Over the past 25 years deep brain stimulation (DBS) has emerged as an effective treatment for the symptoms of Parkinson's disease (PD). Despite its success, the mechanisms of DBS are not yet fully understood. Moreover, patients can experience side effects and poor control of symptoms associated with suboptimal programming of stimulus parameters. Current DBS systems operate in an 'open-loop' configuration with stimulus parameters (amplitude, pulse duration and frequency) empirically set and remaining fixed over time. Closed-loop DBS offers an alternative approach that has the potential to overcome current limitations and increase therapeutic efficacy, while reducing side-effects and increasing battery life, by automatically adjusting stimulation parameters as required. Although the potential benefits of closed-loop DBS are widely recognised, these systems have not yet been implemented clinically. Building on the results of computational modelling studies from the parent ERC project DBSmodel, we have implemented wireless closed-loop DBS in the 6-OHDA rat PD model. Efficacy and feasibility have been demonstrated for a number of different closed-loop DBS algorithms, with improvements in motor control comparable to conventional open loop DBS for lower overall power delivered. The experimental validation supported by the ERC PoC award establishes the PD rodent model as suitable for preclinical testing of closed-loop DBS and confirms the efficacy of a prototype solution providing a path towards translation to human studies.
