Computational Psychiatry

Large Language Models are increasingly used in consumer-facing mental health tools, many of which claim that prompt engineering alone can ensure safe therapeutic behavior. This study evaluates that assumption by testing 20 proprietary and open-source LLMs on high-risk psychiatric scenarios, using prompts grounded in behavioral therapy principles. Prompt engineering reduced some predictable risks, such as explicit endorsement of self-harm, but consistently failed in ambiguous or clinically nuanced situations. Models frequently validated harmful statements, colluded with hallucinations, minimized symptoms, or used stigmatizing language, including in the newest and largest models. These failures reflect structural limitations such as lack of memory, insufficient contextual reasoning, and training-related biases. Prompt engineering alone is therefore insufficient for safe AI-mediated psychotherapy; clinician-guided fine-tuning, integrated safety mechanisms, and system-level oversight will be required. This work provides early evidence motivating deeper clinician-led evaluation and safety-oriented model development.

Importance: Patients who achieve remission from major depressive disorder (MDD) often face a preference-sensitive decision between continued antidepressant maintenance and discontinuation with active monitoring. Quantifying the tradeoff between depression burden and long-term medication exposure may support more individualized shared decision-making. Objective: To quantify tradeoffs between continuous antidepressant maintenance and active monitoring after MDD remission, and to identify preference thresholds favoring each strategy across relapse-risk strata. Design: Individual-level decision-analytic health-state transition model calibrated to randomized maintenance-discontinuation trials and a longitudinal first depressive episode cohort, with a 5-year time horizon. Setting: Outpatient clinical decision after completion of an 8-month continuation phase following remission from MDD. Participants: Adults in remission from MDD, represented across 4 clinically anchored relapse-risk strata ranging from very low risk after a first mild episode to high risk after highly recurrent depression. Exposures: Continuous antidepressant maintenance vs discontinuation with active monitoring and antidepressant restart after detected relapse. Main Outcomes and Measures: Severity-weighted depression-months, antidepressant medication-years, medication-years per depression-month averted, and net benefit across preference thresholds defined as the maximum additional medication-years a patient would be willing to accept to avert 1 depression-month. Results: Continuous maintenance reduced depression burden but required substantially more medication exposure, with efficiency strongly dependent on relapse risk. Medication-years per depression-month averted ranged from 11.8 (95% uncertainty interval [UI], 7.8-19.6) in the very low-risk group to 1.5 (95% UI, 0.8-3.0) in the high-risk group. At a preference threshold of 3 medication-years per depression-month averted, maintenance was preferred for moderate- and high-risk patients; at a threshold of 2, only for high-risk patients; and at a threshold of 1, for no risk group. Conclusions and Relevance: In this decision-analytic model, the value of continuous antidepressant maintenance depended strongly on baseline relapse risk and patient preferences regarding long-term medication exposure. These findings provide a quantitative framework for shared decision-making about antidepressant maintenance after remission from MDD.

The emotional content of suicide notes is typically examined using categorical coding, where each labeled passage is treated in isolation from its surrounding language. In contrast, dimensional models of psychopathology propose that affective content varies along continuous gradients. We evaluated this proposition directly. Excerpts from 884 annotated suicide notes were embedded in a semantic space defined solely by their linguistic properties, and we investigated whether human-assigned emotion labels changed smoothly across this space. They did: affective tone showed clear spatial autocorrelation (Moran's $I = 0.18$, $z = 19.68$, $p < 0.001$), an effect that replicated across three different encoders and remained after removing all within-note dependencies. Emotions occupied recognizable yet overlapping regions rather than forming distinct clusters and varied substantially in how tightly they were concentrated: love and hopelessness appeared with similar frequency, but love was far more localized ($z = 15.7$ versus $10.8$). Among all emotions, hopelessness was the most linguistically diffuse, implying that a single categorical label is capturing multiple, qualitatively different manifestations of suicidal distress.

