Over the past two decades, the conceptualization of neurodevelopmental disorders (NDDs) has undergone a profound transformation, shifting from a primarily brain-centric framework toward a systems-level perspective that integrates peripheral physiological processes. Among these, the gut microbiota has emerged as a critical determinant of neurodevelopmental trajectories. Through its involvement in immune modulation, metabolic signaling, and neural communication, the microbiota–gut–brain axis (MGBA) exerts a pervasive influence on brain maturation and function. A growing body of evidence indicates that early-life disruptions of microbiota composition—commonly referred to as dysbiosis—are associated with an increased risk of NDDs, including autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and epilepsy. These disruptions are frequently driven by environmental exposures such as antibiotic use, cesarean delivery, dietary patterns, and psychosocial stress. Despite the expanding recognition of these associations, current therapeutic strategies aimed at restoring microbiota balance, including probiotics and fecal microbiota transplantation, have yielded inconsistent and often transient results. In this context, Vagus Nerve Stimulation (VNS), particularly in its non-invasive forms (nVNS), has emerged as a promising approach capable of modulating environmental exposures through the host-centered regulatory mechanisms of the MGBA. By influencing autonomic tone, activating the cholinergic anti-inflammatory pathway, and modulating neurotransmitter systems, nVNS may restore microbiota homeostasis while simultaneously improving neurodevelopmental outcomes. This article provides a comprehensive and integrative review of the mechanistic, preclinical, and clinical evidence supporting the role of nVNS as a microbiota-modulating intervention. We further discuss its potential as a safe, cost-effective and promising therapeutic strategy for neurodevelopmental disorders, with a particular emphasis on pediatric populations.
Sex-related molecular phenotypes in anxiety-depressive disorders: a machine learning analysis of routine blood biomarkers
BackgroundAnxiety disorders and depressive disorders are the most prevalent mental disorders worldwide. Their diagnosis has long relied on clinical symptom assessment, and objective blood−based biomarkers remain lacking. Sex is a critical risk factor for these disorders; however, sex−specific divergence in blood biochemical profiles has yet to be systematically characterized.MethodsThis retrospective study enrolled 778 patients diagnosed with anxiety−depressive state at China−Japan Friendship Hospital. Demographic data, complete blood count parameters, and blood biochemical parameters were collected. Following missing value processing and multiple imputation, Mann–Whitney U tests were applied to identify sex−differentially expressed biomarkers. A random forest classifier was constructed to evaluate the discriminative capacity of combined multi−marker panels, with model performance comprehensively assessed through receiver operating characteristic curve analysis, SHAP−based explainability analysis, and multi−classifier probability projection. Age−stratified analyses were performed with a threshold of 50 years to explore the potential modifying effect of age on sex differences.ResultsSeveral biomarkers exhibiting significant differences between males and females were identified (FDR < 0.05), among which creatinine, hemoglobin, hematocrit, red blood cell count, and uric acid demonstrated the largest effect sizes. The random forest model achieved an area under the receiver operating characteristic curve of 0.902 on the independent test set. Multi−classifier probability projection following hyperparameter tuning yielded a Silhouette coefficient of 0.464 in the two−dimensional space, with permutational multivariate analysis of variance confirming highly significant centroid differences between groups (p < 0.001). Age−stratified analysis using hemoglobin as an example revealed that levels in males were significantly higher than those in females across both age strata, with the magnitude of the sex difference attenuated in the ≥50−year group compared with the <50−year group.ConclusionsRobust sex−related signals are embedded in routine blood biochemical markers. Although complete separation is difficult to achieve under unsupervised dimensionality reduction, these signals can be efficiently integrated through ensemble learning algorithms. This study provides a molecular phenotypic basis related to sex in patients with anxiety−depressive state and underscores the importance of fully considering sex as a variable in clinical laboratory testing.
