Identifying tremor-related characteristics of basal ganglia nuclei during movement in the Parkinsonian patient

Active and Passive Cycling Decrease Subthalamic β Oscillations in Parkinson's Disease

BACKGROUND

Preserved cycling capabilities in patients with Parkinson's disease, especially in those with freezing of gait are still poorly understood. Previous research with invasive local field potential recordings in the subthalamic nucleus has shown that cycling causes a stronger suppression of β oscillations compared to walking, which facilitates motor continuation.

METHODS

We recorded local field potentials from 12 patients with Parkinson's disease (six without freezing of gait, six with freezing of gait) who were bilaterally implanted with deep brain stimulation electrodes in the subthalamic nucleus. We investigated β (13-30 Hz) and high γ (60-100 Hz) power during both active and passive cycling with different cadences and compared patients with and without freezing of gait. The passive cycling experiment, where a motor provided a fixed cadence, allowed us to study the effect of isolated sensory inputs without physical exercise.

RESULTS

We found similarly strong suppression of pathological β activity for both active and passive cycling. In contrast, there was stronger high γ band activity for active cycling. Notably, the effects of active and passive cycling were all independent of cadence. Finally, β suppression was stronger for patients with freezing of gait, especially during passive cycling.

CONCLUSIONS

Our results provide evidence for a link between proprioceptive input during cycling and β suppression. These findings support the role of continuous external sensory input and proprioceptive feedback during rhythmic passive cycling movements and suggest that systematic passive mobilization might hold therapeutic potential. © 2023 International Parkinson and Movement Disorder Society.

Suppression and Rebound of Pallidal Beta Power: Observation Using a Chronic Sensing DBS Device

Pallidal deep brain stimulation (DBS) is an increasingly used therapy for Parkinson’s disease (PD). Here, we study the effect of DBS on pallidal oscillatory activity as well as on symptom severity in an individual with PD implanted with a new pulse generator (Medtronic Percept system) which facilitates chronic recording of local field potentials (LFP) through implanted DBS lead. Pallidal LFPs were recorded while delivering stimulation in a monopolar configuration using stepwise increments (0.5 mA, every 20 s). At each stimulation amplitude, the power spectral density (PSD) was computed, and beta power (13–30 Hz) was calculated and correlated with the degree of bradykinesia. Pallidal beta power was reduced when therapeutic stimulation was delivered. Beta power correlated to the severity of bradykinesia. Worsening of parkinsonism when excessive stimulation was applied was associated with a rebound in the beta band power. These preliminary results suggest that pallidal beta power might be used as an objective marker of the disease state in PD. The use of brain sensing from implanted neural interfaces may in the future facilitate clinical programming. Detection of rebound could help to optimize benefits and minimize worsening from overstimulation.

Conveyance of cortical pacing for parkinsonian tremor-like hyperkinetic behavior by subthalamic dysrhythmia

Parkinson's disease is characterized by both hypokinetic and hyperkinetic symptoms. While increased subthalamic burst discharges have a direct causal relationship with the hypokinetic manifestations (e.g., rigidity and bradykinesia), the origin of the hyperkinetic symptoms (e.g., resting tremor and propulsive gait) has remained obscure. Neuronal burst discharges are presumed to be autonomous or less responsive to synaptic input, thereby interrupting the information flow. We, however, demonstrate that subthalamic burst discharges are dependent on cortical glutamatergic synaptic input, which is enhanced by A-type K+ channel inhibition. Excessive top-down-triggered subthalamic burst discharges then drive highly correlative activities bottom-up in the motor cortices and skeletal muscles. This leads to hyperkinetic behaviors such as tremors, which are effectively ameliorated by inhibition of cortico-subthalamic AMPAergic synaptic transmission. We conclude that subthalamic burst discharges play an imperative role in cortico-subcortical information relay, and they critically contribute to the pathogenesis of both hypokinetic and hyperkinetic parkinsonian symptoms.

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.

Veillance

This chapter builds upon the concept of Uberveillance introduced in the seminal research of M. G. Michael and Katina Michael in 2006. It begins with an overview of sousveillance (underwatching) technologies and examines the “We're watching you but you can't watch us” hypocrisy associated with the rise of surveillance (overwatching). Surveillance cameras are often installed in places that have “NO CAMERAS” and “NO CELLPHONES IN STORE, PLEASE!” signage. The author considers the chilling effect of this veillance hypocrisy on LifeGlogging, wearable computing, “Sixth Sense,” AR Glass, and the Digital Eye Glass vision aid. If surveillance gives rise to hypocrisy, then to what does its inverse, sousveillance (wearable cameras, AR Glass, etc.), give rise? The opposite (antonym) of hypocrisy is integrity. How might we resolve the conflict-of-interest that arises in situations where, for example, police surveillance cameras capture the only record of wrongdoing by the police? Is sousveillance the answer or will centralized dataveillance merely turn sousveillance into a corruptible uberveillance authority?

An external portable device for adaptive deep brain stimulation (aDBS) clinical research in advanced Parkinson's Disease

Compared to conventional deep brain stimulation (DBS) for patients with Parkinson's Disease (PD), the newer approach of adaptive DBS (aDBS), regulating stimulation on the basis of the patient's clinical state, promises to achieve better clinical outcomes, avoid adverse-effects and save time for tuning parameters. A remaining challenge before aDBS comes into practical use is to prove its feasibility and its effectiveness in larger groups of patients and in more ecological conditions. We developed an external portable aDBS system prototype designed for clinical testing in freely-moving PD patients with externalized DBS electrodes. From a single-channel bipolar artifact-free recording, it analyses local field potentials (LFPs), during ongoing DBS for tuning stimulation parameters, independent from the specific feedback algorithm implemented. We validated the aDBS system in vitro, by testing both its sensing and closed-loop stimulation capabilities, and then tested it in vivo, focusing on the sensing capabilities. By applying the aDBS system prototype in a patient with PD, we provided evidence that it can track levodopa and DBS-induced LFP spectral power changes among different patient's clinical states. Our system, intended for testing LFP-based feedback strategies for aDBS, should help understanding how and whether aDBS therapy works in PD and indicating future technical and clinical advances.

Clinical implications of local field potentials for understanding and treating movement disorders

BACKGROUND

Deep brain stimulation (DBS) for the treatment of movement disorders has provided researchers with an opportunity to record electrical oscillatory activity from electrodes implanted in deep brain structures. Extracellular activity recorded from a population of neurons, termed local field potentials (LFPs), has shed light on the pathophysiology of movement disorders and holds the potential to lead to refinement in existing treatments.

OBJECTIVE

This paper reviews the clinical significance of LFPs recorded from macroelectrodes implanted in basal ganglia and thalamic targets for the treatment of Parkinson's disease, essential tremor and dystonia.

METHODS

Neural population dynamics and subthreshold events, which are undetectable by single-unit recordings, can be examined with frequency band analysis of LFPs (frequency range: 1-250 Hz).

RESULTS

Of clinical relevance, reliable correlations between motor symptoms and components of the LFP power spectrum suggest that LFPs may serve as a biomarker for movement disorders. In particular, Parkinson's rigidity has been shown to correlate with the power of beta oscillations (13-30 Hz), and essential tremor coheres with oscillations of 8-27 Hz. Furthermore, evidence indicates that the optimal contacts for DBS programming can be predicted from the anatomic location of beta and gamma bands (48-200 Hz).

CONCLUSION

LFP analysis has implications for improved electrode targeting and the development of a real-time, individualized, 'closed-loop' stimulation system.