Psychiatry and Clinical Neurosciences

BackgroundBrain disorders are leading contributors to increasing disability and spending worldwide. In 2022 the Swiss Brain Health Plan (SBHP) was launched to promote brain health and prevent brain disorders. To guide the implementation of the SBHP, we performed a detailed analysis of the health and economic burden of brain disorders in Switzerland. MethodsWe analyzed Global Burden of Disease 2023 disability-adjusted life years (DALYs) and Institute for Health Metrics and Evaluation (IHME) cause-specific health-care spending estimates for Switzerland. DALYs were quantified for 1990 - 2023. Spending was analyzed for 2000 - 2019 across six types of care. We examined age and sex patterns, spending distribution, and international comparisons with six other countries (Germany, France, Denmark, Norway, Italy, Singapore). To assess short- and longer-term association between burden and spending estimates, we fitted panel regression models with disorder and year fixed effects under one-year and five-year lag specifications. FindingsBoth disease burden and spending were highly concentrated in a small number of conditions in Switzerland. In 2023, ten brain disorders accounted for 82{middle dot}9% of Switzerlands total DALY burden. In 2019, ten brain disorders accounted for 86{middle dot}0% of all direct brain-health spending, with dementia alone comprising 29{middle dot}5% of total expenditures. Among seven analyzed comparator countries, Switzerland had the highest per-capita brain-health spending and the highest spending per DALY. In fixed-effects panel models that accounted for spending persistence, lagged DALYs were not statistically associated with subsequent spending. Suicide prevention and addiction showed significant lower-than-expected health-sector spending (self-harm: {beta} = -0{middle dot}23; drug use disorders: {beta} = -0{middle dot}08 to -0{middle dot}18 across lag models). InterpretationBrain disorders generate a large burden in Switzerland. Within the IHME estimates, the burden-spending relationship over time appears limited. The implementation of the SBHP will refer to the current data and call for a burden-informed financing to guide strategic cross-sectorial allocation and prevention investments. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe drew on evidence from the Global Burden of Disease (GBD) 2023 estimates on neurological and mental health conditions, and on cause-specific health-care spending data from the Institute for Health Metrics and Evaluation for Switzerland and selected high-income countries. These sources show that brain disorders are major contributors to disability and premature mortality, and that Switzerland is among the worlds highest spenders per capita on health care. Prior work has described the costs of individual brain disorders and drivers of health expenditure growth; however, it has often treated burden and spending as partly separate domains, leaving the country-level link between cause-specific disability-adjusted life-years (DALYs) and cause-specific spending, over time and in either direction, poorly characterized. Added value of this studyTo our knowledge, this is the first study in a single country to systematically link cause-specific DALYs and cause-specific direct health-care spending for brain disorders and to examine their longitudinal and bi-directional associations. Using harmonized GBD 2023 estimates and IHME 2019 cause-specific health spending data, we quantify the health and economic burden of 23 brain disorders in Switzerland across age, sex, care setting, and time, and benchmark patterns against six other high-income countries. By applying panel regression models with disorder and year fixed effects, we assess whether modeled spending shows any association with prior modeled burden once spending persistence is accounted for, and identify conditions with higher or lower spending relative to burden. Implications of all the available evidenceSwitzerland bears a major burden of brain disorders and devotes substantial resources to their care, yet within the modeled estimates, spending does not consistently correspond to burden over time. Disorders with long-standing multisectoral programs tended to show lower spending-to-burden ratios, suggesting that coordinated action beyond the health sector may reduce downstream health-sector demand. For Switzerland and similar health systems, these findings support national brain-health strategies that strengthen life-course prevention and early intervention, and that integrate financing with burden data to inform priority setting and periodic reassessment of resource allocation.

