Symptom optimization through sensing local field potentials: Balancing beta and gamma in Parkinson's disease

Abstract

Deep brain stimulation (DBS) is an important clinical therapeutic tool to control motor symptoms of Parkinson’s disease (PD) for many patients whose symptoms are not well controlled by medication alone (Malek, 2019 [1]). DBS therapy involves the delivery of an electrical current through an electrode that is surgically placed in the brain, traditionally in the subthalamic nucleus (STN) or globus pallidus internus (GPi). Recent technology has allowed these devices to not only deliver an electrical current, but also measure local field potentials (LFPs). LFPs are electrical potentials, produced by the activation of neurons, that act as markers for the frequency and severity of specific motor symptoms in PD (Neumann et al., 2017 [2], Swann et al., 2016 [3]). These markers can give insight into the efficacy of a given treatment regiment, by providing data to use as clinical benchmarks for the treatment team. The utility of LFPs in the treatment of PD is illustrated in this case series of three patients with complex motor fluctuations such as dyskinesias, Levodopa wearing-off and muscle rigidity. Here we demonstrate how we used LFP sensing through Timeline, Streaming and Event Capture to gain insight to the patients’ symptoms and offer data driven therapy optimization. The first case is a 72-year-old y/o female patient with PD, who presented with severe dyskinesias and motor fluctuations. By monitoring real-time LFPs, the treatment team was able to identify significant dyskinesias and optimize medication and stimulation frequency, which led to more personalized and efficacious treatment. The second case is a 66 y/o male patient with PD, hoping to achieve more on time without troublesome dyskinesias. Using chronic LFP data the treatment team was able to adjust therapy amplitude and pulse width to better manage the patient’s symptoms. In addition, the LFP data was used to educate the patient on when the patient programmer could be used situationally for changing programming groups. The third case is a 65 y/o male patient with a ten-year history of PD, experiencing abrupt and unpredictable medication wearing off times and severe dyskinesias, which compromised gait and activities of daily living. Real time and chronic LFP sensing supported the optimization of his stimulation amplitude and pulse width and medication regimen, ultimately decreasing his fluctuations and greatly improving his gait and quality of life.