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

Subthalamic nucleus local field potential stability in patients with Parkinson's disease

BACKGROUND

Despite the large body of work on local field potentials (LFPs), a measure of oscillatory activity in patients with Parkinson's disease (PD), the longitudinal evolution of LFPs is less explored.

OBJECTIVE

To determine LFP fluctuations collected in clinical settings in patients with PD and STN deep brain stimulation (DBS).

METHODS

Twenty-two STN-DBS patients (age: 67.6 ± 8.3 years; 9 females; disease duration: 10.3 ± 4.5 years) completed bilateral LFP recordings over three visits in the OFF-stimulation setting. Peak and band power measures were calculated from each recording.

RESULTS

After bilateral LFP recordings, at least one peak was detected in 18 (81.8%), 20 (90.9%), and 22 (100%) patients at visit 1, 2, and 3, respectively. No significant differences were seen in primary peak amplitude (F = 2.91, p = 0.060) over time. Amplitude of the second largest peak (F = 5.49, p = 0.006) and low-beta (F = 6.89, p = 0.002), high-beta (F = 13.23, p < 0.001), and gamma (F = 12.71, p < 0.001) band power demonstrated a significant effect of time. Post hoc comparisons determined low-beta power (Visit 1-Visit 2: t = 3.59, p = 0.002; Visit 1-Visit 3: t = 2.61, p = 0.031), high-beta (Visit 1-Visit 2: t = 4.64, p < 0.001; Visit 1-Visit 3: t = 4.23, p < 0.001) and gamma band power (Visit 1-Visit 2: t = 4.65, p < 0.001; Visit 1-Visit 3: t = 4.00, p < 0.001) were significantly increased from visit 1 recordings to both follow-up visits.

CONCLUSION

Our results provide substantial evidence that LFP can reliably be detected across multiple real-world clinical visits in patients with STN-DBS for PD. Moreover, it provides insights on the evolution of these LFPs.

Removal of electrical stimulus artifact in local field potential recorded from subthalamic nucleus by using manifold denoising

BACKGROUND

Deep brain stimulation (DBS) is an effective treatment for movement disorders such as Parkinson's disease (PD). However, local field potentials (LFPs) recorded through lead externalization during high-frequency stimulation (HFS) are contaminated by stimulus artifacts, which require to be removed before further analysis.

NEW METHOD

In this study, a novel stimulus artifact removal algorithm based on manifold denoising, termed Shrinkage and Manifold-based Artifact Removal using Template Adaptation (SMARTA), was proposed to remove artifacts by deriving a template for each stimulus artifact and subtracting it from the signal. Under a low-dimensional manifold assumption, a matrix denoising technique called optimal shrinkage was applied to design a similarity metric such that the template for stimulus artifacts could be accurately recovered.

RESULT

SMARTA was evaluated using semirealistic signals, which were the combination of semirealistic stimulus artifacts recorded in an agar brain model and LFPs of PD patients with no stimulation, and realistic LFP signals recorded in patients with PD during HFS. The results indicated that SMARTA removes stimulus artifacts with a modest distortion in LFP estimates.

COMPARISON WITH EXISTING METHODS

SMARTA was compared with moving-average subtraction, sample-and-interpolate technique, and Hampel filtering.

CONCLUSION

The proposed SMARTA algorithm helps the exploration of the neurophysiological mechanisms of DBS effects.

Abnormally low sensorimotor α band nonlinearity serves as an effective EEG biomarker of Parkinson's disease

Biomarkers obtained from the neurophysiological signals of patients with Parkinson's disease (PD) have objective value in assessing their motor condition for effective diagnosis, monitoring, and clinical intervention. Prominent cortical biomarkers of PD have typically been derived from various β band wave features. This study approached the topic from an alternative perspective and attempted to estimate a recently suggested measure representing α band nonlinear autocorrelative memory from a publicly available EEG dataset that involves 15 patients with earlier-stage PD (dopaminergic medication OFF and ON states) and 16 age-matched healthy controls. The cortical nonlinearity was elevated for the PD ON state compared with the OFF state for bilateral sensorimotor channels C3 and C4 (n = 26; P = 0.003). A similar statistical difference was also identified between PD OFF state and healthy subjects (n = 26; P = 0.049). Analysis over all channels revealed that the α band nonlinearity induced upon medication was constrained to sensorimotor regions. The α nonlinearity measure was compared with a well-accepted cortical biomarker of β-γ phase-amplitude coupling (PAC). They were in moderate negative correlation (r = -0.412; P = 0.036) for only healthy subjects, but not for the patients. The nonlinearity measure was found to be insusceptible to the nonstationary variations within the particular data. Our study provides further evidence that the α band nonlinearity measure can serve as a promising cortical biomarker of PD. The suggested measure can be estimated from a noninvasive low-resolution single scalp EEG channel of patients with relatively early-stage PD, who did not yet need to undergo deep brain stimulation operation.NEW & NOTEWORTHY This study suggests a nonlinearity measure that differentiates Parkinson's disease (PD) dopamine OFF-state scalp EEG data from those of dopamine ON-state patients and healthy subjects. Unlike typical PD cortical biomarkers based on β band activity, this metric shows elevation upon dopaminergic medication in the α band. We provide evidence supporting its potential as an early-stage promising PD biomarker that can be estimated from noninvasive EEG recordings with low resolution and SNR.

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.

Spectral Topography of the Subthalamic Nucleus to Inform Next-Generation Deep Brain Stimulation

BACKGROUND

The landscape of neurophysiological symptoms and behavioral biomarkers in basal ganglia signals for movement disorders is expanding. The clinical translation of sensing-based deep brain stimulation (DBS) also requires a thorough understanding of the anatomical organization of spectral biomarkers within the subthalamic nucleus (STN).

OBJECTIVES

The aims were to systematically investigate the spectral topography, including a wide range of sub-bands in STN local field potentials (LFP) of Parkinson's disease (PD) patients, and to evaluate its predictive performance for clinical response to DBS.

