Multisite Delayed Feedback for Electrical Brain Stimulation.

Oleksandr V Popovych,Peter A Tass

doi:10.3389/fphys.2018.00046

Abstract

Demand-controlled deep brain stimulation (DBS) appears to be a promising approach for the treatment of Parkinson's disease (PD) as revealed by computational, pre-clinical and clinical studies. Stimulation delivery is adapted to brain activity, for example, to the amount of neuronal activity considered to be abnormal. Such a closed-loop stimulation setup might help to reduce the amount of stimulation current, thereby maintaining therapeutic efficacy. In the context of the development of stimulation techniques that aim to restore desynchronized neuronal activity on a long-term basis, specific closed-loop stimulation protocols were designed computationally. These feedback techniques, e.g., pulsatile linear delayed feedback (LDF) or pulsatile nonlinear delayed feedback (NDF), were computationally developed to counteract abnormal neuronal synchronization characteristic for PD and other neurological disorders. By design, these techniques are intrinsically demand-controlled methods, where the amplitude of the stimulation signal is reduced when the desired desynchronized regime is reached. We here introduce a novel demand-controlled stimulation method, pulsatile multisite linear delayed feedback (MLDF), by employing MLDF to modulate the pulse amplitude of high-frequency (HF) DBS, in this way aiming at a specific, MLDF-related desynchronizing impact, while maintaining safety requirements with the charge-balanced HF DBS. Previously, MLDF was computationally developed for the control of spatio-temporal synchronized patterns and cluster states in neuronal populations. Here, in a physiologically motivated model network comprising neurons from subthalamic nucleus (STN) and external globus pallidus (GPe), we compare pulsatile MLDF to pulsatile LDF for the case where the smooth feedback signals are used to modulate the amplitude of charge-balanced HF DBS and suggest a modification of pulsatile MLDF which enables a pronounced desynchronizing impact. Our results may contribute to further clinical development of closed-loop DBS techniques.

Highlights

High-frequency (HF) deep brain stimulation (DBS) is the standard therapy for medically refractory Parkinson’s disease (PD), where electrical pulse trains are permanently delivered via depth electrodes at high frequencies (> 100 Hz) (Benabid et al, 1991, 2009; Kuncel and Grill, 2004; Johnson et al, 2008)
We introduce a pulsatile multisite linear delayed feedback (MLDF) for electrical brain stimulation and test it on a physiologically motivated model of interacting neuronal populations of subthalamic nucleus (STN) and external globus pallidus (GPe) neurons (Terman et al, 2002; Rubin and Terman, 2004)
For a more detailed comparison, we fix optimal stimulation delay τ = 90 ms for pulsatile MLDF and τ = 60 ms for pulsatile linear delayed feedback (LDF), where the stimulation induces strongest desynchronization, see Figures 4–6, and increase the stimulation intensity K. We find that both pulsatile MLDF and LDF stimulations with larger intensity can induce stronger desynchronization, and the first order parameter R1 decreases when K increases, see Figure 7A for MLDF and Figure 7B for LDF

Summary

Introduction

High-frequency (HF) deep brain stimulation (DBS) is the standard therapy for medically refractory Parkinson’s disease (PD), where electrical pulse trains are permanently delivered via depth electrodes at high frequencies (> 100 Hz) (Benabid et al, 1991, 2009; Kuncel and Grill, 2004; Johnson et al, 2008). HF DBS may cause side effects by stimulation of the target as well as surrounding structures (Ferraye et al, 2008; Moreau et al, 2008; van Nuenen et al, 2008; Xie et al, 2012). It is, desirable to reduce the integral current delivered. Closed-loop aDBS approach received recent development in pre-clinical and clinical studies (Graupe et al, 2010; Rosin et al, 2011; Carron et al, 2013; Little et al, 2013; Priori et al, 2013; Yamamoto et al, 2013; Grahn et al, 2014; Hosain et al, 2014; Rosa et al, 2015)

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Conclusion