Computational Psychiatry

Transdiagnostic psychiatric research on reward-guided learning has largely focused on simple associative processes, leaving it unclear whether or how higher-level processes are disrupted. Here, we studied how abstraction, the ability to extract relevant features from complex information, and metacognition, the ability to monitor and evaluate one's own mental processes, map onto specific transdiagnostic dimensions. Using an online sample (N = 249), we examined associations between these processes and three cross-culturally robust transdiagnostic dimensions derived from a large existing dataset (N = 19,505): Compulsive hypersensitivity, Social withdrawal, and Addictive behaviours. Computational modelling of an abstract representation learning task with confidence judgments revealed that Compulsive hypersensitivity was negatively associated with both abstraction ability (pboot = 0.003) and metacognitive sensitivity (pboot = 0.005), while Social withdrawal was positively associated with metacognitive sensitivity alone (pboot = 0.002). Moreover, transdiagnostic dimensions revealed more coherent associations with higher-order cognition than symptom-level analyses, highlighting the added value of examining psychopathology at the factor rather than the symptom level. These findings portray a hierarchical view of cognitive dysfunctions in psychopathology and point to representational and metacognitive processes as potential targets for transdiagnostic intervention.

Suicidal ideation in obsessive-compulsive disorder (OCD) is common and clinically significant, yet much of the existing literature conceptualizes suicide risk through the lens of comorbid depressive symptomatology. The present study examined whether other clinical features can identify clinically meaningful patterns associated with SI. Participants included 231 individuals with clinically significant OCD symptoms. SI was operationalized using Item 9 of the Beck Depression Inventory-II and binarized to reflect the presence or absence of suicidal thoughts. Depression severity scores were intentionally excluded from the predictive feature set, and three machine learning models (ElasticNet, Random Forest, and Explainable Boosting Machines) were evaluated using repeated nested cross-validation. All three algorithms showed comparable predictive performance. Given this overlap, the EBM was selected for interpretation due to its ability to model nonlinear relationships and interaction effects transparently. The model identified quality of life, obsessive-compulsive trait severity, somatic burden, and conscientiousness as prominent predictors of SI. Risk functions suggested nonlinear increases in estimated suicide risk at elevated levels of obsessive-compulsive traits and reduced quality of life. Additionally, interaction analyses indicated that severe obsessive-compulsive traits combined with elevated somatic burden were associated with higher estimated suicide risk than either factor alone. These findings suggest that interpretable machine learning can support clinically relevant phenotypic hypothesis generation. They also highlight somatic burden, functional impairment, obsessive-compulsive trait severity, and conscientiousness as potentially underappreciated targets for SI risk assessment in OCD, beyond the traditional focus on depressive comorbidity.

Impulsivity is a core dimension of ADHD and a transdiagnostic vulnerability factor for a wide range of adverse psychiatric and somatic outcomes, that could be mitigated through more effective screening of at-risk individuals. However, laboratory-based measures of impulsivity show weak convergence across paradigms and limited prediction of real-world behavior, constraining their utility. We tested whether combining repeated ecological assessment with computational modeling of response-time (RT) dynamics improves measurement of impulsivity and its cross-paradigm validity. Sixty participants, including adolescents with ADHD, individuals with 22q11.2 deletion syndrome, and healthy controls, completed a total of 1347 smartphone-based Balloon-Analogue-Risk-Task (D-BART) assessments repeatedly in daily life, alongside a single-session Conners CPT-3. RT was modeled using linear mixed-effects models as a function of objective risk and subjective uncertainty, with random effects capturing between- and within-person variability. Dynamic RT parameters were integrated with conventional performance metrics and related to CPT-3 variables using partial least squares analysis. External validity was evaluated against parent-rated behavioral symptoms. RT increased with both risk and uncertainty, consistent with adaptive modulation of speed-accuracy trade-offs. These effects varied substantially across individuals and repeated assessments. Dynamic RT parameters differentiated clinical from control participants, whereas traditional aggregate metrics did not. A PLS latent component linked D-BART and CPT-3 patterns and was associated with real-world hyperactivity/impulsivity, whereas CPT-3-derived scores alone were not. Experimental manipulation of ecological sampling density directly impacted D-BART predictive accuracy. These findings show that ecological repetition combined with parsimonious RT-dynamics modeling enhances construct validity, cross-paradigm convergence, and behavioral relevance of impulsivity measures, providing a scalable framework for capturing dynamic cognitive-control processes.

