Self-Tuning Deep Brain Stimulation Controller for Suppression of Beta Oscillations: Analytical Derivation and Numerical Validation

Multi-timescale neuromodulation strategy for closed-loop deep brain stimulation in Parkinson's disease

Objective.Beta triggered closed-loop deep brain stimulation (DBS) shows great potential for improving the efficacy while reducing side effect for Parkinson's disease. However, there remain great challenges due to the dynamics and stochasticity of neural activities. In this study, we aimed to tune the amplitude of beta oscillations with different time scales taking into account influence of inherent variations in the basal ganglia-thalamus-cortical circuit.Approach. A dynamic basal ganglia-thalamus-cortical mean-field model was established to emulate the medication rhythm. Then, a dynamic target model was designed to embody the multi-timescale dynamic of beta power with milliseconds, seconds and minutes. Moreover, we proposed a closed-loop DBS strategy based on a proportional-integral-differential (PID) controller with the dynamic control target. In addition, the bounds of stimulation amplitude increments and different parameters of the dynamic target were considered to meet the clinical constraints. The performance of the proposed closed-loop strategy, including beta power modulation accuracy, mean stimulation amplitude, and stimulation variation were calculated to determine the PID parameters and evaluate neuromodulation performance in the computational dynamic mean-field model.Main results. The Results show that the dynamic basal ganglia-thalamus-cortical mean-field model simulated the medication rhythm with the fasted and the slowest rate. The dynamic control target reflected the temporal variation in beta power from milliseconds to minutes. With the proposed closed-loop strategy, the beta power tracked the dynamic target with a smoother stimulation sequence compared with closed-loop DBS with the constant target. Furthermore, the beta power could be modulated to track the control target under different long-term targets, modulation strengths, and bounds of the stimulation increment.Significance. This work provides a new method of closed-loop DBS for multi-timescale beta power modulation with clinical constraints.

Nonlinear effects at the electrode-tissue interface of deep brain stimulation electrodes

Objective.The electrode-tissue interface provides the critical path for charge transfer in neurostimulation therapies and exhibits well-established nonlinear properties at high applied currents or voltages. These nonlinear properties may influence the efficacy and safety of applied stimulation but are typically neglected in computational models. In this study, nonlinear behavior of the electrode-tissue interface impedance was incorporated in a computational model of deep brain stimulation (DBS) to simulate the impact on neural activation and safety considerations.Approach.Nonlinear electrode-tissue interface properties were incorporated in a finite element model of DBS electrodesin vitroandin vivo,in the rat subthalamic nucleus, using an iterative approach. The transition point from linear to nonlinear behavior was determined for voltage and current-controlled stimulation. Predicted levels of neural activation during DBS were examined and the region of linear operation of the electrode was compared with the Shannon safety limit.Main results.A clear transition of the electrode-tissue interface impedance to nonlinear behavior was observed for both current and voltage-controlled stimulation. The transition occurred at lower values of activation overpotential for simulatedin vivothanin vitroconditions (91 mV and 165 mV respectively for current-controlled stimulation; 110 mV and 275 mV for voltage-controlled stimulation), corresponding to an applied current of 30μA and 45μA, or voltage of 330 mV at 1 kHz. The onset of nonlinearity occurred at lower values of the overpotential as frequency was increased. Incorporation of nonlinear properties resulted in activation of a higher proportion of neurons under voltage-controlled stimulation. Under current-controlled stimulation, the predicted transition to nonlinear behavior and Faradaic charge transfer at stimulation amplitudes of 30μA, corresponds to a charge density of 2.29μC cm-2and charge of 1.8 nC, well-below the Shannon safety limit.Significance.The results indicate that DBS electrodes may operate within the nonlinear region at clinically relevant stimulation amplitudes. This affects the extent of neural activation under voltage-controlled stimulation and the transition to Faradaic charge transfer for both voltage- and current-controlled stimulation with important implications for targeting of neural populations and the design of safe stimulation protocols.

Multivariable closed-loop control of deep brain stimulation for Parkinson's disease

Objective. Closed-loop deep brain stimulation (DBS) methods for Parkinson's disease (PD) to-date modulate either stimulation amplitude or frequency to control a single biomarker. While good performance has been demonstrated for symptoms that are correlated with the chosen biomarker, suboptimal regulation can occur for uncorrelated symptoms or when the relationship between biomarker and symptom varies. Control of stimulation-induced side-effects is typically not considered.Approach.A multivariable control architecture is presented to selectively target suppression of either tremor or subthalamic nucleus beta band oscillations. DBS pulse amplitude and duration are modulated to maintain amplitude below a threshold and avoid stimulation of distal large diameter axons associated with stimulation-induced side effects. A supervisor selects between a bank of controllers which modulate DBS pulse amplitude to control rest tremor or beta activity depending on the level of muscle electromyographic (EMG) activity detected. A secondary controller limits pulse amplitude and modulates pulse duration to target smaller diameter axons lying close to the electrode. The control architecture was investigated in a computational model of the PD motor network which simulated the cortico-basal ganglia network, motoneuron pool, EMG and muscle force signals.Main results.Good control of both rest tremor and beta activity was observed with reduced power delivered when compared with conventional open loop stimulation, The supervisor avoided over- or under-stimulation which occurred when using a single controller tuned to one biomarker. When DBS amplitude was constrained, the secondary controller maintained the efficacy of stimulation by increasing pulse duration to compensate for reduced amplitude. Dual parameter control delivered effective control of the target biomarkers, with additional savings in the power delivered.Significance.Non-linear multivariable control can enable targeted suppression of motor symptoms for PD patients. Moreover, dual parameter control facilitates automatic regulation of the stimulation therapeutic dosage to prevent overstimulation, whilst providing additional power savings.