A comparison between seven scales of neuropsychological assessments for cognitive impairment screening in Chinese older population: a cross-sectional study in Chongqing, China
BackgroundThe Clinical Dementia Rating Scale (CDR), the Ascertain Dementia 8 (AD8), the Mini-Cog, the Verbal Fluency Test (VFT), the Community Screener for Dementia (CSI-D), the Rey Auditory Verbal Learning Test (RAVLT), and the Activities of Daily Living Scale (ADL) represent seven commonly employed methods for community cognitive impairment screening in China that have garnered limited attention. This study aimed to assess the discriminatory power of these seven tests when administered concurrently in a single screening to detect cognitive impairment in the absence of a gold standard.MethodsWe conducted a cross-sectional survey among 1,506 elderly people aged 60 and above in the community. The cognitive and social functions of the elderly population were evaluated by using the Seven scales. The characteristics related to demographics and health were collected through questionnaire surveys, and the correlations and consistencies of the Seven scales with cognitive impairment were analyzed respectively. Multifactor logistic regression model was used to analyze the risk factors of cognitive impairment in each scale.ResultsThe positive screening proportions for the seven scales—CDR, ADL, AD8, CSI-D, Mini-Cog, VFT, and RAVLT—were 61.1%, 33.7%, 38.0%, 37.1%, 53.7%, 39.3%, and 52.9%, respectively. Agreement on cognitive impairment risk between each pair of scales was fair-to-moderate (Kappa: 0.32–0.645, all p < 0.001). Screening results differed significantly across the seven scales by age, educational, marital status, epilepsy, cerebral infarction, brain atrophy, and depression (all P < 0.001). Notably, Screen-positive proportions for cognitive impairment rose significantly with increasing age (p < 0.001); the ≥ 80-year-old group showed the highest proportions across scales. Conversely, higher educational attainment was associated with lower risk of screening positive for cognitive impairment (p < 0.001).ConclusionsOur study identified a relatively high screening positivity proportion for cognitive impairment in Chongqing, Age, Sex, Education, Marital status, Brain atrophy, Anxiety, and Depression are risk factors for screening positivity. The identified screening positivity for cognitive impairment varied across different neuropsychological assessment methods, indicating that assessment choice should be tailored to population characteristics rather than applying a one-size-fits-all approach. Selecting the appropriate tool can improve sensitivity and reduce missed diagnoses.
Changes in depression, anxiety, and post-traumatic stress symptoms among children and adolescents exposed to adverse childhood experiences following participation in the PROACT intervention in Nairobi, Kenya
IntroductionGlobally, children and adolescents exposed to Adverse Childhood Experiences (ACEs) face an increased risk of developing mental health disorders. The prevalence of these mental disorders is further amplified by the lack of access to specialised mental health treatment especially in low resource settings. There is an urgent need for scalable mental health interventions that can effectively address the needs of these vulnerable populations. Non-specialist-delivered interventions, such as PROACT (Psychoeducation, Relaxation, Problem-solving, Activation, and Cognitive Coping Therapy), represent a promising scalable approach that could help bridge the existing mental health treatment gap in low-resource settings.ObjectivesThis study aimed to assess changes in depression, anxiety, and post-traumatic stress symptoms among children and adolescents exposed to adverse childhood experiences following participation in the PROACT intervention delivered by trained social workers in Nairobi, Kenya.MethodologyMixed-methods pre-post study design was employed. Twenty purposively selected sites across Nairobi County each contributed one social worker (N = 20), who received training to deliver the intervention. A total of 40 children participated and received 4–6 PROACT sessions. Quantitative data were analysed using STATA version 17. Paired t-tests were used to compare baseline and endline scores, while mixed-effects linear regression models with participant ID as a random effect were fitted to estimate changes in outcomes over time and account for repeated measures. Statistically significant improvements were observed across all mental health outcomes. Mean anxiety scores decreased from 6.2 at baseline to 2.7 at endline (mean difference: −3.5; 95% CI: −4.7 to −2.2; p < 0.001), while mean depression scores decreased from 6.4 to 2.9 (mean difference: −3.6; 95% CI: −4.9 to −2.3; p < 0.001). Mean PTSD scores decreased from 16.7 (95% CI: 12.8–20.5) at baseline to 7.3 (95% CI: 4.4–10.1) at endline (mean difference: −9.4; 95% CI: −14.6 to −9.3; p < 0.001). Mixed-effects linear regression analyses corroborated these findings, demonstrating significant reductions in PTSD (β = −9.44), anxiety (β = −3.48), and depression (β = −3.59) symptoms (all p < 0.001).ConclusionThe PROACT intervention was feasible and acceptable when delivered by social workers in Nairobi primary healthcare facilities and was associated with improvements in mental health outcomes among children and adolescents. These findings highlight the potential of task-sharing approaches to expand access to mental healthcare in low- and middle-income countries (LMICs) and warrant further evaluation in controlled studies.
