In diesem Semester wird die im WiSe 2022/23 erfolgreich gestartete Vortragsreihe Medical Information Sciences fortgesetzt. Renommierte Wissenschaftlerinnen und Wissenschaftler unterschiedlicher Fachdisziplinen und Forschungsstandorte geben jeden Donnerstag ab 16:00 Uhr Einblicke in aktuelle Fragestellungen und Anwendungsgebiete des breiten Forschungsfeldes Medical Information Sciences.

Dystonia comprises complex motor network disorders characterized by involuntary abnormal postures and aberrant movement patterns that, to date, can only be quantified objectively to a limited extent. I here present a translational approach that links preclinical dystonia rodent models and patients with dystonia through shared kinematic signatures. The starting point is the DYT-TOR1A rat model, in which movement-dependent dystonic patterns are induced by repeated overuse of the forepaw and by peripheral nerve injury using a nerve crush paradigm. These movements are quantified using AI-based computer vision and time-resolved motion analysis to define characteristic kinematic profiles of dystonic movements in the animal model.

In a second step, I investigated to what extent these kinematic features can also be identified in humans. To this end, patients with cervical and other forms of dystonia were analyzed using comparable computer-vision tools applied to standardized video recordings. This enabled a data-driven characterization of dystonia subtypes, an objective assessment of treatment effects, for example under botulinum toxin therapy or deep brain stimulation, and a systematic search for structural similarities in movement kinematics between animals and humans. Overall, i here illustrate how kinematic signatures derived from overuse and nerve injury models can be translated into an AI-based translational framework that may provide new biomarkers for subtype classification, treatment monitoring, and, in the longer term, disease-modifying interventions in dystonia.