Rudolf N. Cardinal

Background and Hypothesis Schizophrenia research routinely reduces antipsychotic treatment to a single number, but the available summaries disagree. Chlorpromazine equivalents, the WHO Defined Daily Dose (DDD) and the percentage of the licensed maximum (%BNFmax) rank the same patient differently, most of all for clozapine, whose dose-concentration relationship varies several-fold with sex and smoking. We hypothesised that referencing clozapine dose to a target individualised for sex and smoking would track plasma clozapine better than dose-proportional measures. Study Design In the Antipsychotic Load (AP_Load2026), each daily dose is expressed as a percentage of a reference dose, summed without a cap. Clozapine is referenced to a target individualised for sex and cigarettes per day, under two published anchor schemes, and other antipsychotics to a dose reconciling the WHO DDD and the licensed maximum. The clozapine term was tested against plasma clozapine (1316 assessments, 255 patients) with log-log mixed-effects models, and the non-clozapine reference against independently derived published doses. Study Results The individualised reference tracked plasma clozapine better than any dose-proportional measure (marginal R^2 0.249 vs. 0.166; deltaR^2 0.082, 95% CI 0.044-0.120). The scheme with the larger smoking correction fitted better (0.249 vs. 0.231), an ordering robust to quality-control variants and co-prescription. Of five candidate references for other antipsychotics, %BNFmax was the least consistent with external criteria. Conclusions AP_Load2026 is a transparent framework for normalising prescribed antipsychotic dose. Its clozapine reference is validated against a biomarker, whereas the reference for other antipsychotics is a calibrated convention at least comparable with existing alternatives. The index is particularly suited to treatment-resistant and clozapine-enriched cohorts.

Despite rising global rates for cocaine addiction, treatments options are limited. Psychological treatments remain the standard option but have only modest effectiveness. One major barrier to treatment success is that patients often make disadvantageous decisions that go against recovery, highlighting the need for strategies to support decision-making. Converging evidence indicates that decision-making deficits in cocaine addiction are driven by impaired processing of outcomes and risks. We investigated how patients with cocaine addiction use explicit outcomes and risk information to guide decision-making. One-hundred-and-five patients with moderate-to-severe cocaine use disorder (CUD) and 80 healthy control volunteers completed a decision-making task in which reward, loss, and probability information were provided explicitly. A subset of participants underwent diffusion-weighted magnetic resonance imaging to investigate the orbitofronto-striatal white matter tracts that underpin decision-making. We used generalized linear mixed-effects models to determine how rewards, losses, and probability influence decisions, and combined probabilistic tractography with principal component analysis to characterize the white matter tracts. We found that patients incorporated explicit probability information into their decisions to the same degree as control participants but showed reduced integration of outcomes. Notably, patients were less likely to integrate losses into their decisions than rewards. Although orbitofronto-striatal white matter tracts did not differ between groups, individual variability predicted the extent to which reward and loss information guided decisions, irrespective of diagnosis. Overall, these findings reveal fundamental challenges in using outcomes, particularly negative ones, to guide decision-making in patients with CUD, emphasizing the need for additional support to promote adaptive choices during recovery.

Background; Psychosocial interventions are recommended as first-line approaches for children and young people (CYP) experiencing mental health (MH) difficulties. However, evidence on outcomes of routinely provided psychosocial interventions in real-world settings remains limited. This study examined associations between four commonly used therapies - Cognitive Behavioural Therapy, Dialectical Behaviour Therapy, creative therapy, and psychodynamic/counselling-based therapy, and MH outcomes among CYP receiving specialist MH services. We also examined whether these associations differed by sociodemographic characteristics, including, ethnicity, socioeconomic deprivation, and social work involvement. Methods: Data were extracted from anonymised clinical records (k=9,793) of CYP attending state MH services in a large NHS Trust. Natural language processing (NLP) identified intervention exposure from clinical case notes. Multilevel mixed-effects regression models examined changes in MH difficulties following psychosocial intervention and whether these associations differed by sociodemographic characteristics.Findings: Psychosocial interventions were consistently associated with improvements in MH outcomes over time (β = –1·69, 95% CI [–2·37, –1·00]). Positive associations were observed across intervention types, and trajectories of change did not differ meaningfully by ethnicity, socioeconomic deprivation and social work involvement, despite differences in baseline difficulty levels. Interpretation: Psychosocial interventions delivered in MH services are associated with reduction in MH difficulties among CYP. This provides important real-world evidence supporting the value of psychosocial intervention in routine care and highlights the need for adequate resourcing to expand their delivery.

