Genetic risk variants for common diseases are predominantly located in non-coding regulatory regions and modulate gene expression. Although bulk tissue studies have elucidated shared mechanisms of regulatory and disease-associated genetics, the cellular specificity of these mechanisms remains largely unexplored. Here we present a comprehensive, single-nucleus multi-ancestry atlas of genetic regulation of gene expression in the human prefrontal cortex, comprising 5.6 million nuclei from 1,384 donors of diverse ancestries. Through multi-resolution analyses spanning eight major cell classes and 27 subclasses, we identify genetic regulation for 14,258 genes, with 981 showing cell type-specific regulatory effects at the class level and 857 at the subclass level. Colocalization of genetic variants associated with gene regulation and disease traits uncovers novel cell type-specific genes implicated in Alzheimer's disease, schizophrenia and other disorders that were not detectable in bulk tissue analyses. Analysis of dynamic genetic regulation at the single-nucleus level identifies 2,073 genes with regulatory effects that vary across developmental trajectories, inferred from a broad age range of donors. We also uncover 1,655 genes with trans-regulatory effects, revealing distal regulation of gene expression. This high-resolution atlas provides insight into the cell type-specific regulatory architecture of the human brain, and offers novel mechanistic targets for understanding the genetic basis of neuropsychiatric and neurodegenerative diseases.

Neurodegenerative and neuropsychiatric diseases impose a considerable societal and public health burden. However, our understanding of the molecular mechanisms underlying these highly complex conditions remains limited^1,2. Here, to gain deeper insights into the aetiology of different brain diseases, we used specimens from 1,494 unique donors to generate a population-scale single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex, comprising over 6.3 million individual nuclei. The cohort includes neurotypical controls, as well as donors affected by eight common and complex brain disorders: Alzheimer's disease (AD), diffuse Lewy body disease (DLBD), vascular dementia (Vas), Parkinson's disease (PD), tauopathy, frontotemporal dementia, schizophrenia, and bipolar disorder. We show that interindividual variation accounts for a substantial portion of gene expression variation. By comparing transcriptomic variation across diseases, we reveal universal signatures enriched in basic cellular functions such as mRNA processing and protein localization. After discounting these cross-disease signatures, we show stronger genetic and transcriptomic concordance among AD, DLBD, Vas and PD. Furthermore, we characterize transcriptomic variation among different AD phenotypes, distinct from those observed in healthy ageing, revealing a reduction in neuronal abundance in individuals with more severe AD, coupled with an increase in immune and vascular cell populations. Exploring the neuropsychiatric symptoms (NPSs) that frequently accompany AD, we find an increased abundance of deep-layer excitatory neurons associated with a broad range of NPSs. By constructing transcriptome trajectories that capture AD progression, we implicate cell-type-specific responses in the early and late stages of AD. Our disease atlas provides a perspective of the transcriptomic landscape in neurodegenerative and neuropsychiatric disorders, shedding light on shared and distinct processes involving the neurological-immune-vascular systems, and identifying potential targets for therapeutic intervention.

Common brain disorders impose a substantial health burden, but localizing their genetic risk in the brain remains challenging¹. Although genome-wide association studies have identified numerous loci associated with neuropsychiatric and neurodegenerative disorders, many of these loci lie in non-coding regions that influence gene expression in specific cell types^2-5. Traditional bulk brain transcriptomic analyses, which often focus on European ancestry cohorts, average over cellular diversity, obscuring genetic risk-related changes in gene expression. Here we use single-nucleus gene expression profiles from the dorsolateral prefrontal cortex in the multi-ancestry PsychAD cohort to develop transcriptomic imputation models of genetically regulated expression across major brain cell types. Applying these models to neuropsychiatric and neurodegenerative disorders reveals thousands of gene-trait associations that are undetectable in bulk tissue analyses and resolves many signals to discrete neuronal, glial and immune cell populations. Cross-ancestry analyses in the Million Veteran Program confirm these associations, reveal pleiotropic effects of cell-type-specific predicted expression and demonstrate that trait-related dysregulation is conserved across ancestries, enabling mapping of causal genes and pathways. Together, these findings provide a cell-type-resolved and ancestry-aware atlas of genetically regulated expression in the human prefrontal cortex and illustrate how single-nucleus transcriptomics can sharpen gene discovery and therapeutic target prioritization for complex brain disorders.