<![CDATA[Why shared decision-making stalls in mental health—and how a quick “wind check” adapts recovery care to culture without losing autonomy.]]>
Child Mind Institute Launches Research Initiative To Inform Safer AI Systems for Youth
New project will develop tools to study youth mental health during AI chatbot use
New York, NY — Today, the Child Mind Institute announced a new research initiative, launched with support from the OpenAI Foundation, to build the research infrastructure needed to measure and better understand youth mental health while using AI chatbots and over time. The effort aims to identify valid markers that can inform the design and testing of safer AI systems.
Millions of children in the United States are struggling with mental health or learning challenges, yet there continues to be a dire shortage of qualified mental health professionals. That, along with stigma and misinformation about mental health, is pushing young people to explore using readily available, often free AI tools for support, including general-purpose chatbots, digital companions, or “therapy bots.”
Novel and emerging digital technologies are shaping mental health faster than the pace of science and the development of evidence-based care can keep up. This new initiative will help advance research studying the impact of these tools and better support youth mental health.
“As the leading nonprofit dedicated to improving youth mental health through science, education, and care, the Child Mind Institute is well positioned to help tackle the key issues affecting the well-being of young people, including technology and the proliferation of AI. It is our belief that with appropriate safeguards and evidence, digital tools may complement care from trained clinicians,” said Harold S. Koplewicz, MD, president and medical director at the Child Mind Institute. “We are excited about filling a gap that currently exists in the research of AI tools and to work toward creating a safer online experience for youth around the globe.”
Using clinical assessments, digital journals, de-identified AI chat histories, real-time behavioral measures, and existing youth mental health datasets, the Child Mind Institute will build an infrastructure to better understand interactions between youth users and AI conversational platforms. With the goal of strengthening our understanding of the mental state in AI chatbot interactions, this initial one-year project will enable the research team to identify and begin scaling the signals needed to meaningfully assess and monitor youth mental health alongside their use of AI tools.
The Child Mind Institute has long been committed to advancing technologies that make mental health research and care more precise, measurable, and connected to real-world settings. Grounded in science and focused on impact, the organization brings together scientific rigor, clinical expertise, and product development experience to build digital tools that accelerate research, improve care, and expand access without compromising safety, quality, or accountability.
“Many of our youth are turning to AI chatbots for important areas of their lives — and mental health is no exception. It is our responsibility to more holistically comprehend the impact of these tools on mental health, both in the long and short term,” said Michael P. Milham, MD, PhD, chief science officer at the Child Mind Institute. “Digital platforms provide an opportunity to rethink the way we conduct mental health research, but they also introduce new challenges and risks. It is critical to better understand the relationship between AI use and youth mental health, and to examine whether, and under what conditions, AI tools can strengthen evidence-based care, support clinician training, and expand access to high-quality mental health services.”
