Acta Neuropsychiatrica

Abstract Background: Glycosylated RNAs (GlycoRNAs) participate in immune recognition and intercellular communication, but their relationship with brain cell-type transcriptional signatures in major depressive disorder (MDD) remains unclear. We examined GlycoRNA-related transcriptional coupling with microglial and other cellular signatures in the dorsolateral prefrontal cortex (DLPFC). Method: Postmortem DLPFC transcriptomic data from 22 controls and 26 MDD patients (GSE102556) were analyzed. GlycoRNA-related pathway enrichment was quantified using gene set variation analysis (GSVA), whereas microglial, neuronal, astrocytic, and oligodendrocytic signatures were derived using single-sample gene set enrichment analysis (ssGSEA). Pearson correlations, linear regression, and generalized estimating equations (GEE) evaluated cell-type-specific associations and diagnostic interactions. Exploratory GEE results underwent false discovery rate (FDR) correction. Results: No differentially expressed genes survived FDR correction, and GlycoRNA-related and cell-type signatures did not differ significantly between groups. In controls, GlycoRNA enrichment was positively associated with the microglial signature (beta = 0.447, FDR-adjusted P = 0.006), with significantly weaker associations for neurons, astrocytes, and oligodendrocytes (all FDR-adjusted P < 0.05). The two-way GlycoRNA x is_microglia interaction was nominally significant (beta = 0.210, P = 0.048), confirming that GlycoRNA transcriptional coupling is substantially stronger for microglia than for the other three cell types combined. The microglial association was numerically attenuated in MDD (beta difference = -0.273, P = 0.167), but diagnostic interactions were nonsignificant. Conclusions: GlycoRNA pathway transcriptional enrichment exhibits preferential microglial association in control DLPFC. Its possible attenuation in MDD remains to be validated. keyword: Major depressive disorder; GlycoRNA; microglia; dorsolateral prefrontal cortex; generalized estimating equation; ssGSEA; bioinformatics

Brief assessments reduce clinician and patient burden and increase utilization. Recently, five core depression symptoms (low mood, poor concentration, low self-worth, loss of interest, low energy) have been shown to be particularly sensitive to treatment effects in adults. How these core depressive symptom domains perform for screening or diagnostic purposes in youth is unknown. To address this question, we extracted five analogous symptom items from four widely used symptom assessment tools for children and adolescents (Child Behavior Checklist [CBCL], Youth Self Report [YSR], Parent General Behavior Inventory [P-GBI], and Adolescent General Behavior Inventory [A-GBI]) to create 5-item short forms. We evaluated their psychometric properties and diagnostic utility in a large (N=1185), racially diverse sample (48% Black youth) across community and academic outpatient mental health settings, with diagnoses established through LEAD consensus. All short forms correlated highly with full-length measures (r=.74-.92) and demonstrated comparable inter-item correlations (r =.19-.46). The short forms did not differ meaningfully from the full measures in correlations with psychiatric diagnoses, demographics, or diagnostic interviews. Area under the curve (AUC) values for identifying depression and any mood disorder diagnosis (determined by semi-structured diagnostic interviews) were fair to good (AUC=0.60-0.79), which were not worse than full measures, except for the P-GBI. The CBCL-5, YSR-5, P-GBI-5, and A-GBI-5 may be useful brief depression assessments in youth. How these five symptom domains perform as outcome measures in treatment deserves study.