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.

Background & Hypothesis: Dysfunctional predictive processing (PP), specifically the aberrant weighting of priors, is a frequently-proposed mechanism for psychosis and psychosis-like phenomena (schizotypy). Evidence for this theory mostly originates from single-task studies, which assume that all tasks load onto a single latent construct of PP performance, but the underlying factor structure of PP tasks is unknown. PP deficits in psychosis may be better described by a two-factor, hierarchical model: weakened lower-level (perceptual) priors compensated by higher-level (cognitive) priors. Study Design: This study implements a multi-paradigm approach in healthy participants to investigate latent constructs underlying PP and their relationship to schizotypy. Participants (N = 73) completed 6 tasks measuring reliance on priors across language, memory, visual, and auditory domains. A factor analysis investigated whether performance across tasks is captured by a single or two-factor model. Study Results: Although a two-factor model best described performance, factors reflected within-task correlations rather than a PP hierarchy. Cross-task PP measures were poorly correlated, suggesting that individuals' weighting of priors was task-specific. A full model including all task outcomes (not factors) significantly predicted the severity of schizotypal aberrant beliefs but no other schizotypal measures. Conclusions: These results do not evidence a single factor underpinning PP performance. It is therefore inappropriate to use results from single tasks to propose a generalised PP deficit in psychosis. Variation was also not captured by a two-factor hierarchical model of priors. Further multi-paradigm research is required to evaluate alternative models or additional variables that describe aberrant PP in psychosis.

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.

Recent advances in artificial intelligence (AI) have raised interest in its potential to similarly progress biological psychiatry. This study investigates the current utility of AI models in predicting psychiatric phenotypes in youth - a critical window for psychiatric diagnosis - using neuroimaging data from a large developmental clinical cohort. We assessed the predictive performance of machine learning models on diverse psychiatric and non-psychiatric targets. We show that while models are able to predict various targets from EEG and fMRI data, simple models using only readily-available factors such as demographics and recording site match their performance for clinical phenotypes. This pattern holds across clinical targets and replicates for state-of-the-art deep neural networks, suggesting that either neuroimaging data contains limited disease-specific information or that current methods cannot reliably identify such patterns. These benchmarking results provide important context regarding promises of modern AI in the field of biological psychiatry with a focus on youth.

Background: Hyperbolic tapering is an increasingly recognized approach for discontinuing serotonin reuptake inhibitor (SRI) antidepressants that involves non-linear dose reductions with equal stepwise reductions in serotonin transporter (SERT) occupancy to mitigate withdrawal symptoms. Its theoretical basis is the hyperbolic relationship between SRI dose and SERT occupancy reported in radioligand imaging studies. Hyperbolic tapering implicitly assumes that changes in SERT occupancy approximate changes in biologic effect and withdrawal risk. Because SERT occupancy plateaus across the therapeutic dose range of SRIs, this framework predicts relatively small biologic effects and withdrawal risk within this range. However, SERT occupancy influences serotonergic activity only indirectly via its effects on extracellular serotonin concentrations, and the relationship between these two variables is poorly characterized. Methods: We developed a two-pathway clearance model derived from mass-action kinetics to evaluate the steady-state relationship between SERT occupancy and extracellular serotonin concentrations under chronic SRI treatment. Results: Our analysis indicates that serotonin concentrations increase hyperbolically as transporter occupancy increases, suggesting that biologically meaningful differences in serotonergic signaling persist across the therapeutic dose range of SRIs despite plateauing occupancy. Conclusions: Our model predicts a hyperbolic relationship between SERT occupancy and extracellular serotonin concentrations, suggesting that changes in occupancy may not map proportionally onto serotonergic effect. These findings provide a potential mechanistic explanation for dose-dependent clinical effects of SRIs despite plateauing transporter occupancy and generate testable hypotheses regarding antidepressant tapering strategies. Empirical validation is warranted.

