Journal
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
Volume 37, Issue 3, Pages 505-521Publisher
SPRINGER
DOI: 10.1007/s10827-014-0523-7
Keywords
Basal ganglia; Deep brain stimulation; Parkinson's disease; Parameter tuning; Computational model; Oscillations; Phase-response curves
Funding
- MnDrive Fellowship
- National Science Foundation [IGERT DGE-1069 104]
- NSF [1264432]
- Medtronic
- Netoff CAREER [0954797]
- Neuroscience R21 institutional training grant [2T32GM008471]
- Directorate For Engineering [1264535] Funding Source: National Science Foundation
- Directorate For Engineering
- Div Of Chem, Bioeng, Env, & Transp Sys [0954797] Funding Source: National Science Foundation
- Div Of Chem, Bioeng, Env, & Transp Sys [1264535] Funding Source: National Science Foundation
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1264432] Funding Source: National Science Foundation
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Efficacy of deep brain stimulation (DBS) for motor signs of Parkinson's disease (PD) depends in part on post-operative programming of stimulus parameters. There is a need for a systematic approach to tuning parameters based on patient physiology. We used a physiologically realistic computational model of the basal ganglia network to investigate the emergence of a 34 Hz oscillation in the PD state and its optimal suppression with DBS. Discrete time transfer functions were fit to post-stimulus time histograms (PSTHs) collected in open-loop, by simulating the pharmacological block of synaptic connections, to describe the behavior of the basal ganglia nuclei. These functions were then connected to create a mean-field model of the closed-loop system, which was analyzed to determine the origin of the emergent 34 Hz pathological oscillation. This analysis determined that the oscillation could emerge from the coupling between the globus pallidus external (GPe) and subthalamic nucleus (STN). When coupled, the two resonate with each other in the PD state but not in the healthy state. By characterizing how this oscillation is affected by subthreshold DBS pulses, we hypothesize that it is possible to predict stimulus frequencies capable of suppressing this oscillation. To characterize the response to the stimulus, we developed a new method for estimating phase response curves (PRCs) from population data. Using the population PRC we were able to predict frequencies that enhance and suppress the 34 Hz pathological oscillation. This provides a systematic approach to tuning DBS frequencies and could enable closed-loop tuning of stimulation parameters.
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