Tremor Reduction by Deep Brain Stimulation Is Associated With Gamma Power Suppression in Parkinson's Disease

Current and future applications of local field potential-guided programming for Parkinson's disease with the Percept™ rechargeable neurostimulator

Deep brain stimulation (DBS) has been established as an effective neuromodulatory treatment for Parkinson's disease (PD) with motor complications or refractory tremor. Various DBS devices with unique technology platforms are commercially available and deliver continuous, open-loop stimulation. The Percept™ family of neurostimulators use BrainSense™ technology with five key features to sense local field potentials while stimulating, enabling integration of physiologic data into the routine practice of DBS programming. The newly approved Percept™ rechargeable RC implantable pulse generator offers a smaller, thinner design and reduced recharge time with prolonged recharge interval. In this review, we describe the application of local field potential sensing-based programming in PD and highlight the potential future clinical implementation of closed-loop stimulation using the Percept™ RC implantable pulse generator.

Transient Increases in Neural Oscillations and Motor Deficits in a Mouse Model of Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms like tremors and bradykinesia. PD's pathology involves the aggregation of α-synuclein and loss of dopaminergic neurons, leading to altered neural oscillations in the cortico-basal ganglia-thalamic network. Despite extensive research, the relationship between the motor symptoms of PD and transient changes in brain oscillations before and after motor tasks in different brain regions remain unclear. This study aimed to investigate neural oscillations in both healthy and PD model mice using local field potential (LFP) recordings from multiple brain regions during rest and locomotion. The histological evaluation confirmed the significant dopaminergic neuron loss in the injection side in 6-OHDA lesioned mice. Behavioral tests showed motor deficits in these mice, including impaired coordination and increased forelimb asymmetry. The LFP analysis revealed increased delta, theta, alpha, beta, and gamma band activity in 6-OHDA lesioned mice during movement, with significant increases in multiple brain regions, including the primary motor cortex (M1), caudate-putamen (CPu), subthalamic nucleus (STN), substantia nigra pars compacta (SNc), and pedunculopontine nucleus (PPN). Taken together, these results show that the motor symptoms of PD are accompanied by significant transient increases in brain oscillations, especially in the gamma band. This study provides potential biomarkers for early diagnosis and therapeutic evaluation by elucidating the relationship between specific neural oscillations and motor deficits in PD.

The guiding effect of local field potential during deep brain stimulation surgery for programming in Parkinson's disease patients

BACKGROUND

Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery require subsequent programming, which is complex and cumbersome. The local field potential (LFP) in the deep brain is associated with motor symptom improvement. The current study aimed to identify LFP biomarkers correlated with improved motor symptoms in PD patients after DBS and verify their guiding role in postoperative programming.

METHODS

Initially, the study included 36 PD patients undergoing DBS surgery. Temporary external electrical stimulation was performed during electrode implantation, and LFP signals around the electrode contacts were collected before and after stimulation. The stimulating contact at 6 months of programming was regarded as the optimal and effective stimulating contact. The LFP signal of this contact during surgery was analyzed to identify potential LFP biomarkers. Next, we randomly assigned another 30 PD patients who had undergone DBS to physician empirical programming and LFP biomarker-guided programming groups and compared the outcomes.

RESULTS

In the first part of the study, LFP signals of electrode contacts changed after electrical stimulation. Electrical stimulation reduced gamma energy and the beta/alpha oscillation ratio. The different programming method groups were compared, indicating the superiority of beta/alpha oscillations ratio-guided programming over physician experience programming for patients' improvement rate (IR) of UPDRS-III. There were no significant differences in the IR of UPDRS-III, post-LED, IR-PDQ39, number of programmings, and the contact change rate between the gamma oscillations-guided programming and empirical programming groups.

CONCLUSION

Overall, the findings reveal that gamma oscillations and the beta/alpha oscillations ratio are potential biomarkers for programming in PD patients after DBS. Instead of relying solely on spike action potential signals from single neurons, LFP biomarkers can provide the appropriate depth for electrode placement.

Parkinsonian rest tremor can be distinguished from voluntary hand movements based on subthalamic and cortical activity

OBJECTIVE

To distinguish Parkinsonian rest tremor and different voluntary hand movements by analyzing brain activity.

METHODS

We re-analyzed magnetoencephalography and local field potential recordings from the subthalamic nucleus of six patients with Parkinson's disease. Data were obtained after withdrawal from dopaminergic medication (Med Off) and after administration of levodopa (Med On). Using gradient-boosted tree learning, we classified epochs as tremor, fist-clenching, forearm extension or tremor-free rest.

RESULTS

Subthalamic activity alone was insufficient for distinguishing the four different motor states (balanced accuracy mean: 38%, std: 7%). The combination of cortical and subthalamic features, in contrast, allowed for a much more accurate classification (balanced accuracy mean: 75%, std: 17%). Adding a single cortical area improved balanced accuracy by 17% on average, as compared to classification based on subthalamic activity alone. In most patients, the most informative cortical areas were sensorimotor cortical regions. Decoding performance was similar in Med On and Med Off.

CONCLUSIONS

Electrophysiological recordings allow for distinguishing several motor states, provided that cortical signals are monitored in addition to subthalamic activity.

SIGNIFICANCE

By combining cortical recordings, subcortical recordings and machine learning, adaptive deep brain stimulation systems might be able to detect tremor specifically and to respond adequately to several motor states.

From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation

Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson's disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual "forgetting" and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.

Tuned to Tremor: Increased Sensitivity of Cortico-Basal Ganglia Neurons to Tremor Frequency in the MPTP Nonhuman Primate Model of Parkinson's Disease

Rest tremor is one of the most prominent clinical features of Parkinson's disease (PD). Here, we hypothesized that cortico-basal ganglia neurons tend to fire in a pattern that matches PD tremor frequency, suggesting a resonance phenomenon. We recorded spiking activity in the primary motor cortex (M1) and globus pallidus external segment of 2 female nonhuman primates, before and after parkinsonian state induction with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. The arm of nonhuman primates was passively rotated at seven different frequencies surrounding and overlapping PD tremor frequency. We found entrainment of the spiking activity to arm rotation and a significant sharpening of the tuning curves in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine state, with a peak response at frequencies that matched the frequency of PD tremor. These results reveal increased sensitivity of the cortico-basal ganglia network to tremor frequency and could indicate that this network acts not only as a tremor switch but is involved in setting its frequency.SIGNIFICANCE STATEMENT Tremor is a prominent clinical feature of Parkinson's disease; however, its underlying pathophysiology is still poorly understood. Using electrophysiological recordings of single cortico-basal ganglia neurons before and after the induction of a parkinsonian state, and in response to passive arm rotation, this study reports increased sensitivity to tremor frequency in Parkinson's disease. We found sharpening of the population tuning to the midrange of the tested frequencies (1-13.3 Hz) in the healthy state that further increased in the parkinsonian state. These results hint at the increased frequency-tuned sensitivity of cortico-basal ganglia neurons and suggest that they tend to resonate with the tremor.

Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.