Cognitive flexibility is an executive function that allows individuals to adjust behaviour in response to changing environmental demands. We assessed volitional switching under uncertainty, without rule-based learning, in the Change Your Mind task. Nineteen patients with obsessive-compulsive disorder (OCD), 19 patients with attention-deficit hyperactivity disorder (ADHD) and matched control participants (20 per group) completed the task whilst undergoing a functional MRI scan. The task was a two-alternative forced choice paradigm where each stimulus was presented twice successively, with spurious feedback following the first presentation. This allowed participants the opportunity to repeat or change their response. Participants with ADHD changed their response more frequently than controls following a previously correct response, associated with reduced accuracy on the second trial. This was accompanied with smaller differences between change and repeat trials in the superior frontal gyrus, paracingulate gyrus and frontal pole compared to controls. Participants with OCD did not differ from healthy controls in their performance but exhibited greater activity on both change and repeat trials in the pre- and postcentral gyri than controls. These results point to distinct neurobehavioural differences in patients with ADHD and OCD underlying what is often termed more broadly inflexible behaviour.

Background. UK clinical guidance recommends that structured risk prediction tools and risk stratification should not be used in self-harm, to predict suicide or determine who is offered treatment. Underpinning this position is the premise that routinely collected health data contain no useful predictive signal, which has received little direct scrutiny. Objective. To test whether routinely collected electronic health record data can distinguish groups at higher and lower risk of severe outcomes following paracetamol overdose. Methods. We analysed 4,095 adults presenting to NHS Lothian emergency departments with paracetamol overdose (2017-2023). Elastic-net logistic regression was fitted to 37 routinely collected electronic health record features to predict a composite of death or mental health inpatient admission at 0-7, 8-30 and 31-365 days following attendance, evaluated on a held-out 20% test set with bootstrapping. Findings. Events occurred in 5.5% of patients at 0-7 days, 2.0% at 8-30 days and 7.9% at 31-365 days, dominated by mental health admission. Bootstrap AUROC 95% confidence intervals lay above 0.5 in every window (0.65-0.82, 0.63-0.90, 0.71-0.85): models ranked patients better than chance. Calibration slopes (1.04, 1.14, 1.07) were close to one. Ranking drew primarily on mental health-related features. Conclusions. Routinely collected health data carried predictive signal for severe outcomes after paracetamol overdose, although discrimination fell short of what is needed for individual-level clinical use. Clinical implications. These models are not proposed for clinical deployment; however, treating risk prediction as a settled question will redirect research efforts, potentially excluding this patient population from machine learning advances driving improvements in care in other medical specialties.

Objective Psychosis spectrum symptoms (PSS) are common among children and youth. These symptoms may be clinically significant as studies indicate a heightened risk of mental health disorders, in general, as well as psychotic disorders, specifically, in youth that endorse PSS. This systematic review and meta-analysis investigates the longitudinal association between PSS in children and youth and subsequent mental health diagnosis. Methods A comprehensive search of Ovid Medline, PsycINFO, and EMBASE databases was conducted to identify longitudinal studies that: (i) assess PSS at a baseline timepoint, (ii) in individuals under 25 years, and (iii) assess mental health disorder diagnosis using a structured assessment at a later time point in the same sample. We conducted a meta-analysis and calculated pooled odds ratios (ORs) for mental health and psychotic disorders using random-effects models. Post-hoc meta-regressions were performed to examine the influence of a number of moderators on the relationship between earlier recorded PSS and subsequent mental health disorders or psychotic disorders. Results The search yielded 41 eligible studies of which 25 were included in the meta-analysis. Most included studies assessed PSS using brief self-report measures and recruited their samples from clinical or community settings. Among children and youth without an identified mental health diagnosis at baseline assessment, baseline PSS were associated with a 2-fold (OR = 2.07, CI = 1.61 - 2.66, I2 = 86.92%, p < 0.0001) increased risk of meeting diagnostic criteria for subsequent mental health disorder diagnosis and a 3-fold increased risk (OR = 3.11, CI = 2.11 - 4.58, (I2 = 60.93%, p < 0.0090) of meeting diagnostic criteria for a subsequent psychotic disorder diagnosis with a minimum 1 year follow-up time from baseline assessment. Meta-regression analysis indicated that study quality and sample size explained a substantial proportion of between-study heterogeneity for psychotic disorder outcomes. Conclusions Our results suggest that administration of simple self-report measures of PSS in both clinical and community settings may be helpful to identify children and youth at higher risk of subsequently meeting criteria for a mental disorder generally, and for a severe mental illness (i.e., psychotic disorder), specifically. Future longitudinal studies should focus on improving study design characteristics to increase confidence in identified longitudinal associations. The results of our work suggests that integration of self-report measures of PSS may be useful in a variety of settings to identify youth at increased risk of subsequent mental illness.