Background: Psychiatric disorders pose a significant public health burden, and their complex genetic architecture underlies multiple biological pathways, contributing to a lack of diagnostic biomarkers. Laboratory-Wide Association Studies (LabWAS) leverage electronic health record laboratory data as intermediary phenotypes to link genetic risk to physiology. Methods: Polygenic Scores (PGS) for 25 health-related, psychiatric, and substance use phenotypes were generated for individuals of African (AFR; N=10,473) and European (EUR, N=35,131) ancestry from the Penn Medicine Biobank. Laboratory data was prepared using the LabWAS pipeline, encompassing 78 laboratory tests and 6 vital signs. Linear regression models tested the association of each lab value with PGS adjusting for sex, age, and ten within-ancestry genetic principal components. Results: Four PGS were significantly associated with at least one laboratory value in AFR individuals and 22 PGS in EUR individuals. In EUR individuals, psychiatric PGS partitioned into three physiological profiles: anxiety, attention-deficit/hyperactivity disorder, major depressive disorder, and post-traumatic stress disorder shared a metabolic/inflammatory signature (higher BMI, glucose, and monocytes; lower high density lipoprotein cholesterol and oxygen saturation); anorexia nervosa, obsessive compulsive disorder, and schizophrenia were characterized by lower BMI; and bipolar disorder demonstrated a more circumscribed profile with lower electrolytes and renal makers. Substance use disorder PGS similarly clustered: tobacco use disorder and externalizing showed metabolic/inflammatory profiles; alcohol-related phenotypes showed lower red blood cell indices; and cannabis use disorder had a more circumscribed profile. Conclusions: LabWAS reveals physiologic signatures associated with genetic risk for psychiatric disorders, offering new biological insights into their pathology.

Negative emotional bias, including reduced processing of positive information and negative interpretation of ambiguous stimuli, is a core feature of depression. Esketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist with rapid antidepressant effects in treatment-resistant depression (TRD), reduces depressive symptoms, but its effects on distinct components of emotional processing remain unclear. Fifty-three patients with TRD and 53 healthy controls performed an Emotional Facial Expression Morphing task. Patients were assessed at baseline, 24 hours after the first esketamine administration, and after 4 weeks of treatment. Statistical models examined detection thresholds for happy and sad faces, negative categorisation of neutral faces, and signal detection indices separating emotion-neutral discrimination sensitivity (d') from response criterion (c), indexing the evidence required to report the presence of an emotion. At baseline, patients required greater happy-face intensity for emotion detection and were more likely to categorise detected neutral faces as negative. They also showed a more conservative response criterion without group difference in discrimination sensitivity. Responders showed an early increase in d'; and, by week 4, less negative categorisation of neutral faces than non-responders. A lower baseline response criterion was associated with responder status and, in secondary continuous analyses, with greater subsequent MADRS improvement. These findings indicate that TRD involves abnormalities across detection-related, valence-related, and decisional aspects of facial-emotion processing that are differently associated with esketamine response: baseline response criterion emerged as a candidate prognostic marker, whereas negative categorisation of neutral faces tracked clinical response. Assessing these domains may inform clinical evaluation, pending independent replication.

Biologically informed polygenic scores (PGSs) may help interpret genetic associations with treatment outcomes. Here, we computed neurotransmitter-based PGSs (NeuroPGSs) for patients with major depressive disorder (MDD) and assessed their association with SSRI treatment outcomes. By targeting genetic variants involved in key neurotransmission pathways, we developed PGSs for serotonergic (5228), gamma-aminobutyric acid (GABA, 4103), dopaminergic (7061), and glutamatergic (5690) variants. We leveraged data from two cohorts of patients who received SSRIs for at least 4 weeks: the International SSRI Pharmacogenomics Consortium (ISPC) and the Pharmacogenomics Research Network Antidepressants Medication Pharmacogenomics Study (PGRN-AMPS). Treatment response was defined as [&ge;]50% reduction from baseline depression symptoms, measured using 17-item Hamilton Rating Scale for Depression (HRSD-17) in ISPC and 16-item Quick Inventory of Depressive Symptomatology (QIDS-C16) in PGRN-AMPS. Remission was defined as HRSD-17 [&le;]7 (ISPC) or QIDS-C16[&le;]5 (PGRN-AMPS). The association between each NeuroPGS and antidepressant response or remission was evaluated using logistic regression models, adjusting for age, sex and five genetic principal components, with statistical significance determined at Benjamini Hochberg-adjusted p<0.05. The GABAergic-based PGS was associated with SSRI response (aOR per SD = 1.45, 95%CI: 1.14-1.85). Patients with the highest GABAergic PGS (10th decile) had 2.59-fold higher odds of response (95%CI: 1.46-4.60) compared to those with the lowest decile (1st decile). These exploratory findings support further evaluation of NeuroPGSs; independent replication and assessment of incremental predictive value are needed before clinical application. Keywords: Polygenic score; Neurotransmitters; GABAergic; depression; SSRIs; pharmacogenomics

