Effects of subthalamic nucleus deep brain stimulation on objective sleep outcomes in Parkinson's disease

Fundamentals of deep brain stimulation for Parkinson's disease in clinical practice: part 1

Deep brain stimulation (DBS) is recognized as an established therapy for Parkinson's disease (PD) and other movement disorders in the light of the developments seen over the past three decades. Long-term efficacy is established for PD with documented improvement in the cardinal motor symptoms of PD and levodopa-induced complications, such as motor fluctuations and dyskinesias. Timing of patient selection is crucial to obtain optimal benefits from DBS therapy, before PD complications become irreversible. The objective of this first part review is to examine the fundamental concepts of DBS for PD in clinical practice, discussing the historical aspects, patient selection, potential effects of DBS on motor and non-motor symptoms, and the practical management of patients after surgery.

Effects of deep brain stimulation on quantitative sleep electroencephalogram during non-rapid eye movement in Parkinson's disease

Introduction

Sleep dysfunction is frequently experienced by people with Parkinson's disease (PD) and negatively influences quality of life. Although subthalamic nucleus (STN) deep brain stimulation (DBS) can improve sleep in PD, sleep microstructural features such as sleep spindles provide additional insights about healthy sleep. For example, sleep spindles are important for better cognitive performance and for sleep consolidation in healthy adults. We hypothesized that conventional STN DBS settings would yield a greater enhancement in spindle density compared to OFF and low frequency DBS.

Methods

In a previous within-subject, cross-sectional study, we evaluated effects of low (60 Hz) and conventional high (≥130 Hz) frequency STN DBS settings on sleep macroarchitectural features in individuals with PD. In this post hoc, exploratory analysis, we conducted polysomnography (PSG)-derived quantitative electroencephalography (qEEG) assessments in a cohort of 15 individuals with PD who had undergone STN DBS treatment a median 13.5 months prior to study participation. Fourteen participants had unilateral DBS and 1 had bilateral DBS. During three nonconsecutive nights of PSG, the participants were assessed under three different DBS conditions: DBS OFF, DBS LOW frequency (60 Hz), and DBS HIGH frequency (≥130 Hz). The primary objective of this study was to investigate the changes in sleep spindle density across the three DBS conditions using repeated-measures analysis of variance. Additionally, we examined various secondary outcomes related to sleep qEEG features. For all participants, PSG-derived EEG data underwent meticulous manual inspection, with the exclusion of any segments affected by movement artifact. Following artifact rejection, sleep qEEG analysis was conducted on frontal and central leads. The measures included slow wave (SW) and spindle density and morphological characteristics, SW-spindle phase-amplitude coupling, and spectral power analysis during non-rapid eye movement (NREM) sleep.

Results

The analysis revealed that spindle density was significantly higher in the DBS HIGH condition compared to the DBS LOW condition. Surprisingly, we found that SW amplitude during NREM was significantly higher in the DBS LOW condition compared to DBS OFF and DBS HIGH conditions. However, no significant differences were observed in the other sleep qEEG features during sleep at different DBS conditions.

Conclusion

This study presents preliminary evidence suggesting that conventional HIGH frequency DBS settings enhance sleep spindle density in PD. Conversely, LOW frequency settings may have beneficial effects on increasing slow wave amplitude during sleep. These findings may inform mechanisms underlying subjective improvements in sleep quality reported in association with DBS. Moreover, this work supports the need for additional research on the influence of surgical interventions on sleep disorders, which are prevalent and debilitating non-motor symptoms in PD.

Sleep outcomes and related factors in Parkinson's disease after subthalamic deep brain electrode implantation: a retrospective cohort study

Background

Subthalamic nucleus deep brain stimulation (STN-DBS) improves sleep qualities in Parkinson's disease (PD) patients; however, it remains elusive whether STN-DBS improves sleep by directly influencing the sleep circuit or alleviates other cardinal symptoms such as motor functions, other confounding factors including stimulation intensity may also involve. Studying the effect of microlesion effect (MLE) on sleep after STN-DBS electrode implantation may address this issue.

