Psychiatry and Clinical Neurosciences

Background: Concerns about "AI psychosis" have swirled in the media since ChatGPT's release, but few systematic analyses exist. We therefore conducted an electronic health record (EHR) analysis to identify the frequency, clinical characteristics, and quality of AI interactions in patients experiencing psychosis treated in a medical center. Methods: AI keywords (e.g., ChatGPT, AI) were used to search Vanderbilt University Medical Center's EHR from 12/1/2022-4/1/2026. Records were discarded if they were not AI-related or if the primary diagnosis did not include psychosis. Three raters read notes to determine if a patient was experiencing AI psychosis and classified the interactions using 4 a-priori categories (Catalyst, Amplifier, Co-Author, Object) formulated to explain how AI-related negative outcomes emerge. Findings: 73 patients met our criteria. 28 patients were rated as experiencing AI psychosis, 17 had neutral interactions, and 28 expressed delusional content related to AI without documented evidence of conversational AI use. ChatGPT was the matching keyword for 53.6% patients experiencing AI psychosis. The majority of AI psychosis cases were documented after ChatGPT's "4o" model was released in May 2024. Notably, the AI Psychosis group had significantly more patients experiencing a first psychotic episode (60.7%) compared to the other two groups. Amplifier was the most common (64.3%) qualitative rating in the AI Psychosis group. Interpretation: "AI psychosis" is an infrequent but real phenomenon observed in clinical practice. Most affected patients were experiencing their first psychotic episode and presented with AI psychosis following the release of the more sycophantic GPT-4o. Among the affected patients, AI most often exacerbated an existing condition by reinforcing distorted ideas.

Preliminary evidence suggests that the combination of magnesium and ibogaine, an atypical psychedelic, may be a promising treatment for post-traumatic stress disorder (PTSD), opioid use disorder, and traumatic brain injury (TBI). The "mystical" experience elicited by ibogaine, which is characterized by feelings of awe, selflessness, and transcendence, is correlated with improvements in PTSD symptoms. Mystical experiences with other psychedelics are associated with acute decreases in the activity and connectivity of the default mode network (DMN), which mediates self-related cognition. However, the dynamic effects of ibogaine on the DMN have not yet been studied. At baseline, immediately (3-4 days) after ibogaine, and one month after ibogaine, we acquired resting-state functional magnetic resonance imaging data in an open-label, observational trial of magnesium-ibogaine treatment for 30 U.S. veterans with TBI. Magnesium-ibogaine did not significantly alter static DMN connectivity at either the immediate-post or one-month timepoint. Since static measures cannot capture time-evolving changes in connectivity, we next used Hidden Markov Models (HMM) to measure the post-acute dynamics of DMN activity. Magnesium-ibogaine was associated with significant, sustained decreases in the switching rate (i.e., increases in the duration of) a dynamic DMN substate, which was significantly correlated with the mean score on the Revised Mystical Experience Questionnaire and clinical improvements at one month-post treatment. This DMN substate exhibited a lateral-medial spatial gradient, which was significantly associated with a gradient of externally oriented (i.e., directed to the environment) to internally oriented (i.e., self-related) perception and cognition. Taken together, our results indicate that magnesium-ibogaine alters specific dynamic substates of the DMN, which correlate with its subjective and therapeutic effects.

Adolescence is a period of rapid neurodevelopment during which psychiatric symptoms may emerge, yet symptom-specific markers show inconsistent associations with cognition and brain structure and can rarely be generalised longitudinally. Using data from the ABCD Study, we derived a transdiagnostic mental-health burden measure that integrates multiple symptom domains and examined its cognitive and structural brain correlates in early adolescence longitudinally. Adolescents with higher burden showed consistently lower performance in vocabulary, memory, and processing-speed, alongside widespread reductions in whole-brain, cortical, and white-matter volumes at baseline and after 2 years. These effects were strongest in a subgroup with persistent high burden and replicated in cross-sectional analyses. After 4 years, mental-health differences remained robust, although brain-behaviour associations weakened, likely reflecting developmental reorganisation and reduced sample size. Our study demonstrates that global mental-health burden provides a scalable, developmentally appropriate marker of early psychiatric vulnerability that overcomes limitations of symptom-specific approaches.

