Frontiers in Psychiatry

This paper presents our system employed for the Social Media Mining for Health 2023 Shared Task 4: Binary classification of English Reddit posts self-reporting a social anxiety disorder diagnosis. We systematically investigate and contrast the efficacy of hybrid and ensemble models that harness specialized medical domain-adapted transformers in conjunction with BiLSTM neural networks. The evaluation results outline that our best performing model obtained 89.31% F1 on the validation set and 83.76% F1 on the test set.

Amidst the growing interest in developing task-autonomous AI for automated mental health care, this paper addresses the ethical and practical challenges associated with the issue and proposes a structured framework that delineates levels of autonomy, outlines ethical requirements, and defines beneficial default behaviors for AI agents in the context of mental health support. We also evaluate ten state-of-the-art language models using 16 mental health-related questions designed to reflect various mental health conditions, such as psychosis, mania, depression, suicidal thoughts, and homicidal tendencies. The question design and response evaluations were conducted by mental health clinicians (M.D.s). We find that existing language models are insufficient to match the standard provided by human professionals who can navigate nuances and appreciate context. This is due to a range of issues, including overly cautious or sycophantic responses and the absence of necessary safeguards. Alarmingly, we find that most of the tested models could cause harm if accessed in mental health emergencies, failing to protect users and potentially exacerbating existing symptoms. We explore solutions to enhance the safety of current models. Before the release of increasingly task-autonomous AI systems in mental health, it is crucial to ensure that these models can reliably detect and manage symptoms of common psychiatric disorders to prevent harm to users. This involves aligning with the ethical framework and default behaviors outlined in our study. We contend that model developers are responsible for refining their systems per these guidelines to safeguard against the risks posed by current AI technologies to user mental health and safety. Trigger warningContains and discusses examples of sensitive mental health topics, including suicide and self-harm.

BackgroundPatients of certain diseases are less likely to approach the healthcare system but remain active in social media. Young Social Anxiety Disorder (SAD) patients, in particular, are a hard-to-reach population due to disease symptomatology, unmet need and age-related barriers, which makes obtaining first-hand access to patient perspectives challenging. ObjectiveTo create a curated cohort of patients from social media that report their age in the range of 13 to 25 years old and confirm having a SAD diagnosis or having received therapy for SAD, and to assess the value of the content posted by these users for observational studies of SAD. MethodsWe collected 535k posts by 118k Reddit users from the r/SocialAnxiety subreddit. We then developed precise regular expressions to extract age, diagnosis and therapy mentions. We manually annotated the full set of expressions extracted and double-annotated 5% of the age mentions and 10% of the diagnosis and therapy mentions. Using similar methodology, we identified mentions of comorbidities and substance use. ResultsOur validated cohort includes 37,073 posts by 1,102 users that meet the inclusion criteria. The age, diagnosis, and therapy mention detection had a precision of 68%, 31%, and 44%, respectively, with an inter-annotator agreement of 0.96, 0.96, and 0.78. Sixty-one percent of the users in the cohort report having one or more comorbidities on top of their SAD diagnosis (Fleisss Kappa=0.79) and 13% report a concerning use of drugs or alcohol (Fleisss Kappa=0.87). We compared the characteristics of our social media cohort to the published literature on SAD. ConclusionsPatients with SAD post actively on Reddit and their perspectives can be captured and studied directly from these data. Extracting age, therapy, substance abuse and comorbidities (and potentially other patient data) can address realworld data source biases. Thus, social media is a valuable source to create cohorts of hard-to-reach patient populations that may not enter the healthcare system.

