disturbances are to a clinical population with attenuated psychotic symptoms. This may ultimately inform clinical care and pathoaetiological models of psychosis.

Normative Modeling of Brain Morphometry in Clinical High Risk for Psychosis.

Importance: The lack of robust neuroanatomical markers of psychosis risk has been traditionally attributed to heterogeneity. A complementary hypothesis is that variation in neuroanatomical measures in individuals at psychosis risk may be nested within the range observed in healthy individuals. Objective: To quantify deviations from the normative range of neuroanatomical variation in individuals at clinical high risk for psychosis (CHR-P) and evaluate their overlap with healthy variation and their association with positive symptoms, cognition, and conversion to a psychotic disorder. Design, Setting, and Participants: This case-control study used clinical-, IQ-, and neuroimaging software (FreeSurfer)-derived regional measures of cortical thickness (CT), cortical surface area (SA), and subcortical volume (SV) from 1340 individuals with CHR-P and 1237 healthy individuals pooled from 29 international sites participating in the Enhancing Neuroimaging Genetics Through Meta-analysis (ENIGMA) Clinical High Risk for Psychosis Working Group. Healthy individuals and individuals with CHR-P were matched on age and sex within each recruitment site. Data were analyzed between September 1, 2021, and November 30, 2022. Main Outcomes and Measures: For each regional morphometric measure, deviation scores were computed as z scores indexing the degree of deviation from their normative means from a healthy reference population. Average deviation scores (ADS) were also calculated for regional CT, SA, and SV measures and globally across all measures. Regression analyses quantified the association of deviation scores with clinical severity and cognition, and 2-proportion z tests identified case-control differences in the proportion of individuals with infranormal (z < -1.96) or supranormal (z > 1.96) scores. Results: Among 1340 individuals with CHR-P, 709 (52.91%) were male, and the mean (SD) age was 20.75 (4.74) years. Among 1237 healthy individuals, 684 (55.30%) were male, and the mean (SD) age was 22.32 (4.95) years. Individuals with CHR-P and healthy individuals overlapped in the distributions of the observed values, regional z scores, and all ADS values. For any given region, the proportion of individuals with CHR-P who had infranormal or supranormal values was low (up to 153 individuals [<11.42%]) and similar to that of healthy individuals (<115 individuals [<9.30%]). Individuals with CHR-P who converted to a psychotic disorder had a higher percentage of infranormal values in temporal regions compared with those who did not convert (7.01% vs 1.38%) and healthy individuals (5.10% vs 0.89%). In the CHR-P group, only the ADS SA was associated with positive symptoms (ß = -0.08; 95% CI, -0.13 to -0.02; P = .02 for false discovery rate) and IQ (ß = 0.09; 95% CI, 0.02-0.15; P = .02 for false discovery rate). Conclusions and Relevance: In this case-control study, findings suggest that macroscale neuromorphometric measures may not provide an adequate explanation of psychosis risk.

Combining Clinical With Cognitive or Magnetic Resonance Imaging Data for Predicting Transition to Psychosis in Ultra High-Risk Patients: Data From the PACE 400 Cohort.

BACKGROUND: Multimodal modeling that combines biological and clinical data shows promise in predicting transition to psychosis in individuals who are at ultra-high risk. Individuals who transition to psychosis are known to have deficits at baseline in cognitive function and reductions in gray matter volume in multiple brain regions identified by magnetic resonance imaging. METHODS: In this study, we used Cox proportional hazards regression models to assess the additive predictive value of each modality-cognition, cortical structure information, and the neuroanatomical measure of brain age gap-to a previously developed clinical model using functioning and duration of symptoms prior to service entry as predictors in the Personal Assessment and Crisis Evaluation (PACE) 400 cohort. The PACE 400 study is a well-characterized cohort of Australian youths who were identified as ultra-high risk of transitioning to psychosis using the Comprehensive Assessment of At Risk Mental States (CAARMS) and followed for up to 18 years; it contains clinical data (from N = 416 participants), cognitive data (n = 213), and magnetic resonance imaging cortical parameters extracted using FreeSurfer (n = 231). RESULTS: The results showed that neuroimaging, brain age gap, and cognition added marginal predictive information to the previously developed clinical model (fraction of new information: neuroimaging 0%-12%, brain age gap 7%, cognition 0%-16%). CONCLUSIONS: In summary, adding a second modality to a clinical risk model predicting the onset of a psychotic disorder in the PACE 400 cohort showed little improvement in the fit of the model for long-term prediction of transition to psychosis.

Treatment of schizotypal disorder: a protocol for a systematic review of the evidence and recommendations for clinical practice.

INTRODUCTION: Schizotypal disorder is associated with a high level of disability at an individual level and high societal costs. However, clinical recommendations for the treatment of schizotypal disorder are scarce and based on limited evidence. This review aims to synthesise the current evidence on treatment for schizotypal disorder making recommendations for clinical practice. METHODS AND ANALYSIS: This systematic review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search will be performed in PsychArticles, Embase, Medline and Cochrane Central Register of Controlled Trials. Additionally, we will search for relevant articles manually. Inclusion criteria are published studies including individuals diagnosed with schizotypal personality disorder according to Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, or schizotypal disorder according to International Classification of Diseases (ICD) criteria. We will include interventional studies comprising any pharmacological and non-pharmacological treatment trials for patients with schizotypal disorder, and all relevant outcome measures will be reported. Risk of bias will be assessed by Cochrane risk-of-bias tools. Data will be synthesised using narrative or thematic analysis and, if suitable, through meta-analysis. ETHICS AND DISSEMINATION: No original data will be collected as part of this study and ethics approval is, therefore, not applicable. The results will be disseminated through peer-reviewed publication and presented at international scientific meetings. We will aim at submitting the final paper for publication within 4 months of completion of analyses. Furthermore, this systematic review will inform clinicians and researchers on the current state of evidence on treatment for schizotypal disorder. Findings may guide proposals for further research and potentially guide recommendations for clinical practice using the Grading of Recommendations Assessment, Development and Evaluation. PROSPERO REGISTRATION NUMBER: CRD42022375001.