Resilience, the ability to adapt positively in the face of adversity, is shaped by combined influences of risk and protective factors. Previous neuroimaging studies on resilience have predominantly focused on single factors, often operationalizing resilience dichotomously as the absence of psychiatric disorders despite adversity. In this prospective magnetic resonance imaging study, we defined resilience as "better-than-expected" depressive symptom severity (Hamilton Depression Rating Scale) relative to cumulative risk across 22 risk and protective variables. Using ridge-regularized regression in N = 1804 participants (955 healthy, 849 depressed) from the Marburg-Münster Affective Disorders Cohort Study, we predicted symptom severity and derived residuals as measures of resilience. Residuals were then used to predict gray matter volume (GMV) and cortical thickness at baseline (T1) and two-year follow-up (T2; N = 808). This approach was complemented by extreme-group comparisons of resilient (better-than-expected outcome) and vulnerable (worse-than-expected outcome) individuals. Cumulative risk explained 49.6% of variance in depressive symptoms at T1 and 40.1% at T2. Residual scores showed moderate temporal stability (r = 0.32, p < 0.001). Region-of-interest and whole-brain analyses revealed no morphometric associations with resilience at T1. In contrast, higher resilience at T1 predicted lower GMV in the left inferior orbitofrontal gyrus (IOFG) and temporal pole at T2 (ROI, p_FWE(peak)<0.001, r_partial = 0.18), with no changes in cortical thickness. Taken together, resilience to cumulative risk, defined as better-than-expected depressive symptom severity, was not associated with immediate brain structural differences. However, prospective analyses revealed smaller GMV in the IOFG and temporal pole over time, potentially reflecting greater neural efficiency or delayed biological costs.

Alzheimer's disease (AD) is highly heterogeneous and driven by diverse molecular and cellular mechanisms. Functional genomics investigates these mechanisms from genetic variants to gene expression and regulation. We performed personalized functional genomics analysis on population-scale single-nucleus RNA-seq data, with cross-cohort validation across multiple cohorts comprising over 1900 individual brains, capturing donor-level cell type interactions and gene regulatory networks. Using a knowledge-guided graph neural network, we learned latent representations of each donor's functional genomics that accurately classified AD phenotypes, identified molecularly defined subpopulations, and traced disease progression trajectories. Our importance scores, derived from graph attentions, identified significant inter-donor differences and prioritized personalized cell type genes and regulatory networks. Finally, we identified gene regulatory QTLs (grQTLs) linking genetic variants to donor-level regulatory changes, providing insights into gene regulatory relationships beyond traditional eQTLs. All results are summarized into a personalized functional genomics atlas for AD, including an open-source framework, iBrainMap, for general use.

Artificial intelligence (AI) is increasingly integrated into psychiatric practice, raising not only technical but also epistemological and ethical questions. Beyond performance, the acceptability of AI remains a critical condition for its implementation, particularly in a field deeply rooted in subjectivity and clinical judgment.

To assess the acceptability of AI-based tools in psychiatry using a scenario-based approach, and to explore the structural relationships between its key dimensions (utility, usability, reliability, risk, and professional alignment) through network analysis.

We conducted a cross-sectional study using the Script Method, presenting three clinical vignettes reflecting successive stages of care: diagnostic support, AI-assisted therapeutic guidance, and digital monitoring via wearable devices. A total of 1533 participants were recruited, of whom 727 were included in the final analysis. Acceptability was assessed using Likert-scale items derived from UTAUT models and a pragmatic/social acceptability framework. Psychometric validation included internal consistency analyses and Unique Variable Analysis. Network analysis and Exploratory Graphical Analysis were performed to identify structural patterns of acceptability.

Acceptability emerged as a multidimensional construct structured around four communities: (1) reliability and ease of use, (2) usefulness and professional acceptability, (3) risks related to professional culture, and (4) practical risks and implementation. The most central node was related to the preservation of the clinician's role in decision-making, highlighting the centrality of professional identity. Risk perception was globally low, with ceiling effects on several items. Digital literacy and theoretical orientation significantly influenced acceptability, with structural differences observed in network connectivity. Gender differences were particularly marked in the digital monitoring scenario.

The acceptability of AI in psychiatry depends less on technical performance than on its alignment with professional values and clinical reasoning. AI is more readily accepted as a support to clinical judgment than as an autonomous system. These findings suggest that the successful integration of AI will require the development of epistemically compatible systems that preserve clinician autonomy and the therapeutic relationship.

Alcohol, drugs and self-harm are important public health issues that often coincide with socioeconomic inequalities. While national policy frameworks highlight these as important issues, data describing their prevalence in unscheduled care are lacking.

To conduct the first national-level description of the number, demographic characteristics and routes of care involving alcohol, drugs or self-harm emergencies across all unscheduled care services in Scotland.