Lithium has been the gold standard for the treatment and prevention of relapse in bipolar disorder for over 60 years. Guidance from the National Institute for Health and Clinical Excellence states explicitly to 'offer lithium as a first-line, long-term pharmacological treatment for bipolar disorder'. Yet, in the last two decades its use has been in decline with clinicians favouring anticonvulsants or antipsychotics when treating this condition. In this study, we have used three openly available datasets containing prescribing data from primary and secondary care to explore trends in the use of lithium in England, showing both regional and temporal variance between 2015-2024. We have shown that lithium use declined in primary care by 20.9% in the last ten years (2015-2024) and 10.9% overall in the last five years (2019 to 2025). We have also shown how there is some regional variation in the source of lithium for patients, although the vast majority is prescribed in primary care. Further research into clinical behaviour is needed to understand what is driving the decrease in lithium usage, and what barriers and enablers may influence its use across the country.

Executive function (EF) deficits are central features of schizophrenia and strongly influence long-term functional outcomes. Conventional cognitive assessments often lack ecological validity and cultural relevance. This study introduces the Luk Chup Augmented Reality (LCAR) tool a video guided, clay modeling task delivered through wearable AR that integrates culturally familiar activity with realtime neurophysiological monitoring. Thirty individuals diagnosed with schizophrenia (mean age = 38.9, SD. = 7.15 years) completed a series of modeling and memory tasks using LCAR while undergoing quantitative EEG (QEEG). Task duration and theta/beta power were analyzed across procedural and color shape memory phases. Memory phases took significantly longer to complete and were associated with decreased lateral prefrontal theta and increased frontal midline theta activity (Fz, Cz), indicating higher EF demand. A repeated-measures ANOVA revealed significant condition, site, and interaction effects on theta power. The LCAR tool shows promise as a culturally grounded, dual-mode assessment of EF in schizophrenia. It offers a novel integration of performance-based and neurophysiological metrics that may inform future interventions in psychiatric rehabilitation.

Abstract Background Despite a rising global psychiatric burden, a treatment gap persists where the majority of symptomatic individuals remain unmedicated. Traditional epidemiological analyses treat this untreated population as a single, uniform block, obscuring specific barriers to care. This study uses an unsupervised machine learning pipeline to identify distinct socio-behavioural and biological sub-populations within the untreated cohort to guide targeted public health interventions. Methods Data were pooled from the 2015-2018 National Health and Nutrition Examination Survey (NHANES) cycles (N=11,848 total adult respondents). A symptomatic cohort of 3,075 individuals experiencing daily or weekly anxiety or depression symptoms was isolated, excluding severe liver pathology outliers ("GGT"[&ge;]80" U/L" ). A 22-feature matrix combining continuous clinical biomarkers (systolic blood pressure, waist circumference, HbA1c) and categorical social variables was projected using Factor Analysis of Mixed Data (FAMD). Latent sub-populations were identified via Gaussian Mixture Modelling (GMM), optimized by the Bayesian Information Criterion (BIC). Results The broad baseline population revealed a substantial mental health burden, with 30.4% reporting active psychiatric symptoms, of whom 71.6% were entirely unmedicated. The GMM pipeline successfully isolated three distinct sub-populations (k=3) separated by age, clinical strain, and treatment rates: Cluster 0 (Mature Adults, mean age 55.03): high psychiatric severity (34.1% severe untreated), central obesity, and hypertensive strain (135.82 mmHg), with 64.1% untreated despite frequent primary care contact; Cluster 1 (Working Professionals, mean age 38.38): highly educated, female-dominated (70.5%), with 77.7% untreated driven by moderate distress; Cluster 2 (Emerging Youth, mean age 18.49): a highly vulnerable late-adolescent group with a staggering 90.2% untreated rate. Conclusion The unmedicated symptomatic population is highly diverse and segmented by life stage. These profiles show that the treatment gap is driven by age-specific barriers, specifically workforce-age symptom masking and late-adolescent developmental transitions. Closing this deficit requires shifting from uniform public health approaches toward targeted interventions, such as digital peer support networks for youth and integrated primary care screenings for older adults.