Background General-purpose large language models increasingly encounter emotional and therapy-like conversation, yet are not developed or evaluated as clinical systems. Existing safety evaluations rely largely on brief exchanges, although harms often unfold over extended interactions. Whether models maintain safety-relevant performance as conversations accumulate context remains unknown. Methods In this preregistered study, 400 clinician-validated statements, with or without suicidal ideation, were inserted at 0-200 speaker turns in 5 psychotherapy and 3 synthetic transcripts. Forty-nine LLMs and 8 clinicians performed the same binary classification task. Mixed-effects models estimated the effects of conversational depth, model scale, and model version on F1. Twelve top models were tested to 1,500 turns across conversational trajectories, with or without instruction restatement. Results F1 declined with depth across model families (p<0.001). Larger, newer models performed better but still degraded. Clinicians showed no decline (mean F1 0.86 at both 0 and 200 turns), but eight of nine proprietary models exceeded their performance at 200 turns. Conversational content, not length alone, explained F1 changes; the largest decrease was under adversarial context (p<0.001). Restating instructions increased F1 on therapy to near baseline (median {Delta}F1 +0.12; p<0.001; 89% median recovery) versus MSJ ({Delta}F1 +0.08; p=0.04; 38% recovery). Conclusions LLM detection of suicidal ideation degraded with conversational depth and trajectory, whereas clinician performance remained stable despite the strongest models exceeding most clinicians in absolute performance. Mental health AI safety evaluations should test sustained performance across realistic and adversarial trajectories rather than relying on short-prompt benchmarks.

Working memory depends on gamma oscillations generated across sensory and prefrontal cortices. In sensory cortices such as primary visual cortex (V1), stimulus-locked gamma oscillations encode stimulus information, while in prefrontal cortex (PFC), persistent gamma oscillations maintain this information after the stimulus is removed. In schizophrenia (SZ), gamma power is reduced in both V1 and PFC, consistent with deficits in sensory encoding and working memory maintenance in the illness. These two regimes of gamma oscillations arise from a canonical microcircuit involving pyramidal neurons (PNs) and parvalbumin-expressing interneurons (PVIs). Yet, whether stimulus-locked and persistent gamma oscillations are similarly or differentially vulnerable to synaptic alterations within this circuit in SZ remains unknown. To investigate this question, we used a mean-field model of the PN-PVI circuit generating either stimulus-locked or persistent gamma oscillations. We then assessed the effects of three synaptic alterations found in SZ: lower excitatory drive to PVIs (E[-&gt;]I), lower inhibitory drive to PNs (I[-&gt;]E), and greater variability in E[-&gt;]I synaptic strength. Each alteration produced larger gamma power deficits in the persistent regime than in the stimulus-locked regime. When applied together, these alterations interacted synergistically to reduce gamma power in both regimes, with the persistent regime exhibiting a more pronounced deficit. Among the three parameters, E[-&gt;]I synaptic strength was the strongest contributor to the synergistic loss of gamma power. Two-dimensional bifurcation analyses further revealed that this differential vulnerability arises from a narrower margin of oscillatory stability in the persistent regime, where the parameter values producing maximum gamma power sit closer to the Hopf bifurcation boundary. Together, these findings identify the persistent regime as intrinsically more fragile than the stimulus-locked regime, with the implications for understanding regional patterns of synaptic pathology and cortical gamma oscillations with distinct dynamics in SZ. Author summaryWorking memory depends on stimulus-locked gamma oscillations in sensory cortices such as primary visual cortex (V1) for encoding stimulus information, and persistent gamma oscillations in prefrontal cortex (PFC) for maintaining this information after stimulus offset. In schizophrenia (SZ), gamma power is reduced in both V1 and PFC, and postmortem human brain studies suggest that the underlying synaptic alterations are more severe in V1 than in PFC. Our computational modeling results suggest that this regional pattern arises because persistent gamma oscillations are intrinsically more fragile than stimulus-locked gamma oscillations, so that smaller synaptic alterations are sufficient to disrupt gamma oscillations in PFC while larger alterations are required to produce comparable disruption in V1. Together, these findings give rise to a differential vulnerability model of cortical gamma oscillations in SZ, linking the regional patterns of synaptic pathology to the deficits in gamma oscillations observed across sensory and prefrontal cortices in the illness.

