Acta Neuropsychiatrica

Background Diabetes is common in people with bipolar disorder, particularly men. Both type 2 diabetes and bipolar disorder are associated with evening chronotype, and men of working-age have later chronotypes than women. However, it is not known whether elevated rates of diabetes in bipolar are explained by evening chronotype, nor whether sex differences in chronotype contribute to higher rates of diabetes observed in men with bipolar disorder. Aim To determine whether evening chronotype increases the likelihood of comorbid type 2 diabetes in bipolar disorder, relative to those with no mental health problems. Additionally, to determine whether any effect differs between men and women. Methods Using data from the Our Future Health cohort, we estimated odds ratios for type 2 diabetes by bipolar disorder diagnosis, chronotype, and their interaction, adjusting for relevant covariates. Results A total of 1,136,694 participants (N = 6,846 with bipolar disorder) were included in the adjusted models. Type 2 diabetes was independently associated with bipolar disorder and evening chronotype. There were no significant differences in the interaction effects of chronotype on the odds of type 2 diabetes in bipolar across the whole sample or when stratified by sex. Conclusions The high prevalence of type 2 diabetes in people (and in men, specifically) with bipolar disorder may be independent of evening chronotype.

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

Background: Dissociative identity disorder (DID) is associated with frequent psychiatric comorbidity. Its complex and overlapping symptom profile can complicate recognition of both DID and co-occurring psychiatric conditions, contributing to diagnostic uncertainty. Evidence regarding psychiatric comorbidity is limited and prevalence estimates vary across studies. Aims: This meta-analysis aimed to examine the prevalence of psychiatric comorbidities in DID and factors associated with variation in prevalence estimates. Methods: Eligible studies were identified through PubMed, Scopus, Embase, PsycINFO, and MEDLINE from 1980 to 25 April 2026 (PROSPERO, no. CRD420261335819). Studies reporting psychiatric comorbidity in adults with DID were independently screened by two reviewers. Risk of bias was assessed. Random-effects meta-analyses using logit-transformed prevalence estimates were conducted. Meta-regressions examined moderators, whilst influence and sensitivity analyses assessed robustness. Certainty was evaluated using the GRADE approach. Results: Twenty-four studies (N = 858) contributed to the meta-analysis across 23 psychiatric comorbidities. Among individuals with DID, posttraumatic stress disorder had the highest pooled prevalence (87.44%, 95% CI 71.79-94.96), followed by major depressive disorder (62.64%, 95% CI 33.45-84.84), and borderline personality disorder (47.05%, 95% CI 34.21-60.32). Substantial heterogeneity was noted across most estimates. Meta-regressions identified significant associations with participant characteristics, recruitment, study quality, and diagnostic method. Risk of bias and certainty of evidence were heterogenous. Conclusions: Psychiatric comorbidity in DID is substantial and spans multiple diagnostic categories, although considerable heterogeneity and methodological variation limit confidence in prevalence estimates. Standardised diagnostic assessment, more representative sampling, and longitudinal research are needed to clarify the extent and clinical significance of comorbidity in DID.

Objective: The Alimetry Gut-Brain Wellbeing (AGBW) Survey is a validated 10-item scale developed to assess mental health in patients with chronic gastroduodenal symptoms. While the original scale demonstrated strong psychometric properties overall, as part of an ongoing commitment to continuous instrument optimization, this study evaluated a revised, unidirectional version of the scale (AGBW-II) to enhance measurement precision. Methods: The psychometric properties of the revised AGBW-II were evaluated via an anonymous online questionnaire in a sample of 161 patients with chronic gastroduodenal symptoms. Score distributions, factor analysis, reliability, and validity were analyzed and compared with data from the validation study of the original scale (n= 311). Results: The AGBW-II total and subscale scores demonstrated robust reliability and validity, outperforming the original AGBW Survey. Notable improvements in the reliability and validity of the stress subscale were observed following item rewording. Confirmatory factor analysis verified the three-factor model separating the depression, stress, and anxiety subscales, with the AGBW-II showing improved model fit indices. Conclusions: The revised, unidirectional AGBW-II demonstrated improved reliability and validity, while maintaining the underlying psychometric properties of the original scale. Consequently, the AGBW-II provides a reliable, valid, and brief mental health assessment for patients with chronic gastroduodenal symptoms.

