IntroductionIrritable bowel syndrome (IBS) is a common gut-brain axis disorder characterized by abdominal pain and altered bowel habits, and it shows high comorbidity with psychiatric disorders. However, the shared genetic mechanisms underlying these associations remain incompletely understood.MethodsWe performed a large-scale meta-analysis of IBS in individuals of European ancestry by integrating genome-wide association study (GWAS) summary statistics from the UK Biobank, Bellygenes, and the Million Veteran Program (MVP), thereby increasing statistical power to detect novel IBS loci. We further conducted global genetic correlation analyses with psychiatric traits, followed by multi-trait analysis of GWAS (MTAG) and conditional false discovery rate (condFDR) analyses to identify pleiotropic loci. Transcriptomic, methylomic, and expression quantitative trait locus (eQTL) data were integrated to explore potential regulatory mechanisms.ResultsThe meta-analysis identified up to ten previously unreported IBS loci, several of which were supported by colonic and brain eQTL effects. Global genetic correlation analyses confirmed substantial genetic overlap between IBS and psychiatric traits, particularly major depressive disorder and neuroticism. MTAG and condFDR analyses uncovered more than 100 pleiotropic loci, including signals at SORCS1, SLC35D1, COA1, and TLE1. Integrative analyses of transcriptome- and methylome-wide data highlighted regulatory mechanisms spanning colonic, immune, and neuronal tissues, supporting neuro-immune crosstalk and mitochondrial involvement.DiscussionOur findings provide a comprehensive genetic characterization of IBS, refine its heritable basis, reveal pleiotropic links with psychiatric disorders, and implicate molecular pathways across the gut-brain axis. These results advance mechanistic understanding of IBS and may inform future therapeutic development for IBS and its psychiatric comorbidities.
Can the treatment effects of human-animal interaction be maintained? A randomized controlled trial including follow-up in people with severe mental illness
IntroductionThere are persistent demands for well-designed randomized controlled trials (RCTs), including follow-up measurements, in studies on animal-assisted treatment (AAT). In addition, a possible dose-response relationship is under discussion. The aim of the present study was to investigate the efficacy of a single-session AAT with sheep, including a booster exercise, over a follow-up period of four weeks.MethodsIn an RCT, a single-session AAT with sheep in a group setting, including an imaginative booster exercise conducted in the week following the AAT session, was compared to treatment as usual (TAU). Sixty psychiatric inpatients with severe mental illness were assessed for positive and negative emotions, mindfulness, and self-efficacy expectancy at baseline (PRE), immediately after the intervention (POST), and at one-week and four-week follow-ups.ResultsThe results indicate significant differences between the two groups at POST and still in the one-week follow-up (FU1) in three of four outcomes. Within the intervention group, within-group analyses demonstrated significant improvements from PRE to POST and from PRE to FU1 across all outcomes, with large effect sizes. At the four-week follow-up, all significant effects had diminished.ConclusionsAn imaginative booster exercise conducted within one week after an AAT session was effective in maintaining large effect sizes for up to one week. However, the results did not persist at the four-week follow-up. Longer follow-up periods, variations in the number of sessions, and the inclusion of active control groups are therefore necessary for further AAT studies.Trial registrationhttps://drks.de/search/de/trial/DRKS00031347, identifier DRKS 00031347
Cortical high-threshold and low-activation characteristics in adolescent depression: a cross-age differential analysis
BackgroundAdolescent depression exhibits distinct neurophysiological features, with marked age heterogeneity particularly in the resting motor threshold (RMT) measured during repetitive transcranial magnetic stimulation (rTMS), and substantial variability in clinical therapeutic efficacy. Functional near-infrared spectroscopy (fNIRS) enables the assessment of cortical excitability levels; however, research investigating the association between RMT and cortical activation in adolescent patients with depression remains limited. This study aims to elucidate the underlying neural mechanisms from the perspective of cortical hemodynamics, which is crucial for further optimizing neuromodulation strategies in patients with depression.MethodsWe collected data from 85 treatment-naive patients with depression who underwent rTMS therapy. All patients completed RMT measurement, fNIRS examination, and Hamilton