METHODS

STN-LFPs were recorded from 70 PD patients (130 hemispheres) awake and at rest using multicontact DBS electrodes. A comprehensive spatial characterization, including hot spot localization and focality estimation, was performed for multiple sub-bands (delta, theta, alpha, low-beta, high-beta, low-gamma, high-gamma, and fast-gamma (FG) as well as low- and fast high-frequency oscillations [HFO]) and compared to the clinical hot spot for rigidity response to DBS. A spectral biomarker map was established and used to predict the clinical response to DBS.

RESULTS

The STN shows a heterogeneous topographic distribution of different spectral biomarkers, with the strongest segregation in the inferior-superior axis. Relative to the superiorly localized beta hot spot, HFOs (FG, slow HFO) were localized up to 2 mm more inferiorly. Beta oscillations are spatially more spread compared to other sub-bands. Both the spatial proximity of contacts to the beta hot spot and the distance to higher-frequency hot spots were predictive for the best rigidity response to DBS.

CONCLUSIONS

The spatial segregation and properties of spectral biomarkers within the DBS target structure can additionally be informative for the implementation of next-generation sensing-based DBS. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

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.

NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC

Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel counts and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227μJ/class energy efficiency in a compact area of 0.014mm2/channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In-vivo neural recordings using soft μECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease (PD) from human local field potentials (LFPs) was demonstrated.

Clinical neurophysiology of Parkinson's disease and parkinsonism

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This review is part of the series on the clinical neurophysiology of movement disorders and focuses on Parkinson’s disease and parkinsonism.

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The pathophysiology of cardinal parkinsonian motor symptoms and myoclonus are reviewed.

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The recordings from microelectrode and deep brain stimulation electrodes are reported in detail.

This review is part of the series on the clinical neurophysiology of movement disorders. It focuses on Parkinson’s disease and parkinsonism. The topics covered include the pathophysiology of tremor, rigidity and bradykinesia, balance and gait disturbance and myoclonus in Parkinson’s disease. The use of electroencephalography, electromyography, long latency reflexes, cutaneous silent period, studies of cortical excitability with single and paired transcranial magnetic stimulation, studies of plasticity, intraoperative microelectrode recordings and recording of local field potentials from deep brain stimulation, and electrocorticography are also reviewed. In addition to advancing knowledge of pathophysiology, neurophysiological studies can be useful in refining the diagnosis, localization of surgical targets, and help to develop novel therapies for Parkinson’s disease.

Design of a Parkinsonian Biomarkers Combination Optimization Method Using Rodent Model

Adaptive Deep Brain Stimulation (aDBS) has been proposed in literature to avoid the negative consequences associated with the continuous stimulation delivered through traditional deep brain stimulation. This work seeks to determine a group of neural biomarkers that a classification algorithm could use on an aDBS device using rodent animal models. The neural activities were acquired from the primary motor cortex of four Parkinsonian model rats and four healthy rats from a control group. To overcome the variability introduced from the small rat sample size, this work proposes a novel method for combining and running Genetic Feature Selection and Forward Stepwise Feature Selection in an environment where classification accuracy varies greatly based on how the folds are organized before cross-validation. Three separate classification algorithms, Logistic Regression, k-Nearest Neighbor, and Random Forest are used to verify the proposed method. For Logistic Regression, the set of Alpha Power (7-12 Hz), High Beta Power (20-30 Hz), and 55-95 Hz Gamma Power shows the best performance in classification. For k-Nearest Neighbor, the characterizing features are Low Beta Power (12-20 Hz), High Beta Power, All Beta Power (12-30 Hz), 55-95 Hz Gamma Power, and 95-105 Hz Gamma Power. For Random Forest, they are High Beta Power, All Beta Power, 55-95 Hz Gamma Power, 95-105 Hz Gamma Power, and 300-350 Hz High-Frequency Oscillations Power. With the selected feature set, experimental results show an increasing classification accuracy from 59.08% to 77.69% for Logistic Regression, from 49.53% to 73.44% for k-Nearest Neighbor, and from 54.10% to 71.15% for Random Forest. Clinical Relevance- This experiment provides a method for determining the most effective biomarkers from a larger set for classifying Parkinsonian behavior for an aDBS device.

A translational perspective on pathophysiological changes of oscillatory activity in dystonia and parkinsonism

Intracerebral recordings from movement disorders patients undergoing deep brain stimulation have allowed the identification of pathophysiological patterns in oscillatory activity that correlate with symptom severity. Changes in oscillatory synchrony occur within and across brain areas, matching the classification of movement disorders as network disorders. However, the underlying mechanisms of oscillatory changes are difficult to assess in patients, as experimental interventions are technically limited and ethically problematic. This is why animal models play an important role in neurophysiological research of movement disorders. In this review, we highlight the contributions of translational research to the mechanistic understanding of pathological changes in oscillatory activity, with a focus on parkinsonism and dystonia, while addressing the limitations of current findings and proposing possible future directions.

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."

Subthalamic-Cortical Network Reorganization during Parkinson's Tremor

Tremor, a common and often primary symptom of Parkinson's disease, has been modeled with distinct onset and maintenance dynamics. To identify the neurophysiologic correlates of each state, we acquired intraoperative cortical and subthalamic nucleus recordings from 10 patients (9 male, 1 female) performing a naturalistic visual-motor task. From this task, we isolated short epochs of tremor onset and sustained tremor. Comparing these epochs, we found that the subthalamic nucleus was central to tremor onset, as it drove both motor cortical activity and tremor output. Once tremor became sustained, control of tremor shifted to cortex. At the same time, changes in directed functional connectivity across sensorimotor cortex further distinguished the sustained tremor state.SIGNIFICANCE STATEMENT Tremor is a common symptom of Parkinson's disease (PD). While tremor pathophysiology is thought to involve both basal ganglia and cerebello-thalamic-cortical circuits, it is unknown how these structures functionally interact to produce tremor. In this article, we analyzed intracranial recordings from the subthalamic nucleus and sensorimotor cortex in patients with PD undergoing deep brain stimulation surgery. Using an intraoperative task, we examined tremor in two separate dynamic contexts: when tremor first emerged, and when tremor was sustained. We believe that these findings reconcile several models of Parkinson's tremor, while describing the short-timescale dynamics of subcortical-cortical interactions during tremor for the first time. These findings may describe a framework for developing proactive and responsive neurostimulation models for specifically treating tremor.

