Closed-Loop Control of Deep Brain Stimulation: A Simulation Study

Abstract

Deep brain stimulation (DBS) is an effective therapy to treat movement disorders including essential tremor, dystonia, and Parkinson's disease. Despite over a decade of clinical experience the mechanisms of DBS are still unclear, and this lack of understanding makes the selection of stimulation parameters quite challenging. The objective of this work was to develop a closed-loop control system that automatically adjusted the stimulation amplitude to reduce oscillatory neuronal activity, based on feedback of electrical signals recorded from the brain using the same electrode as implanted for stimulation. We simulated a population of 100 intrinsically active model neurons in the Vim thalamus, and the local field potentials (LFPs) generated by the population were used as the feedback (control) variable for closed loop control of DBS amplitude. Based on the correlation between the spectral content of the thalamic activity and tremor (Hua , 1998), (Lenz , 1988), we implemented an adaptive minimum variance controller to regulate the power spectrum of the simulated LFPs and restore the LFP power spectrum present under tremor conditions to a reference profile derived under tremor free conditions. The controller was based on a recursively identified autoregressive model (ARX) of the relationship between stimulation input and LFP output, and showed excellent performances in tracking the reference spectral features through selective changes in the theta (2-7 Hz), alpha (7-13 Hz), and beta (13-35 Hz) frequency ranges. Such changes reflected modifications in the firing patterns of the model neuronal population, and, differently from open-loop DBS, replaced the tremor-related pathological patterns with patterns similar to those simulated in tremor-free conditions. The closed-loop controller generated a LFP spectrum that approximated more closely the spectrum present in the tremor-free condition than did open loop fixed intensity stimulation and adapted to match the spectrum after a change in the neuronal oscillation frequency. This computational study suggests the feasibility of closed-loop control of DBS amplitude to regulate the spectrum of the local field potentials and thereby normalize the aberrant pattern of neuronal activity present in tremor.

Neural origin of evoked potentials during thalamic deep brain stimulation

“…One potential closed-loop feedback signal is the local field potential (LFP) recorded from the DBS electrode and reflecting synchronized network activity (Marceglia et al 2007;Santaniello et al 2011) that is correlated to motor symptoms in PD (Smirnov et al 2008;Tass et al 2010) and ET (Kane et al 2009). Beta band (13-35 Hz) activity in PD (Brown and Williams 2005) and theta band (4 -7 Hz) activity in ET (Kane et al 2009) are disrupted by DBS (Bronte-Stewart et al 2009;Eusebio et al 2012;Ray et al 2008;Rosa et al 2011;Santaniello et al 2011), suggesting that LFP recordings might be used to identify clinically effective stimulation parameters (Yoshida et al 2010). Additionally, a closed-loop internal globus pallidus (GPi)-DBS system used single-unit activity from primary motor cortex (M1) as a trigger for each stimulus pulse and demonstrated greater symptom reduction than continuous, open-loop stimulation (Rosin et al 2011).…”

Brain-Machine-Brain Interface

“…Bidirectional interfacing also makes a lot of intuitive sense for epilepsy control (Fountas & Smith, 2007) and deep brain stimulation (Santaniello et al, 2011). For these therapies, closedloop bidirectional control would involve sensing when the nervous system was producing pathological activity (epileptic seizures or Parkinsonian tremor) and only stimulating at the minimum level and with the best timing for maximal therapeutic effect.…”

Simulation of Cortico-Basal Ganglia Oscillations and Their Suppression by Closed Loop Deep Brain Stimulation

“…The first uses high-frequency DBS waveforms as currently used in open-loop configuration, but applies closed-loop control to vary the stimulation parameters [10]. The second, termed "mild stimulation", attempts to desynchronise pathological oscillatory networks in an on-demand fashion [11], [12], [13], [14].…”

“…At present, the only implanted stimulation hardware for closed-loop DBS that has been proposed for clinical use is designed to deliver high-frequency DBS [15]. Previous computational models have been developed that aim to improve the efficacy of DBS through closed-loop control of the DBS amplitude [10], and to reduce the energy delivered during stimulation by the automated design of novel stimulation waveforms [16], [17]. Closed-loop control of the DBS amplitude has been shown to be capable of restoring the LFP power spectrum of a parkinsonian STN population to approximate physiological conditions, but the model did not incorporate cortico-basal ganglia feedback [10].…”

“…Previous computational models have been developed that aim to improve the efficacy of DBS through closed-loop control of the DBS amplitude [10], and to reduce the energy delivered during stimulation by the automated design of novel stimulation waveforms [16], [17]. Closed-loop control of the DBS amplitude has been shown to be capable of restoring the LFP power spectrum of a parkinsonian STN population to approximate physiological conditions, but the model did not incorporate cortico-basal ganglia feedback [10]. In addition, side effects of DBS due to activation of adjacent fibers [18], could potentially be reduced by a lowered average stimulation amplitude.…”