Depression Rating Scale (HAMD) assessment prior to rTMS treatment. Participants were divided into three groups according to age: the adolescents group (n=31), the young adult group (n=26), and the middle-aged group (n=28). We compared the differences in RMT among the three groups and explored the relationships between RMT, cortical activation (reflected by prefrontal oxyhemoglobin level changes during the verbal fluency task via fNIRS), and depression severity (assessed by HAMD scores).ResultsThe results demonstrated that the adolescents group had a significantly higher RMT than the other age groups (58.00 ± 11.14, P < 0.001), accompanied by the lowest prefrontal Oxy-Hb activation level (0.095 ± 0.06, P < 0.001). A strong negative correlation was observed between RMT and cortical activation (Spearman’s rs= -0.929, P < 0.001), while a strong positive correlation was found between RMT and depression severity (Spearman’s rs = 0.837, P < 0.001). The distinct coupling phenomenon of high threshold-low activation-severe symptoms was most prominently manifested in this age group, which may theoretically reflect an underlying dysregulation in broader emotional networks, though the current direct findings strictly indicate localized alterations in prefrontal activation and motor cortical excitability.ConclusionsThe characteristics of high RMT and low cortical activation in adolescent depression serve as important neurobiological markers for depression severity. This finding provides a novel direction for developing individualized, developmentally tailored neuromodulation strategies (e.g., optimization of rTMS targets and dosages), indicating that interventions for adolescent depression should prioritize promoting the healthy integration of emotional circuits and the functional coordination between the cortex and subcortex.
The Effects of Lemon Essential Oil Inhalation on Attention in Children With Attention Deficit Hyperactivity Disorder (ADHD)
Conditions: Attention Deficit Disorder With Hyperactivity (ADHD); Sustained Attention; Aromatherapy; Essential Oil; Non-Pharmacological Interventions
Interventions: Other: Lemon Essential Oil Inhalation; Other: placebo inhalation
Sponsors: Başakşehir Çam & Sakura City Hospital
Completed
Interventions: Other: Lemon Essential Oil Inhalation; Other: placebo inhalation
Sponsors: Başakşehir Çam & Sakura City Hospital
Completed
I’m scared of everything — what does it mean and how do I get over it?
What you’re describing sounds really overwhelming. I’m glad you reached out. The fears you mention — being scared of doing something against your will, worrying you might not have control, and feeling intensely concerned about being judged — are patterns I often see in people with anxiety and, sometimes, people with obsessive-compulsive disorder (OCD). A hallmark of OCD is a deep doubt about control: the fear that you might act in a way that goes against your values, even though you don’t want to. These kinds of fears are called intrusive thoughts. While intrusive thoughts can feel very real and frightening, they are not things you actually intend to do or predictions of things that you will do — they’re unwanted experiences that don’t define you.
Avoiding sports and other things for fear of being judged is also a symptom of anxiety. I can understand how hard it is to tell your family what you’re going through, especially if you have felt ignored in the past. At the same time, your pain deserves to be heard and taken seriously. I encourage you to try talking to your parents again, but if you truly feel like you can’t, consider telling one safe person — whether that’s another family member, a school counselor, or even a teacher you trust. You can write how you’re feeling in a note if speaking feels too hard.
The physical symptoms you mentioned — neck and shoulder pain, fidgeting — are also common in anxiety because our bodies can hold tension when our brains are on high alert. What this likely means is that your brain is caught in a fear loop, constantly scanning for danger around control and judgment.
The good news is that this is very treatable. A mental health professional may recommend a type of cognitive behavioral therapy called exposure and response prevention (ERP). ERP helps you gradually face the situations or thoughts you fear instead of looking for reassurance from someone else or avoiding those situations or thoughts altogether. Over time, ERP teaches your brain that thoughts are just thoughts, not actions, and that you can tolerate uncertainty without something bad happening.
For now, you might try gently labeling upsetting thoughts as anxiety, not facts, and practicing not accepting them as true when they show up. Taking small steps toward what you’ve been avoiding can help you rebuild your confidence, even if it feels uncomfortable at first.