A systematic review of local field potential physiomarkers in Parkinson's disease: from clinical correlations to adaptive deep brain stimulation algorithms

Deep brain stimulation (DBS) treatment has proven effective in suppressing symptoms of rigidity, bradykinesia, and tremor in Parkinson's disease. Still, patients may suffer from disabling fluctuations in motor and non-motor symptom severity during the day. Conventional DBS treatment consists of continuous stimulation but can potentially be further optimised by adapting stimulation settings to the presence or absence of symptoms through closed-loop control. This critically relies on the use of 'physiomarkers' extracted from (neuro)physiological signals. Ideal physiomarkers for adaptive DBS (aDBS) are indicative of symptom severity, detectable in every patient, and technically suitable for implementation. In the last decades, much effort has been put into the detection of local field potential (LFP) physiomarkers and in their use in clinical practice. We conducted a research synthesis of the correlations that have been reported between LFP signal features and one or more specific PD motor symptoms. Features based on the spectral beta band (~ 13 to 30 Hz) explained ~ 17% of individual variability in bradykinesia and rigidity symptom severity. Limitations of beta band oscillations as physiomarker are discussed, and strategies for further improvement of aDBS are explored.

Binaural acoustic stimulation in patients with Parkinson's disease

Acoustic stimulation can improve motor symptoms in Parkinson's disease (PD) and might therefore represent a potential non-invasive treatment option. Scalp electroencephalography studies in healthy subjects indicate that specifically binaural beat stimulation (BBS) in the gamma frequency range is associated with synchronized cortical oscillations at 40 Hertz (Hz). Several studies suggest that oscillations in the gamma-frequency range (>30 Hz) serve a prokinetic function in PD. In this double-blind, randomized study, 25 PD patients were recruited. The study was conducted with (ON) and without dopaminergic medication (OFF). Each drug condition consisted of two phases (no stimulation and acoustic stimulation). The acoustic stimulation phase was divided into two blocks including BBS and conventional acoustic stimulation (CAS) as a control condition. For BBS, a modulated frequency of 35 Hz was used (left: 320 Hz; right: 355 Hz) and for CAS 340 Hz on both sides. We assessed effects on motor performance using Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and two validated commercially available portable devices (Kinesia ONE™ and Kinesia 360™) measuring motor symptoms such as dyskinesia, bradykinesia, and tremor. Repeated measures ANOVA revealed that BBS improved resting tremor on the side of the more affected limb in the OFF condition, as measured by wearables (F_(2,48) = 3.61, p = 0.035). However, BBS did not exert a general positive effect on motor symptoms as assessed via MDS-UPDRS (F_(2,48) = 1.00, p = 0.327). For CAS, we did not observe an improvement in specific symptoms but rather an overall beneficial effect on motor performance (MDS-UPDRS total score OFF medication: F_(2,48) = 4.17, p = 0.021; wearable scores: F_(2,48) = 2.46, p = 0.097). In this study, we found an improvement of resting tremor when applying BBS in the gamma frequency band OFF medication. Moreover, the positive effects of CAS underline the general positive potential for improvement of motor function by acoustically supported therapeutic approaches. However, more studies are needed to fully characterize the clinical relevance of BBS and to further optimize its ameliorating effects.

Diagnosis and Treatment of Tremor in Parkinson's Disease Using Mechanical Devices

BACKGROUND

Parkinsonian tremors are sometimes confused with essential tremors or other conditions. Recently, researchers conducted several studies on tremor evaluation using wearable sensors and devices, which may support accurate diagnosis. Mechanical devices are also commonly used to treat tremors and have been actively researched and developed. Here, we aimed to review recent progress and the efficacy of the devices related to Parkinsonian tremors.

METHODS

The PubMed and Scopus databases were searched for articles. We searched for "Parkinson disease" and "tremor" and "device".

RESULTS

Eighty-six articles were selected by our systematic approach. Many studies demonstrated that the diagnosis and evaluation of tremors in patients with PD can be done accurately by machine learning algorithms. Mechanical devices for tremor suppression include deep brain stimulation (DBS), electrical muscle stimulation, and orthosis. In recent years, adaptive DBS and optimization of stimulation parameters have been studied to further improve treatment efficacy.

CONCLUSIONS

Due to developments using state-of-the-art techniques, effectiveness in diagnosing and evaluating tremor and suppressing it using these devices is satisfactorily high in many studies. However, other than DBS, no devices are in practical use. To acquire high-level evidence, large-scale studies and randomized controlled trials are needed for these devices.

Hallmarks of neurodegenerative disease: A systems pharmacology perspective

Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

Toward therapeutic electrophysiology: beta-band suppression as a biomarker in chronic local field potential recordings

Adaptive deep brain stimulation (aDBS) is a promising concept for feedback-based neurostimulation, with the potential of clinical implementation with the sensing-enabled Percept neurostimulator. We aim to characterize chronic electrophysiological activity during stimulation and to validate beta-band activity as a biomarker for bradykinesia. Subthalamic activity was recorded during stepwise stimulation amplitude increase OFF medication in 10 Parkinson's patients during rest and finger tapping. Offline analysis of wavelet-transformed beta-band activity and assessment of inter-variable relationships in linear mixed effects models were implemented. There was a stepwise suppression of low-beta activity with increasing stimulation intensity (p = 0.002). Low-beta power was negatively correlated with movement speed and predictive for velocity improvements (p < 0.001), stimulation amplitude for beta suppression (p < 0.001). Here, we characterize beta-band modulation as a chronic biomarker for motor performance. Our investigations support the use of electrophysiology in therapy optimization, providing evidence for the use of biomarker analysis for clinical aDBS.

Diving into the Subcortex: The Potential of Chronic Subcortical Sensing for Unravelling Basal Ganglia Function and Optimization of Deep Brain STIMULATION

Subcortical structures are a relative neurophysiological 'terra incognita' owing to their location within the skull. While perioperative subcortical sensing has been performed for more than 20 years, the neurophysiology of the basal ganglia in the home setting has remained almost unexplored. However, with the recent advent of implantable pulse generators (IPG) that are able to record neural activity, the opportunity to chronically record local field potentials (LFPs) directly from electrodes implanted for deep brain stimulation opens up. This allows for a breakthrough of chronic subcortical sensing into fundamental research and clinical practice. In this review an extensive overview of the current state of subcortical sensing is provided. The widespread potential of chronic subcortical sensing for investigational and clinical use is discussed. Finally, status and future perspectives of the most promising application of chronic subcortical sensing -i.e., adaptive deep brain stimulation (aDBS)- are discussed in the context of movement disorders. The development of aDBS based on both chronic subcortical and cortical sensing has the potential to dramatically change clinical practice and the life of patients with movement disorders. However, several barriers still stand in the way of clinical implementation. Advancements regarding IPG and lead technology, physiomarkers, and aDBS algorithms as well as harnessing artificial intelligence, multimodality and sensing in the naturalistic setting are needed to bring aDBS to clinical practice.

