A population level computational model of the basal ganglia that generates parkinsonian local field potential activity

  • Authors:

  • George L. Tsirogiannis;George A. Tagaris;Damianos Sakas;Konstantina S. Nikita

  • Affiliations:

  • National Technical University of Athens, Biomedical Simulations and Imaging Laboratory, School of Electrical and Computer Engineering, 9 Iroon Polytechneiou Street, 15773, Athens, Greece;“G. Gennimatas” General Hospital of Athens, Department of Neurology, 154 Mesogeion Avenue, 11527, Athens, Greece;University of Athens, “Evangelismos” Hospital, Parkinson’s Disease Surgical Treatment Unit, Department of Neurosurgery, 45–47 Ipsilantou Street, 10675, Athens, Greece;National Technical University of Athens, Biomedical Simulations and Imaging Laboratory, School of Electrical and Computer Engineering, 9 Iroon Polytechneiou Street, 15773, Athens, Greece

  • Venue:
  • Biological Cybernetics
  • Year:
  • 2010

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Abstract

Recordings from the basal ganglia’s subthalamic nucleus are acquired via microelectrodes immediately prior to the application of Deep Brain Stimulation (DBS) treatment for Parkinson’s Disease (PD) to assist in the selection of the final point for the implantation of the DBS electrode. The acquired recordings reveal a persistent characteristic beta band peak in the power spectral density function of the Local Field Potential (LFP) signals. This peak is considered to lie at the core of the causality–effect relationships of the parkinsonian pathophysiology. Based on LFPs acquired from human subjects during DBS for PD, we constructed a computational model of the basal ganglia on the population level that generates LFPs to identify the critical pathophysiological alterations that lead to the expression of the beta band peak. To this end, we used experimental data reporting that the strengths of the synaptic connections are modified under dopamine depletion. The hypothesis that the altered dopaminergic modulation may affect both the amplitude and the time course of the postsynaptic potentials is validated by the model. The results suggest a pivotal role of both of these parameters to the pathophysiology of PD.