Analysis of Oscillatory Neural Activity in Series Network Models of Parkinson's Disease During Deep Brain Stimulation.

Parkinson's disease is a progressive, neurodegenerative disorder, characterized by hallmark motor symptoms. It is associated with pathological, oscillatory neural activity in the basal ganglia. Deep brain stimulation (DBS) is often successfully used to treat medically refractive Parkinson's disease.

Application of describing function analysis to a model of deep brain stimulation.

Deep brain stimulation effectively alleviates motor symptoms of medically refractory Parkinson's disease, and also relieves many other treatment-resistant movement and affective disorders. Despite its relative success as a treatment option, the basis of its efficacy remains elusive. In Parkinson's disease, increased functional connectivity and oscillatory activity occur within the basal ganglia as a result of dopamine loss.

Application of non-linear control theory to a model of deep brain stimulation.

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.

Analysis of the mechanism of action of deep brain stimulation using the concepts of dither injection and the equivalent nonlinearity.

Deep brain stimulation (DBS) is a widely applied clinical procedure for the alleviation of pathological neural activity, and is particularly effective in suppressing the symptoms of Parkinson's disease. The mechanisms of action of DBS remain to be fully elucidated. In this paper, we present an application to DBS of the concepts of dither injection and equivalent nonlinearity from the theory of nonlinear feedback control systems.