Autistic psychiatrists occupy a paradoxical position: trained to recognise and assess autism in others, yet navigating a professional culture in which their own autistic identity remains largely concealed. Despite growing visibility of autistic clinicians, the barriers autistic psychiatrists face to formal diagnosis and professional disclosure remain unexplored. This study used interpretive phenomenological analysis to examine the experiences of seven autistic psychiatrists in relation to diagnosis and disclosure. Data were generated through in-depth interviews and Retzinger's framework for identifying shame in discourse was applied as an analytical tool within the interpretive process. Shame emerged as the overarching theme across the dataset, operating through four group experiential themes. Its origins lay in childhood experiences of difference and perceived defectiveness, transmitted through family, peers, and the broader social environment. In professional life, shame was sustained and amplified by colleagues' misconceptions about autism, anticipated loss of credibility, and the deficit-based diagnostic criteria - which rendered self-recognition difficult and made formal diagnosis a perceived professional liability. Critically, shame did not only create barriers: it functioned as an override mechanism, rendering the known benefits of disclosure - to participants themselves, to colleagues, and to patients - insufficient to translate into action. This override function was not explained by fear of discrimination or rational career protection alone; it reflected shame's operation as an internal prohibition, dissociated from its original social source and persisting even where stigma had been intellectually processed and rejected. These findings reposition shame not as one barrier among many but as the organising force through which all barriers operate. Interventions aimed at increasing disclosure by raising awareness of its benefits misread the operative mechanism. Creating conditions in which autistic psychiatrists can make decisions about their identities freely requires naming and addressing shame - in research, in clinical training, and in the culture of psychiatry.

Background: Emotion dysregulation is a core feature of bipolar disorder (BD), yet its behavioral expression during depressive episodes, and potential differences between its types, BD-I and BD-II, remain unclear. This study used automated facial-expression analysis during naturalistic affective film viewing to examine subtype-specific and context-dependent emotional responding in bipolar depression. Methods: The sample included 135 participants: 69 healthy controls and 66 patients with BD (BD-I, 23; BD-II, 43). Participants viewed nine emotionally evocative film clips spanning negative, positive, neutral, and socially threatening contexts, while their facial expressions were continuously recorded and quantified using computer vision-based facial-expression analysis. Results: Patients with BD-I showed a distinct, context-dependent facial-expression profile, characterized by greater negative responses across multiple contexts than other groups. Specifically, they showed increased sadness during sad, reward, and amusing clips, and elevated anger during sad and neutral clips. In socially threatening contexts, BD-I participants showed a multivalent pattern of elevated anger, fear, and joy, suggesting poorly coordinated or context-incongruent affective expression. In contrast, BD-II participants did not differ significantly from healthy controls on any emotion, despite depressive symptom severity comparable to BD-I participants. Conclusions: These findings suggest that facial-expression patterns in bipolar depression differ across subtypes. BD-I may be characterized by heightened negative reactivity and altered context-appropriate modulation of emotional expression, whereas BD-II may not show comparable alterations in overt facial output. Automated facial-expression analysis during naturalistic stimulation may provide a useful behavioral marker for characterizing subtype-specific affective disturbance in bipolar depression and related psychopathology.