This research initiative is being independently developed and solely executed by the Child Mind Institute. To ensure the safety and security of all study participants, it will be conducted with strong privacy protections, informed consent, ethical oversight, and careful data governance. The project will be co-designed by its principal investigators, Gregory Kiar, PhD, Arno Klein, PhD, and Dr. Milham, who bring expertise in computational methods, digital measurement, clinical science, and youth mental health. Consistent with the Child Mind Institute’s open science philosophy, all data will be shared to help fuel discovery across the field.
About the Child Mind Institute
The Child Mind Institute is an independent nonprofit organization dedicated to transforming the lives of children and families struggling with mental health and learning disorders. Through cutting-edge research, evidence-based clinical care, and public education, the Child Mind Institute builds open science platforms and digital tools to accelerate discovery and improve youth mental health worldwide.
For press questions, contact cmiscience@ssmandl.com or mediaoffice@childmind.org.
The post Child Mind Institute Launches Research Initiative To Inform Safer AI Systems for Youth appeared first on Child Mind Institute.
A conceptual multi-agent architecture for mental health triage in post-conflict Arabic-speaking populations: a theoretical proposition and staged validation argument
Syria’s protracted conflict has produced a mental health crisis of extraordinary scale, with post-traumatic stress, depression, and anxiety estimated at several times global baselines, set against fewer than 0.37 psychiatrists per 100,000 people. Existing AI mental health tools have been developed and evaluated primarily for English-speaking, non-humanitarian populations, and their transfer to this setting is constrained by three simultaneous structural deficiencies—extreme clinical scarcity, Arabic natural-language-processing underperformance for dialect, and cultural misalignment with Syrian idioms of distress—which we term the Triple Gap. This article is a conceptual contribution in the Hypothesis and Theory genre, and its central claim is theoretical rather than technical: that AI-assisted mental-health triage at a safety floor adequate for crisis relevant care in this setting is conditional on the joint satisfaction of three constraints—linguistic adequacy for the local dialect, cultural validity for local idioms of distress and help-seeking, and bounded clinical responsibility through human oversight. These constraints interact, so that a system satisfying fewer than all three is expected to fail in clinically consequential rather than random ways. As one possible design response to this proposition—neither the only one nor a validated one—we describe a conceptual multi-agent architecture aligned with the WHO mhGAP task-shifting model: a four-stage pipeline (screening, risk stratification, routing, follow-up) constrained by a cross-cutting cultural-adaptation layer, augmented by candidate verification mechanisms with explicit abstention, and governed by human oversight in which clinical responsibility rests with a licensed clinician. The proposal is a hybrid clinical decision support hypothesis, not an autonomous system. Because no Syrian Arabic clinical corpus yet exists, the conversational components of the design cannot presently be evaluated; we therefore set out a staged sequencing argument for future work in which the construction of a Syrian Arabic Mental Health Evaluation Corpus (SAMHEC) is the first and rate-limiting condition. We present no prototype, no corpus, and no clinical, cultural, or safety evaluation, and we make no claim of clinical validity, safety, or readiness for deployment. The contribution is the integration of multi-agent triage with task-shifting and cultural adaptation into a single conditional argument whose adequacy can be established only through the staged empirical work we describe.
