Closed-Loop Modulation of the Pathological Disorders of the Basal Ganglia Network

Abstract

A generalized predictive closed-loop control strategy to improve the basal ganglia activity patterns in Parkinson's disease (PD) is explored in this paper. Based on system identification, an input-output model is established to reveal the relationship between external stimulation and neuronal responses. The model contributes to the implementation of the generalized predictive control (GPC) algorithm that generates the optimal stimulation waveform to modulate the activities of neuronal nuclei. By analyzing the roles of two critical control parameters within the GPC law, optimal closed-loop control that has the capability of restoring the normal relay reliability of the thalamus with the least stimulation energy expenditure can be achieved. In comparison with open-loop deep brain stimulation and traditional static control schemes, the generalized predictive closed-loop control strategy can optimize the stimulation waveform without requiring any particular knowledge of the physiological properties of the system. This type of closed-loop control strategy generates an adaptive stimulation waveform with low energy expenditure with the potential to improve the treatments for PD.

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

“…This approach, however, would require sampling multiple points in the parameter space which may not be practical clinically. An alternative controller design approach is to linearize the inputoutput relationship of the system using a model and subsequently design a controller which meets the required closed-loop system response (Santaniello et al, 2011;Liu et al, 2017a;Yang et al, 2018;Su et al, 2019). This approach was used by Santaniello et al (2011), Liu et al (2017a), and Su et al (2019) where autoregressive models were derived from spiking neuron models.…”

“…An alternative controller design approach is to linearize the inputoutput relationship of the system using a model and subsequently design a controller which meets the required closed-loop system response (Santaniello et al, 2011;Liu et al, 2017a;Yang et al, 2018;Su et al, 2019). This approach was used by Santaniello et al (2011), Liu et al (2017a), and Su et al (2019) where autoregressive models were derived from spiking neuron models. To normalize aberrant neural activity during parkinsonian tremor, Santaniello et al (2011) designed a minimum variance controller, while Liu et al (2017a) implemented a generalized predictive control algorithm.…”

“…This approach was used by Santaniello et al (2011), Liu et al (2017a), and Su et al (2019) where autoregressive models were derived from spiking neuron models. To normalize aberrant neural activity during parkinsonian tremor, Santaniello et al (2011) designed a minimum variance controller, while Liu et al (2017a) implemented a generalized predictive control algorithm. In contrast, Su et al (2019) optimized the parameters of a discrete PI controller to track a dynamic target of betaband power which may be associated with fluctuations of the oscillatory activity during voluntary movement.…”

“…Computational modeling provides an alternative approach for designing and testing more complex forms of closed-loop DBS control. Although computational models have been previously used to investigate closed-loop control strategies for DBS (Santaniello et al, 2011;Carron et al, 2013;Gorzelic et al, 2013;Grant and Lowery, 2013;Pasillas-Lepine et al, 2013;Haidar et al, 2016;Liu et al, 2017a;Popovych and Tass, 2019;Su et al, 2019), they typically do not relate well to clinically relevant parameters and, in particular, rarely incorporate both simulation of the LFP and extracellular application of DBS. Simulation of the LFP is desirable for developing computational models that can be readily translated to patients as LFP derived features, such as frequency and time domain features, are currently the most accessible biomarkers for closed-loop DBS in PD (Priori et al, 2013).…”

Toward Adaptive Deep Brain Stimulation in Parkinson’s Disease: A Review

“…Examples of these include recursive autoregressive models and Kalman filter methods. For DBS, some computational models have implemented closed loop control using these methods [71,72].…”