Temporal Patterns of Spontaneous Fixational Eye Movements: The Influence of Basal Ganglia

BACKGROUND

Spontaneity is a unique feature of the nervous system. One of the fundamentally critical and recognized forms of spontaneous motor activity is witnessed in the visuomotor system. Microsaccades, the miniature spontaneous eye movements, are critical for the visual perception. We hypothesized that microsaccades follow specific temporal patterns that are modulated by the basal ganglia output.

METHODS

We used high-resolution video-oculography to capture microsaccades in 48 subjects (31 healthy and 17 with Parkinson's disease) when subjects were asked to hold their gaze on a straight-ahead target projected on white background. We analyzed spontaneous discharge patterns of microsaccades.

RESULTS

The first analysis considering coefficient of variation in intersaccadic interval distribution demonstrated that microsaccades in Parkinson's disease are more dispersed than the control group. The second analysis scrutinized microsaccades' temporal variability and revealed 3 distinct occurrence patterns: regular rhythmic, clustered, and randomly occurring following a Poisson-like process. The regular pattern was relatively more common in Parkinson's disease. Subthalamic DBS modulated this temporal pattern. The amount of change in the temporal variability depended on the DBS-induced volume of tissue activation and its overlap with the subthalamic nucleus. The third analysis determined the autocorrelations of microsaccades within 2-second time windows. We found that Parkinson's disease altered local temporal organization in microsaccade generation, and DBS had a modulatory effect.

CONCLUSION

The microsaccades occur in 3 temporal patterns. The basal ganglia are one of the modulators of the microsaccade spontaneity.

Brain oscillations and Parkinson disease

Brain oscillations have been associated with Parkinson's disease (PD) for a long time mainly due to the fundamental oscillatory nature of parkinsonian rest tremor. Over the years, this association has been extended to frequencies well above that of tremor, largely owing to the opportunities offered by deep brain stimulation (DBS) to record electrical activity directly from the patients' basal ganglia. This chapter reviews the results of research on brain oscillations in PD focusing on theta (4-7Hz), beta (13-35Hz), gamma (70-80Hz) and high-frequency oscillations (200-400Hz). For each of these oscillations, we describe localization and interaction with brain structures and between frequencies, changes due to dopamine intake, task-related modulation, and clinical relevance. The study of brain oscillations will also help to dissect the mechanisms of action of DBS. Overall, the chapter tentatively depicts PD in terms of "oscillopathy."

Influence of inter-electrode distance on subthalamic nucleus local field potential recordings in Parkinson's disease

OBJECTIVES

To evaluate spectra and their correlations with clinical symptoms of local field potentials (LFP) acquired from wide- and close-spaced contacts (i.e. between contacts 0-3 or LFP03, and contacts 1-2 or LFP12 respectively) on the same DBS electrode within the subthalamus (STN) in Parkinson's disease (PD), before and after levodopa administration.

METHODS

LFP12 and LFP03 were recorded from 20 PD patients. We evaluated oscillatory power, local and switched phase-amplitude coupling (l- and Sw-PAC) and correlation with motor symptoms (UPDRSIII).

RESULTS

Before levodopa, both LFP03 and LFP12 power in the α band inversely correlated with UPDRSIII. Differences between contacts were found in the low-frequency bands power. After levodopa, differences in UPDRSIII were associated to changes in LFP03 low-β and LFP12 HFO (high frequency oscillations, 250-350 Hz) power, while a modulation of the low-β power and an increased β-LFO (low frequency oscillations, 15-45 Hz) PAC was found only for LFP12.

CONCLUSION

This study reveals differences in spectral pattern between LFP12 and LFP03 before and after levodopa administration, as well as different correlations with PD motor symptoms.

SIGNIFICANCE

Differences between LFP12 and LFP03 may offer an opportunity for optimizing adaptive deep brain stimulation (aDBS) protocols for PD. LFP12 can be used to detect β-HFO coupling and β power (i.e. bradykinesia), while LFP03 are optimal for low frequency oscillations (dyskinesias).

Subthalamic dynamic neural states correlate with motor symptoms in Parkinson's Disease

OBJECTIVE

This study aims to discriminate the dynamic synchronization states from the subthalamic local field potentials and investigate their correlations with the motor symptoms in Parkinson's Disease (PD).

METHODS

The resting-state local field potentials of 10 patients with PD were recorded from the subthalamic nucleus. The dynamic neural states of multiple oscillations were discriminated and analyzed. The Spearman correlation was used to investigate the correlations between occurrence rate or duration of dynamic neural states and the severity of motor symptoms.

RESULTS

The proportion of long low-beta and theta synchronized state was significantly correlated with the general motor symptom and tremor, respectively. The duration of combined low/high-beta state was significantly correlated with rigidity, and the duration of combined alpha/high-beta state was significantly correlated with bradykinesia.

CONCLUSIONS

This study provides evidence that motor symptoms are associated with the neural states coded with multiple oscillations in PD.

SIGNIFICANCE

This study may advance the understanding of the neurophysiological mechanisms of the motor symptoms and provide potential biomarkers for closed-loop deep brain stimulation in PD.

Neurophysiological biomarkers to optimize deep brain stimulation in movement disorders

Intraoperative neurophysiological information could increase accuracy of surgical deep brain stimulation (DBS) lead placement. Subsequently, DBS therapy could be optimized by specifically targeting pathological activity. In Parkinson’s disease, local field potentials (LFPs) excessively synchronized in the beta band (13–35 Hz) correlate with akinetic-rigid symptoms and their response to DBS therapy, particularly low beta band suppression (13–20 Hz) and high frequency gamma facilitation (35–250 Hz). In dystonia, LFPs abnormally synchronize in the theta/alpha (4–13 Hz), beta and gamma (60–90 Hz) bands. Phasic dystonic symptoms and their response to DBS correlate with changes in theta/alpha synchronization. In essential tremor, LFPs excessively synchronize in the theta/alpha and beta bands. Adaptive DBS systems will individualize pathological characteristics of neurophysiological signals to automatically deliver therapeutic DBS pulses of specific spatial and temporal parameters.

Local field potentials in Parkinson's disease: A frequency-based review

Deep brain stimulation (DBS) surgery offers a unique opportunity to record local field potentials (LFPs), the electrophysiological population activity of neurons surrounding the depth electrode in the target area. With direct access to the subcortical activity, LFP research has provided valuable insight into disease mechanisms and cognitive processes and inspired the advent of adaptive DBS for Parkinson's disease (PD). A frequency-based framework is usually employed to interpret the implications of LFP signatures in LFP studies on PD. This approach standardizes the methodology, simplifies the interpretation of LFP patterns, and makes the results comparable across studies. Importantly, previous works have found that activity patterns do not represent disease-specific activity but rather symptom-specific or task-specific neuronal signatures that relate to the current motor, cognitive or emotional state of the patient and the underlying disease. In the present review, we aim to highlight distinguishing features of frequency-specific activities, mainly within the motor domain, recorded from DBS electrodes in patients with PD. Associations of the commonly reported frequency bands (delta, theta, alpha, beta, gamma, and high-frequency oscillations) to motor signs are discussed with respect to band-related phenomena such as individual tremor and high/low beta frequency activity, as well as dynamic transients of beta bursts. We provide an overview on how electrophysiology research in DBS patients has revealed and will continuously reveal new information about pathophysiology, symptoms, and behavior, e.g., when combining deep LFP and surface electrocorticography recordings.