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.

Objective: To evaluate the psychometric properties, factor structure, validity, and age- and sex-specific norms of the 21- and 10-item parent-report Child Mania Rating Scale (CMRS-P) in a nationally representative U.S. sample of youth aged 6-18 years. Method: Parents of 2,229 youth aged 6-18 years [weighted M(SD)=12.0(3.3) years; 45.9% female] completed the CMRS-P and related measures between July 2025 and March 2026. Data were weighted to represent U.S. demographics. A clinical comparison sample included parents of 522 youth seeking outpatient mental health services. Complete-case analyses evaluated reliability, factor structure, item response theory, measurement equivalence, retest stability, and convergent and discriminant validity. GAMLSS estimated age- and sex-specific norms. Results: Weighted mean scores were 8.5(SD=9.0) for CMRS-P-21 and 4.0(4.3) for CMRS-P-10. Internal consistency was high ({omega}=.97/.93), and forms correlated .95. A general severity factor explained most reliable variance ({omega}H=.94). Three items differed in response thresholds across samples, with an average impact below one point on expected total scores. Both forms correlated strongly with attention problems, suicidality, depression, externalizing symptoms, and emotion dysregulation (r=.51-.63 for CMRS-P-21) and negligibly with pubertal status. Reliable-change thresholds were 14.5 (CMRS-P-21) and 6.8 (CMRS-P-10) points. Weighted GAMLSS CMRS-P-21 95th percentiles declined with age in males (30-23) and increased in females (22-30), with wide bootstrap intervals at age extremes; the female trend was not replicated without weights. Conclusions: Findings support both CMRS-P forms for assessing youth manic symptoms. The first nationally representative U.S. norms provide age- and sex-adjusted benchmarks for interpreting scores and informing further clinical assessment.

Background: Genetic and observational studies have identified metabolic factors potentially contributing to the risk of Major Depression (MD). However, studies to date have concentrated on common genetic variants, many of which are in linkage disequilibrium. This makes it difficult to localise variants to a single gene. To overcome this, we used short read whole-genome sequenced datasets (srWGS) to conduct a targeted rare variant association analysis of the FADS region, which has previously been implicated in metabolic effects on MD. Methods: Using srWGS data from 740,373 individuals from All of Us (AoU) and the UK Biobank (UKB) we tested for associations between rare variants in the FADS region and MD. Additionally we conducted association tests between rare FADS variants and five blood-based metabolites implicated by previous studies of MD. Finally, we applied Mendelian randomisation to identify causal effects of metabolites on MD using rare-variants. Results: We found FADS rare variants were significantly associated with all tested metabolites, but not with MD. Using Wald-Ratio MR we found a putative causal effect of Docosahexaenoic acid (DHA) on MD using a rare-variant instrument in the FADS2 gene. Conclusion: Our study represents the largest rare-variant analysis of MD to date. However, the lack of associations between FADS rare-variants and MD suggests our analysis is underpowered, or that genetic effects on MD are conferred substantially by higher frequency variants. Rare-variant MR suggested a causal effect of DHA on MD, and implicates the FADS2 gene.

In Ethiopia, khat (Catha edulis) leaves, containing amphetamine-like alkaloids, are chewed for their psycho-stimulating effects. Khat use has been associated with psychosis. In a psychosis etiology framework, we investigated whether season, fluctuations of khat availability and khat use are associated with khat-induced psychotic experiences and whether trauma load moderates this association. In Southwestern Ethiopia, a cohort of 685 men aged 18-40 years were interviewed twice, during dry and after rainy season (low vs. high khat availability). Khat use was assessed by urine immunoassay and self-report. Trauma load and psychotic experiences were assessed by trained local interviewers. Khat use increased after the rainy season (p < 0.0001). Khat use and seasonality predicted khat-induced psychotic experiences. Main findings comprise increased odds of psychotic experiences among immunoassay-positive individuals with high trauma load. A potential additional interaction with recent trauma emerged. Findings support sensitization-related mechanisms but require replication in larger samples with longer follow-up.