Deep Brain Stimulation for Tremor: Update on Long-Term Outcomes, Target Considerations and Future Directions

Deep brain stimulation (DBS) of the thalamic ventral intermediate nucleus is one of the main advanced neurosurgical treatments for drug-resistant tremor. However, not every patient may be eligible for this procedure. Nowadays, various other functional neurosurgical procedures are available. In particular cases, radiofrequency thalamotomy, focused ultrasound and radiosurgery are proven alternatives to DBS. Besides, other DBS targets, such as the posterior subthalamic area (PSA) or the dentato-rubro-thalamic tract (DRT), may be appraised as well. In this review, the clinical characteristics and pathophysiology of tremor syndromes, as well as long-term outcomes of DBS in different targets, will be summarized. The effectiveness and safety of lesioning procedures will be discussed, and an evidence-based clinical treatment approach for patients with drug-resistant tremor will be presented. Lastly, the future directions in the treatment of severe tremor syndromes will be elaborated.

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.

High-Frequency Oscillations in the Pallidum: A Pathophysiological Biomarker in Parkinson's Disease?

BACKGROUND

Abnormal oscillatory neural activity in the beta-frequency band (13-35 Hz) is thought to play a role in Parkinson's disease (PD); however, increasing evidence points to alterations in high-frequency ranges (>100 Hz) also having pathophysiological relevance.

OBJECTIVES

Studies have found that power in subthalamic nucleus (STN) high-frequency oscillations is increased with dopaminergic medication and during voluntary movements, implicating these brain rhythms in normal basal ganglia function. The objective of this study was to investigate whether similar signaling occurs in the internal globus pallidus (GPi), a nucleus increasingly used as a target for deep brain stimulation (DBS) for PD.

METHODS

Spontaneous and movement-related GPi field potentials were recorded from DBS leads in 5 externalized PD patients on and off dopaminergic medication, as well as from 3 rhesus monkeys before and after the induction of parkinsonism with the neurotoxin 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine.

RESULTS

In the parkinsonian condition, we identified a prominent oscillatory peak centered at 200-300 Hz that increased during movement. In patients the magnitude of high-frequency oscillation modulation was negatively correlated with bradykinesia. In monkeys, high-frequency oscillations were mostly absent in the naive condition but emerged after the neurotoxin 1-methyl-4-phenyl-1,2,3,6 tetrahydropyridine. In patients, spontaneous high-frequency oscillations were significantly attenuated on-medication.

CONCLUSIONS

Our findings provide evidence in support of the hypothesis that exaggerated, movement-modulated high-frequency oscillations in the GPi are pathophysiological features of PD. These findings suggest that the functional role(s) of high-frequency oscillations may differ between the STN and GPi and motivate additional investigations into their relationship to motor control in normal and diseased states.

Closed-Loop Deep Brain Stimulation for Essential Tremor Based on Thalamic Local Field Potentials

BACKGROUND

High-frequency thalamic stimulation is an effective therapy for essential tremor, which mainly affects voluntary movements and/or sustained postures. However, continuous stimulation may deliver unnecessary current to the brain due to the intermittent nature of the tremor.

OBJECTIVE

We proposed to close the loop of thalamic stimulation by detecting tremor-provoking movement states using local field potentials recorded from the same electrodes implanted for stimulation, so that the stimulation is only delivered when necessary.

METHODS

Eight patients with essential tremor participated in this study. Patient-specific support vector machine classifiers were first trained using data recorded while the patient performed tremor-provoking movements. Then, the trained models were applied in real-time to detect these movements and triggered the delivery of stimulation.

RESULTS

Using the proposed method, stimulation was switched on for 80.37 ± 7.06% of the time when tremor-evoking movements were present. In comparison, the stimulation was switched on for 12.71 ± 7.06% of the time when the patients were at rest and tremor-free. Compared with continuous stimulation, a similar amount of tremor suppression was achieved while only delivering 36.62 ± 13.49% of the energy used in continuous stimulation.

CONCLUSIONS

The results suggest that responsive thalamic stimulation for essential tremor based on tremor-provoking movement detection can be achieved without any requirement for external sensors or additional electrocorticography strips. Further research is required to investigate whether the decoding model is stable across time and generalizable to the variety of activities patients may engage with in everyday life. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Spectral characteristics of subthalamic nucleus local field potentials in Parkinson's disease: Phenotype and movement matter

Parkinson's disease (PD) is clinically heterogeneous across patients and may be classified in three motor phenotypes: tremor dominant (TD), postural instability and gait disorder (PIGD), and undetermined. Despite the significant clinical characterization of motor phenotypes, little is known about how electrophysiological data, particularly subthalamic nucleus local field potentials (STN-LFP), differ between TD and PIGD patients. This is relevant since increased STN-LFP bandpower at α-β range (8-35 Hz) is considered a potential PD biomarker and, therefore, a critical setpoint to drive adaptive deep brain stimulation. Acknowledging STN-LFP differences between phenotypes, mainly in rest and movement states, would better fit DBS to clinical and motor demands. We studied this issue through spectral analyses on 35 STN-LFP in TD and PIGD patients during rest and movement. We demonstrated that higher β₂ activity (22-35 Hz) was observed in PIGD only during rest. Additionally, bandpower differences between rest and movement occurred at the α-β range, but with different patterns as per phenotypes: movement-induced desynchronization concerned lower frequencies in TD (10-20 Hz) and higher frequencies in PIGD patients (21-28 Hz). Finally, when supervised learning algorithms were employed aiming to discriminate PD phenotypes based on STN-LFP bandpower features, movement information had improved the classification accuracy, achieving peak performances when TD and PIGD movement-induced desynchronization ranges were considered. These results suggest that STN-LFP β-band encodes phenotype-movement dependent information in PD patients.