Objective A diversity of approaches and cohorts have been published in research literature for suicide risk estimation, leading to some uncertainty around which factors and conditions are most strongly contributing to risk. Methods exist for weighting estimates by propensity score, removing simplifying the interpretation of model effects. Few studies have explored the use of these tools to assess when the effect of certain factors is uncertain or inconsistent. Method A mix of 1,001,737 suicide decedents and living patients were evaluated for suicide risk, relying on a subset of commonly identified factors: demographics, psychiatric conditions, substance use disorders, and social needs. Care utilization patterns were then assessed controlling for these factors in a hospital care setting. Four methods were considered for controlling multivariate confounding of odds predicting death by suicide: multivariate logistic regression, 1-to-1 sample matching, inverse propensity weighting (IPTW), and entropy balancing (EBAL). Results Test statistics showed suicide decedents were much more likely to have various psychiatric and substance use diagnoses, social needs and more likely to heightened care utilization across sampling intervals. Greater utilization was found close to date of death. Effect sizes after IPTW and EBAL weighting were also generally greater than would have been documented by regression alone. Conclusions Propensity weighting is a useful alternative to exploring nuanced effects, especially when the direction and strength of an association differ from traditional statistics. By weighting, the overall imbalance of cases remains consistent and effect terms might reflect more nuanced associations than regression or sample matching alone.

Objective: Mindfulness is a robust predictor of mental health, yet the psychometric properties of the short-form Five Facet Mindfulness Questionnaire (FFMQ-15) have not been examined in Iranian populations. This study evaluated the psychometric properties, factor structure, and diagnostic utility of the FFMQ-15 among medical sciences students. Participants: A total of 348 students (178 females, 170 males) from Kermanshah University of Medical Sciences participated. Methods: Participants completed the FFMQ-15, Penn State Worry Questionnaire (PSWQ), Difficulties in Emotion Regulation Scale (DERS), and Emotion Regulation Questionnaire (ERQ). Data were analyzed using exploratory and confirmatory factor analyses, composite reliability, and ROC curve analysis. Results: Both EFA and CFA supported the five-factor model, revealing a culturally unique item loading pattern. The FFMQ-15 demonstrated strong convergent and discriminant validity. ROC analysis indicated acceptable diagnostic accuracy for distinguishing high- and low-worry groups (AUC = 0.72, 95% CI: 0.66-0.78). Conclusions: The FFMQ-15 demonstrated acceptable psychometric properties in Iranian medical students. The non-reactivity facet exhibited cultural variability, suggesting that cross-cultural adaptation is essential. The instrument shows promise for screening in high-stress educational settings.

Introduction: Neuroinflammation is a known pathophysiologic mechanism in multiple sclerosis and a proposed mechanism in fibromyalgia and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Positron Emission Tomography (PET) imaging of the translocator protein (TSPO) has been widely used to measure neuroinflammation, though second-generation ligands are highly sensitive to a single nucleotide polymorphism, rs6971, resulting in high, mixed, and low affinity binders. It is unknown whether this polymorphism is associated with neuroinflammatory disorders. Materials and Methods: Eighty-six total participant genotypes for TSPO binding affinity were identified through Sanger sequencing (30 fibromyalgia, 21 ME/CFS, 11 multiple sclerosis, and 24 healthy controls). Associations between patient group and genotype were assessed with Fishers Exact test. Associations between genotype and symptom severity from self-report questionnaires were assessed with Kruskal-Wallis tests. Results: In the present study individuals with fibromyalgia, (ME/CFS), or multiple sclerosis were more likely to have a mixed- or low-affinity genotypes compared to healthy controls. Presence of the low or mixed genotypes were also associated with greater self-reported fatigue severity and depressive symptoms. Discussion: These findings should be confirmed in larger samples but suggest functional consequences of rs6971 polymorphism that may be related to the pathophysiology of pain and fatigue illnesses.

