Dynamics of Coupled Oscillator Systems - Abstract
Adaptive deep brain stimulation (aDBS) is a closed-loop method, where high-frequency (HF) DBS is turned on and off according to a feedback signal, whereas the conventional continuous HF DBS (cDBS) is delivered permanently. Using a computational model of subthalamic nucleus and external globus pallidus, we extend the concept of adaptive stimulation by adaptively controlling not only continuous HF DBS, but also demand-controlled stimulation. Apart from aDBS and cDBS, we consider continuous pulsatile delayed feedback stimulation specifically designed to induce desynchronization. Additionally, we combine adaptive on-off delivery with continuous delayed feedback modulation of HF pulse train by introducing adaptive pulsatile delayed feedback stimulation, where the stimulation is turned on and off using pre-defined amplitude thresholds. By varying the stimulation parameters we obtain optimal parameter ranges and reveal a simple relation between the thresholds of the local field potential and the extent of the stimulation-induced desynchronization as well as the integral stimulation time required. We find that adaptive stimulation can be more efficient in suppressing abnormal synchronization than continuous simulation, and delayed feedback still remains more efficient and also causes a stronger reduction of the beta burst length. Hence, adaptive on-off delivery may further improve the intrinsically demand-controlled delayed feedback stimulation.