Adaptive closed-loop control strategy inhibiting pathological basal ganglia oscillations

The presence of pathological basal ganglia oscillations in the beta (12-35 Hz) frequency band is associated with Parkinson's disease (PD). Suppressing the abnormal beta rhythm can effectively alleviate prominent PD movement disorders such as bradykinesia and rigidity. Brain stimulations, such as deep brain stimulation or transcranial stimulation, are effective therapeutic methods in managing the beta rhythm. However, electrostimulation using the current open-loop paradigm for stimulation is not optimal, especially when the controlled system experiences a substantial unknown disturbance. In this work we propose an adaptive radial basis function (ARBF) neural network strategy to achieve closed-loop brain stimulation based on real-time observed neural oscillation feedback. The underlying system is assumed to be an unknown nonlinear system, and the closed-loop strategy adaptively modulates the stimulation signal to cope with the abnormal neuronal discharge fluctuations, so as to eliminate the beta rhythm of the STN-GPe network. The proposed ARBF neural network closed-loop scheme is tested in a neural mass model composed of the subthalamic nucleus and external globus pallidus. It is shown that the performance of the ARBF controller is robust, including when internal synaptic connections within the basal ganglia network are enhanced to endogenously impact pathological conditions, and also when pathological oscillations are induced by exogenous cortical inputs. Simulation results demonstrate the effectiveness of the proposed closed-loop neuromodulation pattern based on an ARBF neural network. This work may help to develop DBS control systems with adaptive optimization and less network complexity.