Background: Childhood adversity (CA) has been identified as one of the most robust risk factors for psychotic disorders; several treatable mediating mechanisms have been proposed. Aims: To conduct a systematic review and meta-analysis examining mediating pathways linking CA and psychosis. Method: This PRISMA-compliant systematic review (PROSPERO: CRD42024542972). consisted of a search conducted in January 2026 on Ovid (PsycINFO, Medline, and Embase) using search terms related to psychosis, CA, and mediation analyses. Evidence was appraised by calculating the percentage of the total effect mediated in each study, grouping mediators into meaningful groups. When possible, meta-analyses using two-stage meta-analytic structural equation modelling (METASEM) were conducted. Results: 117 studies were included (54 in clinical samples, 59 in non-clinical samples, and four studies in both clinical and non-clinical samples). 107 studies examined psychological mediators and 12 examined biological. The median percentages of total effect mediated across all analyses per mediator family were: 49% for dissociation (k = 24), 45% for psychosocial stressors (k = 6), 37.9% for negative schemas (k = 23), 35.2% for post-traumatic symptoms (k = 10), 31.5% for depressive symptoms (k = 14), 27.8% for anxiety (k = 11), 27.1% for attachment styles (k = 12), and 8.7% for mentalization domains (k = 5). Meta-analyses confirmed a robust mediating effect of dissociation (k = 7; N = 2143; indirect effect (I.E) =0.42 [0.17, 0.66] on psychosis; 50.49%), on delusions (k =7; N = 1053; I.E = 0.36, [0.27, 0.46]; 46.44%]) and on hallucinations (k =10; N = 5705; I.E = 0.28 [0.20, 0.36]; 57.59%). Robust mediation via depression (k =5; N= 5028; indirect effect= 0.33 [0.31, 0.35]; 31.05%) and negative schemas of the association between trauma and psychosis broadly defined (k = 7; N=10791; I.E= 0.26 [0.17, 0.35]; 26.36%) was also observed. High heterogeneity was observed across all meta-analyses. Fewer studies examined biological mediators, preventing quantitative synthesis. Conclusions: Childhood adversity impacts psychosis through psychosocial mediators, particularly dissociation. Further work is required to on the potential role of biological mechanisms and its interplay with psychological mechanisms.

Background Brain abnormalities related to childhood adversity (CA) have been reported across clinical presentations in psychotic disorder (PD) and bipolar disorder (BD). This systematic review and meta-analysis examined gray matter volume (GMV) alterations linked to CA in PD and BD. Methods A PRISMA-compliant systematic review was conducted (PROSPERO ID: CRD42022351133). The EMBASE, MEDLINE, and PsycINFO databases were searched from inception to June 2024 for studies investigating CA and structural brain imaging in PD and BD. Study quality was assessed with the Newcastle Ottawa Scale (NOS). Data were extracted and synthesized accounting for sex differences and CA subtypes with brain findings categorized by the presence and direction of associations. Meta-analyses were performed for hippocampal and amygdala volumes. Results In the systematic review (k = 29), 3,056 participants with PD and BD (mean age = 36.6; SD =16.1; 47% female), published between 2011 and 2023, were included. Study quality was fair, with high heterogeneity. Most studies reported significant negative associations between CA and GMV, especially in prefrontal regions, while findings for the hippocampus and amygdala were largely null or inconsistent. Meta-analyses of a study subset identified no significant association between CA and hemisphere-specific and combined volumes of the hippocampus (k = 5; p [&ge;] 8805; 0.66) or amygdala (k = 4; p [&ge;] 8805; 0.87). Conclusion CA was not consistently associated with hippocampal or amygdala volume alterations in PD and BD. More consistent evidence emerged for reduced GMV in prefrontal regions, suggesting that neurobiological impact of CA may be more robustly captured at the cortical level.

AO_SCPLOWBSTRACTC_SCPLOWThere is a gap between neuroscientific theories of learning and the speed of learning observed in many experiments. Since the Cognitive Revolution of the 1950s, compositionality has played a central role in efforts to bridge this gap. Roughly, a compositional system is one where distinct modules are combined according to a set of rules in order to accomplish complex tasks. Recently, significant progress has been made in understanding the emergence of modules in both biological and artificial neural systems. How, and under what conditions, the rules of module recombination are represented in these systems remains an open question. Here we present a neural model that can leverage these rules to dramatically speed up learning. We first show that when faced with multiple tasks which share subcomponents, models learn a low-dimensional representation that captures how subcomponents are reused across the task set. These low-dimensional spaces encode the structure that governs how modules should be recombined. Restricting learning to these subspaces greatly reduces the amount of experience needed to acquire a novel task, even when learning from reinforcement on single trials. In some cases, we can leverage the geometric regularities of these representations to reduce learning to a form of hypothesis testing over a small set of discrete points. Finally, we use this theory to model both behavioral and neural data from non-human primates performing a compositional task, and show that key features in this data are consistent with a model in which exploration during learning is restricted to these low-dimensional spaces. Overall, this work shows that the advantages of modularity in neural systems can be greatly improved upon when models represent the structure of module reuse. Both these features working in tandem lead to learning on timescales similar to biological intelligences, and hence provide a model for how such fast, adaptable behavior can emerge from systems of neurons.