Rapid motor fluctuations reveal short-timescale neurophysiological biomarkers of Parkinson's disease

Objective.

Identifying neural activity biomarkers of brain disease is essential to provide objective estimates of disease burden, obtain reliable feedback regarding therapeutic efficacy, and potentially to serve as a source of control for closed-loop neuromodulation. In Parkinson’s disease (PD), microelectrode recordings (MER) are routinely performed in the basal ganglia to guide electrode implantation for deep brain stimulation (DBS). While pathologically-excessive oscillatory activity has been observed and linked to PD motor dysfunction broadly, the extent to which these signals provide quantitative information about disease expression and fluctuations, particularly at short timescales, is unknown. Furthermore, the degree to which informative signal features are similar or different across patients has not been rigorously investigated. We sought to determine the extent to which motor error in PD across patients can be decoded on a rapid timescale using spectral features of neural activity.

Approach.

Here, we recorded neural activity from the subthalamic nucleus (STN) of subjects with PD undergoing awake DBS surgery while they performed an objective, continuous behavioral assessment that synthesized heterogenous PD motor manifestations to generate a scalar measure of motor dysfunction at short timescales. We then leveraged natural motor performance variations as a ‘ground truth’ to identify corresponding neurophysiological biomarkers.

Main results.

Support vector machines using multi-spectral decoding of neural signals from the STN succeeded in tracking the degree of motor impairment at short timescales (as short as one second). Spectral power across a wide range of frequencies, beyond the classic ‘β’ oscillations, contributed to this decoding, and multi-spectral models consistently outperformed those generated using more isolated frequency bands. While generalized decoding models derived across subjects were able to estimate motor impairment, patient-specific models typically performed better.

Significance.

These results demonstrate that quantitative information about short-timescale PD motor dysfunction is available in STN neural activity, distributed across various patient-specific spectral components, such that an individualized approach will be critical to fully harness this information for optimal disease tracking and closed-loop neuromodulation.

Independently together: subthalamic theta and beta opposite roles in predicting Parkinson's tremor

Tremor is a core feature of Parkinson’s disease and the most easily recognized Parkinsonian sign. Nonetheless, its pathophysiology remains poorly understood. Here, we show that multispectral spiking activity in the posterior-dorso-lateral oscillatory (motor) region of the subthalamic nucleus distinguishes resting tremor from the other Parkinsonian motor signs and strongly correlates with its severity. We evaluated microelectrode-spiking activity from the subthalamic dorsolateral oscillatory region of 70 Parkinson’s disease patients who underwent deep brain stimulation surgery (114 subthalamic nuclei, 166 electrode trajectories). We then investigated the relationship between patients’ clinical Unified Parkinson’s Disease Rating Scale score and their peak theta (4–7 Hz) and beta (13–30 Hz) powers. We found a positive correlation between resting tremor and theta activity (r = 0.41, P < 0.01) and a non-significant negative correlation with beta activity (r = −0.2, P = 0.5). Hypothesizing that the two neuronal frequencies mask each other’s relationship with resting tremor, we created a non-linear model of their proportional spectral powers and investigated its relationship with resting tremor. As hypothesized, patients’ proportional scores correlated better than either theta or beta alone (r = 0.54, P < 0.001). However, theta and beta oscillations were frequently temporally correlated (38/70 patients manifested significant positive temporal correlations and 1/70 exhibited significant negative correlation between the two frequency bands). When comparing theta and beta temporal relationship (r θ β) to patients’ resting tremor scores, we found a significant negative correlation between the two (r = −0.38, P < 0.01). Patients manifesting a positive correlation between the two bands (i.e. theta and beta were likely to appear simultaneously) were found to have lower resting tremor scores than those with near-zero correlation values (i.e. theta and beta were likely to appear separately). We therefore created a new model incorporating patients’ proportional theta–beta power and r θ βscores to obtain an improved neural correlate of resting tremor (r = 0.62, P < 0.001). We then used the Akaike and Bayesian information criteria for model selection and found the multispectral model, incorporating theta–beta proportional power and their correlation, to be the best fitting model, with 0.96 and 0.89 probabilities, respectively. Here we found that as theta increases, beta decreases and the two appear separately—resting tremor is worsened. Our results therefore show that theta and beta convey information about resting tremor in opposite ways. Furthermore, the finding that theta and beta coactivity is negatively correlated with resting tremor suggests that theta–beta non-linear scale may be a valuable biomarker for Parkinson’s resting tremor in future adaptive deep brain stimulation techniques.

Subthalamic Single Cell and Oscillatory Neural Dynamics of a Dyskinetic Medicated Patient With Parkinson's Disease

