Changes in Neuronal Entropy in a Network Model of the Cortico-Basal Ganglia during Deep Brain Stimulation

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.

Functional Neural Changes after Low-Frequency Bilateral Globus Pallidus Internus Deep Brain Stimulation for Post-Hypoxic Cortical Myoclonus: Voxel-Based Subtraction Analysis of Serial Positron Emission

Post-hypoxic myoclonus (PHM) and Lance–Adams syndrome (LAS) are rare conditions following cardiopulmonary resuscitation. The aim of this study was to identify functional activity in the cerebral cortex after a hypoxic event and to investigate alterations that could be modulated by deep brain stimulation (DBS). A voxel-based subtraction analysis of serial positron emission tomography (PET) scans was performed in a 34-year-old woman with chronic medically refractory PHM that improved with bilateral globus pallidus internus (Gpi) DBS implanted three years after the hypoxic event. The patient required low-frequency stimulation to show myoclonus improvement. Using voxel-based statistical parametric mapping, we identified a decrease in glucose metabolism in the prefrontal lobe including the dorsolateral, orbito-, and inferior prefrontal cortex, which was suspected to be the origin of the myoclonus from postoperative PET/magnetic resonance imaging (MRI) after DBS. Based on the present study results, voxel-based subtraction of PET appears to be a useful approach for monitoring patients with PHM treated with DBS. Further investigation and continuous follow-up on the use of PET analysis and DBS treatment for patients with PHM are necessary to help understanding the pathophysiology of PHM, or LAS.

Simulation of Closed-Loop Deep Brain Stimulation Control Schemes for Suppression of Pathological Beta Oscillations in Parkinson's Disease

This study presents a computational model of closed-loop control of deep brain stimulation (DBS) for Parkinson's disease (PD) to investigate clinically viable control schemes for suppressing pathological beta-band activity. Closed-loop DBS for PD has shown promising results in preliminary clinical studies and offers the potential to achieve better control of patient symptoms and side effects with lower power consumption than conventional open-loop DBS. However, extensive testing of algorithms in patients is difficult. The model presented provides a means to explore a range of control algorithms in silico and optimize control parameters before preclinical testing. The model incorporates (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The performance of on-off and dual-threshold controllers for suppressing beta-band activity by modulating the DBS amplitude were first verified, showing levels of beta suppression and reductions in power consumption comparable with previous clinical studies. Proportional (P) and proportional-integral (PI) closed-loop controllers for amplitude and frequency modulation were then investigated. A simple tuning rule was derived for selecting effective PI controller parameters to target long duration beta bursts while respecting clinical constraints that limit the rate of change of stimulation parameters. Of the controllers tested, PI controllers displayed superior performance for regulating network beta-band activity whilst accounting for clinical considerations. Proportional controllers resulted in undesirable rapid fluctuations of the DBS parameters which may exceed clinically tolerable rate limits. Overall, the PI controller for modulating DBS frequency performed best, reducing the mean error by 83% compared to DBS off and the mean power consumed to 25% of that utilized by open-loop DBS. The network model presented captures sufficient physiological detail to act as a surrogate for preclinical testing of closed-loop DBS algorithms using a clinically accessible biomarker, providing a first step for deriving and testing novel, clinically suitable closed-loop DBS controllers.