Non-conventional deep brain stimulation in a network model of movement disorders.

Abstract

Conventional deep brain stimulation (DBS) for movement disorders is a well-established clinical treatment. Over the last few decades, over 200,000 people have been treated by DBS worldwide for several neurological conditions, including Parkinson's disease and Essential Tremor. DBS involves implanting electrodes into disorder-specific targets in the brain and applying an electric current. Although the hardware has developed in recent years, the clinically used stimulation pattern has remained as a regular frequency square pulse. Recent studies have suggested that phase-locking, coordinated reset or irregular patterns may be as or more effective at desynchronising the pathological neural activity. Such studies have shown efficacy using detailed neuron models or highly simplified networks and considered one frequency band. We previously described a population level model which generates oscillatory activity in both the beta band (20 Hz) and the tremor band (4 Hz). Here we use this model to look at the impact of applying regular, irregular and phase dependent bursts of stimulation, and show how this influences both tremor- and beta-band activity. We found that bursts are as or more effective at suppressing the pathological oscillations compared to continuous DBS. Importantly however, at higher amplitudes we found that the stimulus drove the network activity, as seen previously. Strikingly, this suppression was most apparent for the tremor band oscillations, with beta band pathological activity being more resistant to the burst stimulation compared to continuous, conventional DBS. Furthermore, our simulations showed that phase-locked bursts of stimulation did not convey much improvement on regular bursts of oscillation. Using a genetic algorithm optimisation approach to find the best stimulation parameters for regular, irregular and phase-locked bursts, we confirmed that tremor band oscillations could be more readily suppressed. Our results allow exploration of stimulation mechanisms at the network level to formulate testable predictions regarding parameter settings in DBS.