Single cell neuronal activity (SUA) and local field potentials (LFP) in the subthalamic nucleus (STN) of unmedicated Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery have been well-characterized during microelectrode recordings (MER). However, there is limited knowledge about the changes in the firing patterns and oscillations above and within the territories of STN after the intake of dopaminergic medication. Here, for the first time, we report the STN single cell and oscillatory neural dynamics in a medicated patient with idiopathic PD using intraoperative MER. We recorded LFP and SUA with microelectrodes at various depths during bilateral STN-DBS electrode implantation. We isolated 26 neurons in total and observed that tonic and irregular firing patterns of individual neurons predominated throughout the territories of STN. While burst-type firings have been well-characterized in the dorsal territories of STN in unmedicated patients, interestingly, this activity was not observed in our medicated subject. LFP recordings lacked the excessive beta (8-30 Hz) activity, characteristic of the unmedicated state and signal energy was mainly dominated by slow oscillations below 8 Hz. We observed sharp gamma oscillations between 70 and 90 Hz within and above the STN. Despite the presence of a broadband high frequency activity in 200-400 Hz range, no cross-frequency interaction in the form of phase-amplitude coupling was noted between low and high frequency oscillations of LFPs. While our results are in agreement with the previously reported LFP recordings from the DBS lead in medicated PD patients, the sharp gamma peak present throughout the depth recordings and the lack of bursting firings after levodopa intake have not been reported before. The lack of bursting in SUA, the lack of excessive beta activity and cross frequency coupling between HFOs and lower rhythms further validate the link between bursting firing regime of neurons and pathological oscillatory neural activity in PD-STN. Overall, these observations not only validate the existing literature on the PD electrophysiology in healthy/medicated animal models but also provide insights regarding the underlying electro-pathophysiology of levodopa-induced dyskinesias in PD patients through demonstration of multiscale relationships between single cell firings and field potentials.

Basal ganglia oscillations as biomarkers for targeting circuit dysfunction in Parkinson's disease

Oscillations are a naturally occurring phenomenon in highly interconnected dynamical systems. However, it is thought that excessive synchronized oscillations in brain circuits can be detrimental for many brain functions by disrupting neuronal information processing. Because synchronized basal ganglia oscillations are a hallmark of Parkinson's disease (PD), it has been suggested that aberrant rhythmic activity associated with symptoms of the disease could be used as a physiological biomarker to guide pharmacological and electrical neuromodulatory interventions. We here briefly review the various manifestations of basal ganglia oscillations observed in human subjects and in animal models of PD. In this context, we also review the evidence supporting a pathophysiological role of different oscillations for the suppression of voluntary movements as well as for the induction of excessive motor activity. In light of these findings, it is discussed how oscillations could be used to guide a more precise targeting of dysfunctional circuits to obtain improved symptomatic treatment of PD.

Parkinson patients without tremor show changed patterns of mechanical muscle oscillations during a specific bilateral motor task compared to controls

The pathophysiology of Parkinson's disease (PD) is still not understood. There are investigations which show a changed oscillatory behaviour of brain circuits or changes in variability of, e.g., gait parameters in PD. The aim of this study was to investigate whether or not the motor output differs between PD patients and healthy controls. Thereby, patients without tremor are investigated in the medication off state performing a special bilateral isometric motor task. The force and accelerations (ACC) were recorded as well as the Mechanomyography (MMG) of the biceps brachii, the brachioradialis and of the pectoralis major muscles using piezoelectric-sensors during the bilateral motor task at 60% of the maximal isometric contraction. The frequency, a specific power ratio, the amplitude variation and the slope of amplitudes were analysed. The results indicate that the oscillatory behaviour of motor output in PD patients without tremor deviates from controls: thereby, the 95%-confidence-intervals of power ratio and of amplitude variation of all signals are disjoint between PD and controls and show significant differences in group comparisons (power ratio: p = 0.000-0.004, r = 0.441-0.579; amplitude variation: p = 0.000-0.001, r = 0.37-0.67). The mean frequency shows a significant difference for ACC (p = 0.009, r = 0.43), but not for MMG. It remains open, whether this muscular output reflects changes of brain circuits and whether the results are reproducible and specific for PD.

Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering

OBJECTIVE

Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning methods for more accurate detection of rest tremor in PD.

METHODS

We analyzed the local field potential (LFP) recordings from the subthalamic nucleus region in 12 patients with PD (16 recordings). To explore the optimal biomarkers and the best performing classifier, the performance of state-of-the-art machine learning (ML) algorithms and various features of the subthalamic LFPs were compared. We further used a Kalman filtering technique in feature domain to reduce the false positive rate.

RESULTS

The Hjorth complexity showed a higher correlation with tremor, compared to other features in our study. In addition, by optimal selection of a maximum of five features with a sequential feature selection method and using the gradient boosted decision trees as the classifier, the system could achieve an average F1 score of up to 88.7% and a detection lead of 0.52 s. The use of Kalman filtering in feature space significantly improved the specificity by 17.0% (p = 0.002), thereby potentially reducing the unnecessary power dissipation of the conventional DBS system.

CONCLUSION

The use of relevant features combined with Kalman filtering and machine learning improves the accuracy of tremor detection during rest.

SIGNIFICANCE

The proposed method offers a potential solution for efficient on-demand stimulation for PD tremor.

Oscillations in cortico-basal ganglia circuits: implications for Parkinson’s disease and other neurologic and psychiatric conditions

Cortico-basal ganglia circuits are thought to play a crucial role in the selection and control of motor behaviors and have also been implicated in the processing of motivational content and in higher cognitive functions. During the last two decades, electrophysiological recordings in basal ganglia circuits have shown that several disease conditions are associated with specific changes in the temporal patterns of neuronal activity. In particular, synchronized oscillations have been a frequent finding suggesting that excessive synchronization of neuronal activity may be a pathophysiological mechanism involved in a wide range of neurologic and psychiatric conditions. We here review the experimental support for this hypothesis primarily in relation to Parkinson’s disease but also in relation to dystonia, essential tremor, epilepsy, and psychosis/schizophrenia.

Resting Tremor Detection in Parkinson's Disease with Machine Learning and Kalman Filtering

Adaptive deep brain stimulation (aDBS) is an emerging method to alleviate the side effects and improve the efficacy of conventional open-loop stimulation for movement disorders. However, current adaptive DBS techniques are primarily based on single-feature thresholding, precluding an optimized delivery of stimulation for precise control of motor symptoms. Here, we propose to use a machine learning approach for resting-state tremor detection from local field potentials (LFPs) recorded from subthalamic nucleus (STN) in 12 Parkinson's patients. We compare the performance of state-of-the-art classifiers and LFP-based biomarkers for tremor detection, showing that the high-frequency oscillations and Hjorth parameters achieve a high discriminative performance. In addition, using Kalman filtering in the feature space, we show that the tremor detection performance significantly improves (F(1,15)=32.16, p<0.0001). The proposed method holds great promise for efficient on-demand delivery of stimulation in Parkinson's disease.