While you can practice managing anxiety or intrusive thoughts on your own, it’s better to have help. Once you talk to someone you know and trust, have them help you reach out to a mental health professional who can provide a more thorough assessment and the appropriate treatment for you. You don’t have to go through this alone, and with the right support, this can get much better.
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Equitable Digital Frailty Screening for Marginalized Older Adults Using Audio Computer-Assisted Self-Interview: Collaborative Development Guide and User Testing Study
Background: Older adults facing social or structural marginalization for reasons such as lower literacy, digital exclusion, financial constraints, restricted living environments, or complex health histories, face persistent barriers to much-needed health screening. Digital health tools, particularly those using audio computer-assisted self-interview (ACASI) technology, offer potential to overcome these barriers (audio-delivered and self-administrable), but their application to marginalized populations remains underexplored. Moreover, guidance is limited for developing such tools which require collaboration within cross-disciplinary teams. This paper presents development insights and user testing findings from the ASCAPE (Audio App-Delivered Screening for Cognition and Age-Related Health in Prisoners) project, which aimed to develop equitable digital frailty and cognition screening for older people in Australian prisons. Objective: This study aims to describe the collaborative development of the “ASCAPE-HS” prototype, a tablet-based ACASI-delivered Frailty Index and aging screener, and to synthesize key lessons from the project that can inform equitable digital health tool development in hard-to-reach older adults. Also, to present findings on the usability and acceptability of ASCAPE-HS in a diverse community sample. Methods: The ASCAPE-HS prototype was developed through an iterative process involving researchers, clinicians, software developers, and end users under a digital health equity framework. The prototype included a self-administered, audio-delivered Frailty Index, alongside other items relevant to aging. The design process prioritized accessibility, sociocultural relevance, and technical feasibility, with regular multidisciplinary consultation and iterative refinement. Exploratory user testing with 20 older adults (aged 47‐93 years, including n=5 who had not finished secondary schooling, n=3 people with previous imprisonment history, and n=9 with mild or moderate cognitive impairment) provided feedback on usability and acceptability. Results: A 50-item Frailty Index was developed, alongside an additional selection of holistic questions that could meaningfully capture age-related health, and transferred to an iOS app (Apple, Inc), with ACASI features. Key elements included lay wording, consistent interface, simple “tapping” response options with repeatable audio feedback, a tutorial, and artificial intelligence–generated audio guidance. Key development considerations were synthesized into a checklist for teams undertaking similar projects. Successful strategies for the collaborative design process included diverse teams abreast of emerging literature and policy with varying expectations for engagement during development, and dedicating time to flexible, iterative development processes. Acceptability (median scores ≥4 out of 5 across 6 constructs) and usability (mean System Usability Scale score 79.0, SD 8.8) were high. Conclusions: A collaborative approach can produce ACASI-based health screening tools that are well-received by older adults. We highlight the feasibility of integrating frailty and aging assessment into a usable and acceptable digital tool, and offer actionable principles for collaborative, evidence-based development of equitable health screening tools in diverse, hard-to-reach populations.
