Continuous deep brain stimulation (cDBS) of the subthalamic nucleus (STN) or globus pallidus (GP) is an effective treatment for the motor symptoms of Parkinson’s Disease (PD). The relative benefit of one region over the other is of great interest but cannot usually be compared in the same patient. Simultaneous DBS of both regions may synergistically increase the therapeutic benefit. CDBS is limited by a lack of responsiveness to dynamic, fluctuating symptoms intrinsic to the disease. Adaptive DBS (aDBS) adjusts stimulation in response to biomarkers to improve efficacy, side effects, and efficiency. We combined bilateral DBS of both STN and GP (dual target DBS) in a prospective within-participant, clinical trial in six PD patients (n = 6, 55 to 65 years, two women). Dual target cDBS was tested for PD symptom control annually over 2 years, measured by motor rating scales, ON time without dyskinesia, and medication reduction. Random amplitude experiments probed system dynamics to estimate parameters for aDBS. We then implemented proportional-plus-integral (PI) aDBS using a novel distributed (off-implant) architecture. In the home setting we collected tremor and dyskinesia scores as well as individualized beta and DBS amplitudes. Dual target cDBS reduced motor symptoms as measured by UPDRS to a greater degree than either region alone (p < 0.05, Linear Mixed Model) in the cohort. The amplitude of beta oscillations in the STN correlated to the speed of hand grasp movements for five of six participants (p < 0.05, Pearson correlation). Random amplitude experiments provided insight into temporal windowing to avoid stimulation artifacts and demonstrated a correlation between STN beta amplitude and DBS amplitude. PI control of aDBS reduced average power, while preserving Unified Parkinson’s Disease Rating Scale Motor Examination (UPDRS III) scores in the clinic (p = 0.28, Wilcoxon signed rank), and tremor and dyskinesia scores during blinded testing at home (n =3, p > 0.05, Wilcoxon ranked sum). In the home setting, DBS power reductions were slight but significant. Dual target cDBS may offer an improvement in treatment of motor symptoms of PD over DBS of either the STN or GP alone. When combined with PI aDBS, stimulation power may be reduced while preserving the increased benefit of dual target DBS.

Neural oscillations represent synchronous neuronal activation and are ubiquitous throughout the brain. Oscillatory activity often includes brief high-amplitude bursts in addition to background oscillations, and burst activity may predict performance on working memory, motor, and comprehension tasks. We evaluated beta burst activity as a possible biomarker for motor symptoms in Parkinson's disease (PD). The relationship between beta amplitude dynamics and motor symptoms is critical for adaptive DBS for treatment of PD. We applied threshold-based and support vector machine (SVM) analyses of burst parameters to a defined on/off oscillator and to intraoperative recordings of local field potentials from the subthalamic nucleus of 16 awake patients with PD. Filtering and time-frequency analysis techniques critically influenced the accuracy of identifying burst activity. Threshold-based analysis lead to biased results in the presence of changes in long-term beta amplitude and accurate quantification of bursts with thresholds required unknowable a priori knowledge of the time in bursts. We therefore implemented an SVM analysis, and we did not observe changes in burst fraction, rate, or duration with the application of cDBS in the participant data, even though SVM analysis was able to correctly identify bursts of the defined on/off oscillator. Our results suggest that cDBS of the STN may not change beta burst activity. Additionally, threshold-based analysis can bias the fraction of time spent in bursts. Improved analysis strategies for continuous and adaptive DBS may achieve improved symptom control and reduce side-effects.

Deep brain stimulation (DBS) has shown great promise toward treating motor symptoms caused by Parkinson's disease (PD), by delivering electrical pulses to the Basal Ganglia (BG) region of the brain. However, DBS devices approved by the U.S. Food and Drug Administration (FDA) can only deliver continuous DBS (cDBS) at a fixed amplitude; this energy inefficient operation reduces battery lifetime of the device, cannot adapt treatment dynamically for activity, and may cause significant side-effects (e.g., gait impairment). In this work, we introduce an offline reinforcement learning (RL) framework, allowing the use of past clinical data to train an RL policy to adjust the stimulation amplitude in real time, with the goal of reducing energy use while maintaining the same level of treatment (i.e., control) efficacy as cDBS. Moreover, clinical protocols require the safety and performance of such RL controllers to be demonstrated ahead of deployments in patients. Thus, we also introduce an offline policy evaluation (OPE) method to estimate the performance of RL policies using historical data, before deploying them on patients. We evaluated our framework on four PD patients equipped with the RC+S DBS system, employing the RL controllers during monthly clinical visits, with the overall control efficacy evaluated by severity of symptoms (i.e., bradykinesia and tremor), changes in PD biomakers (i.e., local field potentials), and patient ratings. The results from clinical experiments show that our RL-based controller maintains the same level of control efficacy as cDBS, but with significantly reduced stimulation energy. Further, the OPE method is shown effective in accurately estimating and ranking the expected returns of RL controllers.

Traditional deep brain stimulation (DBS) at fixed regular frequencies (>100 Hz) is effective in treating motor symptoms of Parkinson's disease (PD). Temporally non-regular patterns of DBS are a new parameter space that may help increase efficacy and efficiency. To compare the effects of temporally non-regular patterns of DBS to traditional regularly-spaced pulses. We simultaneously recorded local field potentials (LFP) and monitored motor symptoms (tremor and bradykinesia) in persons with PD during DBS in subthalamic nucleus (STN). We quantified both oscillatory activity and DBS local evoked potentials (DLEPs) from the LFP. Temporally non-regular patterns were as effective as traditional pulse patterns in modulating motor symptoms, oscillatory activity, and DLEPs. Moreover, one of our novel patterns enabled recording of longer duration DLEPs during clinically effective stimulation. Stimulation gaps of 50 ms can be used to increase efficiency and to enable regular assessment of long-duration DLEPs while maintaining effective symptom management. This may be a promising paradigm for closed-loop DBS with biomarker assessment during the gaps.

Deep brain stimulation (DBS) of the ventral intermediate nucleus (VIM) of the thalamus (TH) is an effective therapy for suppressing tremor. One of the critical challenges to optimizing VIM-DBS therapy is the lack of robust neural biomarkers that correlate well with tremor. To quantify biomarkers for tremor using intraoperative TH local field potential (LFP) recorded from DBS electrodes. Further, we used computational modeling to understand the biophysical basis of the recorded LFP signal. We simultaneously recorded intraoperative TH LFP and tremor from the hand dorsum (32 participants) and during DBS at different frequencies (16 participants). Then, we simulated the effects of DBS and spatial distribution of tremor cells on calculated LFPs in a TH model. There was a moderate correlation between tremor and LFP spectral power in the theta and alpha bands (r = 0.445 and 0.389, respectively). There was a strong correlation between tremor and peak coherence between LFP and tremor signal (r = 0.559). Postural tremor was decoded from the LFP signal with an area under the curve of ∼0.7. High frequency DBS reduced spectral power in the theta and alpha bands and tremor could be decoded from the LFP spectral power in the presence of DBS (0.429 goodness of fit R²). The theta power in the simulated LFP signal varied substantially with the specific location of the bipolar contact pair of the DBS electrode used for the LFP recordings as well as the spatial distribution of tremor cells. Theta power alone was not sufficient for prediction of tremor control. Simulations indicated that the number and distribution of tremor cells surrounding the DBS lead may explain the lack of a strong correlation between tremor and theta power.