While Artificial intelligence (AI) is increasingly being integrated into psychotherapy, little is known about how prospective patients evaluate different levels of AI involvement. Participants (n = 1,003) with depression evaluated vignettes describing psychotherapy options, from treatment delivered without AI integration (Human therapist) to treatments involving different degrees of AI integration (Assistive, Collaborative, Fully Autonomous AI). Participants consistently evaluated Human therapists more favorably, reporting a greater likelihood of seeking treatment, less hesitancy, and greater treatment acceptability. Economically, compared with a Human therapist, participants were willing to pay 31.6% less for therapists using Assistive or Collaborative AI and 57.2% less for Fully Autonomous AI. 18-19% of participants indicated that they would refuse treatment involving Assistive or Collaborative AI, compared with 31% for Fully Autonomous AI. Preference for Human over AI therapists was particularly pronounced at higher levels of therapist experience (doctoral/masters vs. peer/trainee). Overall, participants valued psychotherapy delivered by Human therapists more highly than AI integrated psychotherapy. These findings suggest that, despite increasing AI integration in mental health care, prospective patients continue to view psychotherapy as a distinctly human endeavor.

Suicide risk assessment in clinical conversations relies on the sequential structure of therapist-patient dialogue, yet formal models of how suicide-related content emerges, transitions, and re-ciprocates across conversational turns are lacking. We applied network analysis to C-SSRS-coded utterances from psychotherapy sessions to characterize the topology and dynamics of suicide-related clinical communication. Transcripts of 110 psychotherapy sessions from the SPEAK-SAFE study were classified for suicidality relevance using the Qwen3-32B large language model with an adapted Columbia-Suicide Severity Rating Scale (C-SSRS) schema. From the suicidali-ty-relevant utterances, we constructed a weighted directed category transition network. Centrali-ty, reciprocity, clustering, assortativity, and path length metrics were computed. Independent human expert ratings for a session-level subsample of 30 sessions from this cohort showed sub-stantial agreement with the LLM classifications (Spearman {rho} = 0.885, ICC[2,1] = 0.708). The network was dominated by therapist inquiry. Reciprocity was high, and closeness centrality de-creased monotonically with C-SSRS severity. Active suicidal ideation functioned as a bridge node from which escalation pathways to method, plan, and attempt categories originated. The network exhibited a hub-and-spoke topology with small-world characteristics. Suicide-related clinical dialogue is structured as a therapist-centered hub-and-spoke network with high reciproci-ty and a consistent severity-peripherality gradient. The extension of network theory from intra-individual symptom structures to inter-individual communication dynamics represents a method-ological innovation demonstrated here as a proof of concept, with potential applications to clini-cal training, automated decision support, and multimodal risk assessment. clinical interviews clin-ical interviews

Home-based 2 mA transcranial direct current stimulation (tDCS) was recently approved by the US Food and Drug Administration for depression, but tDCS amplitude has been limited largely by skin tolerance, and higher-intensity, accelerated dosing may be more efficacious or faster-acting. Using a novel high-capacity tDCS (HC-tDCS) stimulator and hydrogel electrodes, we evaluated the safety, tolerability and feasibility of accelerated HC-tDCS (20-minute bifrontal 6 mA sessions, twice daily for five days) in 20 patients with major depressive disorder. A Safety Review Committee reviewed data from the first three participants, who completed a within-day escalation to 6 mA, before the remaining 17 were enrolled. Retention and adherence were 100% (203 of 203 sessions), and 19/20 tolerated the full 6 mA amplitude without dose reduction. There were no serious adverse events. The most common symptoms were fatigue (49% of sessions), burning (46%) and tingling (43%) at the electrode, and difficulty concentrating (40%). Electrode-site erythema occurred in all participants, with no severe reaction or skin breakdown. At 1 week, 7 of 20 reported a residual skin change, mostly peeling or redness, none painful; at 1 month, hyperpigmentation at the electrode site persisted in 2 participants, both Black. At the end of the five-day course, PHQ-9 scores decreased from 15.8 to 7.2 (Hedges g = 1.62); 50% of participants responded, 45% remitted, and improvement was maintained at 1-month follow-up. Accelerated HC-tDCS was feasible, well tolerated, and warrants a sham-controlled trial.

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.