Evaluating the Impact of Digital Behavioral Interventions to Address Insomnia and Depression in Breast Cancer Survivors Using an Adaptive Design
Conditions: Insomnia; Depression
Interventions: Behavioral: Digital Insomnia Program; Behavioral: Digital Mental Health Program; Behavioral: Psychoeducation
Sponsors: University of Virginia; National Cancer Institute (NCI)
Not yet recruiting
Interventions: Behavioral: Digital Insomnia Program; Behavioral: Digital Mental Health Program; Behavioral: Psychoeducation
Sponsors: University of Virginia; National Cancer Institute (NCI)
Not yet recruiting
Group Treatment for Self-harming Adolescents and Their Parents: a Randomized Controlled Trial
Conditions: Self-Harm; Self-Harm, Deliberate; Self-harm Behavior; Non-Suicidal Self Injury; Non-suicidal Self-injury (NSSI)
Interventions: Behavioral: Emotion Regulation Group Therapy for Self-harming Adolescents, ERA; Behavioral: Functional habits for mental health, FUNK
Sponsors: Karolinska Institutet; Västra Götalandsregionen; Region Stockholm
Not yet recruiting
Interventions: Behavioral: Emotion Regulation Group Therapy for Self-harming Adolescents, ERA; Behavioral: Functional habits for mental health, FUNK
Sponsors: Karolinska Institutet; Västra Götalandsregionen; Region Stockholm
Not yet recruiting
Deprexis for Veteran Depression: Open-Label Pilot Trial Examining Feasibility, Acceptability, and Preliminary Efficacy
Background: Depression carries the highest burden of mental health–related disability in the United States. Approximately 13% of military veterans report elevated rates of depression. Despite the availability of evidence-based treatments for depression, nearly 50% of veterans in need of mental health care remain untreated. Internet-based interventions show promise in reducing this gap; however, there are currently no standard self-guided internet-based interventions for depressive symptoms in veterans. Deprexis is one such intervention that leverages cognitive behavioral therapy to target depressive symptoms. Objective: This pilot study evaluated the feasibility, acceptability, and preliminary effectiveness of Deprexis, a fully self-guided internet-based intervention for depression, in US military veterans with mild to severe depressive symptoms. Methods: This open-label pilot trial recruited 19 veterans with mild to severe depression (mean age 55.5, SD 8.2 y; baseline Quick Inventory of Depressive Symptomatology—Self-Report [QIDS-SR]: mean 16.2, SD 4.1) for an 8-week course of Deprexis, with self-report assessments at baseline, posttreatment (8 wk), and follow-up (16 wk). Primary outcomes included depressive symptoms (QIDS-SR), functional disability (World Health Organization Disability Assessment Schedule 2.0), and symptom-related disability (Sheehan Disability Scale). Feasibility was assessed through recruitment and retention rates, and acceptability was measured using validated questionnaires (Credibility and Expectancy Questionnaire and Client Satisfaction Questionnaire). Multilevel models examined change over time, with effect sizes calculated using pooled SDs from unconditional models. Results: Recruitment and retention targets were met, with 15 out of 19 (79%) participants meeting the adherence criteria (ie, ≥60 min of active program use). Of these, 14 participants completed posttreatment questionnaires and were included in the completer analyses. The program received a positive acceptability rating: of the 18 participants who completed follow-up assessments, 78% (n=14) rated services as good or excellent and 72% (n=13) were satisfied with the amount of help received. No safety concerns were reported. Among completers (n=14), QIDS-SR scores decreased from baseline to posttreatment (estimate −2.22, SE 1.44; =.14; =−0.54, 95% CI −1.07 to 0.13) and follow-up (estimate −2.85, SE 1.19; =.02; =−0.70, 95% CI −1.21 to −0.08) with moderate-to-large effect sizes. Effect sizes were similar in the total sample. Functioning (World Health Organization Disability Assessment Schedule 2.0) improved among completers at follow-up (estimate −8.09, SE 3.80; =.045; =−0.41, 95% CI −0.96 to −0.05). Disability (Sheehan Disability Scale) did not significantly improve from baseline to posttreatment or follow-up. Conclusions: This pilot trial demonstrates that Deprexis is feasible and acceptable for veterans with mild to severe depression, with preliminary evidence of effectiveness for depressive symptoms. The delayed emergence of functional improvements and sustained gains at follow-up support the potential of this scalable intervention. The results provide a strong foundation for the ongoing randomized controlled trial. Trial Registration: ClinicalTrials.gov NCT06217198; https://clinicaltrials.gov/study/NCT06217198 International Registered Report Identifier (IRRID): RR2-10.2196/59119
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