Background: Actigraphy-derived rest-activity rhythm (RAR) features are widely used to characterize clinical states in bipolar disorder (BD). Both mean levels and temporal variability of these features have been associated with mood episodes; however, variability measures are often statistically coupled with the mean, particularly in skewed distributions. This raises a question as to whether variability reflects a separate characteristic of the data or whether the observed association arises from statistical properties of the data. Objective: In this study, we aim to determine whether temporal variability of actigraphy-derived RAR features provides standalone information on mood episodes in BD beyond mean activity levels after accounting for mean-variance dependence. Methods: We analyzed actigraphy data from a subset of 72 participants with BD drawn from a larger longitudinal study, extracting 22 daily RAR features aggregated weekly as sample mean (MEAN) and within-week temporal variability computed as sample standard deviation (VAR). Variance-stabilizing transformations (Box-Cox or Yeo-Johnson) were applied to the entire study cohort to reduce mean-variance dependence. Associations with mood episodes and remission (mania: n=34; depression: n=58 annotated participants) were evaluated using generalized linear mixed-effects models with a logistic link function, including univariate (MEAN or VAR) and multivariate (MEAN+VAR) specifications, assessed by likelihood-based metrics and the area under the receiver operating characteristic curve (AUC). Results: Transformations reduced mean-absolute correlations from 0.43 to below 0.06. Temporal variability remained significantly associated with clinical state for 11/22 RAR features in mania and 16/22 features in depression, with all significant associations remaining after false discovery rate correction (p<0.05). Joint models showed modest incremental gains (AUC 3%-4% overall; up to 12% in mania, 7% in depression), with absolute performance remaining limited (AUC 0.50-0.66). In both mania and depression, nearly all significant variability-based regressors contributed incremental information beyond mean-based models. Only sleep duration and activity changes around wake time (+-1 hour), did not improve discrimination between mania and remission. Conclusions: Temporal variability in RAR features can be considered a standalone state marker of mood episodes not captured by mean activity. We found it to be more consistently associated with depression than mania. Its incremental discriminative contribution is modest, suggesting greater utility within multivariate or multimodal frameworks.

Background: Suicide attempts (SA) are elevated in individuals with autism spectrum disorder (ASD), but population-level data characterizing how SA prevalence varies across demographic and clinical subgroups - at the scale and granularity needed to inform evidence-based risk stratification - have been largely unavailable. This study examines SA prevalence across sex, age group, psychiatric comorbidity type, and substance use disorder subtype in the largest real-world ASD cohort to date. Methods: We conducted a retrospective cross-sectional analysis using Epic Cosmos electronic health record data from 2,311,171 individuals with ASD identified by International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes, spanning 2016-2025. SA prevalence was calculated with Wilson score 95% confidence intervals. Modified Poisson regression with robust variance estimation was used to estimate adjusted prevalence ratios (aPRs) for sex, age group, and psychiatric comorbidity. Unadjusted prevalence ratios (uPRs) were calculated separately for individual comorbidity types and substance use disorder subtypes. Results: Overall SA prevalence was 1.7% (38,160 individuals). Females showed higher SA prevalence than males (2.9% vs. 1.2%; aPR 1.61, 95% CI 1.35-1.92). SA prevalence peaked in the 15-24 age group overall (aPR 5.14, 95% CI 3.62-7.29), with sex-stratified analyses revealing that females peaked earlier (15-24 years; aPR 4.33, 95% CI 4.23-4.43) than males (25-34 years; aPR 6.08, 95% CI 5.05-7.31) - a sex-specific divergence in the timing of peak SA prevalence not previously documented in ASD. Having at least one psychiatric comorbidity was associated with a 30-fold higher SA prevalence (aPR 30.56, 95% CI 26.03-35.89), with the effect stronger in females (aPR 36.54, 95% CI 31.04-43.01) than males (aPR 27.77, 95% CI 22.65-34.06). Among comorbidity subtypes, substance-related disorders showed the highest crude SA prevalence (16.9%; uPR 134.20, 95% CI 67.68-266.10). Subtype-level characterization revealed SA prevalence ranging from 19.7% to 24.3% across all five substance use disorder subtypes examined, with stimulant use disorder showing the highest unadjusted prevalence ratio of any subtype (uPR 196.11, 95% CI 100.63-382.20). Conclusion: SA prevalence in ASD is markedly elevated relative to the general population and varies meaningfully by sex, age, and comorbidity profile in clinically important ways. Females carry a disproportionate SA burden relative to males, with peak vulnerability arriving earlier in adolescence; males peak later in young adulthood and remain at elevated risk into midlife. Psychiatric comorbidity - particularly substance use disorders - is associated with the largest relative elevations in SA prevalence. These population-level estimates are directly applicable to EHR-based risk stratification models and can inform the development of ASD-specific clinical decision support tools that concentrate surveillance and intervention on those at demonstrably elevated risk, rather than applying uniform approaches across a heterogeneous population.