Multi-disease Deep Brain Stimulation

Current closed-loop deep brain stimulation (DBS) devices can generally tackle one disorder. This paper presents the design and evaluation of a multi-disease closed-loop DBS device that can sense multiple brain biomarkers, detect a disorder, and adaptively deliver electrical stimulation pulses based on the disease state. The device consists of: (i) a neural sensor, (ii) a controller involving a feature extractor, a disease classifier, and a control strategy, and (iii) neural stimulator. The neural sensor records and processes local field potentials and spikes from within the brain using two low-frequency and high-frequency channels. The feature extractor digitally processes the output of the neural sensor, and extracts five potential biomarkers: alpha, beta, slow gamma, high-frequency oscillations, and spikes. The disease classifier identifies the type of the neurological disorder through an analysis of the biomarkers' amplitude features. The control strategy considers the disease state and supplies the stimulation settings to the neural stimulator. Both the disease classifier and control strategy are based on fuzzy algorithms. The neural stimulator generates electrical stimulation pulses according to the control commands, and delivers them to the target area of the brain. The device can generate current stimulation pulses with specific amplitude, frequency, and duration. The fabricated device has the maximum radius of 15 mm. Its total weight including the circuit board, battery and battery holder is 5.1 g. The performance of the integrated device has been evaluated through six bench and in-vitro experiments. The experimental results are presented, analyzed, and discussed. Six bench and in-vitro experiments were conducted using sinusoidal, normal pre-recorded, and diseased neural signals representing normal, epilepsy, depression and PD conditions. The results obtained through these tests indicate the successful neural sensing, classification, control, and neural stimulating performance.

Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation

Alongside stereotactic magnetic resonance imaging, microelectrode recording (MER) is frequently used during the deep brain stimulation (DBS) surgery for optimal target localization. The aim of this study is to optimize subthalamic nucleus (STN) mapping using MER analytical patterns. 16 patients underwent bilateral STN-DBS. MER was performed simultaneously for 5 microelectrodes in a setting of Ben’s-gun pattern in awake patients. Using spikes and background activity several different parameters and their spectral estimates in various frequency bands including low frequency (2–7 Hz), Alpha (8–12 Hz), Beta (sub-divided as Low_Beta (13–20 Hz) and High_Beta (21–30 Hz)) and Gamma (31 to 49 Hz) were computed. The optimal STN lead placement with the most optimal clinical effect/side-effect ratio accorded to the maximum spike rate in 85% of the implantation. Mean amplitude of background activity in the low beta frequency range was corresponding to right depth in 85% and right location in 94% of the implantation respectively. MER can be used for STN mapping and intraoperative decisions for the implantation of DBS electrode leads with a high accuracy. Spiking and background activity in the beta range are the most promising independent parameters for the delimitation of the proper anatomical site.

EEG and MEG primers for tracking DBS network effects

Deep brain stimulation (DBS) is an effective treatment method for a range of neurological and psychiatric disorders. It involves implantation of stimulating electrodes in a precisely guided fashion into subcortical structures and, at a later stage, chronic stimulation of these structures with an implantable pulse generator. While the DBS surgery makes it possible to both record brain activity and stimulate parts of the brain that are difficult to reach with non-invasive techniques, electroencephalography (EEG) and magnetoencephalography (MEG) provide complementary information from other brain areas, which can be used to characterize brain networks targeted through DBS. This requires, however, the careful consideration of different types of artifacts in the data acquisition and the subsequent analyses. Here, we review both the technical issues associated with EEG/MEG recordings in DBS patients and the experimental findings to date. One major line of research is simultaneous recording of local field potentials (LFPs) from DBS targets and EEG/MEG. These studies revealed a set of cortico-subcortical coherent networks functioning at distinguishable physiological frequencies. Specific network responses were linked to clinical state, task or stimulation parameters. Another experimental approach is mapping of DBS-targeted networks in chronically implanted patients by recording EEG/MEG responses during stimulation. One can track responses evoked by single stimulation pulses or bursts as well as brain state shifts caused by DBS. These studies have the potential to provide biomarkers for network responses that can be adapted to guide stereotactic implantation or optimization of stimulation parameters. This is especially important for diseases where the clinical effect of DBS is delayed or develops slowly over time. The same biomarkers could also potentially be utilized for the online control of DBS network effects in the new generation of closed-loop stimulators that are currently entering clinical use. Through future studies, the use of network biomarkers may facilitate the integration of circuit physiology into clinical decision making.

Use of Functional Magnetic Resonance Imaging to Assess How Motor Phenotypes of Parkinson's Disease Respond to Deep Brain Stimulation

BACKGROUND

Deep brain stimulation (DBS) is a well-accepted treatment of Parkinson's disease (PD). Motor phenotypes include tremor-dominant (TD), akinesia-rigidity (AR), and postural instability gait disorder (PIGD). The mechanism of action in how DBS modulates motor symptom relief remains unknown.

OBJECTIVE

Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) was used to determine whether the functional activity varies in response to DBS depending on PD phenotypes.

MATERIALS AND METHODS

Subjects underwent an fMRI scan with DBS cycling ON and OFF. The effects of DBS cycling on BOLD activation in each phenotype were documented through voxel-wise analysis. For each region of interest, ANOVAs were performed using T-values and covariate analyses were conducted. Further, a correlation analysis was performed comparing stimulation settings to T-values. Lastly, T-values of subjects with motor improvement were compared to those who worsened.

RESULTS

As a group, BOLD activation with DBS-ON resulted in activation in the motor thalamus (p < 0.01) and globus pallidus externa (p < 0.01). AR patients had more activation in the supplementary motor area (SMA) compared to PIGD (p < 0.01) and TD cohorts (p < 0.01). Further, the AR cohort had more activation in primary motor cortex (MI) compared to the TD cohort (p = 0.02). Implanted nuclei (p = 0.01) and phenotype (p = <0.01) affected activity in MI and phenotype alone affected SMA activity (p = <0.01). A positive correlation was seen between thalamic activation and pulse-width (p = 0.03) and between caudate and total electrical energy delivered (p = 0.04).

CONCLUSIONS

These data suggest that DBS modulates network activity differently based on patient motor phenotype. Improved understanding of these differences may further our knowledge about the mechanisms of DBS action on PD motor symptoms and to optimize treatment.