Suicide, a serious public health concern affecting millions of individuals worldwide, refers to the intentional act of ending ones own life. Mental health issues such as depression, frustration, and hopelessness can directly or indirectly influence the emergence of suicidal thoughts. Early identification of these thoughts is crucial for timely diagnosis. In recent years, advances in artificial intelligence (AI) and natural language processing (NLP) have paved the way for revolutionizing mental health support and education. In this proof-of-concept study, we have created MindWatch, a cutting-edge tool that harnesses the power of AI-driven language models to serve as a valuable computer-aided system for the mental health professions to achieve two important goals such as early symptom detection, and personalized psychoeducation. We utilized ALBERT and Bio-Clinical BERT language models and fine-tuned them with the Reddit dataset to build the classifiers. We evaluated the performance of bi-LSTM, ALBERT, Bio-Clinical BERT, OpenAI GPT3.5 (via prompt engineering), and an ensembled voting classifier to detect suicide ideation. For personalized psychoeducation, we used the state-of-the-art Llama 2 foundation model leveraging prompt engineering. The tool is developed in the Amazon Web Service environment. All models performed exceptionally well, with accuracy and precision/recall greater than 92%. ALBERT performed better (AUC=.98) compared to the zero-shot classification accuracies obtained from OpenAI GPT3.5 Turbo (ChatGPT) on hidden datasets (AUC=.91). Furthermore, we observed that the inconclusiveness rate of the Llama 2 model is low while tested for few examples. This study emphasizes how transformer models can help provide customized psychoeducation to individuals dealing with mental health issues. By tailoring content to address their unique mental health conditions, treatment choices, and self-help resources, this approach empowers individuals to actively engage in their recovery journey. Additionally, these models have the potential to advance the automated detection of depressive disorders.

Classification between first episode psychosis (FEP) patients and healthy controls is of particular interest to the study of schizophrenia. However, predicting psychosis with cognitive assessments alone is prone to human errors and often lacks biological evidence to back up the findings. In this work, we combined a multimodal dataset of structural MRI and cognitive data to disentangle the detection of first-episode psychosis with a machine learning approach. For this purpose, we proposed a robust detection pipeline that explores the variables in high-order feature space. We applied the pipeline to Human Connectome Project for Early Psychosis (HCP-EP) dataset with 108 participants in EP and 47 controls. The pipeline demonstrated strong performance with 74.67% balanced accuracy on this task. Further feature analysis shows that the model is capable of identifying verified causative biological factors for the occurrence of psychosis based on volumetric MRI measurements, which suggests the potential of data-driven approaches for the search for neuroimaging biomarkers in future studies.

Addressing mental health problems is a global public health priority. Healthcare trainees are the future professionals responsible for the health of the population, and they also face significant mental health challenges that need our support. Over the past decade, the prevalence of mental health problems among healthcare trainees has increased, highlighting the urgent need to care for those who will care for others. In response, as part of the National Program for Suicide Prevention in Mexico and to address the mental health problems in healthcare trainees in Mexico, we developed MINDS (Mental Health and Intervention for New Doctors and Suicide Prevention), a program designed to evaluate, prevent, and treat mental health problems in healthcare trainees, with a particular focus on suicide risk. MINDS targets students in undergraduate programs who are participating in medical internships and social service programs across all states of Mexico. Here, we aimed to present the MINDS program protocol. The program follows a longitudinal design, beginning with an initial assessment to identify trainees at risk and referring them to clinical treatment as needed. At this stage, participants will also provide a blood or saliva sample to create a DNA biorepository. A follow-up evaluation will occur six months later. MINDS assesses a wide range of mental health domains, including depression, anxiety, substance use disorders, eating behaviors, and suicide risk. By generating critical data and a practical framework for the mental health of medical trainees, MINDS will promote well-being and contribute to a healthier environment within the medical profession in Mexico.

Emotions are complex neuro-physiological states that influence behavior. While emotions have been instrumental to our survival, they are also closely associated with prevalent disorders such as depression and anxiety. The development of treatments for these disorders has relied on animal models, in particular, mice are often used in pre-clinical testing. To compare effects between treatment groups, researchers have increasingly used machine learning to help quantify behaviors associated with emotionality. Previous work has shown that computer vision can be used to detect facial expressions in mice. In this work, we create a novel dataset for depressive-like mouse facial expressions using varying LypoPolySaccharide (LPS) dosages and demonstrate that a machine learning model trained on this dataset was able to detect differences in magnitude via dosage amount.