In treatment-resistant schizophrenia, clozapine treatment has been associated with longitudinal reductions in subcortical volumes, ventricular enlargement, and widespread cortical thinning. However, it is unknown how these structural changes relate to clozapines pharmacological profile and clinical efficacy. We combined five longitudinal datasets with MRI acquired before and on average 5 months after clozapine initiation in 143 individuals to quantify brain structural changes and their association with normative maps relating to neuroreceptor architecture and physiological systems, and improvement in symptom severity. Clozapine treatment was associated with grey matter volume reductions across multiple subcortical regions (including the amygdala, hippocampus, thalamus, caudate, putamen and nucleus accumbens), increases in pallidal volume, ventricular enlargement, and widespread cortical thinning. Cortical regions showing the greatest magnitude of thinning corresponded to areas with higher normative densities of serotonergic 5-HT1A, 5-HT2A and 5-HT4 receptors. Changes in subcortical volume or cortical thickness during clozapine treatment were not associated with changes in total or positive symptom severity. In addition, baseline subcortical volume, cortical thickness, or gyrification prior to starting clozapine did not predict subsequent symptom improvement. Cortical thinning may partly reflect clozapines activity at serotonergic receptors, which have been implicated in cortical network stabilisation and neuroplasticity, however structural remodelling during clozapine treatment may reflect a process independent from its clinical efficacy in improving core symptoms of psychosis.

Forensic patients often have complex and costly healthcare needs, even following discharge from secure care. However, little is known about their health and justice outcomes after community reintegration. To address this gap in the literature, we conducted a systematic review and meta-analysis to estimate the incidence of key post-discharge outcomes among community-discharged forensic patients, including any reoffending, violent reoffending, reconvictions, readmissions, all-cause mortality, and suicide. We systematically searched PsycINFO, Embase, CINAHL, Medline, PubMed, and ProQuest Dissertations from database inception to May 2025 (PROSPERO CRD42024529265). Random-effect meta-analyses were used to generate pooled incidence estimates, with heterogeneity quantified using prediction intervals. A total of 49 studies met inclusion criteria (total patient n = 18,871) and contributed to the meta-analyses. The pooled incidence rate per 100,000 person-years was: any reoffending 3,889 (95% CI 2,055, 7,359; 95% PI 290, 52,136); violent reoffending 1,851 (95% CI 1,229, 2,789; 95% PI 201, 17,068); reconvictions 3,291 (95% CI 2,591, 4,179; 95% PI 950, 11,394); readmissions 7,945 (95% CI 5,507, 11,463; 95% PI 1,225, 51,548); all-cause mortality 1,789 (95% CI 1,341, 2,388; 95% PI 673, 4,756); and suicide 407 (95% CI 319, 519; 95% PI 225, 735). Overall, the reoffending rate for forensic patients discharged to the community was lower than that reported for other cohorts of people charged with general and violent offences. However, despite typically receiving long admission periods, discharged forensic patients continue to experience high rates of readmission, all-cause mortality, and suicide relative to other psychiatric patient groups in the community. Together, our findings highlight a need for enhanced post-discharge suicide support for forensic patients living in the community to better facilitate successful, long-term reintegration.

Mood and anxiety disorders emerge predominantly in adolescence, yet they are usually identified only once symptoms have consolidated, when intervention can only be reactive. A marker that registers the loss of healthy brain function before symptoms crystallise would allow earlier and more targeted treatment, much as caged canaries once warned miners of danger before it became apparent. Here we report such a marker using a single baseline resting-state functional MRI scan in 150 adolescents in the Human Connectome Project Boston Adolescent Neuroimaging of Depression and Anxiety (HCP BANDA) cohort, allowing us to prospectively predict depression and anxiety symptoms one year later in held-out participants at r = 0.60, substantially above the effect-size ceiling reported for functional connectivity in the same data. The marker is not computed from raw functional connectivity but read out from a whole-brain generative model fitted to each individual's dynamics, which gives access to interference structure that covariance-based features cannot represent. The regions driving the prediction, including precuneus, ventromedial prefrontal and anterior cingulate cortices, are among those previously implicated in internalising disorders, and the same signature tracks cognitive variation in healthy participants and is mechanistically linked to the efficiency of task-related computation. These findings establish a mechanistically interpretable and prospectively predictive marker of adolescent mental health and define a clear path towards external validation and clinical use.