Objective: Working memory (WM) deficits are a co-occurring feature to numerous neuropsychiatric disorders, particularly attention-deficit/hyperactivity disorder (ADHD), and there remain no treatments that directly target WM. The coupling between the phase of theta band activity and amplitude of gamma band activity (i.e., TGC) is an established neural correlate of WM. However, no studies have examined WM-related TGC in ADHD or whether neuromodulation can modulate these oscillatory dynamics in youth. This set of studies examined the effects of intermittent theta burst stimulation (iTBS) to the left dorsolateral prefrontal cortex (DLPFC) and left posterior parietal cortex (PPC) on TGC in youth with ADHD. Methods: In two randomized, double-blind, sham-controlled crossover trials, adolescents with ADHD and clinically significant parent-reported WM symptoms first completed a single-session study comparing DLPFC versus PPC iTBS targeting (n = 47) and then a multi-session clinical trial comparing 10 sessions of active versus sham left DLPFC iTBS (n = 29). Participants completed a computerized visuospatial Sternberg WM task with concurrent electroencephalography (EEG) before and after the single sessions, as well as at baseline, midway through treatment, and approximately 24 hours after the final session within the multi-session trial. Phase-amplitude coupling between theta phase and gamma amplitude was quantified using the Kullback Leibler modulation index at frontoparietal electrodes. Linear mixed-effects models examined treatment effects and associations between change in TGC and WM status (including accuracy, reaction time, and clinical symptoms). Results: Across participants, lower TGC was associated with lower symptoms and better WM performance, including higher accuracy, faster and more consistent RT. Active iTBS increased frontoparietal TGC relative to sham stimulation, with effects observed both acutely after a single session and ~24 hours after multiple sessions. DLPFC-targeted iTBS increased TGC, whereas PPC-iTBS had no measurable effect. Change in TGC was associated with change in WM, such that a decrease in TGC was associated with faster RT and decreased RT variability. Higher baseline TGC was associated with greater improvement in WM. Active iTBS decoupled the TGC-WM association observed during sham iTBS, and greater electric field intensity of iTBS was associated with greater improvement in WM accuracy and greater decrease in TGC. Conclusions: Active iTBS to the left DLPFC modulated WM-related TGC in youth with ADHD. These findings provide preliminary evidence that neuromodulation may improve WW by modifying oscillatory dynamics within frontoparietal networks. Larger clinical trials with higher stimulation doses are needed to determine whether targeting oscillatory coupling represents a potential therapeutic strategy for WM deficits.