Schizophrenia spectrum disorders (SSD) have been linked to immune dysregulation and altered intestinal barrier function, although findings across studies remain inconsistent. The present study investigated circulating markers of intestinal permeability and immune activation in individuals with early-stage SSD and healthy controls and examined their associations with clinical outcomes. Serum concentrations of intestinal fatty acid-binding protein (iFABP), zonulin, soluble CD14 (sCD14), bactericidal/permeability-increasing protein (BPI), and lipopolysaccharide-binding protein (LBP) were measured in 36 patients with SSD at baseline and one-year follow-up and in 36 matched healthy controls. Linear mixed-effects models adjusted for age, sex, and body mass index (BMI) were used to evaluate group differences and longitudinal changes. No biomarker differences between patients and controls, and no longitudinal changes within patients, remained statistically significant after correction for multiple testing. Similarly, biomarker concentrations were not associated with symptom severity, global functioning, or neutrophil-to-lymphocyte ratio (NLR). Exploratory analyses identified a robust positive association between BMI and zonulin concentrations and additional associations among iFABP, LBP, and sCD14. Exploratory mixed-effects models identified a negative non-significant association between iFABP and sCD14 after adjustment for age, sex, and BMI. Overall, the findings do not support a generalized alteration of gut permeability or immune activation markers in SSD. Instead, they suggest that variation in gut-related biomarkers may be more strongly influenced by metabolic and individual factors than by diagnosis or current symptom severity.

Abstract Background: Insomnia is highly prevalent in major depressive disorder (MDD), yet the clinical and biological significance of dysfunctional beliefs and attitudes about sleep (DBAS) in MDD remains poorly understood. We examined whether distinct DBAS dimensions are associated with insomnia severity, depressive phenotypes, and neuroimmune-metabolic-oxidative (NIMETOX) biomarkers. Methods: DBAS-30 dimensions, clinical phenotypes, and NIMETOX biomarkers were assessed in 87 hospitalized patients with MDD and 31 healthy controls. Principal component analysis (PCA) was used to ascertain DBAS cognitive dimensions, and associations were examined using multivariable analyses. Results: Compared with controls, MDD patients showed lower scores on "perceived consequences of insomnia," "sleep-related worry and helplessness," and "medication-related beliefs," but higher "causal attributions to aging or biochemical imbalance" scores. A composite DBAS score reflecting these 4 MDD-associated cognitive dimensions was constructed, labeled DBAS dimensional index (DBAS-DI). This score was lower in MDD and inversely associated with Insomnia Severity Index (ISI) scores, overall MDD severity, physiosomatic symptoms, current suicidal ideation, and recurrence of illness. DBAS-DI and the expectations domain jointly explained 35.4% of the variance in ISI scores, with inverse and positive associations, respectively. The DBAS-DI score was inversely associated with indices of the acute-phase inflammatory response, oxidative stress, fecal branched-chain short-chain fatty acids (SCFAs) and positively associated with serum antioxidant capacity and protective SCFAs, such as acetate, propionate, and butyrate. Conclusions: DBAS abnormalities in MDD extend beyond insomnia-related cognitions and are associated with illness severity, suicidality, recurrence, and NIMETOX alterations. These findings suggest that dysfunctional sleep-related cognitions are embedded within broader immune-oxidative and gut microbial-metabolic processes that may contribute to the insomnia-depression interface.

