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Investigating irregularly patterned deep brain stimulation signal design using biophysical models.

Samantha R Summerson1, Behnaam Aazhang2, Caleb Kemere3

  • 1Department of Electrical Engineering and Computer Science, University of California, Berkeley Berkeley, CA, USA.

Frontiers in Computational Neuroscience
|July 14, 2015
PubMed
Summary

Deep brain stimulation (DBS) for Parkinson's disease (PD) shows that irregular pulse patterns in subthalamic nucleus (STN) stimulation can improve motor circuit function by diversifying neural responses and reducing noise.

Keywords:
Parkinson's diseaseantidromiccomputational modelingdeep brain stimulationfiring rate entropy

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Parkinson's disease (PD) involves the loss of dopaminergic neurons in the substantia nigra pars compacta (SNc), leading to motor deficits.
  • Deep brain stimulation (DBS) is a therapeutic intervention for PD that modulates neural activity but requires optimized parameters for efficacy.
  • The precise conditions governing DBS therapeutic efficacy remain incompletely understood.

Purpose of the Study:

  • To develop and utilize a biophysical model of basal ganglia motor circuits to investigate the efficacy of different DBS signal parameters.
  • To compare the performance of various DBS signal paradigms within a computational model.
  • To validate the computational model's physiological relevance using a hemi-Parkinsonian rodent model.

Main Methods:

  • Construction of a computational model using biophysical cell models representing critical motor circuit structures.
  • Simulation of various DBS signal patterns, including frequency and pulse train regularity.
  • Experimental validation using a hemi-Parkinsonian rodent model to assess signal responses.
  • Analysis of antidromic spiking and its impact on cortical and basal ganglia neural activity.

Main Results:

  • Antidromic spiking induced by DBS of the subthalamic nucleus (STN) significantly influences cortical neural activity.
  • The impact of DBS on neural activity is dependent on stimulation frequency and the regularity of the stimulus pulse train.
  • Bounded irregularity in stimulus pulse spacing enhances the diversification of basal ganglia neuron responses.
  • Irregular DBS pulse trains reduce entropic noise in cortical neurons, potentially restoring motor circuit information flow.

Conclusions:

  • Computational modeling provides valuable insights into neural function and novel DBS paradigms for Parkinson's disease.
  • Optimizing DBS signal regularity, specifically employing bounded irregularity, can enhance therapeutic outcomes by modulating neural network dynamics.
  • Findings suggest that irregular DBS pulse trains may be crucial for restoring information processing in the motor circuit affected by Parkinson's disease.