Suicidal thoughts and behaviours (STBs) are heterogeneous in their proximal dynamics, planning, and stress-sensitivity, yet most subtyping efforts remain symptom-driven and rarely validated across independent datasets. Computational mixture modelling offers a principled alternative: by fitting explicit models of learning and action selection and partitioning individuals by their latent parameter profiles, it can identify mechanistically distinct control strategies invisible to cross-sectional symptom measurement. We applied this approach to aversive Go/NoGo performance, jointly clustering two independently collected STB-enriched samples (N = 50 and N = 184) using tasks with the same structure but different duration, reversal timing, and clinical instrumentation. Two recurrent behavioural regimes emerged: a fast/adaptive regime characterised by rapid policy updating and elevated feedback reactivity, and a slow/perseverative regime characterised by slow updating, high choice determinism, and a pronounced cost following contingency reversal. These regimes were stable across initialisations, recovered more parsimoniously in joint than independent solutions, and were largely orthogonal to symptom-based stratification. Critically, stratification by regime exposed clinical-computational coupling structures substantially attenuated in pooled analyses. Pooled, population-level associations were modest and anchored by a broad affective burden axis. Within the slow/perseverative regime, coupling reorganised around learning dynamics and internalizing burden (depression, hopelessness, and active suicidal ideation) with markedly larger effect sizes. Within the fast/adaptive regime, a dissociation between anxious-compulsive and antisocial-disinhibitory profiles emerged along the same computational axis, invisible at the population level. These findings support a view of suicidality heterogeneity in which clinically similar individuals differ in the control strategies they recruit under aversive uncertainty - variation that symptom measurement alone cannot capture.

Human behavioral and mental health outcomes arise from interactions among genetic, environmental, and neurobiological systems. Existing frameworks often model these components jointly, but many treat variables independently or use static representations. This limits their ability to capture system-level dynamics and changes over time. To address this, we developed DynoSys, a unified framework that integrates these signals using three layers: predictive models, relationship exploration models, and mechanism-oriented explanation models. Building on this framework, we introduce DynoSys 2.0, a graph-based temporal modeling approach inspired by the free-energy principle by Karl Friston. In this framework, each individual is represented as a dynamic graph that evolves over time. We hypothesize that healthy development and adverse mental health outcomes correspond to different system states and trajectories. Using longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study, we construct time-indexed graphs that integrate polygenic risk scores (PRS), multi-domain environmental features, and neuroimaging-derived representations. We study six phenotypes: externalizing behavior, internalizing behavior, and sub-stance use initiation (alcohol, nicotine, cannabis, and any substance). In these graphs, nodes represent domain-level features, and edges capture relationships derived from data-driven feature selection and temporal dependencies. We model graph evolution using recurrent neural networks and graph-temporal learning methods. We also define system-level measures, including graph energy and state transitions, to quantify dynamic patterns. Our results show that DynoSys 2.0 can model behavioral development using longitudinal multi-domain data. The framework achieved meaningful prediction for both continuous behavioral symptoms and substance-use initiation outcomes, but performance differed by outcome type. Externalizing behavior was predicted more accurately than internalizing behavior, and alcohol and any substance initiation showed stronger prediction than cannabis and nicotine initiation. Graph-derived energy measures showed clearer separation for high-versus low-symptom externalizing and internalizing groups, suggesting that continuous behavioral symptoms may be linked to different latent system states over time. Overall, DynoSys 2.0 provides a flexible framework for studying behavioral risk as a dynamic developmental process, while rare-event prediction and detailed graph-level interpretation require further work.

