Post-operative delirium (POD) is a serious complication of surgery particularly in older adults, characterized by acute disturbances in consciousness and cognition and associated with increased mortality and long-term cognitive impairment. Despite its clinical relevance, the underlying pathophysiology remains poorly understood. To address this, we performed multi-omics profiling of live brain tissue from patients undergoing neurosurgery. Single-nucleus RNA sequencing revealed POD-specific transcriptional alterations in glial cells, especially microglia, characterized by enhanced neuroinflammatory signatures. Astrocytes also exhibited changes in synaptic and migratory pathways. Upstream analysis implicated external cytokines as potential drivers of glial responses, while downstream analysis linked POD to encephalitis and dementia. DNA methylation profiling identified immune-related epigenetic alterations, suggesting a regulatory role in POD-associated neuroinflammation. Integration of bulk methylation and cell type-specific transcriptomic data suggested that epigenetic changes may influence gene expression during POD pathogenesis. These findings provide the convincing evidence of neuroinflammation and glial involvement as the pathophysiological mechanism of POD based on the first multi-omics analysis using patient brain tissue.

There is an increasing trend towards embedding consumer and community involvement in all health and medical research. The Executive Committee of the International Journal of Eating Disorders (IJED) disseminated a survey for eating disorder researchers investigating their practices and perspectives regarding Patient and Public Involvement (PPI) in research, with the goal of informing our procedures in future.

A survey was distributed globally via eating disorder organizations, professional networks, and individual researchers. Researchers (N = 119) of various career stages completed the survey.

Three quarters of the researchers had consulted people with lived experience to inform their research; 45% said that they employed lived experience and/or peer researchers across all stages of their research, and 75% viewed PPI as improving the quality, relevance, feasibility, adoption, or implementation of research. Concerns were expressed about the challenges associated with PPI, including cost, time, expertise, disclosure of lived experience status, the need to maintain robust scientific principles, concern about the validity of results, and potential harm to the person with lived experience. Most did not want disclosure of PPI to be mandatory or to impact the review process.

These insights informed the development of an approach to recognizing the involvement of PPI in our publishing. Rather than an obligatory statement, IJED encourages use of PPI and disclosures, with inclusion of a description and implications for the interpretation of the research detailed in manuscripts.

Emerging evidence suggests that interoception-the perception and interpretation of internal bodily signals-may play a role in voice perception and voice disorders; however, no self-report measure currently assesses interoception specifically in relation to voice processing. The present research aimed to develop and validate the Interoception in Voice Scale (IVS), a self-report measure designed to capture awareness of bodily cues related to voice production and perception. In total, 33 novel items were developed by drawing on existing interoceptive measures and expert input from speech and language specialists. In Study 1 (N = 584), exploratory factor analysis indicated a 19-item scale with four interpretable factors: Emotional Voice Awareness, Physical Voice Awareness, Voice-related Concern, and Voice-related Attention. These factors demonstrated good-to-excellent internal consistency (ω = .84-.91). In Study 2 (N = 320), confirmatory factor analysis supported the four-factor structure, demonstrating acceptable model fit. Correlational analyses supported construct validity through meaningful associations with dimensions of the Multidimensional Assessment of Interoceptive Awareness (MAIA-2), and Voice-related Concern was consistently associated with self-perceived voice handicap. Together, the findings support the composite reliability and construct validity of the IVS. The scale provides a tool for investigating the role of interoception in voice perception and may be useful for future research and for speech and language therapists engaged in clinical work involving voice disorders. The survey and scoring instructions are available here: https://jswlab.co.uk/IVS.

The objective of this article is to discuss the most updated data on the safety and efficacy of some psychotropic medications during pregnancy, specifically antipsychotic medications, anxiolytic and sedative hypnotics, stimulant medications, and medications for treatment of substance use disorders (SUDs). The common disorders and the risks of not receiving treatment for certain psychiatric conditions, including SUDs, are also discussed.

The objective of this article is to discuss the most updated data on the safety and efficacy of some psychotropic medications during pregnancy, specifically antidepressants and mood-stabilizing medications. The common disorders and the risks of not receiving treatment of certain psychiatric conditions are discussed.

Precision mental health aims to enable personalized care for mental disorders via the identification of brain-behavior associations at the individual level, yet current frameworks often face challenges in generalizability, interpretability, and clinical translation. Contrastive machine learning (CML) has emerged as a promising paradigm for characterizing individual brain variations by extracting brain dimensions that capture both disease-relevant aberrations and inter-subject heterogeneity. In this Review, we synthesize the conceptual foundations and recent methodological advances of CML. We compare CML with related frameworks and illustrate how it integrates with subtyping and predictive modeling pipelines, situating it within the broader landscape of precision mental health. We then review emerging applications that use CML to link brain structure and function to cognition, emotion, and treatment response. Finally, we outline future directions of applications and methodological innovations where CML could further advance personalized diagnosis and intervention in mental health.

Evidence suggests that patients with inflammatory bowel disease (IBD) are at higher risk of psychiatric disorders, yet temporal pattern of risk elevation before/after IBD diagnosis and role of familial factors remain unclear.

Swedish IBD patients diagnosed 2007-2023 were compared with reference individuals in a matched population-based cohort and compared with IBD-free full siblings in a sibling-controlled cohort. Primary outcome was any psychiatric disorder, while secondary outcomes included psychotic disorders, mood disorders, anxiety disorders, eating disorders, substance misuse, personality disorders, attention-deficit/hyperactivity disorder, and autism spectrum disorders. We estimated temporal patterns of hazard ratios (HRs) from five years before to ten years after IBD diagnosis by using flexible parametric survival models.

