
pmid: 22255896
Deep brain stimulation (DBS) effectively alleviates the pathological neural activity associated with Parkinson's disease. Its exact mode of action is not entirely understood. This paper explores theoretically the optimum stimulation parameters necessary to quench oscillations in a neural-mass type model with second order dynamics. This model applies well established nonlinear control system theory to DBS. The analysis here determines the minimum criteria in terms of amplitude and pulse duration of stimulation, necessary to quench the unwanted oscillations in a closed loop system, and outlines the relationship between this model and the actual physiological system.
Neurons, Models, Statistical, Deep Brain Stimulation, Reproducibility of Results, Parkinson Disease, Globus Pallidus, Basal Ganglia, Feedback, Nonlinear Dynamics, Oscillometry, Linear Models, Humans, Algorithms
Neurons, Models, Statistical, Deep Brain Stimulation, Reproducibility of Results, Parkinson Disease, Globus Pallidus, Basal Ganglia, Feedback, Nonlinear Dynamics, Oscillometry, Linear Models, Humans, Algorithms
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