Deep brain stimulation (DBS) is an established therapy for patients with Parkinson's disease.
In silico computer models for DBS allow to pre-select a set of potentially optimal stimulation parameters. If efficacious, they could further carry insight into the mechanism of action of DBS and foster the development of more efficient stimulation approaches. In recent years, the focus has shifted towards DBS-induced firing in myelinated axons, deemed particularly relevant for the external modulation of neural activity.
The scientists in this new medRxiv publication, use the concept of pathway activation modeling, which incorporates advanced volume conductor models and anatomically authentic fiber trajectories to estimate DBS-induced action potential initiation in anatomically plausible pathways that traverse in close proximity to targeted nuclei.
Then the scientists applied the method on a retrospective dataset with the aim of providing a model-based prediction of clinical improvement following DBS (as measured by the motor part of the Unified Parkinsons Disease Rating Scale).
Based on differences in outcome and activation rates for two DBS protocols in a training cohort, the authors computed a theoretical 100% improvement profile and enhanced it by analyzing the importance of profile matching for individual pathways.
Finally, the authors validated the performance of authors' profile-based predictive model in a test cohort.
As a result, the authors demonstrated the clinical utility of pathway activation modeling in the context of motor symptom alleviation in Parkinsons patients treated with DBS.