ObjectiveDepression is a multifaceted disorder with neurobiological, behavioral, and environmental components. This review aims to explore how artificial intelligence (AI) and computational methods are advancing the understanding and treatment of depression, focusing on neurobiological mechanisms, early detection, and behavioral activation (BA) interventions. MethodsA comprehensive literature review was conducted searching PubMed, Scopus, ACM, and Web of Science databases. From 77654 articles identified, 48 studies were selected based on relevance and methodological rigor. These include meta-analyses, randomized controlled trials, or observational studies, focusing on the integration of AI and computational tools in depression research and treatment. ResultsAdvances in AI-driven neuroimaging and machine learning have enhanced the identification of neurobiological changes associated with depression, such as hippocampal atrophy, prefrontal cortex dysfunction, and HPA axis dysregulation. AI models have also facilitated early detection of subtle biomarkers linked to neuroinflammation and reduced BDNF levels. Furthermore, AI-powered digital platforms have optimized BA interventions, personalized treatment and improving access through virtual coaching and mobile applications. AI-enhanced interventions incorporating physical activity monitoring have shown neuroprotective effects, promoting neurogenesis, reducing inflammation, and increasing BDNF levels. ConclusionThe integration of AI and computational approaches into traditional depression therapies holds significant promise. AI-driven tools, when combined with BA interventions, provide scalable, personalized solutions, particularly for individuals with limited access to conventional treatments. The future of depression care can strongly profit from the convergence of AI, neurobiology, and behavioral science, which will enhance diagnostic accuracy, treatment effectiveness, and accessibility.

The rapid growth of tele-counseling and the use of lay counselors in high-volume, low-resource mental health services has created a need for scalable tools for early detection and triage. Effective personalization now requires stratifying individuals by dominant symptom profiles, such as appetite, agency, anxiety, and sleep disturbances. Depression symptoms vary widely, even among those with similar scores, reflecting distinct psychophysiological and cognitive-affective patterns. In tele-mental-health settings, where contextual cues are limited, multimodal behavioral signals from natural interactions can complement traditional assessments. Using synchronized audio, video, and text data from the EDAIC dataset (N=275), we propose a multimodal learning framework to classify five clinically validated outcomes: Depression, Appetite disturbance, Agency impairment, Anxiety, and Sleep problems. We developed a comprehensive multimodal machine-learning pipeline, incorporating automated dataset construction, modality-specific feature extraction (acoustic, facial action unit, linguistic), and supervised learning with cross-validation. Labels were derived from validated scoring rules to ensure clinical relevance. Sentiment analysis revealed lower sentiment scores in participants with high Depression, Anxiety, or Agency scores, but no significant differences in Appetite or Sleep severity. Model performance was assessed across three scenarios: text (transcripts), phone calls (audio + transcript), and video calls (audio + video + transcript). Temporal models (CNN+BiLSTM) achieved over 65% accuracy across modalities, while a fine-tuned temporal model for depression detection using video calls reached an accuracy of 81% with an f1-score of 0.79, demonstrating that our approach performs on par with state-of-the-art methods. XGBoost excelled in phone and video calls, while Ridge classifiers performed best for text-based inputs. SHAPley analysis identified key audio and video features for detecting Depression and other symptoms. A translational avatar-based interface validated system operability, demonstrating the potential for scalable, objective mental-health assessment in tele-counseling.

Childhood mental health disorders such as anxiety, depression, and ADHD are commonly-occurring and often go undetected into adolescence or adulthood. This can lead to detrimental impacts on long-term wellbeing and quality of life. Current parent-report assessments for pre-school aged children are often biased, and thus increase the need for objective mental health screening tools. Leveraging digital tools to identify the behavioral signature of childhood mental disorders may enable increased intervention at the time with the highest chance of long-term impact. We present data from 84 participants (4-8 years old, 50% diagnosed with anxiety, depression, and/or ADHD) collected during a battery of mood induction tasks using the ChAMP System. Unsupervised Kohonen Self-Organizing Maps (SOM) constructed from movement and audio features indicate that age did not tend to explain clusters as consistently as gender within task-specific and cross-task SOMs. Symptom prevalence and diagnostic status also showed some evidence of clustering. Case studies suggest that high impairment (>80th percentile symptom counts) and diagnostic subtypes (ADHD-Combined) may account for most behaviorally distinct children. Based on this same dataset, we also present results from supervised modeling for the binary classification of diagnoses. Our top performing models yield moderate but promising results (ROC AUC .6-.82, TPR .36-.71, Accuracy .62-.86) on par with our previous efforts for isolated behavioral tasks. Enhancing features, tuning model parameters, and incorporating additional wearable sensor data will continue to enable the rapid progression towards the discovery of digital phenotypes of childhood mental health. Clinical RelevanceThis work advances the use of wearables for detecting childhood mental health disorders.