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Machine Learning for Comparative Antidepressant Selection in Major Depressive Disorder: Systematic Review
Background: Major depressive disorder (MDD) affects approximately 1 in 6 adults during their lifetime, yet antidepressant selection relies predominantly on trial-and-error, with response rates of only 42% to 53%. While machine learning (ML) models have shown promise in predicting treatment outcomes, most focus on single treatments rather than comparative selection across therapeutic alternatives, limiting their clinical utility for the medication choice decisions that clinicians face in practice. Objective: This systematic review evaluates ML approaches that examine 2 or more pharmacological interventions for predicting treatment outcomes in MDD, with a focus on their capacity to facilitate comparative treatment selection between medications or medication classes for individual patients. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched PubMed, Scopus, and Web of Science for studies published from 2015 to 2025. We included studies involving adults with MDD that used ML models to predict treatment outcomes across 2 or more pharmacological treatments and reported medication-specific prediction outcomes. Risk of bias was assessed using PROBAST-AI (Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence). We conducted a narrative synthesis organized by modeling strategies, data integration approaches, validation methodologies, and performance patterns. Results: From 5370 initial records, 19 studies met the inclusion criteria, with dataset sample sizes ranging from 49 to 77,226 participants. Studies employed 3 distinct modeling strategies: drug-specific supervised models trained independently for each medication, subtype- or trajectory-based approaches using clustering methods to identify differential response patterns, and a unified differential prediction framework generating calibrated cross-treatment predictions. Performance varied substantially, with area under the curve values ranging from 0.59 to 0.95 and classification accuracies between 62% and 95.4%, though high performance was concentrated in studies with small samples, high-dimensional neurobiological features, and internal-only validation. Only 7 studies conducted external validation, which generally yielded more conservative performance estimates. Feature informativeness was more consistently associated with performance variation than algorithm complexity. Most studies did not formally distinguish between prognostic features predicting general outcomes and predictive features identifying differential medication responses, and none applied formal explainability techniques. Conclusions: ML for comparative antidepressant selection remains in an early stage of development. Only 1 study implemented a unified framework directly supporting patient-level treatment ranking. Key barriers to clinical translation include insufficient distinction between prognostic and predictive markers, limited cross-trial validation, near-absent calibration reporting, and absent explainability. Future research should prioritize unified comparative frameworks with calibrated predictions, rigorous external validation on diverse cohorts, explicit modeling of heterogeneous treatment effects, and integration of explainability into model development.
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Early Detection of Alzheimer’s Disease and Related Dementias From Spontaneous Speech Using Foundation Speech and Language Models: Comparative Evaluation
<strong>Background:</strong> Alzheimer’s disease and related dementias (ADRD) are progressive neurodegenerative conditions where early detection is critical for timely intervention and care planning. However, current diagnostic methods are often inaccessible, costly, and delayed, especially for underserved populations. There is a growing need for scalable, noninvasive tools that can support timely diagnosis. Spontaneous speech contains rich acoustic and linguistic markers that can serve as noninvasive behavioral markers for cognitive decline. Foundation models, pretrained on large-scale audio or text data, generate high-dimensional embeddings that encode rich contextual and acoustic information. <strong>Objective:</strong> This study benchmarks open-source foundation language and speech models to evaluate their effectiveness in detecting ADRD from spontaneous speech as a potential solution for early, noninvasive, and scalable ADRD detection. <strong>Methods:</strong> In this study, we used the Pioneering Research for Early Prediction of Alzheimer’s and Related Dementias EUREKA (PREPARE) Challenge dataset, which consists of audio recordings from over 1600 participants with 3 distinct categories of cognitive decline: healthy control (HC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). We further excluded samples that are non-English, nonspontaneous speech, or of poor quality. Our final samples included 703 (59.13%) HC, 81 (6.81%) MCI, and 405 (34.06%) AD cases. We systematically benchmarked 18 open-source foundation speech and language models to classify cognitive status into 3 categories (HC, MCI, or AD). Post hoc interpretability analysis was performed for the best-performing model using Shapley additive explanations linking high-dimensional embeddings with explainable acoustic and linguistic markers. <strong>Results:</strong> Whisper-medium model achieved the highest performance among speech models at 0.731 accuracy and 0.802 area under the curve, while Bidirectional Encoder Representations from Transformers with pause annotation achieved the top accuracy of 0.662 and 0.744 area under the curve among language models. Overall, ADRD detection based on state-of-the-art automatic speech recognition model-generated audio-embeddings outperformed other models, and the inclusion of nonsemantic information, such as pause patterns, consistently improved the classification performance of text-embedding–based models. <strong>Conclusions:</strong> Our work presents a comprehensive comparative evaluation of state-of-the-art speech and language models for AD and MCI detection on a large, clinically relevant dataset. Embeddings derived from acoustic models, which capture both semantic and acoustic information, show promising performance and highlight the potential for developing a more scalable, noninvasive, and cost-effective early detection tool for ADRD.