Directional communication during movement execution interferes with tremor in Parkinson's disease

BACKGROUND

Both the cerebello-thalamo-cortical circuit and the basal ganglia/cortical motor loop have been postulated to be generators of tremor in PD. The recent suggestion that the basal ganglia trigger tremor episodes and the cerebello-thalamo-cortical circuitry modulates tremor amplitude combines both competing hypotheses. However, the role of the STN in tremor generation and the impact of proprioceptive feedback on tremor suppression during voluntary movements have not been considered in this model yet.

OBJECTIVES

The objective of this study was to evaluate the role of the STN and proprioceptive feedback in PD tremor generation during movement execution.

METHODS

Local-field potentials of the STN as well as electromyographical and electroencephalographical rhythms were recorded in tremor-dominant and nontremor PD patients while performing voluntary movements of the contralateral hand during DBS surgery. Effective connectivity between these electrophysiological signals were analyzed and compared to electromyographical tremor activity.

RESULTS

There was an intensified information flow between the STN and the muscle in the tremor frequencies (5-8 Hz) for tremor-dominant, in comparison to nontremor, patients. In both subtypes, active movement was associated with an increase of afferent interaction between the muscle and the cortex in the β- and γ-frequencies. The γ-frequency (30-40 Hz) of this communication between muscle and cortex correlated inversely with electromyographical tremor activity.

CONCLUSIONS

Our results indicate an involvement of the STN in propagation of tremor-related activity to the muscle. Furthermore, we provide evidence that increased proprioceptive information flow during voluntary movement interferes with central tremor generation. © 2018 International Parkinson and Movement Disorder Society.

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.

Localization of the Epileptogenic Zone Using High Frequency Oscillations

For patients with drug-resistant focal epilepsy, surgery is the therapy of choice in order to achieve seizure freedom. Epilepsy surgery foremost requires the identification of the epileptogenic zone (EZ), defined as the brain area indispensable for seizure generation. The current gold standard for identification of the EZ is the seizure-onset zone (SOZ). The fact, however that surgical outcomes are unfavorable in 40-50% of well-selected patients, suggests that the SOZ is a suboptimal biomarker of the EZ, and that new biomarkers resulting in better postsurgical outcomes are needed. Research of recent years suggested that high-frequency oscillations (HFOs) are a promising biomarker of the EZ, with a potential to improve surgical success in patients with drug-resistant epilepsy without the need to record seizures. Nonetheless, in order to establish HFOs as a clinical biomarker, the following issues need to be addressed. First, evidence on HFOs as a clinically relevant biomarker stems predominantly from retrospective assessments with visual marking, leading to problems of reproducibility and reliability. Prospective assessments of the use of HFOs for surgery planning using automatic detection of HFOs are needed in order to determine their clinical value. Second, disentangling physiologic from pathologic HFOs is still an unsolved issue. Considering the appearance and the topographic location of presumed physiologic HFOs could be immanent for the interpretation of HFO findings in a clinical context. Third, recording HFOs non-invasively via scalp electroencephalography (EEG) and magnetoencephalography (MEG) is highly desirable, as it would provide us with the possibility to translate the use of HFOs to the scalp in a large number of patients. This article reviews the literature regarding these three issues. The first part of the article focuses on the clinical value of invasively recorded HFOs in localizing the EZ, the detection of HFOs, as well as their separation from physiologic HFOs. The second part of the article focuses on the current state of the literature regarding non-invasively recorded HFOs with emphasis on findings and technical considerations regarding their localization.

Toward Electrophysiology-Based Intelligent Adaptive Deep Brain Stimulation for Movement Disorders

Deep brain stimulation (DBS) represents one of the major clinical breakthroughs in the age of translational neuroscience. In 1987, Benabid and colleagues demonstrated that high-frequency stimulation can mimic the effects of ablative neurosurgery in Parkinson's disease (PD), while offering two key advantages to previous procedures: adjustability and reversibility. Deep brain stimulation is now an established therapeutic approach that robustly alleviates symptoms in patients with movement disorders, such as Parkinson's disease, essential tremor, and dystonia, who present with inadequate or adverse responses to medication. Currently, stimulation electrodes are implanted in specific target regions of the basal ganglia-thalamic circuit and stimulation pulses are delivered chronically. To achieve optimal therapeutic effect, stimulation frequency, amplitude, and pulse width must be adjusted on a patient-specific basis by a movement disorders specialist. The finding that pathological neural activity can be sampled directly from the target region using the DBS electrode has inspired a novel DBS paradigm: closed-loop adaptive DBS (aDBS). The goal of this strategy is to identify pathological and physiologically normal patterns of neuronal activity that can be used to adapt stimulation parameters to the concurrent therapeutic demand. This review will give detailed insight into potential biomarkers and discuss next-generation strategies, implementing advances in artificial intelligence, to further elevate the therapeutic potential of DBS by capitalizing on its modifiable nature. Development of intelligent aDBS, with an ability to deliver highly personalized treatment regimens and to create symptom-specific therapeutic strategies in real-time, could allow for significant further improvements in the quality of life for movement disorders patients with DBS that ultimately could outperform traditional drug treatment.