Schizophrenia is often conceptualized as a brain network disorder, yet the organizational principles and heterogeneity underlying widespread cortical abnormalities remain poorly understood. Leveraging multisite MRI data from 3,958 individuals diagnosed with schizophrenia and 5,489 neurotypical individuals, we studied the cortical organization and its subtyping by analyzing individualized cortical network similarity. We used eigenvector decompositions to study spatial patterning of the gradients and graph theory to study small-world topology. Individuals with schizophrenia showed widespread alterations of gradient loadings, which followed inferior-superior and frontal-temporal axes. Alterations in small-world topology were localized in key network hubs, including the insula and anterior cingulate cortex. Brain-symptom association analyses identified a latent dimension linking disorganization symptoms to topological alterations. Finally, clustering cortical alterations identified two robust subtypes, characterized by divergent anterior cingulate (S1) versus temporoparietal (S2) thickness differences aligned with the intrinsic gradient-topology patterns. Both subtypes were present early in the illness and stable across disease stages and age groups. These findings reveal systematic disruptions of cortical organization in schizophrenia, providing a network-level framework for macroscale brain organization and inter-individual heterogeneity.

INTRODUCTIONMounting evidence support exposome influences on brain function and health, complementing genome influences. Understanding the molecular imprint of exposome on brain metabolism and the biochemical communication between the body and brain can impact our fundamental understanding and treatment of neuropsychiatric diseases. METHODSLeveraging two complementary metabolomics platforms, we classified 1400 features in 514 brains from the ROSMAP collection. We evaluated the origin of these compounds using literature and databases. We correlated those metabolites with cognitive function using linear models. RESULTSWe identified over 230 non-endogenous compounds in the brain, including 103 drugs and metabolites, 120 dietary and microbial products and possibly 15 compounds from environmental exposures. Over 20 dietary and gut microbial compounds showed associations with cognition. DISCUSSIONComprehensive profiling of chemicals in the brain and the link to cognitive function provides foundational work to connect body and brain in the study of AD and related dementias.

Psychosis as a symptom manifests in schizophenia and bipolar disorder, two highly heterogeneous psychiatric illnesses with overlapping clinical manifestations. Resting-state functional Magnetic Resonance Imaging (rsfMRI), represents a promising tool for identifying objective biomarkers of functional brain alterations to aid differential diagnosis. In this work, we comparatively evaluate multiple rs-fMRI representations for differentiating schizophrenia and bipolar disorder using intrinsic connectivity network (ICN) temporal profiles and several functional network connectivity (FNC) approaches, including static, dynamic, and high-order connectivity analyses. The study was conducted on a cohort of 371 subjects with psychosis, while evaluation was performed using a separate held-out cohort of 315 subjects. We investigated convolutional neural network architectures applied to ICN temporal profiles, spectrograms, and scalograms, alongside classical machine learning models trained on connectivity-derived features. Across the evaluated approaches, ICN temporal profiles provided the most consistent discriminative performance, with a 1D convolutional neural network achieving the strongest overall results under the benchmark protocol. Among connectivity-based methods, static functional connectivity generally outperformed dynamic and high-order representations, suggesting that increased representational complexity did not necessarily translate into improved generalization. Although the obtained classification performance remained modest, the results highlight the challenges of robust psychosis differentiation using rs-fMRI while emphasizing the relative stability of low-order connectivity representations and temporal ICN features. These findings contribute to ongoing efforts toward reproducible and interpretable neuroimaging biomarkers for psychiatric disorders.

