Subthalamic oscillatory activity and connectivity during gait in Parkinson's disease

Abstract

Local field potentials (LFP) of the subthalamic nucleus (STN) recorded during walking may provide clues for determining the function of the STN during gait and also, may be used as biomarker to steer adaptive brain stimulation devices. Here, we present LFP recordings from an implanted sensing neurostimulator (Medtronic Activa PC + S) during walking and rest with and without stimulation in 10 patients with Parkinson's disease and electrodes placed bilaterally in the STN. We also present recordings from two of these patients recorded with externalized leads. We analyzed changes in overall frequency power, bilateral connectivity, high beta frequency oscillatory characteristics and gait-cycle related oscillatory activity. We report that deep brain stimulation improves gait parameters. High beta frequency power (20-30 Hz) and bilateral oscillatory connectivity are reduced during gait, while the attenuation of high beta power is absent during stimulation. Oscillatory characteristics are affected in a similar way. We describe a reduction in overall high beta burst amplitude and burst lifetimes during gait as compared to rest off stimulation. Investigating gait cycle related oscillatory dynamics, we found that alpha, beta and gamma frequency power is modulated in time during gait, locked to the gait cycle. We argue that these changes are related to movement induced artifacts and that these issues have important implications for similar research.

Corticomuscular control of walking in older people and people with Parkinson’s disease

“…This exaggerated pathophysiological beta activity in the basal ganglia has been found associated with bradykinesia and impaired motor function (Brown 2007, Jenkinson & Brown 2011, Oswal et al 2013, Little & Brown 2014. Interestingly, walking and cycling has been found to modulate/normalise beta activity in the subthalamic nucleus (STN) in people with PD (Storzer et al 2017, Hell et al 2018). Walking and cycling was found to decrease STN beta power in people with PD (Storzer et al 2017).…”

“…For instance, at rest in an untreated state (after a medication wash-out period and STN deep brain stimulating (DBS) electrodes switched off), beta power in the STN and beta activity amongst basal ganglia-cortical networks was increased compared to an optimally medicated state and STN DBS switched on (Litvak et al 2011, Cagnan et al 2015, de Hemptinne et al 2015, Kurani et al 2015, Quinn et al 2015. Notably, walking and cycling was found to decrease STN beta power in people with PD (Storzer et al 2017, Hell et al 2018. Moreover, stepping movements have been shown to modulate STN beta power relative to the movement phase (suppressed during contralateral foot lift, Fischer et al 2018).…”

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

“…Nevertheless, it was recently demonstrated that beta activity reflects bradykinesia even during phases of relative desynchronization during continuous movement [63]. Moreover, specific patterns for walking [64][65][66][67] and even riding the bicycle [68] have been reported for beta activity in PD patients. Studies on the spatial distribution of subthalamic beta activity have found a robust overlap between the location of peak beta power with the optimal target location in the dorsolateral STN [69].…”

“…From this effort, one can primarily conclude that producing an implantable device that is capable of recording voltage fluctuations in deep nuclei poses significant difficulties. High-noise floor, cardioballistic artifacts, and internal clock-related spikes in combination with very low amplitude signals, limited battery life in primary cells, and low storage capacity of the IPGs have been identified by both researchers and manufacturers as the next engineering challenges toward reliable chronic adaptive stimulation [67,179].…”

“…Importantly, this approach can learn an almost infinite amount of spectral, spatial, and temporal patterns of activity, given that both concurrent and preceding activities are used to inform the model. This strategy may have significant benefit in the detection of rhythmic activity, such as walking [64,67], speaking, tremor, [60,61] or sleep [77], as well as in the prediction of clinical symptom severity [34,35,59], where a continuous increase of pathological activity could predict upcoming symptoms more robustly than single time points. This could also account for symptom-specific differences in the time lag to therapeutic response.…”