Local field potentials of subthalamic nucleus contain electrophysiological footprints of motor subtypes of Parkinson's disease

Although motor subtypes of Parkinson's disease (PD), such as tremor dominant (TD) and postural instability and gait difficulty (PIGD), have been defined based on symptoms since the mid-1990s, no underlying neural correlates of these clinical subtypes have yet been identified. Very limited data exist regarding the electrophysiological abnormalities within the subthalamic nucleus (STN) that likely accompany the symptom severity or the phenotype of PD. Here, we show that activity in subbands of local field potentials (LFPs) recorded with multiple microelectrodes from subterritories of STN provide distinguishing neurophysiological information about the motor subtypes of PD. We studied 24 patients with PD and found distinct patterns between TD (n = 13) and PIGD (n = 11) groups in high-frequency oscillations (HFOs) and their nonlinear interactions with beta band in the superior and inferior regions of the STN. Particularly, in the superior region of STN, the power of the slow HFO (sHFO) (200-260 Hz) and the coupling of its amplitude with beta-band phase were significantly stronger in the TD group. The inferior region of STN exhibited fast HFOs (fHFOs) (260-450 Hz), which have a significantly higher center frequency in the PIGD group. The cross-frequency coupling between fHFOs and beta band in the inferior region of STN was significantly stronger in the PIGD group. Our results indicate that the spatiospectral dynamics of STN-LFPs can be used as an objective method to distinguish these two motor subtypes of PD. These observations might lead to the development of sensing and stimulation strategies targeting the subterritories of STN for the personalization of deep-brain stimulation (DBS).

Unilateral deep brain stimulation suppresses alpha and beta oscillations in sensorimotor cortices

Deep brain stimulation (DBS) is an established therapy to treat motor symptoms in movement disorders such as Parkinson's disease (PD). The mechanisms leading to the high therapeutic effectiveness of DBS are poorly understood so far, but modulation of oscillatory activity is likely to play an important role. Thus, investigating the effect of DBS on cortical oscillatory activity can help clarifying the neurophysiological mechanisms of DBS. Here, we aimed at scrutinizing changes of cortical oscillatory activity by DBS at different frequencies using magnetoencephalography (MEG). MEG data from 17 PD patients were acquired during DBS of the subthalamic nucleus (STN) the day after electrode implantation and before implanting the pulse generator. We stimulated the STN unilaterally at two different stimulation frequencies, 130 Hz and 340 Hz using an external stimulator. Data from six patients had to be discarded due to strong artefacts and two other datasets were excluded since these patients were not able to finalize the paradigm. After DBS artefact removal, power spectral density (PSD) values of MEG were calculated for each individual patient and averaged over the group. DBS at both 130 Hz and 340 Hz led to a widespread suppression of cortical alpha/beta band activity (8-22 Hz) specifically over bilateral sensorimotor cortices. No significant differences were observed between the two stimulation frequencies. Our finding of a widespread suppression of cortical alpha/beta band activity is particularly interesting as PD is associated with pathologically increased levels of beta band activity in the basal ganglia-thalamo-cortical circuit. Therefore, suppression of such oscillatory activity might be an essential effect of DBS for relieving motor symptoms in PD and can be achieved at different stimulation frequencies above 100 Hz.

Occurrence of thalamic high frequency oscillations in patients with different tremor syndromes

OBJECTIVE

To assess whether high frequency oscillations (HFOs, >150 Hz), known to occur in basal ganglia nuclei, can be observed in the thalamus.

METHODS

We recorded intraoperative local field potentials from the ventral intermediate nucleus (VIM) of the thalamus in patients with Essential Tremor (N = 16), Parkinsonian Tremor (3), Holmes Tremor (2) and Dystonic Tremor (1) during implantation of electrodes for deep brain stimulation. Recordings were performed with up to five micro/macro-electrodes that were simultaneously advanced to the stereotactic target.

RESULTS

Thalamic HFOs occurred in all investigated tremor syndromes. A detailed analysis of the Essential Tremor subgroup revealed that medial channels recorded HFOs more frequently than other channels. The highest peaks were observed 4 mm above target. Macro- but not microelectrode recordings were dominated by peaks in the slow HFO band (150-300 Hz), which were stable across several depths and channels.

CONCLUSION

HFOs occur in the thalamus and are not specific to any of the tremors investigated. Their spatial distribution is not homogeneous, and their appearance depends on the type of electrode used for recording.

SIGNIFICANCE

The occurrence of HFOs in the thalamus of tremor patients indicates that HFOs are not part of basal ganglia pathophysiology.

[Pathophysiology of tremor]

Tremor is clinically defined as a rhythmic, oscillating movement of parts of the body, which functionally leads to impairment of the coordination and execution of targeted movements. It can be a symptom of a primary disease, such as resting tremor in Parkinson's disease or occur as an independent disease, such as essential or orthostatic tremor. For the development of tremor, cerebral components as well as mechanisms at the spinal and muscular level play an important role. This review presents the results of new imaging and electrophysiological studies that have led to important advances in our understanding of the pathophysiology of tremor. We discuss pathophysiological models for the development of resting tremor in Parkinson's disease, essential and orthostatic tremor. We describe recent developments starting from the classical generator model, with an onset of pathological oscillations in distinct cerebral regions, to a network perspective in which tremor arises and spreads through existing anatomical or newly emerged pathological brain networks. In particular translational approaches are presented and discussed. These could serve in the future as a basis for the development of new therapeutic strategies.

Bicycling suppresses abnormal beta synchrony in the Parkinsonian basal ganglia

OBJECTIVE

Freezing of gait is a poorly understood symptom of Parkinson disease, and can severely disrupt the locomotion of affected patients. However, bicycling ability remains surprisingly unaffected in most patients suffering from freezing, suggesting functional differences in the motor network. The purpose of this study was to characterize and contrast the oscillatory dynamics underlying bicycling and walking in the basal ganglia.

METHODS

We present the first local field potential recordings directly comparing bicycling and walking in Parkinson disease patients with electrodes implanted in the subthalamic nuclei for deep brain stimulation. Low (13-22Hz) and high (23-35Hz) beta power changes were analyzed in 22 subthalamic nuclei from 13 Parkinson disease patients (57.5 ± 5.9 years old, 4 female). The study group consisted of 5 patients with and 8 patients without freezing of gait.