Importance: The impact of COVID-19 containment policies (e.g., physical distancing, school closures) on population anxiety has been debated and difficult to resolve. Objective: To estimate the joint effects of state-level COVID-19 containment policies on anxiety symptoms during the early pandemic. Design: Retrospective analysis of a prospective cohort with cross-sectional outcome assessment. Setting: All of Us Research Program, a U.S. national research cohort. Participants: 40,610 adult participants who completed the All of Us COPE survey in July 2020. Exposures: Seven state-level COVID-19 containment policies (school closures, workplace closures, cancellation of public events, restrictions on gatherings, public transport closures, stay-at-home requirements, and restrictions on internal movement) measured from March 22 to May 23, 2020, via the Oxford COVID-19 Government Response Tracker (OxCGRT). Main outcomes and measures: The primary outcome was anxiety symptoms (GAD-7) in July 2020. Using quantile g-computation, we classified policies as anxiety-increasing or anxiety-decreasing by the sign of their training-set contributions, then re-estimated joint effects in a holdout testing set. Results: Among participants (64% female; mean age: 57.8 years), 13.3% (n=5398) reported moderate-to-severe anxiety (GAD-7 score 10-21) in July 2020. The joint effect of all seven containment policies was not significant ({beta} = 1.88, 95% CI: -0.51 to 4.28, p = 0.12). An anxiety-increasing joint effect from 4 policies (school, workplace, public events, internal movement; {beta} = 2.98, 95% CI: 0.30 to 5.66, p = 0.03) and an anxiety-decreasing joint effect from 3 policies (gatherings, public transport, stay-at-home; {beta} = -1.10, 95% CI: -1.75 to -0.44, p = 0.002) reached significance. Effects were largest in adults 18-44 (anxiety-increasing {beta} = 8.93, 95% CI: 1.50 to 16.37, p = 0.02; anxiety-decreasing {beta} = -2.81, 95% CI: -4.98 to -0.64, p = 0.01), with no significant effects in adults 45 and older. Conclusions and Relevance: Modeling seven containment policies jointly showed no net anxiety effect, a result that masked opposing-direction effects. Partitioning by effect direction revealed significant joint effects exceeding single-policy estimates, with young-adult point estimates above the 4-point GAD-7 minimal clinically important difference (MCID) though lower CI bounds fell below it. These findings may inform the use of containment policies in future pandemics, given their differing association with population anxiety

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.

Background and Hypothesis: Schizophrenia (SZ) and bipolar disorder (BD) share overlapping yet distinct clinical profiles and system-wide brain alterations. Macroscale functional connectivity gradients capture principal axes of cortical organization, including the separation of unimodal and transmodal systems, offering a low-dimensional lens on individual differences in brain architecture. Whether these axes reflect shared or diagnosis-specific variation across the SZ-BD spectrum is unknown. Study Design: Using resting-state fMRI from 187 adults (110 HC, 37 SZ, 40 BD) from the UCLA Consortium for Neuropsychiatric Phenomics, we derived individual low-dimensional gradients and applied three analyses: case-control comparisons at both the cortical network and subcortical region-of-interest level, Partial Least Squares (PLS) regression linking gradients to clinical phenotypes, and individual-level similarity indices (SI-PLS) positioning participants within a gradient-behaviour space. Study Results: While the gradient structure (G1: visual-somatomotor and G2: unimodal-transmodal) was preserved across groups, patient groups showed greater deviations along both axes. Network analyses revealed transdiagnostic frontoparietal compression in G2, alongside disorder-specific effects: visual pole contraction and subcortical amygdala displacement in SZ, and somatomotor displacement in BD. PLS identified a BD-associated profile of preserved gradient architecture and lower symptom burden, contrasting with an SZ-associated profile of greater cognitive impairment and symptom severity. SI-PLS scores placed SZ and BD in distinct regions of a shared two-dimensional neural space, with HC between them. Conclusions: Differences across the SZ-BD spectrum organize along two principal axes, revealing transdiagnostic alterations in higher-order association systems alongside disorder-specific sensory signatures. These findings support a multi-axis dimensional framework for understanding clinical heterogeneity in psychosis.