The study included 43,862 IBD patients (mean age at diagnosis: 41.6 years; 47.3% female) and 178,821 reference individuals. The HR of any psychiatric disorder started to increase from 2-3 years before IBD diagnosis (e.g., HR=1.15 [95%CI: 1.09-1.21] at -2 years), peaked immediately after IBD diagnosis (HR=1.50 [1.41-1.59] at 0.5 year), and decreased rapidly thereafter but remained increased even at 10 years after diagnosis (HR=1.19 [1.13-1.24]). The increased HR was mainly driven by major depressive disorder, anxiety disorders, and substance misuse. No increase was observed for psychotic disorders, personality disorders, and attention-deficit hyperactivity disorder, although IBD patients were more likely to have autism spectrum disorders shortly after diagnosis and eating disorders from ≥5 years onwards. Similar temporal HR patterns were observed across all subtypes of IBD: Crohn's disease, ulcerative colitis, and IBD-unclassified. Similar risk increases for psychiatric disorders were observed in the sibling-controlled cohort.

Patients with IBD were at an increased risk of several psychiatric disorders from 2-3 years before diagnosis through up to 10 years after, and this risk increase cannot be fully explained by shared familial factors.

Personality disorders (PDs) are increasingly recognized and highly prevalent, yet remarkably poorly understood, diagnosed and treated mental conditions. Common PDs include antisocial, avoidant, borderline, dependent, histrionic, narcissistic, obsessive-compulsive, and paranoid disorders. Experimental (animal) models are a valuable tool to study various brain disorders, including PDs. However, characterized by complex and frequently overlapping aberrant behaviors, PDs pose major challenges for their preclinical modeling. Here, we discuss the potential utility of zebrafish (Danio rerio) as complementary translational models relevant to studying PDs. Paralleling clinical symptoms and traits of PDs to specific behavioral phenotypes, biomarkers and paradigms in these fish, we critically evaluate the existing challenges and limitations of such models, and provide a conceptual framework for further research in this field. Despite these conceptual and practical challenges, we argue that zebrafish models not only provide a powerful platform for elucidating neurobiological mechanisms of a wide range of key PD traits (e.g., impulsivity, aggression, emotional dysregulation), but may markedly accelerate both translational modeling of these disorders and therapeutic discovery.

Clinical decision-making in psychiatry has traditionally relied on rating scales and clinical impressions documented in the electronic health record (EHR). Yet, clinical interviews contain rich behavioral signals that remain underutilized in psychiatric care. Recent advances in artificial intelligence (AI) now enable quantification of these signals, and prior work demonstrates that computational measures of speech, language, and facial expression can inform diagnosis and estimate symptom severity. Despite this progress, most prediction efforts remain confined to single modalities and individual diagnoses and focus on diagnostic classification rather than clinically actionable outcomes such as treatment discontinuation or the need for crisis care. Here, we contextualize advances in behavioral quantification and multimodal data fusion, and present the Phenotypes REimagined to Define Clinical Treatment and Outcome Research (PREDiCTOR) study, a prospective cohort study of 2100 patients entering outpatient mental health care. PREDiCTOR is designed to develop and validate dynamic, multimodal prediction signatures that predict treatment discontinuation, emergency department visits, and hospitalizations over a one-year follow-up period. The study audiovisual recordings of clinical encounters, EHR data, cognitive assessments, smartphone passive sensing, therapeutic alliance measures, and audio/text diaries within a Contextual Bandit framework that continuously updates individualized outcome estimates as new data become available. Both interpretable features and learned embeddings are leveraged, with large language models serving as feature extractors rather than clinical decision-makers. We describe the study design, data collection, and processing pipelines, hybrid predictive modeling approach, and prospective validation strategy, and discuss the potential for translating dynamic behavioral quantification into individualized clinical prognostics.

Aberrations in neuro-immune, metabolic, and oxidative stress (NIMETOX) pathways are implicated in major depressive disorder (MDD). First-episode simple dysmood disorder (FE-SDMD) without metabolic syndrome offers a unique model to investigate early lipid alterations using a systems biology approach.

Plasma samples were collected from 88 university students (44 FE-SDMD, 44 healthy controls). Participants underwent psychiatric assessments, including adverse childhood experiences (ACEs), negative life events (NLEs), depression, anxiety, suicidal behaviors, and insomnia. Untargeted lipid profiling was performed using LC-QTOF-MS, while indices of oxidative and nitrosative stress (ONS) and lecithin-cholesterol acyltransferase (LCAT) activity were assessed. Data was analyzed using machine learning approaches with recursive feature elimination and cross-validation.

FE-SDMD was characterized by increased ceramides (CER), diacylglycerides (DAG), triacylglycerides (TG), sphingomyelins (SM), bis-monoacylglycerol phosphates (BMP), cholestone, and fatty-acyl amino acids (FAAA). DAG, CER, and BMP were the strongest predictors of depression severity and physiosomatic symptoms, whereas cholestone, CER, and SM predicted suicidal behaviors. These lipid modules, together with lowered LCAT and increased ONS, explained substantial variance in depression severity (46.4%), physiosomatic symptoms (42.4%), cognitive-affective symptoms (37.9%), suicidal behaviors (30.1%), insomnia (32%), and anxiety (19.5%). ACEs and NLEs were strongly associated with CER (p < 0.001), DAG (p < 0.01), and cholestone (p < 0.01), revealing coordinated lipidomic networks within a systems biology framework.

Early-stage MDD is characterized by lipid dysregulations, which are linked to psychosocial stress exposure, oxidative stress and attenuated reverse cholesterol transport. These lipid modules identify biologically functional pathways associated with early disease and may inform the development of future therapeutic strategies.