Over the past two decades, deep brain stimulation (DBS) has become established as an effective treatment for movement disorders including Parkinson’s disease, essential tremor, and dystonia. By electrically stimulating neurons deep within the brain, DBS aims to disrupt pathological, or abnormal, activity of neural circuits that promote pathological firing and are associated with the development of patient symptoms. DBS consists of high frequency electrical stimulation within affected brain regions applied via small surgically implanted electrodes. The stimulator parameters including electrical pulse strength, frequency, and duration are externally tuned within the clinic. Device re-tuning is done on a trial and error basis until an optimal parameter setting is found for each individual patient. Despite its high success in controlling movement symptoms, the exact way in which DBS works is not well understood. Furthermore, at the moment there is a strong clinical need to improve DBS methods to provide better control of symptoms for a wider range of patients, limit side-effects and extend stimulator battery life.
The project DBSmodel aims to address this need by developing an alternative ‘closed-loop’ approach for DBS that would automatically adjust stimulation parameters as needed to deliver the optimal electrical stimulation to control a patient’s symptoms at each instant in time. This type of approach offers the potential to alter the stimulation parameters, such as the strength of stimulation current, to optimise clinical benefit, minimise side effects, and reduce power consumption.
To do this, we have developed a new multiscale computational model of the neuromuscular system. The model enables sensing and stimulation of neurons within the brain to be simulated ‘in silico’, incorporating details of the individual neurons lying in the vicinity of the DBS electrode through to the muscles that control movement. The model encompasses the electric field around the electrodes, the effect on individual neurons and neural networks, and the generation of muscle force.
In parallel, we are conducting experiments to measure muscle activity in individuals with Parkinson’s disease and in healthy volunteers. These experiments will help us to better understand the changes occurring within the nervous system in Parkinson’s disease and the way in which DBS helps to overcome them. Information extracted from these experiments is used to validate the computational model, and also to help identify new biomarkers of neural activity that could be used to enable continuous monitoring of patient symptoms. Using the model developed, in combination with insights gained through the experimental studies, we are designing novel control strategies for closed-loop DBS. The identification of suitable closed-loop approaches for adaptive deep brain stimulation has the potential to advance the next generation of neuromodulation devices and provide more effective stimulation for patients, enabling greater control of symptoms and side-effects and improving patient outcomes.