Understanding how language reflects the experiences of individuals with attention-deficit/hyperactivity disorder (ADHD) is important for developing targeted support strategies. This research investigates the linguistic patterns exhibited in discussions within women-centric and general ADHD population on Reddit, exploring how gender influences language use. By analyzing language, the study uncovers unique linguistic patterns and illuminates how women with ADHD express themselves, share experiences, and seek support compared to the general ADHD population. By leveraging deep learning-based embedding, clustering and named entity recognition, the study conducts a comprehensive analysis. Results reveal that women with ADHD emphasize themes like "ADHD Awareness and Diagnosis" and "Emotional Well-being". Conversely, the general population highlights "Medication and Treatment", and "Work and Academic Challenges". Statistical tests confirm significant differences in both linguistics and emotional expression between the two groups, emphasizing the importance of gender considerations in understanding ADHD language nuances and tailoring interventions accordingly.

IntroductionPreventive transdiagnostic interventions for depression and anxiety symptoms in children and adolescents are important in reducing the development of disorders later in life, and emotion regulation (ER) is one potentially relevant factor to consider. Impairments in executive functions play a critical role in emotion development and regulation in these ages. Immersive Virtual Reality (IVR) technology has exhibited potential to augment traditional cognitive training interventions. In this project, we propose to use Enhance IVR, a gamified cognitive training that can be tailored to specific ER strategies that individuals may face, such as difficulty with attention control, inhibition, or cognitive flexibility. Methods and analysisThis is a longitudinal, parallel, single-blind, randomized, controlled pilot trial with an estimated sample of 160 participants. The first group (experimental group) will receive the gamified IVR program (Enhance IVR), while the second group (active control group) will receive a comparable IVR relaxation experience. Both Enhance IVR and the control IVR interventions will last five weeks, two times a week, 30 minutes (10 sessions per participant). Participants will undergo a baseline assessment that includes several measures related to mental health, ER, executive functioning, and cognitive performance, and another post intervention assessment with the same measures. Finally, variables related to system usability and cybersickness with the IVR tool will be evaluated for both groups. Ethics and disseminationThis study was approved by the Ethical Committee of the International University of Catalonia (PSI-2023-04). The findings will be disseminated through peer-reviewed journals, reports, conferences, and other scientific events. Strengths and limitations of this studyO_LIThis is a controlled, randomized, and single-blind design, which provides rigorous evidence for a virtual reality (VR) intervention effect. C_LIO_LIThe intervention has a strong ecological validity as it is done in the participants natural context (i.e. school) and the assessment involves students, family, and school. C_LIO_LIThe gamified VR design is innovative and engaging, and there is an equivalent active-based VR control condition. C_LIO_LIOne limitation is there is no follow-up assessment and no double-blind experimental design due to the nature of the study. C_LIO_LIA second limitation is the potential drop-out rates due the preventive nature of the interventions. C_LI

In 2019, the National Basketball Association (NBA) expanded its mental health rules to include mandating that each team have at least one mental health professional on their full-time staff and to retain a licensed psychiatrist to assist when needed. In this work, we investigate the NBA players discussion of mental health using historical data from players public Twitter accounts. All current and former NBA players with Twitter accounts were identified, and each of their last 800 tweets were scraped, yielding 920,000 tweets. A list of search terms derived from the DSM5 diagnoses was then created and used to search all of the nearly one million tweets. In this work, we present the most common search terms used to identify tweets about mental health, present the change in month-by-month tweets about mental health, and identify the impact of players discussing their own mental health struggles on their box score statistics before and after their first tweet discussing their own mental health struggles.

Mental health is in a state of crisis with demand for mental health services significantly surpassing available care. As such, building scalable and objective measurement tools for mental health evaluation is of primary concern. Given the usage of spoken language in diagnostics and treatment, it stands out as potential methodology. Here a model is built for mental health status evaluation using natural language processing. Specifically, a RoBERTa-based model is fine-tuned on text from psychotherapy sessions to predict mental health status with prediction accuracy on par with clinical evaluations at 74%.