A Population Model of Deep Brain Stimulation in Movement Disorders From Circuits to Cells

For more than 30 years, deep brain stimulation (DBS) has been used to target the symptoms of a number of neurological disorders and in particular movement disorders such as Parkinson’s disease (PD) and essential tremor (ET). It is known that the loss of dopaminergic neurons in the substantia nigra leads to PD, while the exact impact of this on the brain dynamics is not fully understood, the presence of beta-band oscillatory activity is thought to be pathological. The cause of ET, however, remains uncertain, however pathological oscillations in the thalamocortical-cerebellar network have been linked to tremor. Both of these movement disorders are treated with DBS, which entails the surgical implantation of electrodes into a patient’s brain. While DBS leads to an improvement in symptoms for many patients, the mechanisms underlying this improvement is not clearly understood, and computational modeling has been used extensively to improve this. Many of the models used to study DBS and its effect on the human brain have mainly utilized single neuron and single axon biophysical models. We have previously shown in separate models however, that the use of population models can shed much light on the mechanisms of the underlying pathological neural activity in PD and ET in turn, and on the mechanisms underlying DBS. Together, this work suggested that the dynamics of the cerebellar-basal ganglia thalamocortical network support oscillations at frequency range relevant to movement disorders. Here, we propose a new combined model of this network and present new results that demonstrate that both Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arise from the dynamics of such a network. We find regions in the parameter space demonstrating the different dynamics and go on to examine the transition from one oscillatory regime to another as well as the impact of DBS on these different types of pathological activity. This work will allow us to better understand the changes in brain activity induced by DBS, and allow us to optimize this clinical therapy, particularly in terms of target selection and parameter setting.

Tremor and myoclonus

Tremor and myoclonus are two common hyperkinetic movement disorders. Tremor is characterized by rhythmic oscillatory movements while myoclonic jerks are usually arrhythmic. Tremor can be classified into subtypes including the most common types: essential, enhanced physiological, and parkinsonian tremor. Myoclonus classification is based on its anatomic origin: cortical, subcortical, spinal, and peripheral myoclonus. The clinical presentations are unfortunately not always classic and electrophysiologic investigations can be helpful in making a phenotypic diagnosis. Video-polymyography is the main technique to (sub)classify the involuntary movements. In myoclonus, advanced electrophysiologic testing, such as back-averaging, coherence analysis, somatosensory-evoked potentials, and the C-reflex can be of additional value. Recent developments in tremor point toward a role for intermuscular coherence analysis to differentiate between tremor subtypes. Classification of the movement disorder based on clinical and electrophysiologic features is important, as it enables the search for an etiological diagnosis and guides tailored treatment.

A Miniature Dual-Biomarker-Based Sensing and Conditioning Device for Closed-Loop DBS

In this paper, a dual-biomarker-based neural sensing and conditioning device is proposed for closing the feedback loop in deep brain stimulation devices. The device explores both local field potentials (LFPs) and action potentials (APs) as measured biomarkers. It includes two channels, each having four main parts: (1) a pre-amplifier with built-in low-pass filter, (2) a ground shifting circuit, (3) an amplifier with low-pass function, and (4) a high-pass filter. The design specifications include miniature-size, light-weight, and 100 dB gain in the LFP and AP channels. This device has been validated through bench and in-vitro tests. The bench tests have been performed using different sinusoidal signals and pre-recorded neural signals. The in-vitro tests have been conducted in the saline solution that mimics the brain environment. The total weight of the device including a 3 V coin battery, and battery holder is 1.2 g. The diameter of the device is 11.2 mm. The device can be used to concurrently sense LFPs and APs for closing the feedback loop in closed-loop deep brain stimulation systems. It provides a tetherless head-mountable platform suitable for pre-clinical trials.

Neural Circuit and Clinical Insights from Intraoperative Recordings During Deep Brain Stimulation Surgery

Observations using invasive neural recordings from patient populations undergoing neurosurgical interventions have led to critical breakthroughs in our understanding of human neural circuit function and malfunction. The opportunity to interact with patients during neurophysiological mapping allowed for early insights in functional localization to improve surgical outcomes, but has since expanded into exploring fundamental aspects of human cognition including reward processing, language, the storage and retrieval of memory, decision-making, as well as sensory and motor processing. The increasing use of chronic neuromodulation, via deep brain stimulation, for a spectrum of neurological and psychiatric conditions has in tandem led to increased opportunity for linking theories of cognitive processing and neural circuit function. Our purpose here is to motivate the neuroscience and neurosurgical community to capitalize on the opportunities that this next decade will bring. To this end, we will highlight recent studies that have successfully leveraged invasive recordings during deep brain stimulation surgery to advance our understanding of human cognition with an emphasis on reward processing, improving clinical outcomes, and informing advances in neuromodulatory interventions.

Axial symptoms predict mortality in patients with Parkinson disease and subthalamic stimulation

Objective

To characterize how disease progression is associated with mortality in a large cohort of patients with Parkinson disease (PD) with long-term follow-up after subthalamic nucleus deep brain stimulation (STN-DBS).

Methods

Motor and cognitive disabilities were assessed before and 1, 2, 5, and 10 years after STN-DBS in 143 consecutive patients with PD. We measured motor symptoms “off” and “on” levodopa and STN-DBS and recorded causes of death. We used linear mixed models to characterize symptom progression, including interactions between treatment conditions and time to determine how treatments changed efficacy. We used joint models to link symptom progression to mortality.

Results

Median observation time was 12 years after surgery, during which akinesia, rigidity, and axial symptoms worsened, with mean increases of 8.8 (SD 6.5), 1.8 (3.1), and 5.4 (4.1) points from year 1–10 after surgery (“on” dopamine/“on” STN-DBS), respectively. Responses to dopaminergic medication and STN-DBS were attenuated with time, but remained effective for all except axial symptoms, for which both treatments and their combination were predicted to be ineffective 20 years after surgery. Cognitive status significantly declined. Forty-one patients died, with a median time to death of 9 years after surgery. The current level of axial disability was the only symptom that significantly predicted death (hazard ratio 4.30 [SE 1.50] per unit of square-root transformed axial score).

Conclusions

We quantified long-term symptom progression and attenuation of dopaminergic medication and STN-DBS treatment efficacy in patients with PD and linked symptom progression to mortality. Axial disability significantly predicts individual risk of death after surgery, which may be useful for planning therapeutic strategies in PD.

Longitudinal Recordings Reveal Transient Increase of Alpha/Low-Beta Power in the Subthalamic Nucleus Associated With the Onset of Parkinsonian Rest Tremor

Functional magnetic resonance imaging studies suggest that different subcortico-cortical circuits control different aspects of Parkinsonian rest tremor. The basal ganglia were proposed to drive tremor onset, and the cerebellum was suggested to be responsible for tremor maintenance (“dimmer-switch” hypothesis). Although several electrophysiological correlates of tremor have been described, it is currently unclear whether any of these is specific to tremor onset or maintenance. In this study, we present data from a single patient measured repeatedly within 2 years after implantation of a deep brain stimulation (DBS) system capable of recording brain activity from the target. Local field potentials (LFPs) from the subthalamic nucleus and the scalp electroencephalogram were recorded 1 week, 3 months, 6 months, 1 year, and 2 years after surgery. Importantly, the patient suffered from severe rest tremor of the lower limbs, which could be interrupted voluntarily by repositioning the feet. This provided the unique opportunity to record many tremor onsets in succession. We found that tremor onset and tremor maintenance were characterized by distinct modulations of subthalamic oscillations. Alpha/low-beta power increased transiently immediately after tremor onset. In contrast, beta power was continuously suppressed during tremor maintenance. Tremor maintenance was additionally associated with subthalamic and cortical power increases around individual tremor frequency. To our knowledge, this is the first evidence of distinct subthalamic LFP modulations in tremor onset and tremor maintenance. Our observations suggest the existence of an acceleration signal for Parkinsonian rest tremor in the basal ganglia, in line with the “dimmer-switch” hypothesis.