Introduction Limitations of current classifications of schizophrenia, schizoaffective disorder, and bipolar disorder are evident from their overlapping symptoms, aetiologies, treatments, and outcomes, and present a barrier to novel treatment discovery. Alternative conceptualisations are needed to address nosological validity, align diagnosis to aetiology, and improve prognostication and treatment choice. We aimed to identify latent classes across the psychosis spectrum based on premorbid functioning and outcomes, and assess these in relation to genetic liability and symptom dimensions. Method Participants with a diagnosis of schizophrenia, schizoaffective disorder, or bipolar disorder type 1, were ascertained from four UK clinical cohorts (total n=5,043). Latent class analysis was conducted using phenotypes not included within the diagnostic criteria, including premorbid functioning, age at illness onset, and measures of severity and course. Polygenic scores (PGS) for psychiatric disorders and behavioural traits were tested for associations with latent classes. We tested if diagnosis explained associations between PGS and classes. Results A three-class model provided the best fit. Class one had poorer premorbid functioning, lower rates of recovery, and higher PGS for schizophrenia and ADHD. Class three had the highest functioning, higher rates of psychosocial stressors before onset, higher intelligence PGS and lower PGS for psychiatric disorders. Class two was intermediate between classes one and three on measures of functioning, but was characterised by high levels of involuntary hospital admissions and high bipolar disorder PGS. Diagnosis only partially explained associations between PGS and class membership. Conclusions We identified classes across the psychosis spectrum characterised by different premorbid functioning and outcomes, that cut across diagnostic categories and captured genetic liability not explained by diagnosis. Our findings suggest alternative conceptualisations of psychotic disorders may complement diagnoses in mapping to the aetiology of these conditions, and could be useful to advance precision psychiatry.

The ability to adjust to changing environments (cognitive flexibility) and optimal decision-making are pivotal brain functions that govern successful human behavior. Anxiety and depressive disorders are strongly pervasive psychiatric conditions across the lifespan that profoundly disrupt mechanisms of attention, working memory, and decision-making. Although existing task evidence documents impaired decision-making and flexibility outcomes for both anxiety and depression, there is a growing need to systematically evaluate the role of anxiety and depression and to quantitatively compare the effects of these disorders on these domains. In the present study, we conducted a meta-analysis of anxiety and depression on decision-making and cognitive flexibility. We utilized a random-effects approach, given that a large amount of between-subject heterogeneity was anticipated. Given the scope of this meta-analysis, we used the machine learning tool asReview to more efficiently conduct a meta-analytic search. Across all outcomes, results showed anxiety and depression were associated with reduced cognitive flexibility and decision-making. These effect sizes were then tested for significance using a fixed-effects (plural) model. Subgroup analyses revealed no significant differences between anxiety and depression for either decision-making or flexibility outcomes, consistent with a transdiagnostic perspective. Results are contextualized in light of the biopsychosocial model and potential transdiagnostic factors.

Predicting real-world functional outcomes in schizophrenia (SCZ) remains a clinical priority, but existing models are limited by methodological constraints and a lack of established clinical utility. Cognition is a commonly used predictor, and the Normative Latent Cognitive Structure (N-LCS) approach provides a structure-informed representation that may address limitations of conventional domain-level scores. Data from two merged COBRE cohorts (163 SCZ, 180 healthy controls) were used to develop ridge regression models for economic (EF), occupational (OF), and social (SF) functioning, using N-LCS deviation metrics alongside a priori selected demographic and clinical predictors. Score-based models using MCCB domain T-scores were developed for comparison. Performance was evaluated using bootstrap-corrected AUC, balanced accuracy, and calibration for binary outcomes, and weighted kappa and log-loss for SF. Decision curve analysis (DCA) was used to assess clinical utility for the binary outcomes. The EF model achieved a corrected AUC of 0.76 and balanced accuracy of 0.73. The OF model achieved 0.72 and 0.71, respectively. The SF model showed modest performance (weighted kappa = 0.33). DCA indicated net benefit across the full threshold range for EF and above 0.37 for OF. N-LCS models demonstrated comparable or modestly superior performance to score-based models while using fewer predictors and showing better calibration for EF. These findings support the predictive utility of N-LCS for functional outcomes in SCZ and underscore the need for external validation in independent cohorts as a next step toward clinical application.