French Child and Adolescent Psychiatry (CAP) faces significant issues, primarily due to an overwhelming increase in demand and insufficient capacity. In response, the French Society for Child and Adolescent Psychiatry and Allied Professions (SFPEADA) initiated an action research project in June 2023 aimed at reimagining the future of CAP in France for the second quarter of the 21st century. Employing a holistic qualitative methodology that merges bottom-up and top-down approaches, the project progressed through four phases: interviews with informed individuals, consultations with trade unions or associations, synthesis of findings using thematic analysis and AI technologies, and public dissemination via a symposium at the ministry of health. The project identified 5 main themes: "CAP and Society", "Knowledge Integration", "Healthcare Delivery", "Caregivers", "System Organization". This initiative underscores the importance of a collaborative, multidisciplinary approach to address the important needs of child and adolescent mental health in France, advocating for significant systemic changes to enhance CAPs efficacy and accessibility.

The safety of large language models (LLMs) as mental health chatbots is not fully established. This study evaluated the risk escalation responses of publicly available ChatGPT conversational agents when presented with prompts of increasing depression severity and suicidality. The average referral point to a human was at the midpoint of escalating prompts. However, most agents only definitively recommended professional help at the highest level of risk. Few agents included crisis resources like suicide hotlines. The results suggest current LLMs may fail to escalate mental health risk scenarios appropriately. More rigorous testing and oversight are needed before deployment in mental healthcare settings.

Integrating multimodal biological, cognitive, and clinical data is crucial for uncovering distinct psychiatric disease subgroups, enabling precision diagnosis, personalized treatment, and more targeted drug development. However, a significant gap remains between traditional clustering approaches and the growing need for advanced methods that can integrate and jointly analyze multimodal biological and clinical data to achieve more biologically meaningful subtyping. This study introduces the Mixed INtegrative Data Subtyping (MINDS) method, a Bayesian hierarchical joint model designed to identify subtypes of Attention-Deficit/Hyperactivity Disorder (ADHD) and Obsessive-Compulsive Disorder (OCD) in adolescents using multimodal data from the Adolescent Brain Cognitive Development (ABCD) Study. MINDS integrates clinical assessments, neuro-cognitive measures, and neuroimaging biomarkers while simultaneously performing clustering and dimension reduction. By leveraging Polya-Gamma augmentation, we propose an efficient Gibbs sampler to improve computational efficiency and provide subtype identification. Simulation studies demonstrate the superior robustness of MINDS compared to traditional clustering techniques. Application to the ABCD study reveals more reliable and clinically meaningful subtypes of ADHD and OCD with distinct cognitive and behavioral profiles. These findings show the potential of multimodal model-based clustering for advancing precision psychiatry in mental health.

In vivo exposure is a highly effective but rarely implemented treatment for agoraphobia. Most of the patients receive medication or cognitive therapy without exposure because of a high expenditure of money and time for in vivo exposure. Exposure in virtual reality (VR) is easier to implement but the effectiveness of stimulating fear compared to in vivo exposure is still questionable. Therefore, in this study, the effects of in vivo and VR exposure on subjective symptom burden and heart rate variability (HRV) were assessed. 30 healthy individuals with fears in narrow rooms went through in vivo and VR exposure in a randomized order while HRV parameters (RMSSD, HF) and subjective symptom burden was assessed. Linear mixed models were calculated. The effect of condition (VR vs. in vivo), scenario (several narrow rooms) and slot (first 30 seconds, peak, last 30 seconds) on RMSSD and HF was assessed. A random effect for participants (random-intercept term) to allow the intercept to vary across participants was included. Regarding RMSSD and HF, participants showed significantly higher levels during in vivo exposure compared to exposure in VR (RMSSD: p = .005; HF: p < .001), reflecting a stronger activation of the parasympathetic nervous system during in vivo exposure or presumably higher stress levels during VR exposure. This study highlights the necessity of assessing subjective and objective parameters allowing the evaluation of the effectiveness of fear stimulation by exposure approaches. The effectiveness of VR exposure for agoraphobic patients needs to be assessed in future studies.