Objective: To determine whether plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are elevated in hospitalised patients with delirium relative to patients with dementia and primary psychiatric disorders (PPD). Methods and analysis: This was a cross-sectional observational study conducted at the Royal Melbourne Hospital, Melbourne, Australia, between April and October 2024, with comparator samples from a research biobank. In Phase 1 (Delirium Discovery Cohort), we measured plasma NfL and GFAP using the Single molecule array (Simoa) platform in hospitalised patients with delirium and compared them with dementia and PPD groups. Biomarker concentrations were the primary outcome and were compared using general linear models adjusted for age, sex, and estimated glomerular filtration rate, with bootstrapped confidence intervals. Discriminative performance was assessed as a secondary outcome using receiver operating characteristic (ROC) curve analysis. In Phase 2, we applied this threshold to independent dementia and PPD groups. Results: In Phase 1 (n =176), inpatients with delirium showed higher concentrations of both NfL and GFAP than patients with dementia ({beta} = 0.78; 95% CI [0.37, 1.20] and {beta} = 0.39 [0.07, 0.73] respectively) or PPD groups ({beta} = 1.17; [0.69, 1.66] and {beta} = 0.83 [0.51, 1.24] respectively). ROC curve analysis for differentiating delirium from dementia and PPD groups yielded an optimal NfL threshold of 24.1 pg/mL (AUC = 0.89, 96% sensitivity, 70% specificity). In comparison, GFAP showed lower accuracy (AUC = 0.85), and combining NfL with GFAP (AUC = 0.90) did not significantly improve performance. In Phase 2 (n = 154), the NfL threshold of > 24 pg/ml maintained strong performance (AUC = 0.95, 96% sensitivity, and 77% specificity). Conclusions: Plasma NfL shows promise as a biomarker for delirium in hospitalised patients, distinguishing it with high sensitivity from patients with dementia and PPD. Larger multicentre studies are needed to refine cut-offs, evaluate temporal dynamics, and determine integration into acute hospital delirium pathways.

Objective: Amphetamine (AMP) and its derivates are commonly prescribed for the management of attention-deficit/hyperactivity disorder (ADHD). This study aimed to 1) quantify AMP's transfer into human milk in lactating women receiving therapeutic doses and 2) contextualize infant exposure. Methods: Milk samples and dyad health histories of thirteen lactating women treated with varying doses of either mixed AMP, dextroamphetamine, or lisdexamfetamine were released for this study from the InfantRisk Human Milk Biorepository. Milk drug concentrations were quantified by validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) and used to calculate standard relative infant dose (RIDmat) and RID standardized to the defined daily dose (RIDDDD) of 15 mg AMP base. Results: Average milk concentrations (Cavg) of AMP base ranged from 23.3 to 297.0 ng/mL and increased linearly (R^2 = 0.92) with maternal daily dose. The RIDmat ranged 3.6-10.8% and RIDDDD ranged 1.4-21.8%. The fitted regression line crossed the 10% RIDDDD threshold at 1.17x DDD, with the upper 95% prediction limit crossing at 0.84x DDD. No adverse event reported by the mother-infant dyads were attributed to AMP exposure, notably including absence of reduced milk supply. Conclusions:The levels of AMP observed in human milk followed a dose predictable manner. The average patient was predicted to remain below the 10% RID DDD safety threshold at total daily doses at or below approximately 28 mg of mixed AMP salts or 59 mg of lisdexamfetamine. Ninety-five percent of patients were predicted to remain below the threshold at or below approximately 20 mg and 40 mg, respectively.