Background: Internet-based cognitive behavioral therapy (iCBT) is efficacious for panic disorder (PD), yet the mechanisms of change remain underspecified. Anxiety sensitivity (AS) is theoretically central to PD maintenance, but its role as a mediator has not been formally tested in Spanish-speaking populations using minimal-contact formats. This study evaluates the efficacy of the "Free from Anxiety" iCBT program and examines AS as a mediator of clinical outcomes. Methods: In a randomized controlled trial, 95 adults meeting DSM-IV-TR criteria for PD were assigned to an 8-week iCBT program with optional email support (n = 49) or a waiting-list control (n = 46). Primary outcome was PD severity (PDSS); secondary outcomes included anxiety sensitivity (ASI-3), general anxiety (BAI), and depression (BDI-II). Mediation was assessed via Baron and Kenny's framework with bootstrapping (5,000 resamples) to estimate the indirect effect of ASI-3 change on PDSS reduction. Results: The treatment group showed significant improvements across all measures compared to controls (PDSS: d = 0.76, 95% CI [0.10, 1.42]; mean d = 1.30). Mediation analysis confirmed that ASI-3 change partially mediated the treatment effect on PDSS (indirect effect = 1.85, 95% CI [0.36, 3.70]), accounting for 27.4% of the total effect. The direct effect remained significant (b = 4.89, p < .001). Intent-to-treat (ITT) analyses supported robustness (d = 0.47 to 1.47). Gains were maintained at 6-month follow-up (d = 1.19 to 1.26). Conclusions: iCBT reduces anxiety sensitivity as a partial mechanism of change, aligning with cognitive models of panic. These findings support Free from Anxiety as an evidence-based, viable first-step intervention for Spanish-speaking clinical populations within stepped-care pathways.

Alcohol demand is commonly assessed using hypothetical alcohol purchase tasks (APTs), from which individual demand curves are constructed and yield multiple indices of reinforcing value. Procedurally, APTs can confer participant burden, and existing brief alternatives cannot produce demand curves or derived indices. Thus, we evaluated a novel, adjusting APT that efficiently and idiographically assesses alcohol demand while preserving the benefits of a full task. Adults reporting past-six-month alcohol use (n=897) completed either the adjusting or full APT, the former utilizing a binary-search-style algorithm to administer six prices from the full APT's price set based on level of alcohol demand. The adjusting APT reduced item burden by 49% and produced well-fitting individual demand curves. Average demand intensity and elasticity estimates did not differ significantly by modality, whereas Omax and breakpoint estimates were significantly higher on the adjusting APT, though only by $3 each. All demand indices from both APTs were positively associated with alcohol use and problems, with similar magnitude by modality. Results provide support for the adjusting APT as a brief measure of alcohol demand that retains demand-curve-based indices of reinforcing value.