Objective

To examine the influence of MLE on sleep quality and related factors in PD, as well as the effects of regional and lateral specific correlations with sleep outcomes after STN-DBS electrode implantation.

Study Design

Case-control study; Level of evidence, 3.

Data Sources and Methods

In 78 PD patients who underwent bilateral STN-DBS surgery in our center, we compared the sleep qualities, motor performances, anti-Parkinsonian drug dosage, and emotional conditions at preoperative baseline and postoperative 1-month follow-up. We determined the related factors of sleep outcomes and visualized the electrodes position, simulated the MLE-engendered volume of tissue lesioned (VTL), and investigated sleep-related sweet/sour spots and laterality in STN.

Results

MLE improves sleep quality with Pittsburgh Sleep Quality Index (PSQI) by 13.36% and Parkinson's Disease Sleep Scale-2 (PDSS-2) by 17.95%. Motor (P = 0.014) and emotional (P = 0.001) improvements were both positively correlated with sleep improvements. However, MLE in STN associative subregions, as an independent factor, may cause sleep deterioration (r = 0.348, P = 0.002), and only the left STN showed significance (r = 0.327, P = 0.004). Sweet spot analysis also indicated part of the left STN associative subregion is the sour spot indicative of sleep deterioration.

Conclusion

The MLE of STN-DBS can overall improve sleep quality in PD patients, with a positive correlation between motor and emotional improvements. However, independent of all other factors, the MLE in the STN associative subregion, particularly the left side, may cause sleep deterioration.

Quantitative Sleep Electroencephalogram in Parkinson's Disease: A Case-Control Study

BACKGROUND

Sleep disorders are common in Parkinson's disease (PD) and include alterations in sleep-related EEG oscillations.

OBJECTIVE

This case-control study tested the hypothesis that patients with PD would have a lower density of Scalp-Slow Wave (SW) oscillations and higher slow-to-fast frequencies ratio in rapid eye movement (REM) sleep than non-PD controls. Other sleep-related quantitative EEG (qEEG) features were also examined, including SW morphology, sleep spindles, and Scalp-SW spindle phase-amplitude coupling.

METHODS

Polysomnography (PSG)-derived sleep EEG was compared between PD participants (n = 56) and non-PD controls (n = 30). Following artifact rejection, sleep qEEG analysis was performed in frontal and central leads. Measures included SW density and morphological features of SW and sleep spindles, SW-spindle phase-amplitude coupling, and spectral power analysis in Non-REM (NREM) and REM. Differences in qEEG features between PD and non-PD controls were compared using two-tailed Welch's t-tests, and correction for multiple comparisons was performed per the Benjamini-Hochberg method.

RESULTS

SW density was lower in PD than in non-PD controls (F = 13.5, p' = 0.003). The PD group also exhibited higher ratio of slow REM EEG frequencies (F = 4.23, p' = 0.013), higher slow spindle peak frequency (F = 24.7, p' <  0.002), and greater SW-spindle coupling angle distribution non-uniformity (strength) (F = 7.30, p' = 0.034).

CONCLUSION

This study comprehensively evaluates sleep qEEG including SW-spindle phase amplitude coupling in PD compared to non-PD controls. These findings provide novel insights into how neurodegenerative disease disrupts electrophysiological sleep rhythms. Considering the role of sleep oscillatory activity on neural plasticity, future studies should investigate the influence of these qEEG markers on cognition in PD.

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.

Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior

One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of cognition. In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain–computer interfaces for applications in neuroprosthetics has supported a genuine revolution in the field. However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such as attention, motivation and decision making. While improvement in this field of BCIs is growing fast, a new research focus has emerged from the development of strategies for decoding neural activity. In this review, we aim at exploring how the advanced in decoding of brain activity is becoming a major neuroscience tool moving forward our understanding of brain functions, providing a robust theoretical framework to test predictions on the relationship between brain activity and cognition and behavior.