Adaptive Stimulations in a Biophysical Network Model of Parkinson’s Disease

Deep brain stimulation (DBS)—through a surgically implanted electrode to the subthalamic nucleus (STN)—has become a widely used therapeutic option for the treatment of Parkinson’s disease and other neurological disorders. The standard conventional high-frequency stimulation (HF) that is currently used has several drawbacks. To overcome the limitations of HF, researchers have been developing closed-loop and demand-controlled, adaptive stimulation protocols wherein the amount of current that is delivered is turned on and off in real-time in accordance with a biophysical signal. Computational modeling of DBS in neural network models is an increasingly important tool in the development of new protocols that aid researchers in animal and clinical studies. In this computational study, we seek to implement a novel technique of DBS where we stimulate the STN in an adaptive fashion using the interspike time of the neurons to control stimulation. Our results show that our protocol eliminates bursts in the synchronized bursting neuronal activity of the STN, which is hypothesized to cause the failure of thalamocortical neurons (TC) to respond properly to excitatory cortical inputs. Further, we are able to significantly decrease the TC relay errors, representing potential therapeutics for Parkinson’s disease.

An in-silico framework for modeling optimal control of neural systems

Introduction

Brain-machine interfaces have reached an unprecedented capacity to measure and drive activity in the brain, allowing restoration of impaired sensory, cognitive or motor function. Classical control theory is pushed to its limit when aiming to design control laws that are suitable for large-scale, complex neural systems. This work proposes a scalable, data-driven, unified approach to study brain-machine-environment interaction using established tools from dynamical systems, optimal control theory, and deep learning.

Methods

To unify the methodology, we define the environment, neural system, and prosthesis in terms of differential equations with learnable parameters, which effectively reduce to recurrent neural networks in the discrete-time case. Drawing on tools from optimal control, we describe three ways to train the system: Direct optimization of an objective function, oracle-based learning, and reinforcement learning. These approaches are adapted to different assumptions about knowledge of system equations, linearity, differentiability, and observability.

Results

We apply the proposed framework to train an in-silico neural system to perform tasks in a linear and a nonlinear environment, namely particle stabilization and pole balancing. After training, this model is perturbed to simulate impairment of sensor and motor function. We show how a prosthetic controller can be trained to restore the behavior of the neural system under increasing levels of perturbation.

Discussion

We expect that the proposed framework will enable rapid and flexible synthesis of control algorithms for neural prostheses that reduce the need for in-vivo testing. We further highlight implications for sparse placement of prosthetic sensor and actuator components.

Efficient suppression of parkinsonian beta oscillations in a closed-loop model of deep brain stimulation with amplitude modulation

Introduction

Parkinson's disease (PD) is a movement disorder characterized by the pathological beta band (15-30 Hz) neural oscillations within the basal ganglia (BG). It is shown that the suppression of abnormal beta oscillations is correlated with the improvement of PD motor symptoms, which is a goal of standard therapies including deep brain stimulation (DBS). To overcome the stimulation-induced side effects and inefficiencies of conventional DBS (cDBS) and to reduce the administered stimulation current, closed-loop adaptive DBS (aDBS) techniques were developed. In this method, the frequency and/or amplitude of stimulation are modulated based on various disease biomarkers.

Methods

Here, by computational modeling of a cortico-BG-thalamic network in normal and PD conditions, we show that closed-loop aDBS of the subthalamic nucleus (STN) with amplitude modulation leads to a more effective suppression of pathological beta oscillations within the parkinsonian BG.

Results

Our results show that beta band neural oscillations are restored to their normal range and the reliability of the response of the thalamic neurons to motor cortex commands is retained due to aDBS with amplitude modulation. Furthermore, notably less stimulation current is administered during aDBS compared with cDBS due to a closed-loop control of stimulation amplitude based on the STN local field potential (LFP) beta activity.

Discussion

Efficient models of closed-loop stimulation may contribute to the clinical development of optimized aDBS techniques designed to reduce potential stimulation-induced side effects of cDBS in PD patients while leading to a better therapeutic outcome.

