Parkinsonian daytime sleep-wake classification using deep brain stimulation lead recordings.

Excessive daytime sleepiness is a recognized non-motor symptom that adversely impacts the quality of life of people with Parkinson's disease (PD), yet effective treatment options remain limited. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for PD motor signs. Reliable daytime sleep-wake classification using local field potentials (LFPs) recorded from DBS leads implanted in STN can inform the development of closed-loop DBS approaches for prompt detection and disruption of sleep-related neural oscillations. We performed STN DBS lead recordings in three nonhuman primates rendered parkinsonian by administrating neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Reference sleep-wake states were determined on a second-by-second basis by video monitoring of eyes (eyes-open, wake and eyes-closed, sleep). The spectral power in delta (1-4 Hz), theta (4-8 Hz), low-beta (8-20 Hz), high-beta (20-35 Hz), gamma (35-90 Hz), and high-frequency (200-400 Hz) bands were extracted from each wake and sleep epochs for training (70% data) and testing (30% data) a support vector machines classifier for each subject independently. The spectral features yielded reasonable daytime sleep-wake classification (sensitivity: 90.68 ± 1.28; specificity: 88.16 ± 1.08; accuracy: 89.42 ± 0.68; positive predictive value; 88.70 ± 0.89, n = 3). Our findings support the plausibility of monitoring daytime sleep-wake states using DBS lead recordings. These results could have future clinical implications in informing the development of closed-loop DBS approaches for automatic detection and disruption of sleep-related neural oscillations in people with PD to promote wakefulness.

Basal ganglia engagement during REM sleep movements in Parkinson's disease.

To elucidate the role of the basal ganglia during REM sleep movements in Parkinson's disease (PD) we recorded pallidal neural activity from four PD patients. Unlike desynchronization commonly observed during wakeful movements, beta oscillations (13-35 Hz) synchronized during REM sleep movements; furthermore, high-frequency oscillations (150-350 Hz) synchronized during movement irrespective of sleep-wake states. Our results demonstrate differential engagement of the basal ganglia during REM sleep and awake movements.

Fully Closed Loop Test Environment for Adaptive Implantable Neural Stimulators Using Computational Models.

Implantable brain stimulation devices continue to be developed to treat and monitor brain conditions. As the complexity of these devices grows to include adaptive neuromodulation therapy, validating the operation and verifying the correctness of these systems becomes more complicated. The new complexities lie in the functioning of the device being dependent on the interaction with the patient and environmental factors such as noise and artifacts. Here, we present a hardware-in-the-loop (HIL) testing framework that employs computational models of pathological neural dynamics to test adaptive deep brain stimulation (DBS) devices prior to animal or human testing. A brain stimulation and recording electrode array is placed in the saline tank and connected to an adaptive neuromodulation system that measures and processes the synthetic signals and delivers stimulation back into the saline tank. A data acquisition system is used to detect the stimulation and provide feedback to the computational model in order to simulate the effects of stimulation on the neural dynamics. In this study, we used real-time computational models to emulate the dynamics of epileptic seizures observed in the anterior nucleus of the thalamus (ANT) in epilepsy patients and beta band (11-35 Hz) oscillations observed in the subthalamic nucleus (STN) of Parkinson's disease (PD) patients. These models simulated neuronal responses to electrical stimulation pulses and the saline tank tested hardware interactions between the detection algorithms and stimulation interference. We tested and validated the operation of adaptive DBS algorithms for seizure and beta band power suppression embedded in an implantable DBS system (Medtronic Summit RC+S). This study highlights the utility of the proposed hardware-in-the-loop framework to systematically test the adaptive DBS systems in the presence of system aggressors such as environmental noise and stimulation-induced electrical artifacts. This testing procedure can help ensure correctness and robustness of adaptive DBS devices prior to animal and human testing.

Directional deep brain stimulation leads reveal spatially distinct oscillatory activity in the globus pallidus internus of Parkinson's disease patients.

The goal of this study was to characterize the spectral characteristics and spatial topography of local field potential (LFP) activity in the internal segment of the globus pallidus (GPi) in patients with Parkinson's disease utilizing directional (segmented) deep brain stimulation (dDBS) leads. Data were collected from externalized dDBS leads of three patients with idiopathic Parkinson's disease after overnight withdrawal of parkinsonian medication at rest and during a cued reach-to-target task. Oscillatory activity across lead contacts/segments was examined in the context of lead locations and contact orientations determined using co-registered preoperative 7 Tesla (T) MRI and postoperative CT scans. Each of the three patients displayed a unique frequency spectrum of oscillatory activity in the pallidum, with prominent peaks ranging from 5 to 35 Hz, that modulated variably across subjects during volitional movement. Despite subject-specific spectral profiles, a consistent finding across patients was that oscillatory power was strongest and had the largest magnitude of modulation during movement in LFPs recorded from segments facing the postero-lateral "sensorimotor" region of GPi, whereas antero-medially-directed segmented contacts facing the internal capsule and/or anterior GPi, had relatively weaker LFP power and less modulation in the 5 to 35 Hz. In each subject, contact configurations chosen for clinically therapeutic stimulation (following data collection and blinded to physiology recordings), were in concordance with the contact pairs showing the largest amplitude of LFP oscillations in the 5-35 Hz range. Although limited to three subjects, these findings provide support for the hypothesis that the sensorimotor territory of the GPi corresponds to the site of maximal power of oscillatory activity in the 5 to 35 Hz and provides the greatest benefit in motor signs during stimulation in the GPi. Variability in oscillatory activity across patients is likely related to Parkinson's disease phenotype as well as small differences in recording location (i.e. lead location), highlighting the importance of lead location for optimizing stimulation efficacy. These data also provide compelling evidence for the use of LFP activity for the development of predictive stimulation models that may optimize patient benefits while reducing clinic time needed for programming.