Background: Serial glial fibrillary acidic protein (GFAP) trajectories have become an important framework for contextualizing evolving secondary-injury pathophysiology after moderate-to-severe traumatic brain injury (msTBI). However, total GFAP pools release and clearance signals that may be less useful for longitudinal bedside decisions than a proteoform-resolved assay. We compared total GFAP with neoGFAP, defined here as calpain-generated GFAP proteoforms intended to index active astroglial proteolysis during the subacute phase. Methods: We analyzed 651 serial serum samples from 95 msTBI patients from a previously described single-site cohort. Total GFAP and neoGFAP were measured on the same MSD platform from 6 to 240 hours after injury. Early (6 to 72 h) and late (96 to 240 h) windows, data-derived tertiles, and serial trajectory summaries were calculated directly from serial samples. Models were benchmarked against age plus admission post-resuscitation Glasgow Coma Scale (GCS) and the admission IMPACT extended risk score using five-fold stratified cross-validation. Outcomes were unfavorable outcome (GOSE 1 to 4), less-than-good recovery (GOSE 1 to 6), Disability Rating Scale (DRS) [≥]15, mortality, and neuroimaging worsening at 6 months. Results: The cohort contributed 95 serial biomarker profiles, with 90 participants evaluable for 6-month GOSE and 89 for DRS. Unfavorable outcome occurred in 57/90 (63.3%), and less-than-good recovery in 79/90 (87.8%). For unfavorable outcome, IMPACT plus early neoGFAP reached AUROC 0.85 versus 0.84 for IMPACT plus early total GFAP and 0.81 for IMPACT alone. For less-than-good recovery, IMPACT plus late neoGFAP achieved AUROC 0.90 versus 0.84 for late total GFAP and 0.82 for IMPACT alone. Secondary analyses for DRS, mortality, and neuroimaging worsening showed smaller differences. Conclusions: In this retrospective analysis, neoGFAP provided clearer incremental value than total GFAP for recovery-oriented monitoring, especially when late-window reassessment of patients who remained at risk for less-than-good recovery was required. Results support prospective testing of neoGFAP as a pathophysiology-informed adjunct to serial bedside decision making, repeat-assessment thresholds, and recovery stratification.

The increased vulnerability to mental health problems in adolescence is frequently reported but poorly understood, hampered by a rigid diagnostic system which fails to capture intertwining symptoms and only loosely aligns with biological axes of variability. Here, we reconceptualised the mental health symptoms of young adolescents in the ABCD cohort (N=11862) as a latent topology of overlapping symptom dimensions, using an unsupervised machine learning algorithm to establish how transdiagnostic dimensions co-occur and overlap within individuals. Combining this with a novel classification approach, we delineated zones within this landscape, within which specific profiles of symptoms were robustly represented. These data-driven profiles were leveraged to establish associated resting-state functional connectivity and genetic characteristics. In doing so we recaptured the commonly reported p-factor axis as well as further symptom-subtype dimensions. Gene ontology analysis revealed that shared neurobiological and cellular mechanisms embedded in both the genome and transcriptome may confer risk for psychopathology.

Severe worry is a transdiagnostic syndrome associated with significant morbidity in older adults. In this study, we aim to infer worry-related mental states though brain activity timeseries. We acquired fMRI on two cohorts (N=116 and N=88), using an in-scanner worry induction and reappraisal task. We trained a recurrent long short-term memory (LSTM) neural network, using the first cohort as the train/validation and the second cohort as an independent test set. We predicted worry induction, reappraisal, and neutral states (area under the curve 0.89, 0.77, 0.91 for the test set and 0.78, 0.63, 0.81 for the independent set). The model was most accurate when participants reported high worry during the induction state. Dorsal attention network, and networks seeded on the anterior hippocampus, and supplementary motor area were most important for predicting worry states. The LSTM approach may have critical translational implications for identifying and treating severe worry in older adults.