Schizophrenia is a complex psychiatric disorder whose pathophysiology spans multiple spatial and temporal scales. Structural and functional neuroimaging studies have identified a broad range of disease-associated markers encompassing cortical atrophy, white matter disruptions, and aberrant functional connectivity patterns. Their application to personalized diagnosis and treatment selection has remained elusive. Here, we introduce the first Virtual Brain Twin (VBT) pipeline that integrates individual connectome-based network models with multimodal neuroimaging data, incorporating patient-specific structural connectivity, cortical thickness, and resting-state fMRI features to construct personalised whole-brain dynamical models. Dopaminergic and serotonergic signaling pathways are embedded within a mean-field framework, and simulation-based inference (SBI) is used to recover key pathophysiological parameters from individual patient data. The validity of this inference is first established using synthetic patients with known ground truth parameters, confirming that the pipeline can accurately identify underlying neurochemical states from simulated functional data. Applied to a cohort of 33 subjects in three clinical centers, the framework identifies personalized pathophysiological parameter regimes consistent with current neurobiological hypotheses of schizophrenia, including reduced cortical dopaminergic drive and elevated subcortical dopaminergic drive relative to healthy controls. Simulated pharmacological interventions within the VBT generate individualized medication effect trajectories that align retrospectively with known treatment outcomes (66.6% accuracy), demonstrating the frameworks capacity to capture patient-specific pharmacological responses. These results establish a principled and extensible computational foundation for neuroimaging-guided personalized medicine in psychiatry, with direct implications for two prospective clinical trials conducted in Marseille and Munich as part of the Virtual Brain Twin project, designed to evaluate VBT-guided individualised antipsychotic treatment selection.

Background: Psychotic disorders such as schizophrenia have been associated with older-appearing brain structure, commonly quantified using the brain-age paradigm. However, it remains unclear whether these alterations are present at illness onset and whether antipsychotic treatment modifies their trajectory. Methods: In this study, 61 (28 females and 33 males) antipsychotic-naive people with first-episode psychosis were randomised to receive either a second-generation antipsychotic (risperidone or paliperidone) or placebo over a 6-month treatment period, alongside intensive psychosocial therapy. A healthy control group (n = 27, 17 females, 10 males) was also recruited. Structural MRI scans were collected at baseline, 3 months, and 12 months. Brain age was estimated using two pretrained and validated models (Pyment and CentileBrain). Results: Brain-predicted age difference (brain-PAD) did not differ between patients and healthy controls at baseline (F(1,80) = 1.30; p = 0.26). There were also no significant effects of time, treatment group (antipsychotic, placebo, healthy control), or their interaction on brain-PAD across the first year (all p > 0.26). Findings were consistent across both brain-age models, and brain-PAD was not associated with clinical and lifestyle measures. Conclusion: These findings suggest that altered structural brain ageing is not evident during the earliest stages of psychosis and is not modified by early antipsychotic exposure over the first year of illness. Longer follow-up and approaches that account for illness heterogeneity may be needed to clarify when brain-age alterations emerge in psychotic disorders.

Schizophrenias substantial heterogeneity poses a major challenge for understanding its neurobiological mechanisms and predicting treatment response. Moving toward precision psychiatry, we identified clinically meaningful subtypes and characterised their neural and pharmacological profiles. Clustering of multidimensional clinical feature space revealed two distinct patient subtypes, primarily differentiated by degree of illness insight. In parallel, three symptom-severity groups defined by positive and negative psychopathology dimensions provided a complementary stratification framework. Resting-state fMRI analyses revealed that higher-insight patients exhibited greater dynamic reconfiguration of regional functional connectivity, emerging as the primary neuroimaging feature differentiating subtypes. Multivariate classification and feature importance analysis confirmed the discriminative value of neuroimaging metrics. Across both subtyping approaches, regional flexibility was spatially associated with cortical receptor density maps in a subtype-specific manner, particularly for D2 and 5-HT2A when accounting for estimated antipsychotic receptor occupancies. Additionally, pharmacological-clinical associations were stronger and more spatially widespread in specific subtypes, indicating subtype-dependent pharmacodynamic relationships. Furthermore, structural equation modelling demonstrated that neuroimaging measures mediate receptor pharmacologys influence on clinical outcomes. These findings together show that integrating clinical, neuroimaging, and pharmacological data can uncover biologically grounded schizophrenia subtypes, identify functional biomarkers, and inform personalised therapeutic strategies.

