Chronic high-frequency electrical stimulation of the brain, called deep brain stimulation (DBS), has emerged as a well-established therapy for the treatment of movement disorders, including essential tremor and Parkinson's disease (PD). Although the clinical benefits of DBS are well documented, fundamental questions remain about the mechanisms of action, and this lack of understanding will limit the full development and optimization of this promising treatment. One of the hallmarks of DBS is the strong dependence of symptom relief on the frequency of stimulation. High frequency DBS (> 100 Hz) relieves the symptoms of movement disorders, while low frequency stimulation (< 50 Hz) is generally not ineffective. During the prior grant period we established that the effects of DBS are also strongly dependent on the temporal pattern of stimulation. Now, we seek to exploit this finding - that the effects of DBS are strongly dependent on the temporal pattern of stimulation - both to understand the relationship between temporal patterns of neural activity and the motor symptoms of PD and to improve the effectiveness and efficiency of DBS through the design of novel optimal temporal patterns of stimulation. We will combine computational modeling, quantitative behavior and single unit neural recording in an animal model of PD, and translational experiments in humans with PD to advance both the understanding and application of DBS. First, we will measure the effects on tremor and bradykinesia of symptogenic temporal patterns of DBS, designed to generate neural typified by either theta-frequency or beta-frequency oscillations, and determine the causality between these temporal patterns of neural activity and the motor symptoms of PD. Second, we will use model-based optimization to design novel temporal patterns of stimulation intended to suppress maximally the abnormal synchronous oscillations in the theta- and beta-frequency bands, and measure the effects of DBS with these patterns on tremor and bradykinesia in a rat model of PD and in persons with PD and STN DBS. Third, we will measure the effects of the frequency and temporal pattern of DBS on neural activity the basal ganglia and cortex. We will use innovative hardware that enables recording of local field potentials during the application of DBS and correlate the changes in neural oscillatory activity with changes in symptoms in persons with PD and STN DBS, as well as in a rat model of PD. The temporal pattern of DBS is a novel and important parameter that we will exploit, both to understand the relationship between the patterns of neural activity and motor symptoms of PD, and as a novel way to improve the efficacy and efficiency of DBS. The outcomes of the proposed research will contribute to understanding the relationship between patterns of neuronal activity and the symptoms of movement disorders, to improving the treatment of Parkinsonian symptoms with DBS, and to uncovering the mechanisms of action of DBS.

Public Health Relevance

The clinical benefits of deep brain stimulation (DBS) to the motor symptoms of Parkinson's disease are well established, but fundamental questions remain about the mechanisms of action. The outcomes of the proposed project will advance the understanding of the mechanisms of action of DBS and develop and test an innovative approach to increase the efficacy and efficiency of DBS.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Method to Extend Research in Time (MERIT) Award (R37)
Project #
5R37NS040894-13
Application #
9331764
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Langhals, Nick B
Project Start
2000-09-30
Project End
2019-08-31
Budget Start
2017-09-01
Budget End
2018-08-31
Support Year
13
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Duke University
Department
Biomedical Engineering
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
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Lee, Hyung-Min; Howell, Bryan; Grill, Warren M et al. (2018) Stimulation Efficiency With Decaying Exponential Waveforms in a Wirelessly Powered Switched-Capacitor Discharge Stimulation System. IEEE Trans Biomed Eng 65:1095-1106
Yi, Guosheng; Grill, Warren M (2018) Frequency-dependent antidromic activation in thalamocortical relay neurons: effects of synaptic inputs. J Neural Eng 15:056001
Cassar, Isaac R; Titus, Nathan D; Grill, Warren M (2017) An improved genetic algorithm for designing optimal temporal patterns of neural stimulation. J Neural Eng 14:066013
So, Rosa Q; McConnell, George C; Grill, Warren M (2017) Frequency-dependent, transient effects of subthalamic nucleus deep brain stimulation on methamphetamine-induced circling and neuronal activity in the hemiparkinsonian rat. Behav Brain Res 320:119-127
Hoang, Kimberly B; Cassar, Isaac R; Grill, Warren M et al. (2017) Biomarkers and Stimulation Algorithms for Adaptive Brain Stimulation. Front Neurosci 11:564
Brocker, David T; Swan, Brandon D; So, Rosa Q et al. (2017) Optimized temporal pattern of brain stimulation designed by computational evolution. Sci Transl Med 9:
Ramirez-Zamora, Adolfo; Giordano, James J; Gunduz, Aysegul et al. (2017) Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank. Front Neurosci 11:734
Medina, Leonel E; Grill, Warren M (2016) Nerve excitation using an amplitude-modulated signal with kilohertz-frequency carrier and non-zero offset. J Neuroeng Rehabil 13:63
Kumaravelu, Karthik; Brocker, David T; Grill, Warren M (2016) A biophysical model of the cortex-basal ganglia-thalamus network in the 6-OHDA lesioned rat model of Parkinson's disease. J Comput Neurosci 40:207-29

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