A Programmable Multi-biomarker Neural Sensor for Closed-loop DBS

Most of the current closed-loop DBS devices use a single biomarker in their feedback loop which may limit their performance and applications. This paper presents design, fabrication, and validation of a programmable multi-biomarker neural sensor which can be integrated into closed-loop DBS devices. The device is capable of sensing a combination of low-frequency (7-45 Hz), and high-frequency (200-1000 Hz) neural signals. The signals can be amplified with a digitally programmable gain within the range 50-100 dB. The neural signals can be stored into a local memory for processing and validation. The sensing and storage functions are implemented via a combination of analog and digital circuits involving preamplifiers, filters, programmable post-amplifiers, microcontroller, digital potentiometer, and flash memory. The device is fabricated, and its performance is validated through: (i) bench tests using sinusoidal and pre-recorded neural signals, (ii) in-vitro tests using pre-recorded neural signals in saline solution, and (iii) in-vivo tests by recording neural signals from freely-moving laboratory mice. The animals were implanted with a PlasticsOne electrode, and recording was conducted after recovery from the electrode implantation surgery. The experimental results are presented and discussed confirming the successful operation of the device. The size and weight of the device enable tetherless back-mountable use in pre-clinical trials.

CONTINUOUS SENSORY ELECTRICAL STIMULATION FOR THE SUPPRESSION OF PARKINSONIAN REST TREMOR

This paper aims to investigate the effect of continuous sensory electrical stimulation (SES) on the suppression of a Parkinsonian rest tremor. Fourteen patients with Parkinson’s disease participated in this study. Three wrist muscles were electrically stimulated on sensory level under motor threshold. Intensity of stimulation was determined for each muscle of each patient as the maximum tolerable current amplitude that does not induce muscle contraction. Tri-axial gyro sensors were attached to three upper limb segments. The angular velocity of each segment was measured for each of the three sessions, i.e., PRE-, ON- and POST- stimulations. Outcome measures were the tremor amplitude and main frequency of each axis in the power spectrum. Decrease in tremor amplitude was significant at ON and POST sessions in finger and at POST session in hand and forearm. Decrease in main frequency was significant mainly at ON session. About one-third of patients showed reduction in tremor power at ON-stimulation and at POST-stimulation. Subjects with suppression of tremor showed greater initial tremor amplitude than those without suppression. Continuous SES suppressed the Parkinsonian rest tremor. The results suggest that the properties of tremor-generating loop may be altered by continuous SES and the effect lasts temporarily.

Frequency-dependent effects of subthalamic deep brain stimulation on motor symptoms in Parkinson's disease: a meta-analysis of controlled trials

This study aims to investigate how the frequency settings of deep brain stimulation (DBS) targeting the subthalamic nucleus (STN) influence the motor symptoms of Parkinson's disease (PD). Stimulation with frequencies less than 100 Hz (mostly 60 or 80 Hz) is considered low-frequency stimulation (LFS) and with frequencies greater than 100 Hz (mostly 130 or 150 Hz) is considered high-frequency stimulation (HFS). We conducted a comprehensive literature review and meta-analysis with a random-effect model. Ten studies with 132 patients were included in our analysis. The pooled results showed no significant difference in the total Unified Parkinson Disease Rating Scale part III (UPDRS-III) scores (mean effect, -1.50; p = 0.19) or the rigidity subscore between HFS and LFS. Compared to LFS, HFS induced greater reduction in the tremor subscore within the medication-off condition (mean effect, 1.01; p = 0.002), while no significance was shown within the medication-on condition (mean effect, 0.01; p = 0.92). LFS induced greater reduction in akinesia subscore (mean effect, -1.68, p = 0.003), the time to complete the stand-walk-sit (SWS) test (mean effect, -4.84; p < 0.00001), and the number of freezing of gait (FOG) (mean effect, -1.71; p = 0.03). These results suggest that two types of frequency settings may have different effects, that is, HFS induces better responses for tremor and LFS induces greater response for akinesia, gait, and FOG, respectively, which are worthwhile to be confirmed in future study, and will ultimately inform the clinical practice in the management of PD using STN-DBS.

Fornical Closed-Loop Stimulation for Alzheimer's Disease

Pharmacological neuromodulation strategies have shown limited efficacy in treating memory deficits related to Alzheimer's disease (AD). Despite encouraging results from a few preclinical studies, clinical trials investigating open-loop deep brain stimulation (DBS) for AD have not been successful. Recent refinements in understanding the various phases of memory processes, animal studies investigating phase-specific modulation of hippocampal activity during memorization, and clinical studies using closed-loop DBS strategies to treat patients with movement disorders, all point to the need to investigate closed-loop fornical DBS strategies to better understand memory dynamics and potentially treat memory deficits in AD preclinical models.

Intra-operative characterisation of subthalamic oscillations in Parkinson's disease

OBJECTIVE

This study aims to use the activities recorded directly from the deep brain stimulation (DBS) electrode to address the focality and distinct nature of the local field potential (LFP) activities of different frequency.

METHODS

Pre-operative and intra-operative magnetic resonance imaging (MRI) were acquired from patients with Parkinson's disease (PD) who underwent DBS in the subthalamic nucleus and intra-operative LFP recording at rest and during cued movements. Images were reconstructed and 3-D visualized using Lead-DBS® toolbox to determine the coordinates of contact. The resting spectral power and movement-related power modulation of LFP oscillations were estimated.

RESULTS

Both subthalamic LFP activity recorded at rest and its modulation by movement had focal maxima in the alpha, beta and gamma bands. The spatial distribution of alpha band activity and its modulation was significantly different to that in the beta band. Moreover, there were significant differences in the scale and timing of movement related modulation across the frequency bands.

CONCLUSION

Subthalamic LFP activities within specific frequency bands can be distinguished by spatial topography and pattern of movement related modulation.

SIGNIFICANCE

Assessment of the frequency, focality and pattern of movement related modulation of subthalamic LFPs reveals a heterogeneity of neural population activity in this region. This could potentially be leveraged to finesse intra-operative targeting and post-operative contact selection.

Parkinsonian Rest Tremor Is Associated With Modulations of Subthalamic High-Frequency Oscillations

BACKGROUND

High frequency oscillations (>200 Hz) have been observed in the basal ganglia of PD patients and were shown to be modulated by the administration of levodopa and voluntary movement.

OBJECTIVE

The objective of this study was to test whether the power of high-frequency oscillations in the STN is associated with spontaneous manifestation of parkinsonian rest tremor.