Purpose: Involuntary psychiatric hospitalization under court orders requires careful balancing of legal obligations and clinical needs. Identifying factors that influence the length of these hospital stays helps clarify the relationship between legal frameworks and psychiatric treatment. This study aims to describe the socio-demographic, clinical, and legal profiles of individuals hospitalized under court warrants and to identify factors independently associated with the duration of forensic hospitalization. Methods: A retrospective study was conducted on 119 patients discharged between 2018 and 2023. Data were collected from medical and legal records, including socio-demographic details, psychiatric diagnoses, offense types, hospital stay lengths, and legal proceedings. Results: Most patients were men (91.6%) diagnosed with schizophrenia or schizoaffective disorder (97.5%), with high rates of comorbid substance use disorder (79.0%) and unemployment (85.7%). The median hospital stay was 19.0 months, representing 40% of the maximum statutory sentence. Patients with low-severity offenses served a larger share of their maximum sentence (47%) than those with high-severity offenses (24%). Time to first discretionary leave was the strongest predictor of total stay duration in univariable analysis. Conclusion: The finding that patients with minor offenses have longer hospital stays than those with serious offenses confirms that clinical factors, rather than offense severity, primarily influence discharge decisions. These findings support moving toward personalized, clinically focused, and family-inclusive forensic discharge planning while maintaining public safety.

Accurate and scalable assessment of quantitative neuroimaging biomarkers, such as white matter hyperintensities (WMH) and hippocampal (HIP) volumes, is essential for understanding and monitoring brain health, preventing neurological diseases and improving healthspan. However, population-level evaluation of these neuroimaging biomarkers relies on inaccessible, costly and time-consuming magnetic resonance imaging (MRI). Here we propose RetiBrain, a cross-modal deep learning framework that predicts these neuroimaging biomarkers from retinal color fundus photography (CFP) images. By distilling latent structural representations from MRI-based models into a CFP-based model, RetiBrain establishes biologically grounded eye-to-brain mapping. In a CFP-MRI paired cohort, RetiBrain accurately estimates six WMH- and HIP-related biomarkers and outperforms the state-of-the-art retinal foundation model RETFound, improving the mean Pearson correlation coefficient by 0.309 (from 0.240 to 0.549) and achieving a coefficient of 0.640 for periventricular WMH prediction. By integrating structural, topological and geometric feature analyses from CFP images, RetiBrain identifies interpretable retinal representations associated with neurodegeneration and cerebrovascular injury, hallmarks of major neurological diseases such as dementia and stroke. In a longitudinal cohort comprising 2,082 participants (4,164 CFP images with up to 15 years of follow-up), RetiBrain-predicted neuroimaging biomarkers robustly estimated neurological disease risk, as illustrated by dementia prediction (AUROC of 0.824, hazard ratio 2.500 per standard deviation increase, 95% CI: 2.201-2.840). RetiBrain provides a robust, scalable, cost-effective and convenient approach for the assessment of neuroimaging biomarkers, and has potential for long-term brain health monitoring in large-scale general population settings.