Exposure to adverse childhood experiences is a pervasive risk factor for psychosis, exhibiting a linear relationship across the psychosis spectrum from subclinical schizotypal traits to schizophrenia spectrum disorders. While this association is often conceptualised within the vulnerability-stress framework, the systemic mechanisms through which childhood trauma reconfigures the brain-personality interactome remain poorly understood. We examined clinical, neuropsychological, and neuroimaging data from a sample of low- and high-schizotypy individuals, and patients with a diagnosis of schizophrenia spectrum disorder (N=120). Our aim was to map how trauma reconfigures interactions between neurobiology and schizotypal phenomenology. We adopted a mixed graphical model approach to jointly estimate conditional dependencies between childhood trauma, regional brain morphometry, and schizotypal traits across the psychosis spectrum. Our results show that childhood trauma reconfigures the brain-personality network, shifting it from a state driven by cognitive processes to one anchored in emotional (limbic) reactivity. This transition is marked by the increased influence of impulsive traits and a significant strengthening of connections within the salience network. These changes converge with a reduced thickness of the frontal executive regions, the brain's control centres, identified in our models. Collectively, our results suggest a structural phenomenological decoupling, where trauma conditioned affective circuits may bypass weakened top-down regulatory controls. These findings highlight the necessity of using integrative frameworks to capture how trauma fundamentally reshapes the relationship between the brain and schizotypal personality.

Machine learning approaches may support individual-level classification in psychiatry, but many EEG-based schizophrenia studies have relied on small samples or conventional summary features. We evaluated whether trial-level auditory oddball event-related potential (ERP) waveforms could support schizophrenia versus healthy-control classification using deep learning. The study included 258 patients with schizophrenia and 142 healthy controls. EEG recordings from an auditory duration oddball paradigm were segmented into -100 to 500 ms epochs, and trial-level mismatch waveforms were generated by subtracting each participant's mean standard response from accepted deviant trials. Models were trained using a fixed participant-level training, validation, and test split, with demographic residualization fit only in the training set. Five deep learning architectures were trained on full residualized ERP waveforms and compared with classical machine learning models trained on 18 conventional ERP summary features. Deep learning models achieved higher test set discrimination than classical feature-based models, with AUROC values ranging from 0.797 to 0.857 versus 0.705 to 0.720. Benchmark analyses suggested that performance depended on the combination of waveform-level input and deep learning architecture. These findings support trial-level auditory oddball ERP waveforms as promising classification inputs and candidate electrophysiological biomarkers of schizophrenia-related neural information processing.

Neighbourhood deprivation is one of the few potential policy-modifiable risk factors for psychiatric and neurological disorders, but the neurobiological pathways underlying these associations remain unclear. We investigated these relationships across three cohorts spanning the life span: the Healthy Brain and Child Development (HBCD) Study (n = 84, aged 0 to 4 weeks postnatal), the Adolescent Brain Cognitive Development (ABCD) Study (n = 4,792, aged 9 to 10 years), and the UK Biobank (UKB; approximately 500,000 adults, aged 44 to 87 years). Neighbourhood deprivation was associated with elevated disease risk, and individual lifestyle factors accounted for only a small fraction of this burden, indicating that the much larger residual effect reflects broader contextual characteristics of deprived environments rather than individual behaviours alone. Across all cohorts, greater deprivation consistently predicted lower cortical and subcortical brain volume, with effects detectable in early development and substantially stronger in adulthood. Across disorders, regional brain volume emerged as a consistent neuroanatomical mediator linking neighbourhood deprivation to neuropsychiatric disease. We further showed that deprivation preferentially affects brain regions intrinsically vulnerable to neuropsychiatric disorders. Spatial decoding analyses implicated dopaminergic and serotonergic neurotransmitter systems together with specific excitatory and inhibitory neuronal classes. Importantly, both the deprivation effects and their neuroanatomical mediation patterns were replicated across independent populations. Our study delivers a translational framework linking neighbourhood deprivation to brain health, which could inform public health policies and preventive interventions.

