Disruption of parkinsonian brain oscillations

TL;DR

This paper discusses the development of closed-loop deep brain stimulation (DBS) for Parkinson's disease, utilizing model-free control to adaptively suppress pathological brain oscillations and improve treatment outcomes.

Contribution

It introduces a novel model-free control approach for closed-loop DBS, addressing uncertainties in brain dynamics for better suppression of Parkinsonian oscillations.

Findings

Potential for adaptive suppression of pathological oscillations

Addresses limitations of simple control algorithms

Proposes a model-free control framework for DBS

Abstract

Deep brain stimulation (DBS) is an advanced surgical treatment for the symptoms of Parkinson's disease (PD), involving electrical stimulation of neurons within the basal ganglia region of the brain. DBS is traditionally delivered in an open-loop manner using fixed stimulation parameters, which may lead to suboptimal results. In an effort to overcome these limitations, closed loop DBS, using pathological subthalamic beta (13--30 Hz) activity as a feedback signal, offers the potential to adapt DBS automatically in response to changes in patient symptoms and side effects. However, clinically implemented closed-loop techniques have been limited to date to simple control algorithms, due to the inherent uncertainties in the dynamics involved. Model-free control, which has already seen successful applications in the field of bioengineering, offers a way to avoid this limitation and provides an alternative method to apply modern control approach to selective suppression of pathological oscillations.