Analysis of Oscillatory Neural Activity in Series Network Models of Parkinson’s Disease During Deep Brain Stimulation
Refereed Original Article
Abstract—Parkinson’s disease is a progressive, neurodegenerative disorder, characterized by hallmark motor symptoms. It is associatedwithpathological,oscillatoryneuralactivityinthebasal ganglia. Deep brain stimulation(DBS) is often successfully used to treat medically refractive Parkinson’s disease. However, the selection of stimulation parameters is based on qualitative assessment of the patient, which can result in a lengthy tuning period and a suboptimal choice of parameters. This study explores fourthorder, control theory-based models of oscillatory activity in the basal ganglia. Describing function analysis is applied to examine possible mechanisms for the generation of oscillations in interactingnucleiandtoinvestigatethesuppressionofoscillationswith high-frequencystimulation.Thetheoreticalresultsforthesuppressionoftheoscillatoryactivityobtainedusingboththefourth-order model, and a previously described second-order model, are optimized to ﬁt clinically recorded local ﬁeld potential data obtained fromParkinsonianpatientswithimplantedDBS.Closeagreement between the power of oscillations recorded for a range of stimulation amplitudes is observed (R2 =0 .69−0.99). The results suggest that the behavior of the system and the suppression of pathological neural oscillations with DBS is well described by the macroscopic models presented. The results also demonstrate that inthisinstance,asecond-ordermodelissufﬁcienttomodeltheclinical data, without the need for added complexity. Describing the systembehaviorwithcomputationallyefﬁcientmodelscouldaidin theidentiﬁcationofoptimalstimulationparametersforpatientsin a clinical environment.
Digital Object Identifer (DOI):
Date Accepted for Publication:
Monday, 31 August, 2015
National University of Ireland, Dublin (UCD)
Open access repository: