Department of Neuroscience, Brown University, Providence, United States; Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, United States; The Warren Alpert Medical School, Brown University, Providence, United States
Shane Lee
Department of Neuroscience, Brown University, Providence, United States; Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, United States; Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, United States; Department of Neurosurgery, Rhode Island Hospital, Providence, United States
Daniel E Amaya
Department of Neuroscience, Brown University, Providence, United States; Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, United States
David D Liu
Department of Neurosurgery, Brigham and Women’s Hospital, Boston, United States
Umer Akbar
Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, United States; The Warren Alpert Medical School, Brown University, Providence, United States; Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, United States; Department of Neurology, Rhode Island Hospital, Providence, United States
Department of Neuroscience, Brown University, Providence, United States; Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, United States; The Warren Alpert Medical School, Brown University, Providence, United States; Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, United States; Department of Neurosurgery, Rhode Island Hospital, Providence, United States
Parkinson’s disease (PD) is characterized by distinct motor phenomena that are expressed asynchronously. Understanding the neurophysiological correlates of these motor states could facilitate monitoring of disease progression and allow improved assessments of therapeutic efficacy, as well as enable optimal closed-loop neuromodulation. We examined neural activity in the basal ganglia and cortex of 31 subjects with PD during a quantitative motor task to decode tremor and bradykinesia – two cardinal motor signs of PD – and relatively asymptomatic periods of behavior. Support vector regression analysis of microelectrode and electrocorticography recordings revealed that tremor and bradykinesia had nearly opposite neural signatures, while effective motor control displayed unique, differentiating features. The neurophysiological signatures of these motor states depended on the signal type and location. Cortical decoding generally outperformed subcortical decoding. Within the subthalamic nucleus (STN), tremor and bradykinesia were better decoded from distinct subregions. These results demonstrate how to leverage neurophysiology to more precisely treat PD.