RESULTS

In patients without freezing of gait, both bicycling and walking led to a suppression of subthalamic beta power (13-35Hz), and this suppression was stronger for bicycling. Freezers showed a similar pattern in general. Superimposed on this pattern, however, we observed a movement-induced, narrowband power increase around 18Hz, which was evident even in the absence of freezing.

INTERPRETATION

These results indicate that bicycling facilitates overall suppression of beta power. Furthermore, movement leads to exaggerated synchronization in the low beta band specifically within the basal ganglia of patients susceptible to freezing. Abnormal ∼18Hz oscillations are implicated in the pathophysiology of freezing of gait, and suppressing them may form a key strategy in developing potential therapies. Ann Neurol 2017;82:592-601.

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.

Localization of beta and high-frequency oscillations within the subthalamic nucleus region

Parkinsonian bradykinesia and rigidity are typically associated with excessive beta band oscillations in the subthalamic nucleus. Recently another spectral peak has been identified that might be implicated in the pathophysiology of the disease: high-frequency oscillations (HFO) within the 150–400 Hz range. Beta-HFO phase-amplitude coupling (PAC) has been found to correlate with severity of motor impairment. However, the neuronal origin of HFO and its usefulness as a potential target for deep brain stimulation remain to be established. For example, it is unclear whether HFO arise from the same neural populations as beta oscillations. We intraoperatively recorded local field potentials from the subthalamic nucleus while advancing DBS electrodes in 2 mm steps from 4 mm above the surgical target point until 2 mm below, resulting in 4 recording sites. Data from 26 nuclei from 14 patients were analysed. For each trajectory, we identified the recording site with the largest spectral peak in the beta range (13–30 Hz), and the largest peak in the HFO range separately. In addition, we identified the recording site with the largest beta-HFO PAC. Recording sites with largest beta power and largest HFO power coincided in 50% of cases. In the other 50%, HFO was more likely to be detected at a more superior recording site in the target area. PAC followed more closely the site with largest HFO (45%) than beta power (27%). HFO are likely to arise from spatially close, but slightly more superior neural populations than beta oscillations. Further work is necessary to determine whether the different activities can help fine-tune deep brain stimulation targeting.

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LFPs were recorded from multiple sites within and around the subthalamic nucleus.

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Sites with largest beta and high-frequency oscillations (HFO) were identified.

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HFO were located slightly more superior than beta oscillations.

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Phase-amplitude coupling more closely followed the site with largest HFO.

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This work hints at different neural generators for beta and HFO.

BrainCycles: Experimental Setup for the Combined Measurement of Cortical and Subcortical Activity in Parkinson's Disease Patients during Cycling

Recently, it has been demonstrated that bicycling ability remains surprisingly preserved in Parkinson's disease (PD) patients who suffer from freezing of gait. Cycling has been also proposed as a therapeutic means of treating PD symptoms, with some preliminary success. The neural mechanisms behind these phenomena are however not yet understood. One of the reasons is that the investigations of neuronal activity during pedaling have been up to now limited to PET and fMRI studies, which restrict the temporal resolution of analysis, and to scalp EEG focused on cortical activation. However, deeper brain structures like the basal ganglia are also associated with control of voluntary motor movements like cycling and are affected by PD. Deep brain stimulation (DBS) electrodes implanted for therapy in PD patients provide rare and unique access to directly record basal ganglia activity with a very high temporal resolution. In this paper we present an experimental setup allowing combined investigation of basal ganglia local field potentials (LFPs) and scalp EEG underlying bicycling in PD patients. The main part of the setup is a bike simulator consisting of a classic Dutch-style bicycle frame mounted on a commercially available ergometer. The pedal resistance is controllable in real-time by custom software and the pedal position is continuously tracked by custom Arduino-based electronics using optical and magnetic sensors. A portable bioamplifier records the pedal position signal, the angle of the knee, and the foot pressure together with EEG, EMG, and basal ganglia LFPs. A handlebar-mounted display provides additional information for patients riding the bike simulator, including the current and target pedaling rate. In order to demonstrate the utility of the setup, example data from pilot recordings are shown. The presented experimental setup provides means to directly record basal ganglia activity not only during cycling but also during other movement tasks in patients who have undergone DBS treatment. Thus, it can facilitate studies comparing bicycling and walking, to elucidate why PD patients often retain the ability to bicycle despite severe freezing of gait. Moreover it can help clarifying the mechanism through which cycling may have therapeutic benefits.

Deep Brain Stimulation Frequency-A Divining Rod for New and Novel Concepts of Nervous System Function and Therapy

The efficacy of Deep Brain Stimulation (DBS) for an expanding array of neurological and psychiatric disorders demonstrates directly that DBS affects the basic electroneurophysiological mechanisms of the brain. The increasing array of active electrode configurations, stimulation currents, pulse widths, frequencies, and pulse patterns provides valuable tools to probe electroneurophysiological mechanisms. The extension of basic electroneurophysiological and anatomical concepts using sophisticated computational modeling and simulation has provided relatively straightforward explanations of all the DBS parameters except frequency. This article summarizes current thought about frequency and relevant observations. Current methodological and conceptual errors are critically examined in the hope that future work will not replicate these errors. One possible alternative theory is presented to provide a contrast to many current theories. DBS, conceptually, is a noisy discrete oscillator interacting with the basal ganglia–thalamic–cortical system of multiple re-entrant, discrete oscillators. Implications for positive and negative resonance, stochastic resonance and coherence, noisy synchronization, and holographic memory (related to movement generation) are presented. The time course of DBS neuronal responses demonstrates evolution of the DBS response consistent with the dynamics of re-entrant mechanisms. Finally, computational modeling demonstrates identical dynamics as seen by neuronal activities recorded from human and nonhuman primates, illustrating the differences of discrete from continuous harmonic oscillators and the power of conceptualizing the nervous system as composed on interacting discrete nonlinear oscillators.