Hoarding disorder (HD) affects approximately 2-3% of adults and is associated with substantial functional disability and limited access to evidence-based care. The aim of the current analysis was to examine the naturalistic effectiveness of therapist-delivered video cognitive-behavioral therapy (CBT) for HD in a large real-world sample, and to characterize individual-level treatment response, time-to-response, and moderators of outcome. This retrospective, observational analysis examined clinical data from 305 adults diagnosed with HD who received therapist-delivered video CBT through an online specialty therapy platform between September 2021 and February 2026. Hoarding symptom severity was assessed using the Hoarding Rating Scale-Self Report (HRS-SR). Linear mixed models examined symptom change from baseline to three timepoints: session 10, session 20, and each patient's final session. HRS-SR scores decreased from M = 22.4 (SD = 7.6) at baseline to M = 16.4 (SD = 8.2) at final session (Hedges' g = 0.81, 95% CI: 0.68-0.94). By the final session, median percent improvement was 25.0% [IQR: 3.0-46.7%]. A total of 39.3% of patients achieved [&ge;]35% HRS-SR reduction, 27.4% of patients who began above the clinical threshold achieved remission, 36.4% demonstrated reliable improvement, and 22.9% of eligible patients achieved clinically significant change. Among patients who achieved and maintained [&ge;]35% reduction through their final session (n = 120), median time to first response was session 9, with 54.2% responding within 10 sessions. Analyses of secondary outcomes showed significant improvements in clutter severity, depressive and anxiety symptoms, stress, quality of life, and functional disability (Hedges' g = 0.21-0.47). Greater baseline severity, more sessions, and longer treatment duration significantly moderated outcomes; prior OCD treatment history did not. Findings suggest that therapist-delivered video CBT for HD, delivered remotely in a real-world setting, produces outcomes consistent with controlled trials and may be a clinically effective and scalable approach for a condition historically underserved by mental health systems.

Relapse after treatment for various mental health disorders has been linked to tendency for reductions in responding to increase over time or following re-exposure to motivating stimuli. Here we show that, in rats, responding reduced through non-contingent outcome delivery does not recover in these ways, and that this learning depends on an intact lateral orbitofrontal cortex. These findings suggest that contingency degradation overwrites original learning which may support the development of relapse-resistant behavioural interventions.

Background: Conventional psychiatric screening instruments summarize symptoms within individual scales and prioritize cases with high single-instrument additive score severity. This design treats items as independent within instruments and ignores cross-instrument covariance structure, making it insensitive to respondents whose responses are distributed across multiple domains in unusual combinations that remain below threshold on every individual scale. Methods: We analyzed two cohorts spanning older and younger adults. Item prompts from depression, stress, anxiety, and sleep instruments were embedded into a shared semantic space using a pretrained sentence encoder. Principal component analysis of the item-prompt embeddings alone---with no use of respondent data at this stage---was used to construct a low-dimensional subspace retaining 80\% of variance in the item embedding matrix. Normalized participant responses were then projected into this subspace, with Jaccard-based stability analysis used as a check on dimensional robustness. Multivariate deviation from the cohort norm was quantified with Mahalanobis distance using Ledoit-Wolf covariance regularization. Candidate outliers were defined by the empirical 95th percentile of the cohort-specific distance distribution. To isolate response configurations not already captured by conventional single-instrument extreme-value logic, we excluded all outlier respondents who had endorsed any individual item at the maximum value of its Likert scale on any instrument. For the remaining outliers, anomalous components were backtracked to their original item loadings for interpretation. Results: In the older-adult Health and Retirement Study (HRS) cohort, principal component analysis of 27 item-prompt embeddings showed that a 10-dimensional subspace provided a stable representation of cross-instrument semantic structure. In the younger-adult Xinxiang cohort the corresponding stable solution was 16-dimensional. In each cohort, seven respondents remained as multivariate outliers despite falling below every single-instrument extreme-value threshold. These cases were not characterized by uniformly severe symptom scores but by unusual cross-domain response configurations that became visible only in the shared semantic covariance subspace. The response structure of the retained configurations differed across cohorts: older-adult cases more often involved weak endorsement of mood-labeled items alongside nonzero body- and sleep-related responses, whereas younger-adult cases more often involved incomplete response configurations spanning mood, sleep, stress, and self-harm-related items. Conclusions: A semantically aligned, auditable covariance subspace provides a practical tool for flagging unusual multivariate response configurations that single-instrument additive screening may not flag. The method is interpretable at the level of original item contributions. It should be understood as a hypothesis-generating screen for unusual response configurations requiring further clinical assessment, not as a diagnostic instrument. Outcome validity remains to be established by prospective study.