Management of Sleep Disturbances in Parkinson's Disease

Parkinson's disease (PD) is defined by its motor symptoms rigidity, tremor, and akinesia. However, non-motor symptoms, particularly autonomic disorders and sleep disturbances, occur frequently in PD causing equivalent or even greater discomfort than motor symptoms effectively decreasing quality of life in patients and caregivers. Most common sleep disturbances in PD are insomnia, sleep disordered breathing, excessive daytime sleepiness, REM sleep behavior disorder, and sleep-related movement disorders such as restless legs syndrome. Despite their high prevalence, therapeutic options in the in- and outpatient setting are limited, partly due to lack of scientific evidence. The importance of sleep disturbances in neurodegenerative diseases has been further emphasized by recent evidence indicating a bidirectional relationship between neurodegeneration and sleep. A more profound insight into the underlying pathophysiological mechanisms intertwining sleep and neurodegeneration might lead to unique and individually tailored disease modifying or even neuroprotective therapeutic options in the long run. Therefore, current evidence concerning the management of sleep disturbances in PD will be discussed with the aim of providing a substantiated scaffolding for clinical decisions in long-term PD therapy.

The Impact of Deep Brain Stimulation on Sleep in Parkinson's Disease: An update

Background:

Parkinson’s disease (PD) can have a significant impact on sleep. Deep brain stimulation (DBS) is an effective treatment for motor features of PD, but less is understood about the impact DBS may have on sleep architecture and various sleep issues commonly seen in PD.

Objective:

To review the impact of DBS on various sleep issues in PD.

Methods:

We reviewed the literature regarding the impact of DBS on sleep patterns, nocturnal motor and non-motor symptoms, and sleep disorders in PD.

Results:

Objective sleep measures on polysomnography (PSG), including sleep latency and wake after sleep onset improve after subthalamic nucleus (STN) and globus pallidus interna (GPi) DBS. Subjective sleep measures, nocturnal motor symptoms, and some non-motor symptoms (nocturia) also may improve. Current evidence suggests STN DBS has no impact on Rapid Eye Movement Behavior Disorder (RBD), while STN DBS may improve symptoms of Restless Legs Syndrome (RLS). There are no studies that have evaluated the impact of GPi DBS on RBD, while it is unclear if GPi has an effect on RLS in PD.

Conclusion:

DBS therapy at either site appears to improve objective and subjective sleep parameters in patients with PD. Most likely, the improvement of motor and some non-motor nocturnal symptoms leads to an increase in total sleep time by up to an hour, as well as reduction of sleep fragmentation. DBS most likely has no impact on RBD, while there is evidence that STN DBS appears to help reduce RLS severity. Further studies are needed.

Machine learning-based personalized subthalamic biomarkers predict ON-OFF levodopa states in Parkinson patients

Objective.Adaptive deep brain stimulation (aDBS) based on subthalamic nucleus (STN) electrophysiology has recently been proposed to improve clinical outcomes of DBS for Parkinson's disease (PD) patients. Many current models for aDBS are based on one or two electrophysiological features of STN activity, such as beta or gamma activity. Although these models have shown interesting results, we hypothesized that an aDBS model that includes many STN activity parameters will yield better clinical results. The objective of this study was to investigate the most appropriate STN neurophysiological biomarkers, detectable over long periods of time, that can predict OFF and ON levodopa states in PD patients.Approach.Long-term local field potentials (LFPs) were recorded from eight STNs (four PD patients) during 92 recording sessions (44 OFF and 48 ON levodopa states), over a period of 3-12 months. Electrophysiological analysis included the power of frequency bands, band power ratio and burst features. A total of 140 engineered features was extracted for 20 040 epochs (each epoch lasting 5 s). Based on these engineered features, machine learning (ML) models classified LFPs as OFF vs ON levodopa states.Main results.Beta and gamma band activity alone poorly predicts OFF vs ON levodopa states, with an accuracy of 0.66 and 0.64, respectively. Group ML analysis slightly improved prediction rates, but personalized ML analysis, based on individualized engineered electrophysiological features, were markedly better, predicting OFF vs ON levodopa states with an accuracy of 0.8 for support vector machine learning models.Significance.We showed that individual patients have unique sets of STN neurophysiological biomarkers that can be detected over long periods of time. ML models revealed that personally classified engineered features most accurately predict OFF vs ON levodopa states. Future development of aDBS for PD patients might include personalized ML algorithms.