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

Importance: Psychiatric emergency department (ED) presentations are difficult to predict using general medical risk stratification tools. Health information exchange (HIE) data may improve prediction by capturing fragmented care across settings. Objective: To develop and temporally validate a machine learning model using HIE and geospatial data to predict 30-day psychiatric ED presentation among outpatients receiving psychiatric care and to compare its performance with standard clinical risk scores. Design, Setting, and Participants: This retrospective cohort study included patients seen at Frontier Psychiatry with records in the Big Sky Care Connect statewide HIE. Structured clinical data were linked to zip code-level sociodemographic measures. The analytic unit was the patient snapshot, defined as all structured data available up to a given point. Models were evaluated in temporally separated train and test sets. Exposures: Predictors derived from HIE structured data, including prior utilization, diagnoses, medications, laboratory data, and zip code-linked geospatial deprivation and vulnerability measures. Main Outcomes and Measures: The primary outcome was psychiatric ED presentation within 30 days, identified from structured encounter-type fields and primary diagnosis codes for psychiatric or substance use disorders. Model discrimination was compared with a parsimonious clinical baseline model and LACE and Elixhauser scores. Results: In the test set, 343 of 16,469 snapshots (2.1%) were followed by a qualifying psychiatric ED presentation within 30 days, corresponding to 102 ED visits among 68 patients. The machine learning model showed discrimination in temporally held-out testing and outperformed the clinical baseline model as well as LACE and Elixhauser scores. At a prespecified decision threshold, the model reduced the number needed to evaluate from more than 40 with universal screening to 3.4 to identify 1 true-positive case, while identifying over two fifths of 30-day psychiatric ED presentations. Conclusions and Relevance: In this retrospective cohort study, a locally developed machine learning model using statewide HIE data showed improved prediction of 30-day psychiatric ED presentation compared with selected general-purpose risk scores. The results support the feasibility of HIE-enabled local psychiatric risk modeling and suggest other practices could develop similarly tailored models. Prospective studies are needed to assess clinical utility and effects on outcomes.

Schizophrenia and bipolar disorder are diagnostically distinct categories that overlap substantially in clinical features and genetic aetiology. Understanding genetic variants that contribute liability specifically to each disorder can offer insights into biological processes that differentiate them. Here we used Case-Case GWAS (CC-GWAS) to identify common genetic variants differentially associated with schizophrenia and bipolar disorder, analysing 67,390 schizophrenia cases and 41,917 bipolar disorder cases. We identified 19 genome-wide significant loci, of which 16 (84%) demonstrated divergent genetic effects with risk alleles showing opposite directions of association between disorders. The CC-GWAS summary statistics had detectable disorder-differentiating heritability (10.27%, SE=0.01) and showed genetic correlations indicating that SCZ-differentiating alleles were associated with lower educational attainment, lower cognitive performance, and increased risk of ADHD, anorexia, autism, BD1 (though not BD2), cannabis use disorder, and OCD. Four loci showed divergent effects despite not reaching genome-wide significance in either individual disorder GWAS, demonstrating enhanced power to detect opposite-direction effects. Functional annotation identified 102 mapped genes significantly enriched for expression across all 13 tested brain regions, with no significant enrichment in peripheral tissues, and gene set enrichment analysis implicated neuronal projection and synaptic compartments as the strongest biological themes differentiating the two disorders. Polygenic risk scores derived from these disorder-differentiating variants were associated with earlier age at onset and more severe negative symptoms in schizophrenia, consistent with these variants marking neurodevelopmental dimensions of illness. Our findings provide targets for understanding pathogenic differences between schizophrenia and bipolar disorder and demonstrate that genuine divergent genetic effects exist beyond the substantial shared liability.

