Automated artifact injection into sensing-capable brain modulation devices for neural-behavioral synchronization and the influence of device state

Abstract

Background: Sensing-enabled deep brain stimulation (DBS) devices enable opportunities to investigate correlations between neural activity and behavior. Unfortunately, these devices do not allow straightforward synchronization of neural data with external events. Objective: To implement and assess an automated neural-behavioral synchronization system for a fully implanted DBS system. Methods and Results: We describe a synchronization strategy that relies on computer-driven artifact injection via event-triggered transcutaneous stimulation (TS). We validated the temporal accuracy of the approach in two patients receiving DBS for treatment of Parkinson's disease, observing consistently low jitter between task events and subsequent TS artifacts during DBS OFF (± 22.9ms) and ON (± 9.08ms) conditions. Notably, we observed that event-triggered TS was modulated by device state, where active circuitry during specific streaming modes influenced artifact injection in the data. Conclusion: We describe a rigorous approach for neural-behavioral alignment using fully implanted DBS systems and demonstrate how accuracy of alignment depends on device state.