Objective: To identify speech features distinguishing adults with depression from healthy controls, evaluate whether these features track with depression severity during naturalistic treatment, and assess whether baseline speech features predict treatment response. Design: Cross-sectional group comparison (Objective A) followed by longitudinal evaluation in an independent sample (Objectives B and C). Candidate features surviving false discovery rate (FDR) correction in the cross-sectional analysis were carried forward to longitudinal and predictive analyses. Setting: A community mental health center in New York. Participants: Dataset I (cross-sectional): 82 adults with chart-diagnosed depressive disorders and 80 healthy controls (N=162), recruited via convenience sampling. Dataset II (longitudinal): 85 adults with depressive disorders followed for up to four timepoints over a mean of 10.4 weeks during naturalistic treatment; 63 with baseline PHQ-9 >=10 and at least one follow-up were included in prediction analyses. Recruitment occurred under protocols approved between 2020 and 2021. Interventions: None; participants received naturalistic clinical care. Outcome Measures: Cross-sectional: diagnostic group membership (depressive disorder vs. healthy control). Longitudinal: Patient Health Questionnaire-9 (PHQ-9) total score. Predictive: treatment response, defined as >=50% PHQ-9 reduction from baseline to last follow-up. Results: Across three speech tasks (journaling, picture description, phonemic fluency), 11 voice quality features, six shimmer variants and five harmonics-to-noise ratio (HNR) variants, survived FDR correction (all q<.05) in all tasks, with higher shimmer and lower HNR in the depressed group (largest adjusted effect beta=1.89, q<.001). In longitudinal analysis, one phonemic fluency voice quality feature was associated with PHQ-9 at FDR<.05 (maximum HNR beta=-1.43, SE=0.41, FDR=.006), with the remaining ten consistent in direction at FDR<.10; no speech x time interactions were significant. Sex moderated voice quality-depression associations in picture description and phonemic fluency. For treatment response prediction, phonemic fluency voice quality features achieved cross-validated AUC=0.727 [95% CI: 0.662, 0.792], comparable to clinical features alone (AUC=0.700-0.715); the combined model across all tasks and clinical features achieved AUC=0.730 [0.639, 0.820]. No pairwise AUC comparisons between predictor configurations reached significance after correction (all corrected p>.54). Conclusions: Voice quality features, particularly shimmer and HNR, were the most consistent speech correlates of depression across cross-sectional, longitudinal, and predictive analyses and across multiple speech tasks. These features are computationally simple to extract from brief recordings and showed preliminary predictive utility comparable to standard clinical variables. However, the modest sample sizes, chart-based diagnoses and heterogeneous treatment exposures preclude clinical application at this stage. Larger, prospectively designed studies with standardized recording protocols, structured diagnostic assessment, and recruitment at treatment initiation are needed before voice quality features can be recommended as clinical biomarkers.

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.

Background: Major depressive disorder (MDD) is clinically heterogeneous, but relationships among cortical hemodynamic abnormalities, peripheral neuroimmune, metabolic, and oxidative stress (NIMETOX) processes, and clinical phenome features remain unclear. We applied a systems-biology approach integrating functional near-infrared spectroscopy (fNIRS), peripheral NIMETOX biomarkers, and multidimensional clinical phenomes. Methods: Patients with MDD (n = 86) and demographically matched healthy controls (HC, n = 40) underwent 89-channel high-density fNIRS during resting-state and verbal fluency task (VFT) conditions. Temporal hemodynamic variability (Difference) and area under the concentration-time curve (AUC) were derived from oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin (HbT) signals. NIMETOX assessments included inflammatory biomarkers, generalized and lipophilic antioxidant defenses, oxidized lipids, lipidomics, and 16S rRNA amplicon sequencing of the stool microbiome. Results: MDD showed reduced VFT Difference and AUC across HbO, HbR, and HbT. Difference reductions were widespread across prefrontal, frontal eye field, language-related, and sensorimotor cortices, whereas AUC abnormalities were more restricted and resting-state alterations were limited. VFT differences significantly differentiated MDD from HC, with further improvement after integrating NIMETOX biomarkers. Lower antioxidants, carnitines/acetylcarnitines, polyunsaturated and even-chain saturated fatty acids, inflammation, indices of gut dysbiosis and leaky gut, and adverse childhood experiences explained substantial variance in HbO, HbR, and HbT Difference. Integrated fNIRS-NIMETOX profiles explained substantial variance in overall illness severity (cross-validated R2= 0.62) and physiosomatic symptoms (cross-validated R2= 0.48). Conclusions: Systems-biology integration identified a multilevel MDD phenotype linking peripheral NIMETOX dysregulation with task-evoked cortical hemodynamic abnormalities and clinical phenome heterogeneity, supporting an interconnected gut-NIMETOX-brain pathophysiology. Key words: major depressive disorder, biomarkers, redox mechanisms, lipophilic antioxidants, oxidized lipids