Major depressive disorder (MDD) is a heterogeneous mental disorder characterised by rumination. Mindfulness-based cognitive therapy (MBCT) is an evidence-based treatment developed to target rumination and recurrence risk. Ongoing studies have begun to identify neural changes associated with treatment effects. However, the low-dimensional organisation underlying whole-brain dynamics remains largely unexplored and may provide a more complete characterisation of the neural processes through which MBCT exerts its therapeutic effects in MDD. Here, we investigated functional magnetic resonance imaging (fMRI) of a randomised controlled trial of MBCT with treatment as usual (TAU), or TAU alone, in a group of MDD patients (N=80). We applied a novel framework, complex harmonics decomposition (CHARM), to uncover low-dimensional manifolds in the spacetime domain, capturing local as well as non-local interactions made possible by brain criticality and amplified by the anatomical long-range connectivity. We successfully identified distinct distributed spatiotemporal manifolds across brain states and outperformed traditional dimensionality reduction techniques. During rumination after MBCT we found consistent recruitment of regions involved in bodily and interoceptive processing integrated within the whole-brain across manifolds, changes in latent configurations associated with clinical and behavioural improvements, and greater flexibility within the reduced space. Integration of bodily and interoceptive processing regions within distributed whole-brain manifolds and greater brain flexibility may be associated with reduced 'stickiness' of ruminative thinking patterns following mindfulness training in depression. Our findings highlight the promise of low-dimensional manifolds and long-range interactions arising from critical brain dynamics in understanding how mindfulness targets depressive ruminative processing.

Identifying generalizable brain-based biotypes across independent cohorts is critical for parsing heterogeneity in Major Depressive Disorder (MDD), yet robust subtypes spanning micro- and macroscales remain poorly defined. We applied stability-based clustering to cortical thickness data from 1,531 MDD individuals in UK Biobank (UKB), with external validation in 144 inpatients from IRCCS Ospedale San Raffaele (HSR). Two distinguishable clusters emerged (accuracy=87.5%), with one showing widespread cortical thinning, anergy-related symptoms, childhood trauma, and diabetes comorbidity. This profile generalized with 96.5% accuracy in a hold-out UKB sample and 80.6% in HSR. Mapping clusters cortical profiles onto Neurosynth meta-analytic activation patterns revealed a ventral-dorsal gradient linked with emotion regulation, interoceptive, and motivational processes. Spatial correlations with 19 neurotransmitter receptors and transporters obtained from positron emission tomography identified dopamine transporter as the dominant contributor in UKB, and histamine receptor H3 in HSR. These findings provide a reproducible framework linking MDD subtypes to multiscale biological complexity.

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

We report a pilot study of deep brain stimulation (DBS) in three individuals with treatment refractory schizophrenia (TRS). DBS target selection was supported by an inpatient brain network mapping paradigm using intracranial electroencephalography. In conjunction with assessing stimulation-dependent symptom improvement, we derived spatiotemporally resolved profiles of psychosis and healthy brain states and identified stimulation targets that best shifted brain networks towards healthy states. Therapeutic stimulation sites were personalized for each participant but converged on salience network nodes including anterior cingulate and anterior insula. No significant adverse events were noted across participants. Moreover, two participants with chronic stimulation and clinical follow-up of at least 4 months reported significant improvement in both positive and negative symptoms, and treatment optimization is underway for the third. These critical pilot data establish the feasibility of personalized DBS guided by concurrent stimulation mapping, behavioral assessment, and biomarker monitoring as a treatment for TRS.

With dense axonal connectivity to every region of cortex, the thalamus plays a central role in the nervous system from sensory processing to cognitive functions. Yet how tissue maturation of the thalamus unfolds during childhood and contributes to typical or atypical development is not clear. Through several large datasets, we provide here a thalamic portrait of fine-scale structural development whose nuclei develop along unique trajectories, some of which diverge from predictions of developmental theory. We find that those thalamic nuclei which show the most protracted development are at the greatest risk for later clinical differences in schizophrenia. The spatial pattern across thalamic nuclei for early psychosis risk is associated with a unique neuroreceptor fingerprint with implications for symptom severity.