Implementation and preclinical testing of a closed-loop control system for deep brain stimulation
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 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 (pulse amplitude, 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. Under the parent ERC project DBSmodel, we have developed biophysically detailed computational models of the neural circuits in the brain during DBS and are using these to develop and test novel algorithms for closed-loop DBS.  Before these can be trialled in humans, however, feasibility must first to be demonstrated through implementation on a prototype device and pre-clinical testing in animal models. ERC proof of concept funding will enable us to do this by implementing and testing a novel closed-loop DBS system in an animal model of PD. The experimental validation will confirm the efficacy of a prototype solution suitable for translation to human studies.