Neuroinformatics and Intelligent Systems for the Study and Support of Youth Mental Health

This Research Topic aims to bring together researchers and practitioners working at the intersection of neuroinformatics, artificial intelligence, computational neuroscience, and mental health sciences to develop and evaluate computational models and analytical tools that support the understanding, monitoring, and support of mental wellbeing in young populations.

This collection centers around the development and application of validated, reproducible computational methods that enable early detection, prevention, and postvention of mental health challenges, including stress, anxiety, and suicidal behavior. In line with Frontiers in Neuroinformatics, we particularly welcome contributions that advance data acquisition, integration, analysis, visualization, dissemination, and sharing of neuroscience- and clinically relevant youth mental health data (e.g., neuroimaging/EEG/physiology, cognitive task data, digital phenotyping, and multimodal behavioral and linguistic data), including FAIR resources, benchmarks, and open-source pipelines. Submissions to the Frontiers in Neuroinformatics track are expected to include a neuroinformatics or neuroscience data component. Submissions to the Frontiers in Digital Health track may focus more broadly on digital tools and AI-based systems for youth mental health, provided they include a clear digital health application and evaluation.

Special attention will be given to approaches leveraging world models, embodied and conversational avatars, and affective computing to simulate, interpret, and interact with human emotional states, where these systems are grounded in interpretable computational models and evaluated with appropriate validation on relevant datasets.

Topics of interest include, but are not limited to:

- Computational and neurocomputational models of emotion, stress, and resilience
- World models and generative architectures for mental state representation AI-driven detection and prediction of stress, burnout, and suicidal risk (including robustness and external validation where applicable)
- Avatars and virtual agents for prevention, postvention, and mental health support (with clearly described computational methods, evaluation, and/or reusable tools)
- Multimodal sensing (physiological, behavioral, linguistic and neuroscience signals where available) for mental health monitoring
- Data/metadata standards, ontologies, and methods for integrating clinical and neuroscience data across cohorts and sites
- Open, reproducible tools and pipelines for analysis/visualization, and benchmark datasets or evaluation frameworks
- Ethical, social, and psychological considerations in AI-based mental health interventions (including privacy-preserving analytics for sensitive youth data, where relevant)
- Computational psychiatry and precision mental health approaches for youth

The Research Topic aims to foster interdisciplinary dialogue between computer scientists, neuroscientists, clinicians, and psychologists to build a shared roadmap toward safe, ethical, and effective neuroinformatics-enabled AI applications in youth mental health, and encourages authors to share data, code, and analysis workflows in appropriate repositories to support reproducibility.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Classification
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: youth mental health, neuroinformatics, digital phenotyping, affective computing, multimodal data integration, AI-driven mental health, computational psychiatry

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.