Slow Wave Sleep and EEG Delta Spectral Power are Associated with Cognitive Function in Parkinson's Disease

BACKGROUND

Cognitive and sleep dysfunction are common non-motor symptoms in Parkinson's disease (PD).

OBJECTIVE

Determine the relationship between slow wave sleep (SWS) and cognitive performance in PD.

METHODS

Thirty-two PD participants were evaluated with polysomnography and a comprehensive level II neurocognitive battery, as defined by the Movement Disorders Society Task Force for diagnosis of PD-mild cognitive impairment. Raw scores for each test were transformed into z-scores using normative data. Z-scores were averaged to obtain domain scores, and domain scores were averaged to determine the Composite Cognitive Score (CCS), the primary outcome. Participants were grouped by percent of SWS into High SWS and Low SWS groups and compared on CCS and other outcomes using 2-sided t-tests or Mann-Whitney U. Correlations of cognitive outcomes with sleep architecture and EEG spectral power were performed.

RESULTS

Participants in the High SWS group demonstrated better global cognitive function (CCS) (p = 0.01, effect size: r = 0.45). In exploratory analyses, the High SWS group showed better performance in domains of executive function (effect size: Cohen's d = 1.05), language (d = 0.95), and processing speed (d = 1.12). Percentage of SWS was correlated with global cognition and executive function, language, and processing speed. Frontal EEG delta power during N3 was correlated with the CCS and executive function. Cognition was not correlated with subjective sleep quality.

CONCLUSION

Increased SWS and higher delta spectral power are associated with better cognitive performance in PD. This demonstrates the significant relationship between sleep and cognitive function and suggests that interventions to improve sleep might improve cognition in individuals with PD.

Randomized, Controlled Trial of Exercise on Objective and Subjective Sleep in Parkinson's Disease

BACKGROUND

Sleep dysfunction is common and disabling in persons with Parkinson's Disease (PD). Exercise improves motor symptoms and subjective sleep quality in PD, but there are no published studies evaluating the impact of exercise on objective sleep outcomes. The goal of this study was to to determine if high-intensity exercise rehabilitation combining resistance training and body-weight interval training, compared with a sleep hygiene control improved objective sleep outcomes in PD.

METHODS

Persons with PD (Hoehn & Yahr stages 2-3; aged ≥45 years, not in a regular exercise program) were randomized to exercise (supervised 3 times a week for 16 weeks; n = 27) or a sleep hygiene, no-exercise control (in-person discussion and monthly phone calls; n = 28). Participants underwent polysomnography at baseline and post-intervention. Change in sleep efficiency was the primary outcome, measured from baseline to post-intervention. Intervention effects were evaluated with general linear models with measurement of group × time interaction. As secondary outcomes, we evaluated changes in other aspects of sleep architecture and compared the effects of acute and chronic training on objective sleep outcomes.

RESULTS

The exercise group showed significant improvement in sleep efficiency compared with the sleep hygiene group (group × time interaction: F = 16.0, P < 0.001, d = 1.08). Other parameters of sleep architecture also improved in exercise compared with sleep hygiene, including total sleep time, wake after sleep onset, and slow-wave sleep. Chronic but not acute exercise improved sleep efficiency compared with baseline.

CONCLUSIONS

High-intensity exercise rehabilitation improves objective sleep outcomes in PD. Exercise is an effective nonpharmacological intervention to improve this disabling nonmotor symptom in PD. © 2020 International Parkinson and Movement Disorder Society.