A retrospective cross-sectional study using linked data from all unscheduled care services in Scotland (NHS 24 telephone triage service; primary care out-of-hours; ambulance; emergency department (ED); acute admission and mental health admission). All continuous care episodes (defined as pathways) were analysed and within these, indicators for involvement of alcohol, drugs and/or self-harm were identified from service level 'flags' and clinical codes. Pathways from 1 April 2022 to 31 March 2023 were included. Descriptive analyses of characteristics, exploration of the top 30 most common pathways and top-down pathway-attributable-cost analyses were undertaken.

Of 2 721 120 pathways, 2.7% involved alcohol, drugs and/or self-harm-indicating considerable overlaps and complexity of care episodes. In 1.9% of all pathways only one of these indicators was identified. For the top 30 pathways (reflecting 79.0% of pathways involving only one indicator), the most common patient journey was ambulance to ED to acute admission, with an overall directly attributable cost to the National Health Service of approximately £216.9 million.

Using current structured data, 1.9% of unscheduled care pathways in Scotland involve alcohol, drugs or self-harm and 2.7% involve one or several of these indicators. Ambulance services and EDs feature in most of these care pathways and are potential settings for intervention. Future work should explore these pathways in greater depth, refine case identification at service level and develop integrated care models within the ED to better serve this patient population and address health inequalities.

Survivors of intensive care unit (ICU) admission and their families frequently experience persistent physical, cognitive and psychological impairments following critical illness, collectively described as post-intensive care syndrome (PICS) and post-intensive care syndrome-family (PICS-F). These sequelae often emerge during the recovery period after hospital discharge and may affect long-term functioning and quality of life. Social connectivity-the extent to which individuals maintain meaningful social relationships and supportive interactions-has been associated with improved health outcomes in several populations. This scoping review aims to map the existing literature describing social connectivity among ICU survivors and their families and examine how it has been studied in relation to recovery outcomes.

This scoping review will be conducted using the Joanna Briggs Institute methodology for scoping reviews and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. MEDLINE, EMBASE, CINAHL and PsycINFO will be searched from database inception to December 2025 without language restrictions. Studies examining social connectivity in relation to PICS or PICS-F among adult ICU survivors or their family members will be included. Data will be extracted independently using a standardised charting form and summarised descriptively.

Ethical approval is not required because this study synthesises previously published literature. Findings will be disseminated through publication in a peer-reviewed journal and may inform future research and patient- and family-centred strategies to support recovery after critical illness.

Chronic psychological stress is a major public health problem with adverse effects on mental health, health-related quality of life (HRQoL), and work functioning. Transcendental Meditation (TM) is a standardised meditation that may reduce stress and enhance wellbeing, yet long-term evidence in workplace settings is limited. This study evaluated the effect of TM on perceived stress and related outcomes among Australian university staff.

This two-arm, open-label randomised controlled trial enrolled staff of an Australian university into TM (5-day training plus regular practice, n = 65) or control (usual wellbeing resources, n = 64) groups. Symptoms of perceived stress (primary outcome), anxiety and depression symptoms, workplace burnout, poor sleep, well-being, HRQoL, and productivity impairment were assessed at baseline, 3, and 6 months. Analyses were conducted using baseline-adjusted linear mixed models under the intention-to-treat principle.

TM significantly reduced perceived stress over 6 months compared with control (adjusted mean difference - 2.25 [95% CI: -3.33, -1.17], p < .001) with a moderate effect size (Hedge g = -0.52). Significant improvements were also observed in symptoms of anxiety, depression, burnout, sleep quality (mean differences -0.25 to -2.89), well-being, and HRQoL (1.08 to 1.64). Exploratory analysis showed that twice-daily practice was significantly associated with improvements in perceived stress, well-being, HRQoL, symptoms of depression and burnout (p < .05). No adverse effects were reported.

TM produced significant improvements in stress, mental health, sleep, and HRQoL over six months and warrants consideration as a safe, effective workplace mental health strategy, but active-comparator trials with longer follow-up are needed.

Alopecia areata (AA) is associated with substantial psychosocial morbidity. Population-based studies have identified increased suicide-related risks, although findings are heterogeneous, which raises an ethical question regarding dermatologists' obligation to screen for psychiatric morbidity. Routine screening for depression and psychologic distress may reasonably form part of AA care. In contrast, formal suicide risk assessment should follow identification of suicidal ideation or other concerning clinical findings rather than be universally administered. This proportionate approach balances beneficence and nonmaleficence with the risks of overmedicalization, false-positive results, and limited referral resources. Screening should occur within a defined response pathway that includes assessment of positive findings, safety measures when indicated, crisis resources, and mental health referral. This approach allows dermatologists to address foreseeable psychologic harm while maintaining appropriate professional boundaries.