Linking Profiles of Pathway Activation with Clinical Motor Improvements – a Retrospective Computational Study

Deep brain stimulation (DBS) is an established therapy for patients with Parkinson9s 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. We 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. We apply 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 Parkinson9s Disease Rating Scale). Based on differences in outcome and activation rates for two DBS protocols in a training cohort, we compute a theoretical 100% improvement profile and enhance it by analyzing the importance of profile matching for individual pathways. Finally, we validate the performance of our profile-based predictive model in a test cohort. As a result, we demonstrate the clinical utility of pathway activation modeling in the context of motor symptom alleviation in Parkinson9s patients treated with DBS.