Background: Children and young people (CYP) with intellectual disability (ID) frequently have co-occurring neurodevelopmental (ND) and mental health (MH) difficulties. While copy number variants (CNVs) are identified as an important aetiology of ID, it is unclear whether and how CNV risk scores predict ND and MH characteristics within the CNV-associated ID population. Methods: We analysed data from the UK-based IMAGINE-ID cohort of CYP (aged 4-19 years) with ID and clinically-reported CNVs (N = 1,640). CNVs were annotated with Gencode 19 in ENSEMBL to calculate CNV risk scores, including summed probability of loss-of-function intolerance (pLI) and dosage sensitivity. Multivariate regression models examined the prediction of CNV variables and inheritance on ND and MH characteristics, assessed via the Development and Well-Being Assessment (DAWBA). Post-hoc analyses explored CNV variable stratification (lower vs. higher range pLI). Results: Higher summed pLI scores (indexing CNV genes' intolerance to loss of function) unexpectedly predicted fewer MH difficulties and a lower likelihood of ND diagnoses, even after accounting for demographic factors and CNV inheritance. Post-hoc analyses identified a threshold effect. Within the lower pLI range, higher pLI scores were associated with greater MH difficulties, consistent with findings from population-based samples. In contrast, within the higher pLI range, higher pLI scores were associated with fewer MH difficulties (among individuals more likely to have severe ID). Conclusion: These findings challenge the assumption that CNV genomic "risk scores" universally predict ND and MH difficulties. Instead, within CNV-associated ID, complex relationships exist between CNV risk scores, inheritance and phenotypes. These insights emphasise the necessity of integrating genomic results with familial and developmental context to understand individual vulnerabilities and support needs.

Purpose: Nonresponse to routinely collected patient-reported outcome measures (PROMs) threatens the representativeness of aggregated data. We characterized patient-, provider-, and clinic-level factors associated with PROMIS Global-10 nonresponse in routine radiation oncology care. Methods: In this retrospective cohort study, all adults seen at five Mass General Brigham radiation oncology clinics over one year were included. The primary outcome was patient-level nonresponse, defined as never completing the portal-administered Global-10 versus completing it at least once. Using iterative mixed-effects logistic regression, we modeled patient-, provider-, and clinic-level factors. Results: Among 12,214 patients, 71 providers, and five clinics, patient- and appointment-level response rates were 35.4% and 10.9%, with patient-level response ranging nearly fivefold across clinics (12.8% to 66.2%). In Model 1, male sex, lower education, not working, and recent surgery had higher odds of nonresponse, and longer time since diagnosis lower odds. After provider- and clinic-level factors were added, patient sex, education, and employment became nonsignificant, whereas recent surgery (adjusted odds ratio [aOR] 1.97) and longer time since diagnosis (aOR 0.46 for >12 months) persisted. A provider's historical collection rate was protective but attenuated at the clinic level. There, a later program launch (aOR 0.29) and higher historical collection rate (aOR 0.79) correlated with lower nonresponse, whereas academic versus community setting did not. Conclusions: Nonresponse to routinely collected PROMs is a multilevel phenomenon driven substantially by clinic-level implementation factors, not patient characteristics alone. Because response rate is only a proxy for representativeness, PROMs programs and PRO-based performance measures should prioritize representative collection over volume.

Controlled human infection models (CHIMs) are increasingly used in infectious disease research to study pathogen dynamics and evaluate interventions under controlled conditions. However, these studies are resource-intensive and involve ethical and safety constraints, making efficient study design critical. Dose-finding is a key early component in CHIMs, where the aim is to identify a challenge dose that achieves a target infection probability. Traditional rule-based designs are commonly used but can be inefficient, motivating the use of model-based adaptive approaches such as the Bayesian Continual Reassessment Method (CRM). Although CRM has been extensively studied and widely adopted in Phase I oncology trials for identifying the maximum tolerated dose of therapeutics, its application in CHIM settings remains limited, particularly when the endpoint of interest is infection. This tutorial provides step-by-step guidance for implementing a Bayesian CRM in dose-finding CHIMs, using an oropharyngeal Neisseria gonorrhoeae challenge as a motivating case study. The framework outlines key design components, including dose-grid specification, dose-response model, prior elicitation, Bayesian updating, decision rules, and stopping criteria, with particular emphasis on a clinically interpretable parameterisation. Trial operating characteristics are evaluated through simulation studies under multiple dose-response scenarios and prior-predictive analyses, and compared with a commonly used '3+3' type rule-based design. This work highlights the advantages of Bayesian model-based designs for dose-finding in CHIMs over classic rule-based designs and provides a structured, reproducible framework for implementing CRM, supporting their application in future CHIM studies.