Correction of Stress-Induced States Using Sensory Stimulation Automatically Modulated by Endogenous Human Rhythms

This article considers the dynamics of the development of a potential approach to correcting stress-induced states in humans, i.e., adaptive neurostimulation. The approach consists of presenting sensory stimulation automatically modulated by intrinsic rhythmic human processes such as the respiratory rhythm, the heartbeat rhythm, and electroencephalograph (EEG) rhythms. Many examples have shown that real-time self-adjustment of the stimulation parameters by these rhythms leads to a high level personalization of therapeutic stimulation and increases in its efficacy in suppressing stress-induced states. The publications reviewed here point to the advantages of this approach for developing innovatory technologies using complex feedback from endogenous human rhythms to correct a wide spectrum of functional disorders.

Stimulation-induced changes at the electrode-tissue interface and their influence on deep brain stimulation

OBJECTIVE

During deep brain stimulation (DBS) the electrode-tissue interface forms a critical path between device and brain tissue. Although changes in the electrical double layer and glial scar can impact stimulation efficacy, the effects of chronic DBS on the electrode-tissue interface have not yet been established.

APPROACH

In this study, we characterised the electrode-tissue interface surrounding chronically implanted DBS electrodes in rats and compared the impedance and histological properties at the electrode interface in animals that received daily stimulation and in those where no stimulation was applied, up to eight weeks post-surgery. A computational model was developed based on the experimental data, which allowed the dispersive electrical properties of the surrounding encapsulation tissue to be estimated. The model was then used to study the effect of stimulation-induced changes in the electrode-tissue interface on the electric field and neural activation during voltage- and current-controlled stimulation.

MAIN RESULTS

Incorporating the observed changes in simulations in silico, we estimated the frequency-dependent dielectric properties of the electrical double layer and surrounding encapsulation tissue. Through simulations we show how stimulation-induced changes in the properties of the electrode-tissue interface influence the electric field and alter neural activation during voltage-controlled stimulation. A substantial increase in the number of stimulated collaterals, and their distance from the electrode, was observed during voltage-controlled stimulation with stimulated ETI properties. In vitro examination of stimulated electrodes confirmed that high frequency stimulation leads to desorption of proteins at the electrode interface, with a concomitant reduction in impedance.

SIGNIFICANCE

The demonstration of stimulation-induced changes in the electrode-tissue interface has important implications for future DBS systems including closed-loop systems where the applied stimulation may change over time. Understanding these changes is particularly important for systems incorporating simultaneous stimulation and sensing, which interact dynamically with brain networks.

Controlling Clinical States Governed by Different Temporal Dynamics With Closed-Loop Deep Brain Stimulation: A Principled Framework

Closed-loop strategies for deep brain stimulation (DBS) are paving the way for improving the efficacy of existing neuromodulation therapies across neurological disorders. Unlike continuous DBS, closed-loop DBS approaches (cl-DBS) optimize the delivery of stimulation in the temporal domain. However, clinical and neurophysiological manifestations exhibit highly diverse temporal properties and evolve over multiple time-constants. Moreover, throughout the day, patients are engaged in different activities such as walking, talking, or sleeping that may require specific therapeutic adjustments. This broad range of temporal properties, along with inter-dependencies affecting parallel manifestations, need to be integrated in the development of therapies to achieve a sustained, optimized control of multiple symptoms over time. This requires an extended view on future cl-DBS design. Here we propose a conceptual framework to guide the development of multi-objective therapies embedding parallel control loops. Its modular organization allows to optimize the personalization of cl-DBS therapies to heterogeneous patient profiles. We provide an overview of clinical states and symptoms, as well as putative electrophysiological biomarkers that may be integrated within this structure. This integrative framework may guide future developments and become an integral part of next-generation precision medicine instruments.

The Need for Digital Health Solutions in Deep Brain Stimulation for Parkinson's Disease in the Time of COVID-19 and Beyond

OBJECTIVES

Deep brain stimulation (DBS) is a well-established therapy for the management of patients with advanced Parkinson's disease and other movement disorders. Patients implanted with DBS require life-long management of the medical device as well as medications. Patients are often challenged to frequently visit the specialized DBS centers and such challenges are aggravated depending on geography, socioeconomic factors, and support systems. We discuss the need for digital health solutions to overcome these barriers to better and safely take care of patients, especially in the current COVID-19 pandemic.

MATERIALS AND METHODS

A review of the literature was conducted for technology and logistics necessary in forming a digital health program.

RESULTS

Digital health encounters can take place in both a synchronous and asynchronous manner. Factors involving patients include cognitive capacity, physical safety, physical capacity, connectivity, and technological security. Physician factors include examining the patient, system diagnostics, and adjusting stimulation or medications. Technology is focused on bridging the gap between patient and physician through integrating the DBS lead, implantable pulse generator (IPG), programmer, novel devices/applications to grade motor function, and teleconference modalities.

CONCLUSIONS

For patients with Parkinson's disease, digital health has the potential to drastically change the landscape after DBS surgery. Furthermore, technology is fundamental in connectivity, diagnostic evaluation, and security in order to create stable and useful patient-focused care.