Key Projects, Innovations, and Breakthroughs
Mathematical Models of Mental Disorders
Our groundbreaking approach bridges mathematics and psychiatry, viewing mental illness through dynamic systems analysis. By conceptualizing mood as a dynamic property of biological systems, we're developing powerful new tools to understand and predict individual patterns of mood regulation - moving beyond traditional static models to capture the true complexity of mental disorders.
Advanced Nonlinear Analysis of Mood Disorders
We've developed sophisticated models using Markov Brains and nonlinear techniques that revolutionize how we understand mood fluctuations. Our research has uncovered that these variations follow deterministic patterns, leading to crucial insights: prediction windows will be inevitably short.
The Stability Paradox
We've discovered something counterintuitive: extremely stable mood might actually signal illness rather than health. Similar to groundbreaking findings in cardiology, where reduced heart rate variability predicts worse outcomes, our research suggests that natural mood variability plays a crucial role in mood regulation.
Wearable Technology Integration:
going beyond step count
Our innovative approach combines physiological data from wearable devices with clinical observations, measuring crucial variables (e.g., sleep and activity parameters, respiratory rate, heart rate variability, and posture) at different points during the disease. This integration allows us to move beyond simple correlations to understand the interconnected nature of our body's regulatory systems, enabling more effective and personalized interventions.
Personalized Predictions
While population-level predictions in psychiatry have advanced significantly, individual predictions remain challenging. Our program tackles this head-on by focusing on individual variability through dynamic analysis. We're developing tools that acknowledge each person's unique patterns, moving away from one-size-fits-all approaches to truly personalized care.
Key Research Initiatives
Episode detection and forecasting in mood disorders using mathematical modeling
The role of chaotic processes in the generation of mood fluctuations: We have shown that mathematical models, not only of groups, but also of individuals, can be used to forecast short-term changes based on current behavior. We have also shown that the underlying architecture of mood variability is in keeping with that of a chaotic system, suggesting that the window for episode prediction in BD will be inevitably short.
Using wearable technology for electronic (e-)monitoring in mood disorders
Our high recruitment rates, in combination with longitudinal follow-up and crisp clinical monitoring have surpassed expectations. Attrition rates are amongst the lowest in the world and adherence rates are close to 80%.
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