Closing the loop of deep brain stimulation

Abstract

High-frequency deep brain stimulation is used to treat a wide range of brain disorders, like Parkinson's disease. The stimulated networks usually share common electrophysiological signatures, including hyperactivity and/or dysrhythmia. From a clinical perspective, HFS is expected to alleviate clinical signs without generating adverse effects. Here, we consider whether the classical open-loop HFS fulfills these criteria and outline current experimental or theoretical research on the different types of closed-loop DBS that could provide better clinical outcomes. In the first part of the review, the two routes followed by HFS-evoked axonal spikes are explored. In one direction, orthodromic spikes functionally de-afferent the stimulated nucleus from its downstream target networks. In the opposite direction, antidromic spikes prevent this nucleus from being influenced by its afferent networks. As a result, the pathological synchronized activity no longer propagates from the cortical networks to the stimulated nucleus. The overall result can be described as a reversible functional de-afferentation of the stimulated nucleus from its upstream and downstream nuclei. In the second part of the review, the latest advances in closed-loop DBS are considered. Some of the proposed approaches are based on mathematical models, which emphasize different aspects of the parkinsonian basal ganglia: excessive synchronization, abnormal firing-rate rhythms, and a deficient thalamo-cortical relay. The stimulation strategies are classified depending on the control-theory techniques on which they are based: adaptive and on-demand stimulation schemes, delayed and multi-site approaches, stimulations based on proportional and/or derivative control actions, optimal control strategies. Some of these strategies have been validated experimentally, but there is still a large reservoir of theoretical work that may point to ways of improving practical treatment.

Robust stabilization of delayed neural fields with partial measurement and actuation

“…It is thus of interest to study the possibility to attenuate these pathological oscillations using closed-loop techniques. As reported in the survey paper (Carron et al, 2013), several attempts have been made in that direction. These include adaptive and on-demand stimulation (Rosin et al, 2011;Graupe et al, 2010;Santaniello et al, 2011;Little et al, 2016;Marceglia et al, 2007), delayed and multi-site stimulation (Lysyansky et al, 2011;Batista et al, 2010;Pfister and Tass, 2010;Tass et al, 2012), optimal control strategies (Feng et al, 2007), and activity regulation (Haidar et al, 2016;Wagenaar et al, 2005;Grant and Lowery, 2013).…”

Recovery of dynamics and function in spiking neural networks by closed-loop control

“…In contrast to open-loop control, closed-loop control can by design deal with all these situations. For this reason there is a growing interest in investigating feedback-control both experimentally [2,[22][23][24] and theoretically [39]. The goal of the experimental work has been to demonstrate that closed-loop control is indeed effective, whereas the theoretical studies aimed at providing a deeper understanding of the underlying conditions and mechanisms.…”

Closed loop deep brain stimulation: an evolving technology

“…Therefore, some researches have been conducted to record internal biomarker from outside human body. This approach may be The spike timing of a neuron can be controlled by relying on a phase model [10] Electrocorticogram (ECoG)…”

“…The stimulation parameter adjustment by the clinician is not in real-time and automated, meaning that such systems are not closed loop systems [9]. Standard OLDBS systems suffer from some limitations including: having no effect on some symptoms, deteriorating certain conditions, causing some side effects, becoming less effective with time, causing speech impairment, increasing the cost of therapy, applying the same stimulation signal regardless of the state of patient, and so on [10]. The invariant stimulation pattern in OLDBS leads to habituation and tolerance resulting in the loss of efficacy.…”

“…The stimulation parameters are electrode configurations, combinations of negative and positive contacts, frequency, pulse width, and pulse amplitude. A real-time adaptation of the stimulation parameters is often necessary because the disease condition of a brain disorder patient changes with time [10]. The adaptive optimization of the stimulation parameters can minimize side effects, maximize therapeutic efficacy, and prolong battery life [15].…”