iCARE Self-Guided Digital Intervention for Postpartum Depression in Danish Mothers: Formative Research Using User-Centered Design
<strong>Background:</strong> Postpartum depression (PPD) is a major public health concern. Despite advancements in treatment, many barriers to accessing care remain. There has been a growing interest in digital interventions for the prevention and treatment of PPD. However, for mothers with mild and moderate symptoms of depression, there is a limited offer of self-guided internet-based interventions developed with user input and with considerations on how to integrate the intervention into stepped care models for PPD. <strong>Objective:</strong> The aim of this study was (1) to describe the process of the design and development of iCARE, a self-guided digital psychological intervention for mothers with mild and moderate symptoms of PPD in Denmark, (2) present the program’s theory illustrated by a logic model, and (3) explore its initial usability and prospective acceptability. <strong>Methods:</strong> Applying user-centered design methods, the intervention development followed six steps: (1) a literature review to identify evidence‑based therapeutic components of self‑guided interventions for PPD, (2) interviews with women with lived experience of PPD and group discussions with mental health experts and home‑visiting providers to identify user needs, (3) iterative design and content development with stakeholder feedback in collaboration with the Department of Digital Psychiatry, (4) prototype testing using think‑aloud usability sessions and interviews with 5 mothers, (5) a group cognitive walkthrough with mental health experts, and (6) final refinement and implementation of the iCARE program with developers and designers. <strong>Results:</strong> Initial interviews with mothers and maternal health care providers emphasized the importance of a digital intervention offering timely psychoeducation, coping strategies, and pathways to in-person care while addressing the diversity of expressions of PPD symptoms. Stakeholders recommended a flexible program, multimodal content, and integration into maternal care systems with community health nurses supporting engagement and participation. The prototype was designed to be user-centered, engaging, and with multiple interactive features. It included components on psychoeducation, cognitive exercises grounded in cognitive behavioral therapy, acceptance and commitment principles, and mood-monitoring. The prototype was designed to be user-centered and engaging, with interactive features and components on psychoeducation, cognitive exercises grounded in cognitive behavioral and acceptance and commitment principles, and mood-monitoring. Prototype testing indicated high prospective acceptability and led to refinements across 6 themes: appropriateness of content; motivation and engagement; inclusivity and gender representation; clarity of instructions and data use; understanding of therapeutic method; and usability, layout, and navigation. <strong>Conclusions:</strong> iCARE is a self-guided internet-based psychological intervention for mothers with mild and moderate symptoms of PPD in Denmark. It was developed with user input by using qualitative methods, user-centered design, and psychological theory. Further research is needed to evaluate the feasibility and effectiveness of the program in a randomized controlled trial and its integration into maternal health care models such as universal PPD screening and home-visiting.
Roles of NRXN1 in neuropsychiatric disorders: from genetic lesion to molecular mechanism
Numerous neuropsychiatric disorders frequently exhibit overlapping genetic risk factors, implying the molecular basis for their comorbidity. Nevertheless, the pathogenesis of these disorders remains elusive, particularly regarding how genetic variations impair the physiological function of risk genes and contribute to disease phenotypes. Neurexin 1 protein, encoded by NRXN1 gene, belongs to the neurexin family of presynaptic adhesion molecules. And neurexin 1 is involved in synaptogenesis and the maintenance of synaptic action. Genetic variations of NRXN1 have been demonstrated to be associated with a spectrum of neuropsychiatric disorders. Herein, this review focuses on the most recent and relevant literature concerning the genetic and molecular mechanisms through which NRXN1 variants contribute to the pathogenesis of neuropsychiatric disorders, particularly schizophrenia and autism spectrum disorder. Among them, we propose the isoform-dependent excitation-inhibition imbalance hypothesis of NRXN1 in autism spectrum disorder. And this hypothesis may account for both the elevated and decreased excitation-inhibition ratios observed in diverse individuals with autism spectrum disorder. Moreover, both schizophrenia and autism spectrum disorder involve deletions and alternative splicing of NRXN1, offering molecular evidence for their comorbidity. Then, we analyzed and summarized the current research status of NRXN1 in other neuropsychiatric disorders, including attention-deficit hyperactivity disorder, insomnia, epilepsy, suicide, and depression. Additionally, available limited researches on NRXN1-targeted therapeutic strategies and associated pharmacological studies are also incorporated. Finally, we discussed existing challenges in NRXN1 research within the context of neuropsychiatric disorders and proposed potential avenues to overcome these obstacles.