Bipolar At-Risk Criteria and Risk of Bipolar Disorder Over 10 or More Years.

Importance: Predicting the onset of bipolar disorder (BD) could facilitate preventive treatments. Among risk measures, bipolar at-risk (BAR) criteria have shown promise in predicting onset of bipolar disorder in the first year in clinical cohorts; however, it is not known whether BAR criteria are associated with the onset of BD in the longer term. Objective: To assess the association of BAR criteria with onset of BD over 10 to 13 years follow-up. Design, Setting, and Participants: This prospective cohort study, completed between May 1, 2020, and November 7, 2022, included consenting people seeking help for nonpsychotic major mental health difficulties, including mood, personality, and substance use disorders, who were originally recruited at ages 15 to 25 years from a tertiary youth mental health setting in metropolitan Melbourne, Victoria, Australia, from May 1, 2008, to September 30, 2010. Exposure: Meeting BAR criteria at baseline. Criteria included subthreshold mania, cyclothymic features, subthreshold depression, and family history of BD. A matched clinical comparison group was recruited from the same help-seeking population. Main Outcomes and Measures: The primary outcome was expert consensus diagnosis of BD I or II based on the Mini International Neuropsychiatric Interview, self-reported information collected through online assessments, and linked data on mental health service utilization in Victoria over 10 to 13 years of follow-up. Results: Among 69 eligible participants, follow-up data were available for 60 (88.2%). The mean (SD) age at the end of follow-up was 32.9 (2.8) years, and 49 (81.7%) were women. A total of 28 participants met BAR criteria, and 32 were in the comparison group. In the BAR group, 8 patients (28.6%) developed BD over a mean (SD) of 11.1 (0.7) years of follow-up, and no patients in the comparison group developed BD. The risk of developing BD was higher in the BAR group than in the non-BAR group (χ21 = 70.0; P < .001). The proportions of transitions to BD were equal in the first and second halves of the follow-up period. Conclusions and relevance: In this cohort study of participants seeking care for mental health difficulties, patients meeting the BAR criteria were significantly more likely to transition to BD over a decade after ascertainment compared with patients not meeting the BAR criteria. The findings suggest that those meeting BAR criteria may benefit from longer-term monitoring and support. Evaluation of predictive properties in longer-term studies using a risk measure will help with implementation of BAR criteria in clinical settings.

Network-Based Spreading of Gray Matter Changes Across Different Stages of Psychosis.

Importance: Psychotic illness is associated with anatomically distributed gray matter reductions that can worsen with illness progression, but the mechanisms underlying the specific spatial patterning of these changes is unknown. Objective: To test the hypothesis that brain network architecture constrains cross-sectional and longitudinal gray matter alterations across different stages of psychotic illness and to identify whether certain brain regions act as putative epicenters from which volume loss spreads. Design, Settings, and Participants: This case-control study included 534 individuals from 4 cohorts, spanning early and late stages of psychotic illness. Early-stage cohorts included patients with antipsychotic-naive first-episode psychosis (n = 59) and a group of patients receiving medications within 3 years of psychosis onset (n = 121). Late-stage cohorts comprised 2 independent samples of people with established schizophrenia (n = 136). Each patient group had a corresponding matched control group (n = 218). A sample of healthy adults (n = 356) was used to derive representative structural and functional brain networks for modeling of network-based spreading processes. Longitudinal illness-related and antipsychotic-related gray matter changes over 3 and 12 months were examined using a triple-blind randomized placebo-control magnetic resonance imaging study of the antipsychotic-naive patients. All data were collected between April 29, 2008, and January 15, 2020, and analyses were performed between March 1, 2021, and January 14, 2023. Main Outcomes and Measures: Coordinated deformation models were used to estimate the extent of gray matter volume (GMV) change in each of 332 parcellated areas by the volume changes observed in areas to which they were structurally or functionally coupled. To identify putative epicenters of volume loss, a network diffusion model was used to simulate the spread of pathology from different seed regions. Correlations between estimated and empirical spatial patterns of GMV alterations were used to quantify model performance. Results: Of 534 included individuals, 354 (66.3%) were men, and the mean (SD) age was 28.4 (7.4) years. In both early and late stages of illness, spatial patterns of cross-sectional volume differences between patients and controls were more accurately estimated by coordinated deformation models constrained by structural, rather than functional, network architecture (r range, >0.46 to <0.57; P < .01). The same model also robustly estimated longitudinal volume changes related to illness (r ≥ 0.52; P < .001) and antipsychotic exposure (r ≥ 0.50; P < .004). Network diffusion modeling consistently identified, across all 4 data sets, the anterior hippocampus as a putative epicenter of pathological spread in psychosis. Epicenters of longitudinal GMV loss were apparent in posterior cortex early in the illness and shifted to the prefrontal cortex with illness progression. Conclusion and Relevance: These findings highlight a central role for white matter fibers as conduits for the spread of pathology across different stages of psychotic illness, mirroring findings reported in neurodegenerative conditions. The structural connectome thus represents a fundamental constraint on brain changes in psychosis, regardless of whether these changes are caused by illness or medication. Moreover, the anterior hippocampus represents a putative epicenter of early brain pathology from which dysfunction may spread to affect connected areas.