METHODS

The electromyogram of both forearms and local field potentials from the STN were recorded in 11 PD patients (10 men, age 58 [9.4] years, disease duration 9.2 [6.3] years). Patients were recorded at rest and while performing repetitive hand movements before and after levodopa intake. High-frequency oscillation power was compared across epochs containing rest tremor, tremor-free rest, or voluntary movement and related to the tremor cycle.

RESULTS

We observed prominent slow (200-300 Hz) and fast (300-400 Hz) high-frequency oscillations. The ratio between slow and fast high-frequency oscillation power increased when tremor became manifest. This increase was consistent across nuclei (94%) and occurred in medication ON and OFF. The ratio outperformed other potential markers of tremor, such as power at individual tremor frequency, beta power, or low gamma power. For voluntary movement, we did not observe a significant difference when compared with rest or rest tremor. Finally, rhythmic modulations of high-frequency oscillation power occurred within the tremor cycle.

CONCLUSIONS

Subthalamic high-frequency oscillation power is closely linked to the occurrence of parkinsonian rest tremor. The balance between slow and fast high-frequency oscillation power combines information on motor and medication state. © 2016 International Parkinson and Movement Disorder Society.

Tremor stability index: a new tool for differential diagnosis in tremor syndromes

See Vidailhet et al. (doi:10.1093/brain/awx140) for a scientific commentary on this article. Misdiagnosis among tremor syndromes is common, and can impact on both clinical care and research. To date no validated neurophysiological technique is available that has proven to have good classification performance, and the diagnostic gold standard is the clinical evaluation made by a movement disorders expert. We present a robust new neurophysiological measure, the tremor stability index, which can discriminate Parkinson’s disease tremor and essential tremor with high diagnostic accuracy. The tremor stability index is derived from kinematic measurements of tremulous activity. It was assessed in a test cohort comprising 16 rest tremor recordings in tremor-dominant Parkinson’s disease and 20 postural tremor recordings in essential tremor, and validated on a second, independent cohort comprising a further 55 tremulous Parkinson’s disease and essential tremor recordings. Clinical diagnosis was used as gold standard. One hundred seconds of tremor recording were selected for analysis in each patient. The classification accuracy of the new index was assessed by binary logistic regression and by receiver operating characteristic analysis. The diagnostic performance was examined by calculating the sensitivity, specificity, accuracy, likelihood ratio positive, likelihood ratio negative, area under the receiver operating characteristic curve, and by cross-validation. Tremor stability index with a cut-off of 1.05 gave good classification performance for Parkinson’s disease tremor and essential tremor, in both test and validation datasets. Tremor stability index maximum sensitivity, specificity and accuracy were 95%, 95% and 92%, respectively. Receiver operating characteristic analysis showed an area under the curve of 0.916 (95% confidence interval 0.797–1.000) for the test dataset and a value of 0.855 (95% confidence interval 0.754–0.957) for the validation dataset. Classification accuracy proved independent of recording device and posture. The tremor stability index can aid in the differential diagnosis of the two most common tremor types. It has a high diagnostic accuracy, can be derived from short, cheap, widely available and non-invasive tremor recordings, and is independent of operator or postural context in its interpretation.

Parkinsonian rest tremor can be detected accurately based on neuronal oscillations recorded from the subthalamic nucleus

OBJECTIVE

To investigate the possibility of tremor detection based on deep brain activity.

METHODS

We re-analyzed recordings of local field potentials (LFPs) from the subthalamic nucleus in 10 PD patients (12 body sides) with spontaneously fluctuating rest tremor. Power in several frequency bands was estimated and used as input to Hidden Markov Models (HMMs) which classified short data segments as either tremor-free rest or rest tremor. HMMs were compared to direct threshold application to individual power features.

RESULTS

Applying a threshold directly to band-limited power was insufficient for tremor detection (mean area under the curve [AUC] of receiver operating characteristic: 0.64, STD: 0.19). Multi-feature HMMs, in contrast, allowed for accurate detection (mean AUC: 0.82, STD: 0.15), using four power features obtained from a single contact pair. Within-patient training yielded better accuracy than across-patient training (0.84vs. 0.78, p=0.03), yet tremor could often be detected accurately with either approach. High frequency oscillations (>200Hz) were the best performing individual feature.

CONCLUSIONS

LFP-based markers of tremor are robust enough to allow for accurate tremor detection in short data segments, provided that appropriate statistical models are used.

SIGNIFICANCE

LFP-based markers of tremor could be useful control signals for closed-loop deep brain stimulation.

The role of deep brain stimulation in Parkinson's disease: an overview and update on new developments

Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by the loss of neuronal dopamine production in the brain. Oral therapies primarily augment the dopaminergic pathway. As the disease progresses, more continuous delivery of therapy is commonly needed. Deep brain stimulation (DBS) has become an effective therapy option for several different neurologic and psychiatric conditions, including PD. It currently has US Food and Drug Administration approval for PD and essential tremor, as well as a humanitarian device exception for dystonia and obsessive-compulsive disorder. For PD treatment, it is currently approved specifically for those patients suffering from complications of pharmacotherapy, including motor fluctuations or dyskinesias, and a disease process of at least 4 years of duration. Studies have demonstrated superiority of DBS and medical management compared to medical management alone in selected PD patients. Optimal patient selection criteria, choice of target, and programming methods for PD and the other indications for DBS are important topics that continue to be explored and remain works in progress. In addition, new hardware options, such as different types of leads, and different software options have recently become available, increasing the potential for greater efficacy and/or reduced side effects. This review gives an overview of therapeutic management in PD, specifically highlighting DBS and some of the recent changes with surgical therapy.

A neural mass model of basal ganglia nuclei simulates pathological beta rhythm in Parkinson's disease

An increase in beta oscillations within the basal ganglia nuclei has been shown to be associated with movement disorder, such as Parkinson's disease. The motor cortex and an excitatory-inhibitory neuronal network composed of the subthalamic nucleus (STN) and the external globus pallidus (GPe) are thought to play an important role in the generation of these oscillations. In this paper, we propose a neuron mass model of the basal ganglia on the population level that reproduces the Parkinsonian oscillations in a reciprocal excitatory-inhibitory network. Moreover, it is shown that the generation and frequency of these pathological beta oscillations are varied by the coupling strength and the intrinsic characteristics of the basal ganglia. Simulation results reveal that increase of the coupling strength induces the generation of the beta oscillation, as well as enhances the oscillation frequency. However, for the intrinsic properties of each nucleus in the excitatory-inhibitory network, the STN primarily influences the generation of the beta oscillation while the GPe mainly determines its frequency. Interestingly, describing function analysis applied on this model theoretically explains the mechanism of pathological beta oscillations.

