Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach.

Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson's disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control.

Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson's disease and obsessive-compulsive disorder in a data-driven computational model.

Objective: Almost 30 years after the start of the modern era of deep brain stimulation (DBS), the subthalamic nucleus (STN) still constitutes a standard stimulation target for advanced Parkinson's disease (PD), but the use of STN-DBS is also now supported by level I clinical evidence for treatment-refractory obsessive-compulsive disorder (OCD). Disruption of neural synchronization in the STN has been suggested as one of the possible mechanisms of action of standard and alternative patterns of STN-DBS at a local level. Meanwhile, recent experimental and computational modeling evidence has signified the efficiency of alternative patterns of stimulation; however, no indications exist for treatment-refractory OCD.

Supporting clinical decision making during deep brain stimulation surgery by means of a stochastic dynamical model.

Objective: During deep brain stimulation (DBS) surgery for the treatment of advanced Parkinson's disease (PD), microelectrode recording (MER) in conjunction with functional stimulation techniques are commonly applied for accurate electrode implantation. However, the development of automatic methods for clinical decision making has to date been characterized by the absence of a robust single-biomarker approach. Moreover, it has only been restricted to the framework of MER without encompassing intraoperative macrostimulation.