Accurate individualized prediction as a basis for rational implementation of early intervention
Better understanding of the determinants of mental health, ill-health, illness and recovery and the use of new technologies for individualized prediction can help inform indication setting and the timing of specialized intervention under consideration of developmental trajectories. We assign particular significance to transdiagnostic prediction in large samples over long periods of time spanning several years to decades, with a special focus on functional and social outcomes.
Research in this priority area includes
- analysis of the neurobiological pathways by which lifestyle (e.g., circadian patterns, sleep and diet) affects mental health;
- use of artificial intelligence methods (e.g. machine learning, deep learning) to investigate the complex dynamics of early psychopathology, and to dynamically track the changing patterns of symptoms, risk behaviours and/or physiological markers and use them to predict short- and long-term outcomes in the form of decision support tools (e.g. risk calculators, medical expert systems);
- identification of mediators, moderators and biomarkers for early illness detection and outcome prediction;
- multivariate analysis of the bio-psycho-social mechanisms mediating change following pharmacological or psychosocial interventions;
Innovative, low-threshold interventions
This priority area aims to develop and test approaches and treatment formats that can be used for low-threshold intervention outside the narrow mental health care setting. These include biotherapies (e.g., interventions targeting circadian rhythms or sleep, nutrition- and gut microbiome-based interventions), neuromodulation approaches (e.g., tDCS), and digital interventions (e.g., online psychotherapy, digital self-management tools, therapeutic videogames and virtual reality approaches). Evaluation models will take into account the patients’ individual context as well as the trans-syndromal nature of mental health issues in youth. Importantly, a critical shift of focus in needed, from the current focus of acute symptom management towards long-term prevention, reducing illness progression and promoting functional rehabilitation.
Sustainability – population outreach, social participation and quality assurance
The area aims to ensure sustainability of the developed approaches by integrating translational research findings in clinical and public health settings.
Therefore, planning of the Center will include measures aiming to
- improve health literacy, facilitate access to information and peer support, and promote participatory research;
- improve networking with professionals in the education and health care sectors and develop adequate training and support measures in order to improve quality of care outside of specialized settings and/or in sparsely populated regions;
- develop methods and strategies to identify effective interventions and promote their uptake into routine practice, including needs-based adjustment of roles and tasks across disciplines und involvement of patients and relatives in the development of ‘extramural’ support networks;
- develop a national standard for transdiagnostic screening, outcome assessment and evaluation of healthcare delivery services, and promote harmonization with international standards;
- establish central structures for data management, methodological support and technical assistance across sites, including instrument training, biometrics and statistics, and artificial intelligence methods;
- support young scientists in conducting translational research