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. 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.

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. Here, we comparatively simulate the desynchronizing effect of standard (regular at 130 Hz) versus temporally alternative (in terms of frequency, temporal variability and the existence of bursts or pauses) patterns of STN-DBS for PD and OCD, by means of a stochastic dynamical model and two microelectrode recording (MER) datasets. APPROACH: The stochastic model is fitted to subthalamic MERs acquired during eight surgical interventions for PD and eight surgical interventions for OCD. For each dynamical system simulated, we comparatively assess the invariant density (steady-state phase distribution) as a measure inversely related to the desynchronizing effect yielded by the applied patterns of stimulation. MAIN RESULTS: We demonstrate that high (130 Hz)-and low (80 Hz)-frequency irregular patterns of stimulation, and low-frequency periodic stimulation interrupted by bursts of pulses, yield in both pathologic conditions a significantly stronger desynchronizing effect compared with standard STN-DBS, and distinct alternative patterns of stimulation. In PD, values of the invariant density measure are proven to be optimal at the dorsolateral oscillatory region of the STN including sites with the optimal therapeutic window. SIGNIFICANCE: 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.

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. Here, we propose an integrated series of novel single-biomarker approaches applicable to the entire electrophysiological procedure by means of a stochastic dynamical model. APPROACH: The methods are applied to MER data pertinent to ten DBS procedures. Considering the presence of measurement noise, we initially employ a multivariate phase synchronization index for automatic delineation of the functional boundaries of the subthalamic nucleus (STN) and determination of the acceptable MER trajectories. By introducing the index into a nonlinear stochastic model, appropriately fitted to pre-selected MERs, we simulate the neuronal response to periodic stimuli (130 Hz), and examine the Lyapunov exponent as an indirect indicator of the clinical effectiveness yielded by stimulation at the corresponding sites. MAIN RESULTS: Compared with the gold-standard dataset of annotations made intraoperatively by clinical experts, the STN detection methodology demonstrates a false negative rate of 4.8% and a false positive rate of 0%, across all trajectories. Site eligibility for implantation of the DBS electrode, as implicitly determined through the Lyapunov exponent of the proposed stochastic model, displays a sensitivity of 71.43%. SIGNIFICANCE: 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.

A population level computational model of the basal ganglia that generates parkinsonian Local Field Potential activity.

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.