BackgroundPsychotic-like experiences (PLEs) have been shown to increase the risk for mental health problems in later life, especially psychosis. Following previously raised methodological concerns about the assessment of PLEs in adolescents, this study sought to differentiate subtypes of PLEs and to explore their trajectories. MethodsLongitudinally matched data of 671 German adolescents from three surveys over the time span of one year were analysed. Four trajectories were identified, including persisting, subsiding, and emerging highly indicative PLEs (HIPs), and non-highly indicative PLEs (non-HIPs), and longitudinally compared in terms of associated distress, psychopathology, and substance use. A differentiated never PLEs group was considered as a control. ResultsThe persisting HIPs group reported greater distress than the non-HIP group over time and was consistently associated with higher rates of internalising symptoms. The subsiding HIP group showed significant reductions in distress and internalising symptoms over time, but no significant changes regarding externalising symptoms and substance use. There was no difference in the extent of substance use between the groups. ConclusionsScreenings of PLEs should be mindful of different subtypes of PLEs that indicate a greater associated risk of poor mental health. PLE screening should not stop at whether PLEs are present but ask which ones.

Background: Adolescents frequently consult online resources regarding sensitive health topics, including suicide. Although content analyses demonstrate that online platforms contain both suicide-preventive and pro-suicide material, they rarely incorporate subjective data from adolescents at elevated suicide risk. Aims: This exploratory study examined associations between reasons for seeking suicide-related information online and the digital media used to access such content among adolescents with suicidal ideation. Method: Over one year, 131 adolescents aged 13 to 18 with current or past suicidal ideation were recruited from the Emergency Department of an urban pediatric hospital to complete a cross-sectional survey. Participants reported whether they had sought suicide-related information online, the type of information sought, and the online locations used. Mixed-effects regression models examined associations between reasons for online searches and digital media used. Results: Of 131 enrolled adolescents, 113 completed the survey; 69 (61.1%) reported ever seeking suicide-related information online. Searches were more often intended to assist with acting on suicidal thoughts than to reduce suicidal thoughts (63.7% vs. 55.1%). Social media were used for help-seeking searches slightly more often than professional health organization websites (42.1% vs. 31.6%). Among adolescents who used social media for their searches, discussion forums were significantly more likely to be used when seeking information to act on suicidal thoughts (OR=7.0, p=.02). Conclusion: The majority of adolescents with suicidal ideation may seek out suicide-related content online, particularly to facilitate suicide planning. Social media may be used as frequently as professional mental health websites like 988 or The Trevor Project for suicide-related searches.

Depression in Alzheimer's disease (AD) is clinically challenging to characterise because affective and cognitive manifestations frequently overlap with the underlying neurodegenerative condition. This study investigates depression-related speech patterns in AD using an explainable multimodal framework that integrates linguistic and acoustic features from elicited picture-description speech. To address the absence of directly observed depression labels in the target AD cohort, we employ a source-informed cross-dataset transfer strategy. Unsupervised clustering identified two distinct speech sub-phenotypes within the AD cohort. By comparing these unsupervised representations to a depression-labelled source corpus, one cluster demonstrated mathematical proximity (measured via cosine similarity in a shared latent space) to the source depression centroid. We explicitly acknowledge that this proximity reflects geometric similarity in a shared latent space, which may encapsulate overlapping domain characteristics alongside affective variance. To validate the structural integrity and separability of these discovered clusters, a proxy supervised classification task was employed. The retained multimodal artificial neural network (ANN) integrated information from both modalities and achieved 92.73\% cluster separability accuracy. This demonstrates that the representation-derived two-cluster partition was highly learnable on the independent internal hold-out test set; it does not establish clinical validity, longitudinal stability, or diagnostic validity of the identified sub-phenotypes. For interpretability, Shapley Additive exPlanations (SHAP) analysis was utilised to extract feature attributions and organise influential acoustic and linguistic features into a multimodal taxonomy. Furthermore, a Multimodal SHAP Attribution Magnitude (MSAM) score was derived as an exploratory model-based attribution score rather than a clinically validated severity measure. Ultimately, this study provides an interpretable computational baseline for characterising depression-related speech variance in AD, establishing a structural foundation for future prospective validation using independently labelled clinical cohorts.

