Adaptive deep brain stimulation in advanced Parkinson disease

Abstract

Objective: Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.

Methods: We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.

Results: Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.

Interpretation: BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.

Copyright © 2013 American Neurological Association.

Figures

Figure 1

Experimental setup for adaptive deep brain stimulation in externalized subjects. Bipolar local field potential (LFP) is passed through a custom built StimRecord amplifier that filters (3–37Hz) and amplifies (×9,100). The analogue (A) output is passed to a 1401 data acquisition unit (Cambridge Electronic Design, Cambridge, UK), which converts it to a digital (D) signal that is displayed on a portable computer using Spike2 software (Cambridge Electronic Design, Cambridge, UK). The signal is digitally filtered around the beta peak in real time and converted to beta amplitude by rectifying and smoothing. A threshold is set that triggers stimulation in a monopolar montage between the 2 bipolar recording electrodes when beta power crosses the threshold. Stimulation terminates when beta power drops again below threshold. [Color figure can be viewed in the online issue, which is available at www.annalsofneurology.org.]

Figure 2

Results in Case 1. (A) Sample section of adaptive deep brain stimulation (aDBS) recordings. Channels are from bottom to top: analogue filtered local field potential (LFP; bipolar contacts 0 and 2), LFP digitally filtered about the beta peak, running average of rectified, beta filtered, amplitude with triggering threshold superimposed, stimulation trigger signal and stimulation (130Hz, 100μS, monopolar contact 1). Boxed area shows a beta burst. Time for this to cross threshold depends on LFP amplitude at onset of beta burst, but thereafter there was only a 30- to 40-millisecond delay to stimulation onset. Note stimulation is ramped, and can be triggered by bursts of beta that are of variable duration, but often last

Figure 3

Clinical improvements. Mean ± standard error of the mean percentage change in hemibody United Parkinson's Disease Rating Scale scores (items 20, 22, and 23) with different stimulation conditions as assessed unblinded during the experimental sessions (A) or from video recordings by blinded experts (B). Asterisks denote significant differences following correction for multiple comparisons by the false discovery rate procedure. All changes were significant from the unstimulated state, with the exception of the blinded score for random stimulation. aDBS = adaptive deep brain stimulation; cDBS = continuous deep brain stimulation.

Figure 4

Decline in triggered stimulation duration over time. Dependency of proportion of time stimulated (% per 10-second block) on duration of adaptive deep brain stimulation is shown for Subject 5. Solid and interrupted lines are result of linear regression and 95% confidence intervals, respectively. r = −0.567, p < 0.001.