Awareness of comorbidities in people with dementia is vital context for dementia research and clinical management. Natural Language Processing (NLP) applied to free text in the electronic health record (EHR) may help ascertain comorbidity presence. Our aim was to assess the validity of the MedCAT Comorbidity NLP app in a cohort of people with dementia. We used text from the EHR of South London and Maudsley NHS Mental Health Trust via Clinical Records Interactive Search (CRIS), linked to structured data from local primary care (Lambeth DataNet) and hospital admission data in a cohort of 3,543 people with dementia. We evaluated MedCAT Comorbidity, examining internal validity via manual review of text and external validity by cross-validation against linked primary care and hospital data; and comparison of the predictive value of comorbidity measures for mortality. Internal precision was highest for atrial fibrillation (100%) and lowest for diabetes (78%). Cross-validation with linked data varied substantially across conditions, with precision from 96% to 59% and recall 77% to 4%. The median number of comorbidities identified was two using any single source, or three when combined. MedCAT Comorbidity demonstrated predictive ability for mortality (HR 1.11 per condition), although the model fit was better using comorbidity from hospital admission data. In people with dementia, the MedCAT Comorbidity app showed high precision for most conditions during internal validation. The variability of precision and recall on external validity assessment partly reflects incomplete documentation. Results from the MedCAT comorbidity app can assist researchers in understanding comorbidity of cohorts, particularly when primary care or hospital data is not available. Our findings highlight the need to develop quality assurance protocols that include internal and external validity checks before deploying general data extraction apps for specific clinical subpopulations.

Large language models (LLMs) are increasingly consulted for health advice, but they can produce fluent statements that are false or unsupported, known as hallucinations, and this risk is poorly characterised in low-resource languages such as Bangla. We measured hallucinations of locally deployable open-weight LLMs in Bangla diabetes question answering and tested whether retrieval-augmented generation (RAG) grounded in Bangladeshs National Guideline on Diabetes Mellitus reduces them. Forty patient questions spanning six clinical sub-domains were each answered twice, under zero-shot prompting, by Qwen3.5-9B, TigerLLM-9B, Qwen3.6-35B-A3B and Gemma-4-26B-A4B, and by BanglaDiaRAG, which adds hybrid dense (BGE-M3) and sparse (BM25) retrieval from a physician-reviewed Bangla guideline knowledge base to Qwen3.5-9B. Two physicians rated the 400 de-identified responses by consensus for medical accuracy, completeness, safety, readability and reliability on a 0-1 scale, and responses with accuracy below 0.70 were classified as hallucinated. Hallucination rates were 2.5% for Gemma-4-26B-A4B, 3.8% for TigerLLM-9B, 22.5% for Qwen3.6-35B-A3B and 77.5% for Qwen3.5-9B. Guideline grounding lowered the rate of Qwen3.5-9B to 11.3% (difference -66.3 percentage points; 95% CI -76.3 to -55.0) and raised every criterion by 0.24 to 0.38 points (Holm-adjusted p < 0.001). BanglaDiaRAG outscored Qwen3.6-35B-A3B on all five criteria and matched or exceeded TigerLLM-9B, but it remained below Gemma-4-26B-A4B in safety, readability and reliability, and its residual errors were specific, confident and potentially harmful. Guideline grounding can turn a weak general-purpose model into a much safer Bangla diabetes assistant, but the choice of base model mattered as much as retrieval and no configuration was free of hallucination, so clinical oversight remains essential before patient-facing use.

Background: Self-harm is a frequent and challenging occurrence in adult inpatient psychiatric units. The use of restrictive practice to manage self-harm incidents continues to be widespread within mental health environments despite increasing national and organisational guidance to minimise its use. Aim: The aim of this project was to introduce and evaluate a continuous quality improvement programme to progressively reduce incidents of self-harm and the use of restrictive practice. The project was set in a female acute mental health inpatient unit. The project sought to embed improvements in an inpatient unit interventions, procedures, and culture and staff mindset, and move towards a sustained collaborative and positive therapeutic and communication-based approach. Methods: A retrospective service evaluation design was employed to assess differences in the number of incidents of self-harm, enhanced observation, seclusion, restraint and rapid tranquillisation in the period following the introduction of a continuous whole-systems multimodal interventional initiative in 2019. Pre- and post-initiation data were compared (2017/18 compared to 2019/2025) to evaluate improvement and whether this had been sustained. Data were analysed using descriptive statistics, including frequencies, percentages, and means. Results: Self-harm incidents reduced between 2017/18 and 2019, with a downward trend sustained between 2019 and 2025 (136 to 72 incidents respectively between 2019 and 2025; 47% reduction). A sustained reduction in the use of restrictive practice was shown in all domains pre- and post-initiation (2017/18 compared to 2025). Clinical implications: No one approach will successfully reduce self-harm and the use of restrictive practice. Approaches employed have to be multi-faceted, multi-interdisciplinary, collaborative, and blend together to deliver improvement. Sustained progress requires commitment to an iterative process of continuous collaboration, improvement and adaptation.