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder characterized by persistent symptoms of inattention, hyperactivity, and impulsivity that significantly affect cognitive, academic, and social functioning. Although electroencephalography (EEG) has emerged as a promising non-invasive tool for identifying objective neurophysiological biomarkers of ADHD because of its high temporal resolution and cost-effectiveness, many existing machine learning studies rely on single-domain EEG features, which may not adequately capture the complex functional organization of the brain. Methods: This study proposes a subject-level EEG classification framework that integrates three complementary feature domains: spectral characteristics, weighted Phase Lag Index (wPLI)-based functional connectivity, and topological descriptors derived from persistent homology. EEG recordings from 121 subjects were preprocessed and segmented into overlapping windows for feature extraction. Window-level features were aggregated to generate subject-level representations. Recursive Feature Elimination (RFE) was employed to select the 110 most informative features from an initial set of 1,043 descriptors. Five machine learning classifiers, including Support Vector Machine (SVM), Random Forest, XGBoost, CatBoost, and Multi-Layer Perceptron (MLP), were evaluated using stratified five-fold cross-validation. An ablation study was further conducted to assess the contribution of each feature domain. Model interpretability was investigated using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), two widely adopted Explainable Artificial Intelligence techniques for interpreting machine learning models. Results: Among the evaluated classifiers, the SVM achieved the highest classification performance, yielding an accuracy of 80.17%, an F1-score of 80.95%, a balanced accuracy of 80.14%, a receiver operating characteristic area under the curve (ROC-AUC) of 86.72%, and a precision recall area under the curve (PR-AUC) of 87.34%. The ablation study demonstrated that combining spectral, functional connectivity, and topological features consistently outperformed individual feature domains, highlighting the complementary nature of these representations. Explainable AI analyses further identified functional connectivity features as the primary contributors to classification performance while confirming the additional value of spectral and topological descriptors. Conclusions: The proposed framework demonstrates that integrating spectral analysis, functional connectivity, and topological data analysis provides an effective and interpretable approach for subject-level ADHD classification from EEG recordings. The combination of recursive feature selection, multi-domain feature fusion, ablation analysis, and Explainable Artificial Intelligence establishes a transparent and reproducible pipeline that may facilitate the development of reliable EEG-based clinical decision-support systems for ADHD.

Abstract Objective: The Nationwide Quality of Life Scale (NQLS) is a brief, public-domain, youth self-report measure designed for quality of life (QoL) reporting in measurement-based care (MBC). Preliminary psychometric support comes from a sample of treatment-seeking youth, but its use in a population-based sample has not been evaluated. This study examined the NQLS psychometric performance in a U.S. nationally representative sample and develops national norms, score interpretation guidance, and clinical significance benchmarks. Method: A nationally representative sample of 1,008 youth ages 12-18 completed the NQLS as part of a larger survey battery. Analyses included exploratory and confirmatory factor analysis, reliability, validity, item response theory, and generalized additive models for location, scale, and shape (GAMLSS) norming. We integrated these results with clinical data from 1,200 youth seeking treatment at a large Midwestern children's hospital to estimate clinical significance benchmarks and compare between groups. Results: One- and two-factor solutions were evaluated. The NQLS demonstrated good internal consistency and convergent validity, with distinguishable 'life' and 'school' factors. Normative percentiles were generally stable across age, with modest narrowing at the youngest and oldest ages. Normative-sample youth showed raw score and latent trait estimates significantly higher than clinical-sample youth. Conclusions: The NQLS provides reliable and valid measurement of QoL in a nationally representative sample. Although two structural factors were identified, the composite score is recommended for clinical decision-making. National norms and clinical significance benchmarks may facilitate MBC applications of the NQLS. Future research should evaluate its performance across additional care settings. Keywords: Quality of life, assessment, psychometrics, youth, measurement-based care, norms