Background: There is growing evidence to suggest a clinically significant overlap between autism spectrum conditions and psychotic disorders. Preliminary evidence suggest that autism diagnoses and autistic traits are associated with poorer outcomes following a first episode of psychosis. Methods: This study used data from the Cambridgeshire and Peterborough National Health Service Foundation Trust (CPFT) Research Database to examine clinical outcomes in autistic and non-autistic people following a first episode of psychosis. We describe patterns of community and inpatient service use, using descriptive statistics, Cox regression, binomial logistic regression, and negative binomial regression. Results: Data from 224 autistic and 7185 non-autistic people with psychosis were analysed. Autism was associated with greater community service use (use of mental health emergency lines, mental health detentions by police), as well as greater likelihood of psychiatric hospital admission (adjusted hazard ratio 1.42, 95% confidence interval 1.08–1.88, p<0.05) and longer inpatient stays (median 112 versus 49 days, p<0.0001). Learning disability played a significant role in the utilisation of community and inpatient services, with lower rates of community service use but longer inpatient admissions. Conclusions: This study indicates a differing pattern of service use between autistic and non-autistic people following psychosis that warrants further research into how best to support autistic people with psychosis. Lay Summary: Autistic people are at greater risk of developing psychosis. This study aimed to compare the outcomes and mental health service use of autistic people with and without psychosis. This study found that autistic people with psychosis were at greater risk of hospitalisation following a psychosis diagnosis. We found that autistic people were more likely to have used emergency mental health services relative to non-autistic people. We also found that people with a learning disability in addition to autism and psychosis used services in different ways.

Background: Routinely collected health data, such as that held by United Kingdom (UK) national health services (NHS), has important research uses. However, its use requires public trust and transparency. Access by commercial/industrial organisations is especially sensitive for the public, as is mental health (MH) data. Although existing MH data science guidelines emphasise patient/public involvement (PPI), they do not cover commercial uses specifically. Objectives: To develop patient- and public-led guidelines for the commercial and industrial use of MH data for research. Though UK-focused, their principles may apply internationally. Methods: A PPI Lived Experience Advisory Group (LEAG) was created within DATAMIND, a UK data hub for MH informatics. Initial discussion yielded a requirement for definitions and explanations of concepts relating to MH data research, developed iteratively. Subsequently, the LEAG developed guidelines via a qualitative quasi-Delphi approach. The agreed scope excluded data provided for research with informed consent, data processing arrangements (e.g. companies hosting electronic systems on the instruction of health services), and compliance with legal minimum requirements. The scope included the use of routinely collected MH data for research by commercial/industrial organisations without explicit consent, and aspects of industry-led MH data collection conducted with consent. Results: Alongside the primer in MH data research concepts, the LEAG provide best-practice guidelines relating to commercial/industrial research use of MH data, for organisations controlling MH data (such as NHS bodies) and for commercial applicants seeking access. Core principles include transparency, patient rights, meaningful PPI, stringent governance, and statistical disclosure control. The guidelines recommend a risk–benefit approach to assessing data access applications, within limits that include avoiding the export of unconsented patient-level data outside NHS-controlled secure data environments, and not providing commercial applicants with access to unconsented free-text MH data. Further recommendations for NHS executive and regulatory bodies relate to public choice and transparency, clarity of guidance to research-active NHS organisations, and support for de-identification. Conclusions: MH data research requires patient/public involvement and understanding. These guidelines reflect the views of people with personal or family experience of mental ill health. We hope they are useful to the MH research community and increase public transparency and trust.

BACKGROUND: Routinely collected health data, such as that held by the United Kingdom (UK) National Health Service/Health and Social Care (collectively "NHS"), has important research uses, but its appropriate use requires public trust and transparency. Commercial/industrial access to routinely collected health data is especially controversial and sensitive for the public, and particular concerns may relate to mental health (MH) data. Existing best-practice MH data science guidelines do not cover commercial uses specifically, but emphasise the importance of patient/public co-development of data science. OBJECTIVES: To develop patient/public-led guidelines for the commercial/industrial use of MH data for research, and to capture relevant background information required by patient/public participants. The focus was on the UK and its constituent nations, but the principles may have wider applicability. METHODS: A patient/public lived experience advisory group (LEAG) was set up within DATAMIND, the Health Data Research UK data hub for MH informatics research development. Initial training and discussion yielded a requirement for definitions and explanations of concepts and processes relating to MH data research, developed iteratively. Subsequently, the LEAG developed guidelines via a qualitative and iterative quasi-Delphi approach. The agreed scope excluded data provided for research with informed consent, data processing arrangements such as companies hosting electronic health records or e-mail systems on the instruction of health services, or compliance with legal minimum requirements. The scope included the use of routinely collected MH data (e.g. NHS data) for research by commercial/industrial organisations without explicit consent, and aspects of MH data collection directly by industry with consent. RESULTS: Alongside the primer in MH data research concepts, the LEAG provide recommendations and best-practice guidelines relating to commercial/industrial research use of MH data, for organisations controlling MH data (such as NHS bodies) and for commercial applicants seeking to use MH data for research. Alongside principles of transparency, patient rights, patient/public involvement in research, stringent governance, and statistical disclosure control, the guidelines recommend a risk-benefit approach to assessing applications for data use, within limits that include avoiding the export of unconsented patient-level data outside NHS-controlled secure data environments, and not providing access to unconsented free-text MH data to commercial applicants. We also provide some recommendations for NHS executive and regulatory bodies, relating to public choice and transparency, clarity of guidance to research-active NHS organisations, and support for de-identification. CONCLUSIONS: Patient/public involvement and understanding is central to MH data research. The primer materials developed here constitute information requested by public advisers prior to considering best practice. The guidelines reflect the views of people with personal or family experience of mental ill health. We hope they are of practical use to the wider MH research community and serve to increase public transparency and trust.