The gut-brain axis has emerged as a critical modulator in neurological diseases, including stroke. While acute post-stroke dysbiosis is well established, it remains unclear whether these alterations persist long-term and can be therapeutically modulated. This study addresses this gap through integrated metagenomic and metabolomic profiling in two independent clinical stroke cohorts, complemented by experimental models. Experimental stroke induced persistent changes in gut microbiota composition and altered short-chain fatty acid profiles. In patients, microbiota composition remained distinct from controls up to three months after ischemic stroke or transient ischemic attack (TIA), accompanied by persistent reductions in propionic and valeric acid and reduced abundance of Prevotella-associated microbial signatures. We performed a randomized, double-blind interventional trial (PRISE) testing a multispecies probiotic versus placebo, using household members as additional controls. Probiotic intervention led to microbiome compositional changes with increased abundance of beneficial taxa and significantly improved functional recovery. The tested probiotic strains showed anti-inflammatory and antioxidant effects in vitro. These findings suggest that targeted modulation of the microbiome represents a promising therapeutic avenue to improve chronic stroke recovery, warranting validation in larger Phase III clinical trials.

INTRODUCTION: The digital clock drawing test (dCDT) is a cognitive screening tool capturing high-resolution pen-movement data. Existing approaches for classifying cognitive impairment from dCDTs rely on selected features or image-based methods. We propose representing dCDTs as structural networks to classify cognitive impairment and characterize spatial organization in clock drawings. METHODS: Weighted networks were constructed from 3,362 Framingham Heart Study participants, with and without cognitive impairment. Network representations were generated separately for command and copy conditions, with nodes representing pre-defined clock components (digits, hands, clock face) and edges capturing pairwise spatial relationships between components. Random forest models and graph convolutional networks were trained to predict cognitive impairment from the networks. Models were benchmarked against demographics-only and established summary feature baselines. RESULTS: Network-based models achieved AUC values comparable to the established summary feature baseline across command-only and copy-only conditions. Differences in spatial organization between the cognitively intact and impaired groups were greatest in the upper-right clock region, involving digits 1 and 2, the clock hands, and the surrounding clock face. Differences between the command and copy networks were larger among cognitively impaired participants. DISCUSSION: The proposed network representations provide an objective, scalable, and flexible framework for representing clock drawings for use in classification models. dCDT networks matched the predictive performance of established summary feature-based approaches and identified clock regions where spatial organization differed by cognitive status. After external validation, this approach has the potential to be incorporated within a clinical workflow to support automated and interpretable cognitive screening.