Automatic Sleep Stage Classification Based on Subthalamic Local Field Potentials

Deep brain stimulation (DBS) is an established treatment for patients with Parkinson's disease (PD). Sleep disorders are common complications of PD and affected by subthalamic DBS treatment. To achieve more precise neuromodulation, chronicsleepmonitoringand closed-loop DBS toward sleep-wake cycles could potentially be utilized. Local field potential (LFP) signals that are sensed by the DBS electrode could be processed as primary feedback signals. This is the first study to systematically investigate the sleep-stage classification based on LFPs in subthalamic nucleus (STN). With our newly developed recording and transmission system, STN-LFPs were collected from 12 PD patients during wakefulness and nocturnal polysomnography sleep monitoring at one month after DBS implantation. Automatic sleep-stage classificationmodels were built with robust and interpretable machine learning methods (support vector machine and decision tree). The accuracy, sensitivity, selectivity, and specificity of the classification reached high values (above90% at most measures) at group and individual levels. Features extracted in alpha (8-13 Hz), beta (13-35 Hz), and gamma (35-50 Hz) bandswere found to contribute the most to the classification. These results will directly guide the engineering development of implantable sleepmonitoring and closed-loopDBS and pave the way for a better understanding of the STN-LFP sleep patterns.

Deep Brain Stimulation and Sleep-Wake Disturbances in Parkinson Disease: A Review

Sleep-wake disturbances are common non-motor manifestations in Parkinson Disease (PD). Complex pathophysiological changes secondary to neurodegeneration in combination with motor symptoms and dopaminergic medications contribute to development of sleep-wake disturbances. The management of sleep complaints in PD is important as this symptom can affect daily activities and impair quality of life. Deep brain stimulation (DBS) is an effective adjunctive therapy for management of motor symptoms in PD. However, its effect on non-motor symptoms including sleep-wake disturbances is not widely understood. In this article, we reviewed studies assessing the effect of DBS at various therapeutic targets on sleep-wake disturbances. Of the studies examining the role of DBS in sleep-wake disturbances, the effect of subthalamic nucleus stimulation is most widely studied and has shown improvement in sleep quality, sleep efficiency, and sleep duration. Although, studies investigating changes in sleep with stimulation of thalamus, globus pallidus interna, and pedunculopontine nucleus are limited, they support the potential for modulation of sleep-wake centers with DBS at these sites. The mechanism by which DBS at different anatomical targets affects sleep-wake disturbances in PD is unclear and may involves multiple factors, including improved motor symptoms, medication adjustment, and direct modulation of sleep-wake centers.

Pedestrian safety in patients with Parkinson's disease: A case-control study

BACKGROUND

Patients with Parkinson's disease experience debilitating motor symptoms as well as nonmotor symptoms, such as cognitive dysfunction and sleep disorders. This constellation of symptoms has the potential to negatively influence pedestrian safety. The objective of this study was to investigate the association of motor symptoms, daytime sleepiness, impaired vigilance, and cognitive dysfunction with pedestrian behavior in patients with Parkinson's disease and healthy older adults.

METHODS

Fifty Parkinson's disease and 25 control participants were evaluated within a virtual reality pedestrian environment and completed assessments of motor performance, daytime sleepiness (Epworth Sleepiness Scale), vigilance (psychomotor vigilance task), and visual processing speed (Useful Field of View) outside the virtual reality environment. The primary outcome measure was time to contact, defined as the time remaining until a participant would have been hit by an approaching vehicle while crossing the virtual street.

RESULTS

The virtual reality pedestrian environment was feasible in all participants. Patients with Parkinson's disease demonstrated riskier pedestrian behavior compared with controls. Among Parkinson's disease participants, walking speed, objective measures of vigilance, and visual processing speed were correlated with pedestrian behavior, with walking speed the strongest predictor of time to contact, explaining 48% of the variance. Vigilance explained an additional 8% of the variance. In controls, vigilance was also important for street-crossing safety, but older age was the most robust predictor of pedestrian safety.

CONCLUSIONS

Walking speed is associated with unsafe pedestrian behavior in patients with Parkinson's disease. In contrast, age was the strongest predictor of pedestrian safety in healthy older adults. © 2017 International Parkinson and Movement Disorder Society.