Background: Alcohol use disorder (AUD) is marked by high relapse rates often driven by craving, yet less is known about whether in vivo, social, and place-based alcohol cues are differentially associated with craving across affective states. This study examined independent and affect-contingent associations of these cues with momentary craving in adults with AUD enrolled in an alcohol intervention study. Methods: Thirty-three adults with AUD completed up to four daily ecological momentary assessments (EMA) for 28 days. EMA prompts assessed craving, in vivo alcohol exposure, being around usual drinking partners, being in usual drinking places, and high-arousal positive affect (PA) and negative affect (NA). Multilevel mixed-effects models adjusted for demographics, intervention phase (1 = post, 0 = pre), AUD severity, and temporal and contextual covariates. Results: EMA compliance was high (median per-participant = 86.6%). Within-person elevations in in vivo alcohol exposure and being around usual drinking partners were independently associated with greater momentary craving, whereas being in usual drinking places was not. In vivo alcohol exposure was more strongly associated with craving during higher-than-usual PA ({beta} = 0.08, p = .032), whereas being in usual drinking places was more strongly associated with craving during higher-than-usual NA ({beta} = 0.06, p = .036), adjusting for intervention phase, which was associated with lower craving. Conclusions: Findings support the need for personalized just-in-time adaptive interventions tailored to high-risk, momentary cue-affect contexts in AUD, beyond low-frequency clinician-delivered feedback that may reduce average craving but not fully address real-time risk. ClinicalTrials.gov registration: NCT05135767.

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"[≥]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.

Human reward processing varies along cue-centric and outcome-centric axes, but a reproducible mechanistic account of individual variation in incentive salience attribution has been lacking. Using fMRI across five cohorts (N-total=1,252; N1=890; N2=245; N3=34; N4=48; N5=34), we identified two robust imaging phenotypes mirroring sign- and goal-tracking (ST-like, GT-like). ST-like individuals showed dominant ventral striatal responses to reward-anticipation cues and sustained incentive salience attribution; GT-like individuals showed heightened responses to reward outcomes. This distinction was replicable across sites and independent samples. Single-dose and repeated-dose D2/D3 antagonism (risperidone, haloperidol, amisulpride) selectively reduced anticipatory ventral striatal activity in ST, with single-dose antagonism additionally producing a parallel drop in self-reported energy. Instead, D2/D3 partial agonism (aripiprazole) increased anticipatory and reduced outcome-phase responses in GT. In a psychosis cohort, antipsychotic D2 affinity was associated with blunted anticipatory signals and higher negative symptom burden, offering a neuroimaging-driven basis for stratifying patients and predicting response to dopaminergic agents.

Cannabis use is widespread, with genetic differences partly explaining variation in individual patterns of use. We performed the largest-to-date genome-wide association study (GWAS) meta-analysis of cannabis ever-use (N=736,322, 76% European ancestry) and various measures of frequency of use (N=269,160 cannabis users, 84% European ancestry). We identified 54 independent genome-wide significant loci for ever-use and 6 for frequency and show that the genetic architecture of ever-use, frequency, and cannabis use disorder (CUD) are overlapping but distinguishable. We identified 63 loci that were associated with common liability ( All-cannabis) to different cannabis use traits in European-ancestry individuals. Across analyses, we identified 75 unique loci that had not previously been implicated in cannabis use. Gene prioritization analyses identified 349 genes for ever-use, 5 genes for frequency of use, and 429 for All-cannabis, including previously identified and novel genes. We found enrichment of genetic signals for cannabis use in biologically meaningful categories and relevant human brain cell types, including excitatory neuronal populations. There were substantial genetic correlations between cannabis use and a range of psychiatric disorders and substance use traits, while cannabis polygenic scores were associated with increased risk of psychiatric disorders. Mendelian Randomization showed evidence for (bidirectional) causal associations between cannabis use and ADHD, bipolar disorder, schizophrenia and PTSD.