BACKGROUND Depression is a disabling disorder with variable outcomes. In severe cases treatment is provided by specialist mental health care services, yet there is a lack of real-world evidence demonstrating how depression is managed within these settings, and consequently, a limited understanding of how to improve care for this population. AIMS We examine the characteristics of patients receiving secondary mental healthcare for depressive disorders within a UK National Health Service (NHS) provider, and the treatments they receive. We investigate when patients receive treatments, and what predicts the use of specific treatments, improvement, and duration with services, with the aim of comparing real-world care to that advised by national guidelines. METHODS A retrospective cohort study was conducted using de-identified electronic patient records of patients with depression referred to Cambridgeshire and Peterborough NHS Foundation Trust (serving a population ∼0·86 million), between January 2013 and June 2021. ANOVA models examined predictor variables of improvement and duration of care, while survival analyses explored treatment initiation rates and predictors of which treatments were used. RESULTS 9,083 patients met the study’s inclusion criteria. Almost half of those with depression had additional psychiatric diagnoses, reflecting the complexity of cases in secondary care. Treatment within secondary care was associated with improvements in both depressive and overall symptoms. Patients with a greater degree of psychiatric co-morbidity and those with lower socio-economic status indicators presented with greater overall illness severity at baseline, were more likely to be admitted into hospital, spent longer with services, and improved less than the average. Treatment patterns differed across age groups, sex/gender, socio-economic status, and psychiatric comorbidities. Some nationally recommended further-line treatments appeared to be under-used. CONCLUSIONS Treatment gaps in further-line treatments for depression exist, highlighting key areas for service improvement. Future work should target patients with complex needs and those who are socio-economically deprived.

Reinforcement learning is a fundamental aspect of adaptive behaviour, since it involves the acquisition and updating of associations between actions and their outcomes based on the rewarding or punishing consequences. Acute experimental manipulations of serotonin have provided compelling evidence for its role in reinforcement learning. However, it remains unknown how more chronic manipulation of serotonin, which holds greater clinical relevance, affects reinforcement learning and the underlying neural mechanisms. Consequently, we aimed to investigate the effect of a three-week administration of the SSRI, escitalopram, on a reinforcement learning paradigm during functional magnetic resonance imaging. The study used a double-blind, placebo-controlled design with 64 healthy volunteers. Participants were semi-randomised, ensuring matched groups for age, sex and intelligence quotient (IQ), to receive either 20 mg of escitalopram (n = 32) or placebo (n = 32) for at least 21 days. We analysed group differences in reinforcement learning using both analysis of covariance as well as innovative hierarchical Bayesian modelling of the reinforcement learning task. Escitalopram reduced learning from punishment during punishment trials. A key novel finding was that there was decreased activation of the intraparietal sulcus in the escitalopram group when compared to the placebo group during reward trials. The involvement of the intraparietal sulcus suggests that escitalopram affects the encoding of value outcome, which may lead to reduced reinforcement sensitivity, and thereby impacting adaptive learning from feedback. Understanding these mechanisms may help to optimize SSRI treatment to mitigate clinical symptoms and improve quality of life for neuropsychiatric patients, by elucidating serotonin’s effects on affect, cognition, and behaviour.

In schizophrenia, anterior hippocampus (aHipp) overactivity is associated with orbitofrontal cortex (OFC) dysfunction, but the contribution to symptomatology is unknown. In rodents, degradation of the hippocampal perineuronal net (PNN) replicates this overactivity, but uncertainty over rodent/human prefrontal homology limits translation to humans. Here, we test the hypothesis that aHipp PNN degradation in a species with a human-like prefrontal cortex, the marmoset, alters aHipp-striatal and aHipp-OFC circuitry. Microdialysis and [ ¹⁸ F]-fluoro- l -dihydroxyphenylalanine positron emission tomography identified increased dopamine synthesis in the associative striatum, but not the nucleus accumbens, as is seen in schizophrenia, and elevated dopamine and noradrenaline in the OFC. Behaviorally, activity was elevated in a marmoset version of the amphetamine-induced activity test, and impaired probabilistic discrimination learning was seen in an OFC/striatum-dependent task that computational modeling suggests was due to loss of goal-directed behavior. Together, these findings demonstrate that a loss of primate aHipp PNNs is sufficient to induce striatal and prefrontal dysfunction consistent with that observed in humans with schizophrenia.