Description

While much research has been dedicated to understanding the pathophysiology of Parkinson's disease (PD), the neural dynamics underlying the manifestation of motor signs remain unclear. Studies over the past two decades have shown a correlation of the amplitude and incidence of beta band oscillations in the subthalamic nucleus (STN) with changes in bradykinesia and rigidity mediated by levodopa or deep brain stimulation (DBS) therapies. Yet, no study has conclusively or deductively demonstrated a causal link. A limitation to establishing causality is the lack of available neuromodulation tools capable of predictably and precisely controlling neural oscillatory activity in the human brain in real time without introducing confounding factors. Establishing these tools and clarifying whether the relationship of beta band oscillations with PD motor signs is causal or epiphenomenon are critical steps to better understand PD pathophysiology and advance personalized DBS technology in PD and other brain conditions. This study aims to address these technology and knowledge gaps by leveraging feedback control engineering and patient-specific computational modeling tools.

In this study, the investigators will employ a neural control approach, referred to as evoked interference closed-loop DBS (eiDBS), to characterize the degree by which controlled suppression or amplification of beta oscillations in the STN influences bradykinesia and rigidity in PD (Specific Aim 1, SA1). The investigators will test the hypothesis that stimulation-induced suppression or amplification of beta oscillations in the STN will result in changes in bradykinesia and rigidity measures. In SA2, the investigators will employ levodopa medication to characterize how changes in bradykinesia and rigidity relate to variations in the amplitude of neural oscillations in the STN and primary motor cortex (MC) evoked by STN stimulation. The investigators will test the hypothesis that levodopa administration will result in a decrease in the amplitude of stimulation-evoked beta oscillations that will correlate with changes in bradykinesia and rigidity. The results from SA2 will help to gain a greater understanding of intrinsic circuit dynamics associated with PD and identify strategies to optimize closed-loop DBS algorithms (e.g., eiDBS) in the face of concurrent levodopa therapy, a step to bring this technology to future clinical trials. Combining electrophysiological data with high-resolution (7T) magnetic resonance (MR) imaging and computational modeling, the investigators will examine which specific neuronal pathways connected with the STN need to be activated to evoke frequency-specific neural responses in the STN and MC (SA3). The data from SA3 will shed light on which sub-circuits are involved in the generation of stimulation-evoked and spontaneous beta oscillations in PD, and inform how to use directional DBS leads to shape electric fields in the STN to selectively modulate the STN via eiDBS or other neurostimulation techniques. The investigators will address the three aims of this study with the participation of PD patients implanted with DBS leads in the STN, whose DBS lead extensions will be externalized and connected to our recording and closed-loop stimulation infrastructure.