Background The excitation-inhibition (E-I) balance is essential for normal brain functioning, while deviations from this balance have been implicated in several psychiatric disorders. However, the extent to which electroencephalography (EEG) and proton magnetic resonance spectroscopy (1H-MRS) E-I markers are altered in schizophrenia spectrum disorders (SSD), how they converge across modalities, and how they relate to cognitive performance and clinical symptoms remain insufficiently characterized. Methods We recruited 111 healthy controls (HC) and 113 individuals with SSD. All participants underwent resting-state EEG and 1H-MRS. Metabolites were measured either in the anterior cingulate cortex (ACC; NSSD = 63, NHC = 58) or in the left dorsolateral prefrontal cortex (lDLPFC; NSSD = 50, NHC = 53), from which gamma-aminobutyric acid (GABA), glutamate + glutamine (Glx), and the Glx/GABA ratio were extracted. Extracted EEG E-I markers included oscillatory activity, aperiodic activity, functional E-I, microstates, multiscale entropy, and neuronal avalanche criticality. Results MRS results showed no group differences in GABA, Glx, or the Glx/GABA ratio. In contrast, most EEG-derived E-I markers indicated increased cortical inhibition in SSD, including steeper aperiodic exponents, prolonged microstate durations, and greater prevalence of subcritical states. However, functional E-I showed a divergent pattern, suggesting balanced dynamics in SSD and relatively inhibition-weighted dynamics in HC. Across groups, higher ACC and lDLPFC GABA predicted a lower kappa index, whereas a higher lDLPFC Glx/GABA ratio was associated with a higher kappa index. In SSD, reduced avalanche criticality was associated with better cognition and less severe symptoms. Conclusion Several EEG-derived E-I proxies, but not MRS measures, indicate an increased cortical inhibition in SSD. Criticality indices best capture frontal neurochemical metabolites and improvements in clinical symptoms, potentially reflecting inhibitory compensation mechanisms in SSD.

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.

Suicidality phenotypes, including suicidal ideation (SI), non-fatal suicide attempt (SA), and suicide death (SD), are heritable and exhibit both shared and phenotype-specific genetic influences. Using genomic structural equation modelling, we estimated the shared genetic architecture across GWAS of SI (176,147 cases, 1,010,300 controls), SA (53,919 cases, 1,063,988 controls), and SD (7,584 cases, 652,070 controls) and conducted a multivariate GWAS of a latent suicidality factor capturing their shared liability. This analysis identified 36 genome-wide significant loci, including seven not previously reported in any suicidality GWAS. Follow-up analyses identified residual genetic variance specific to each phenotype, including three SD-specific genomic risk loci. Conditioning suicidality phenotypes on genetic liability to psychiatric disorders revealed significant residual genetic variance across SI, SA, SD, and the suicidality common factor. Together, these results suggest that suicidality reflects both shared genetic liability and phenotype-specific contributions.

Background: Tobacco use is prevalent in clinical high risk for psychosis (CHR-P) population and has widespread negative health consequences, but understanding of its neural substrates is limited. Abnormal default mode network (DMN) may underlie tobacco dependence in CHR-P. We investigated how tobacco use relates to DMN connectivity and how CHR-P status impacts this relationship. Methods: We used baseline substance use and resting-state functional magnetic resonance imaging data from the North American Prodrome Longitudinal Study (NAPLS2; CHR-P: n=211, mean age 19.2, 37.9% female; healthy control: n=132, mean age 19.9, 47.7% female). Voxel-wise connectivity was calculated from the left lateral parietal (LLP) node of the DMN to the rest of the brain. We regressed LLP-brainwide connectivity against tobacco use frequency in the past month to generate a spatial map of how connectivity relates to current tobacco use. Results: Brainwide connectivity analysis identified two clusters in R hippocampus (peak voxel at MNI [+30,-12,-27]) and in L parahippocampus (peak voxel at MNI [-27,-27,-27]), where higher LLP-cluster connectivity was associated with more frequent tobacco use. LLP - R hippocampus connectivity was higher in current tobacco users compared to non-tobacco users (t=-3.5466, df=101.88, p=0.0006), and higher in CHR-P than controls (t=-2.8651, df=279.47, p=0.0049). Among current tobacco users, there was a significant tobacco-by-diagnosis interaction on LLP - R hippocampus connectivity (estimate=0.306, SE=0.149, t=2.051, p=0.045) such that heavier tobacco use predicted hyperconnectivity only in CHR. Conclusions: More frequent tobacco use was associated with higher DMN-hippocampal connectivity in both CHR-P and controls. CHR-P diagnosis enhanced this relationship.