Background: Predictive-processing accounts propose altered prediction-error signaling in psychosis. However, event-related potential (ERP) findings in first-episode psychosis (FEP) are inconsistent and focused predominantly on mismatch negativity (MMN). Examining anticipatory and later post-stimulus ERP responses alongside MMN within a single passive paradigm may broaden the assessment of predictive processing in FEP. Methods: 34 participants with non-affective FEP and 34 healthy controls (HC) underwent EEG during a passive auditory pattern-based paradigm. Mean amplitudes of contingent negative variation (CNV), MMN, and post-imperative negative variation (PINV) were quantified at five frontocentral electrodes. Exploratory analyses examined N1 and P2 mean amplitudes. Mixed models tested group, electrode, and group-by- electrode effects adjusting for age, sex and years of education. Regressions examined ERP associations with psychotic-like experiences, neurocognition, and social cognition across groups, and with symptoms, functioning, and antipsychotic dose within FEP. Results: FEP participants reported more psychotic-like experiences and showed poorer global and social cognition than HC (all p-FDR [&le;] .042). Standardized adjusted FEP-HC differences at FCz were nonsignificant for CNV ({beta} = -0.46), MMN ({beta} = -0.30), and PINV ({beta} = -0.10; all p-FDR [&ge;] .265). Exploratory analyses identified lower P2 amplitude in FEP at FCz ({beta} = -0.73; p-FDR = .019) and in the averaged frontocentral ROI ({beta} = -0.64; p-FDR = .027). No ERP-phenotype associations survived FDR correction. Conclusions: Despite phenotypic differences between FEP and HC, no significant differences emerged in the primary ERP indices of predictive processing examined. The exploratory P2 finding and its possible relationship to predictive processing and frontal dysfunction in FEP warrant further investigation.

Background: Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental condition, and co-occurring autistic traits may shape its phenotypic expression. Although creativity has been discussed as a relative strength in ADHD, it remains unclear whether autistic traits are associated with different dimensions of creativity in adults with ADHD and whether large-scale brain networks contribute to these associations. Methods: We compared divergent thinking (DT) and real-life creativity between 65 adults with ADHD and 73 neurotypical controls. We used subgroup comparisons, partial correlation analyses, and multiple regression models to determine the effects of autistic traits (ATs) on creativity dimensions within the ADHD cohort. Guided by prior creativity-network research, we analyzed resting-state functional connectivity (FC) of the Default Mode Network (DMN) and Frontoparietal Network (FPN) to uncover the neural mechanisms. Results: The ADHD cohort demonstrated significantly higher scores on both DT (ps = 0.002-0.009) and real-life creativity measures (ps = 0.011-0.017) compared to the neurotypical control group. Importantly, increased ATs within the ADHD group were associated with a dissociated creativity profile, showing positive associations with DT but negative associations with real-life creativity. At the neural level, stronger right DMN FPN connectivity mediated positive associations between ATs and DT performance (ps = 0.007-0.018), but mediated a negative association between ATs and self-reported imagination (p = 0.029). Conclusions: In individuals with ADHD, co-occurring ATs exert a paradoxical effect on creativity, enhancing DT while compromising real-life creative achievement. In this subgroup with ADHD and elevated ATs, interventions should capitalize on ATs-associated ideational strengths while circumventing translational barriers, to enhance real-world creative outcomes.

Exposure to armed conflict can leave persistent effects on mental health, yet its neurophysiological correlates remain poorly characterized, particularly in populations exposed to prolonged violence outside narrowly defined post traumatic stress disorder samples. Here we examined resting state electroencephalographic activity in 122 Colombian adults, including 74 participants exposed to armed conflict and 48 controls. Eyes closed EEG was analysed for 240 seconds per participant. We quantified absolute and relative spectral power across delta, theta, alpha and beta bands and regional functional connectivity using weighted phase lag index in theta, alpha and beta. Group comparisons were adjusted for age, education, sex and assessment year, with correction across 78 EEG contrasts. Median occipital and global alpha power was lower in exposed participants, although adjusted estimates were close to the null and confidence intervals were broad. Temporal theta relative power was lower in exposed participants at nominal significance, but this association did not survive Holm correction. No spectral power or regional connectivity contrast remained significant after multiple testing correction. Exploratory associations between EEG measures and MoCA performance were modest in participants with confirmed scores. These findings provide a systematic characterization of resting EEG after exposure to armed conflict in a population shaped by prolonged violence and displacement. They also indicate that large or widespread alterations in resting spectral power and connectivity were not detectable with the current sample and analytical framework, while defining candidate signals and methodological priorities for larger studies of conflict related brain health.