The suggested comprehensive method exhibits remarkable performance in automatically determining both the acceptable MER trajectories and the optimal stimulation sites, thereby having the potential to accelerate precise target finalization during DBS surgery for PD.

Recordings from the basal ganglia's subthalamic nucleus are acquired via microelectrodes immediately prior to the application of Deep Brain Stimulation (DBS) treatment for Parkinson's Disease (PD) to assist in the selection of the final point for the implantation of the DBS electrode. The acquired recordings reveal a persistent characteristic beta band peak in the power spectral density function of the Local Field Potential (LFP) signals. This peak is considered to lie at the core of the causality-effect relationships of the parkinsonian pathophysiology. Based on LFPs acquired from human subjects during DBS for PD, we constructed a computational model of the basal ganglia on the population level that generates LFPs to identify the critical pathophysiological alterations that lead to the expression of the beta band peak. To this end, we used experimental data reporting that the strengths of the synaptic connections are modified under dopamine depletion. The hypothesis that the altered dopaminergic modulation may affect both the amplitude and the time course of the postsynaptic potentials is validated by the model. The results suggest a pivotal role of both of these parameters to the pathophysiology of PD.

In addition to providing novel insights into the efficiency of low-frequency nonregular patterns of STN-DBS for advanced PD and treatment-refractory OCD, this work points to a possible correlation of a model-based outcome measure with clinical effectiveness of stimulation and may have significant implications for an energy- and therapeutically-efficient configuration of a closed-loop neuromodulation system.

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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. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.