From intentions to actions: Neural oscillations encode motor processes through phase, amplitude and phase-amplitude coupling

Goal-directed motor behavior is associated with changes in patterns of rhythmic neuronal activity across widely distributed brain areas. In particular, movement initiation and execution are mediated by patterns of synchronization and desynchronization that occur concurrently across distinct frequency bands and across multiple motor cortical areas. To date, motor-related local oscillatory modulations have been predominantly examined by quantifying increases or suppressions in spectral power. However, beyond signal power, spectral properties such as phase and phase-amplitude coupling (PAC) have also been shown to carry information with regards to the oscillatory dynamics underlying motor processes. Yet, the distinct functional roles of phase, amplitude and PAC across the planning and execution of goal-directed motor behavior remain largely elusive. Here, we address this question with unprecedented resolution thanks to multi-site intracerebral EEG recordings in human subjects while they performed a delayed motor task. To compare the roles of phase, amplitude and PAC, we monitored intracranial brain signals from 748 sites across six medically intractable epilepsy patients at movement execution, and during the delay period where motor intention is present but execution is withheld. In particular, we used a machine-learning framework to identify the key contributions of various neuronal responses. We found a high degree of overlap between brain network patterns observed during planning and those present during execution. Prominent amplitude increases in the delta (2-4Hz) and high gamma (60-200Hz) bands were observed during both planning and execution. In contrast, motor alpha (8-13Hz) and beta (13-30Hz) power were suppressed during execution, but enhanced during the delay period. Interestingly, single-trial classification revealed that low-frequency phase information, rather than spectral power change, was the most discriminant feature in dissociating action from intention. Additionally, despite providing weaker decoding, PAC features led to statistically significant classification of motor states, particularly in anterior cingulate cortex and premotor brain areas. These results advance our understanding of the distinct and partly overlapping involvement of phase, amplitude and the coupling between them, in the neuronal mechanisms underlying motor intentions and executions.

Long-Term Task- and Dopamine-Dependent Dynamics of Subthalamic Local Field Potentials in Parkinson's Disease

Subthalamic nucleus (STN) local field potentials (LFP) are neural signals that have been shown to reveal motor and language behavior, as well as pathological parkinsonian states. We use a research-grade implantable neurostimulator (INS) with data collection capabilities to record STN-LFP outside the operating room to determine the reliability of the signals over time and assess their dynamics with respect to behavior and dopaminergic medication. Seven subjects were implanted with the recording augmented deep brain stimulation (DBS) system, and bilateral STN-LFP recordings were collected in the clinic over twelve months. Subjects were cued to perform voluntary motor and language behaviors in on and off medication states. The STN-LFP recorded with the INS demonstrated behavior-modulated desynchronization of beta frequency (13-30 Hz) and synchronization of low gamma frequency (35-70 Hz) oscillations. Dopaminergic medication did not diminish the relative beta frequency oscillatory desynchronization with movement. However, movement-related gamma frequency oscillatory synchronization was only observed in the medication on state. We observed significant inter-subject variability, but observed consistent STN-LFP activity across recording systems and over a one-year period for each subject. These findings demonstrate that an INS system can provide robust STN-LFP recordings in ambulatory patients, allowing for these signals to be recorded in settings that better represent natural environments in which patients are in a variety of medication states.

Oscillatory activity in the basal ganglia and deep brain stimulation

Over the past 10 years, research into the neurophysiology of the basal ganglia has provided new insights into the pathophysiology of movement disorders. The presence of pathological oscillations at specific frequencies has been linked to different signs and symptoms in PD and dystonia, suggesting a new model to explain basal ganglia dysfunction. These advances occurred in parallel with improvements in imaging and neurosurgical techniques, both of which having facilitated the more widespread use of DBS to modulate dysfunctional circuits. High-frequency stimulation is thought to disrupt pathological activity in the motor cortex/basal ganglia network; however, it is not easy to explain all of its effects based only on changes in network oscillations. In this viewpoint, we suggest that a return to classic anatomical concepts might help to understand some apparently paradoxical findings. © 2016 International Parkinson and Movement Disorder Society.

Innovations in deep brain stimulation methodology

Deep brain stimulation is a powerful clinical method for movement disorders that no longer respond satisfactorily to pharmacological management, but its progress has been hampered by stagnation in technological procedure solutions and device development. Recently, the combined research efforts of bioengineers, neuroscientists, and clinicians have helped to better understand the mechanisms of deep brain stimulation, and solutions for the translational roadblock are emerging. Here, we define the needs for methodological advances in deep brain stimulation from a neurophysiological perspective and describe technological solutions that are currently evaluated for near-term clinical application. © 2016 International Parkinson and Movement Disorder Society.

Defining Prolonged Length of Acute Care Stay for Surgically and Conservatively Treated Patients with Spontaneous Intracerebral Hemorrhage: A Population-Based Analysis

Background. The definition of prolonged length of stay (LOS) during acute care remains unclear among surgically and conservatively treated patients with intracerebral hemorrhage (ICH). Methods. Using a population-based quality assessment registry, we calculated change points in LOS for surgically and conservatively treated patients with ICH. The influence of comorbidities, baseline characteristics at admission, and in-hospital complications on prolonged LOS was evaluated in a multivariate model. Results. Overall, 13272 patients with ICH were included in the analysis. Surgical therapy of the hematoma was documented in 1405 (10.6%) patients. Change points for LOS were 22 days (CI: 8, 22; CL 98%) for surgically treated patients and 16 days (CI: 16, 16; CL: 99%) for conservatively treated patients. Ventilation therapy was related to prolonged LOS in surgically (OR: 2.2, 95% CI: 1.5–3.1; P < 0.001) and conservatively treated patients (OR: 2.5, 95% CI: 2.2–2.9; P < 0.001). Two or more in-hospital complications in surgical patients (OR: 2.7, 95% CI: 2.1–3.5) and ≥1 in conservative patients (OR: 3.0, 95% CI: 2.7–3.3) were predictors of prolonged LOS. Conclusion. The definition of prolonged LOS after ICH could be useful for several aspects of quality management and research. Preventing in-hospital complications could decrease the number of patients with prolonged LOS.

Parkinson's Disease with Fatigue: Clinical Characteristics and Potential Mechanisms Relevant to α-Synuclein Oligomer

Background and Purpose

The aim of this study was to identify the clinical characteristics and potential mechanisms relevant to pathological proteins in Parkinson's disease (PD) patients who experience fatigue.

Methods

PD patients (n=102) were evaluated using a fatigue severity scale and scales for motor and nonmotor symptoms. The levels of three pathological proteins—α-synuclein oligomer, β-amyloid (Aβ)_1-42, and tau—were measured in 102 cerebrospinal fluid (CSF) samples from these PD patients. Linear regression analyses were performed between fatigue score and the CSF levels of the above-listed pathological proteins in PD patients.

Results

The frequency of fatigue in the PD patients was 62.75%. The fatigue group had worse motor symptoms and anxiety, depression, and autonomic dysfunction. The CSF level of α-synuclein oligomer was higher and that of Aβ_1-42 was lower in the fatigue group than in the non-fatigue group. In multiple linear regression analyses, fatigue severity was significantly and positively correlated with the α-synuclein oligomer level in the CSF of PD patients, after adjusting for confounders.

Conclusions

PD patients experience a high frequency of fatigue. PD patients with fatigue have worse motor and part nonmotor symptoms. Fatigue in PD patients is associated with an increased α-synuclein oligomer level in the CSF.