Artificial intelligence (AI) is increasingly leveraged in mental healthcare for early detection, monitoring, and personalized intervention. However, most existing AI applications are based on categorical diagnostic systems like DSM-5 or ICD-11, which often lead to comorbidity issues, ambiguous diagnoses, and insufficient personalization. These tools typically target specific disorders (e.g., depression or anxiety), neglecting the broader, interconnected nature of psychopathological symptoms. Addressing these limitations, recent innovations in psychopathology emphasize transdiagnostic and network-based approaches, such as the Hierarchical Taxonomy of Psychopathology (HiTOP), which conceptualize mental disorders as dimensional and inter-connected constructs. This study proposes an AI-powered tool that integrates data-driven principles from both the HiTOP and symptom network models to generate individualized risk profiles for internalizing mental disorders (e.g., depression, anxiety, bipolar disorders). Our solution aims to assess individuals current psychopathological traits and symptom components, providing a comprehensive, nuanced profile supporting clinical diagnoses and monitoring. The study unfolds in three phases: (1) model ideation; (2) model implementation in a large-scale Vietnamese sample; and (3) deployment in clinical and psychological practice settings in Vietnam. Central to our method is the development of a Risk-aware Taxonomy-enhanced Symptom Encoder (RiTaSE), which encodes symptom data and their severities into rich representations processed via a Transformer-based model. The model is trained using high-quality, validated datasets mapped to the HiTOP framework. This project is among the first to employ AI for personalized psychopathological profiling in Vietnam,, as well as other low- and middle-income countries. Expected outcomes include an advanced diagnostic-support tool for clinical use, improved crosscultural insights into symptom comorbidity, and practical utility in mental health monitoring and intervention evaluation. Future extensions aim to broaden the scope across all HiTOP dimensions and predict transitions to clinical states through longitudinal and multi-modal data integration.

Substance use disorder (SUD) recovery typically requires transformative change and prioritizing long-term healthy goals. Unfortunately, successful recovery is threatened by relapse rates that often exceed 50% in the first year. We previously reported on an experiential virtual reality (VR) SUD recovery intervention using personalized future self-avatars that produced emotional engagement and positive behavioral change, ie, stronger connection with the future self and future rewards and reduced craving. Here, we used fMRI to identify brain engagement to a future self experience with divergent futures. Twenty adults (14 male, 33 years old) in early SUD recovery (<1 year) interacted with age-progressed versions of themselves in two different VR future realities: an SUD Future Self and a Recovery Future Self. Vivid lifelike visual and audio animation was augmented with a personalized narrative concerning future drug use and recovery. MRI immediately followed. Participants viewed videos of their future selves in the virtual environment and were directed to contemplate what they were seeing. Viewing and contemplating the future selves elicited activation in midline default mode regions (posterior cingulate and ventromedial prefrontal cortices), visual regions including the occipital and fusiform face areas, and left middle frontal gyrus. The Recovery Future Self produced significant left occipital face area activation compared with the SUD Future Self. Midline default mode activation correlated with VR-induced increases in delayed reward preference, and also with greater trait perseverance. Using digital selves as therapeutic agents reveals an entirely novel set of possible interventions and opens exciting new frontiers in behavior change methodology. Future studies targeting decision-making and future behavior could be informed by evaluating increased midline default mode engagement, with uniquely self-focused mechanisms signaled by executive network and face area coactivation. New hope for treatment-resistant mental health conditions is offered by the nearly limitless range of therapeutic experiences enabled by immersive digital therapeutics. Plain Language SummaryHigh relapse rates in early recovery remains a serious challenge. To promote better outcomes, our team recently developed a virtual reality experience where people interacted with future versions of themselves. We used magnetic resonance imaging (MRI) to understand how the brain activated to this experience, and what brain responses were linked to positive outcomes. We worked with 20 adults in early recovery. Each person used virtual reality to interact with two different future selves: one who had returned to substance use, and one who had stayed in recovery. These digital future selves looked and sounded like the participants and were paired with a personalized story about future drug use and recovery. Right after the virtual reality session, participants brains were scanned while they watched videos of these future selves and were asked to think about what they were seeing. When people viewed and reflected on their future selves, brain areas involved in self-reflection and imagining the future became more active, along with regions that process faces. The future selves triggered brain activation in "self-focused" brain networks and in face-processing regions. Activity in key "self-focused" brain regions was linked to choosing larger, delayed rewards over smaller, immediate ones, and to lower impulsivity. These findings suggest that lifelike digital versions of peoples future selves engage brain systems that support thinking ahead, persistence, and valuing long-term outcomes. This creates a promising new avenue for immersive digital therapeutic experiences to encourage lasting behavior change in early recovery from substance use disorder.