UCSF

In Parkinson’s disease, imbalances between “antikinetic” and “prokinetic” patterns of neuronal oscillatory activity are related to motor dysfunction. Invasive brain recordings from the motor network have suggested that medical or surgical therapy can promote a prokinetic state by inducing narrowband gamma rhythms (60-90 Hz), which are associated with dyskinesia. Here, I investigate the behavioral and statistical properties of levodopa-induced and deep brain stimulation-entrained narrowband gamma rhythms. Collaborating with Oxford researchers, we also model the deep brain stimulation induced entrainment of gamma oscillations using interacting neuronal populations and patient-specific features of the levodopa-induced gamma oscillations. Using a sensing-enabled deep brain stimulation system, attached to both motor cortex and subthalamic or pallidal leads, the Starr Lab recorded over 900 hours of multisite field potentials prior to initiating deep brain stimulation, and over 600 hours with deep brain stimulation. I find that levodopa-induced gamma oscillations are more strongly associated with dyskinesia than deep brain stimulation-entrained gamma oscillations. Statistical comparisons revealed that levodopa-induced gamma oscillations exhibit increased variance in peak frequency, decreased spectral power, and higher variance in spectral power. Furthermore, I also work with collaborators at Oxford to show that entrainment can be predicted using neural circuit models fitted to patient data at various stimulation parameters. Put together, this work suggests that gamma oscillations as programming biomarkers should be leveraged distinctly across medical and surgical interventions to mitigate dyskinesia – while excessive levodopa-induced gamma oscillations are a marker for dyskinesia, increased entrained gamma oscillations may be a marker of a non-pathological prokinetic movement state. Our modeling work can subsequently be leveraged to predict the amount of entrained gamma across stimulation parameters.