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Proportional-Integral Controller-Based Deep Brain Stimulation Strategy for Controlling Excitatory-Inhibitory Network

Aanuoluwapo V Olumuyiwa1, Gautam Kumar1

  • 1Department of Chemical and Materials Engineering, San Jose State University, CA 95192, U.S.A.

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PubMed
Summary
This summary is machine-generated.

This study introduces a closed-loop system for deep brain stimulation (DBS) using a novel Forced Temporal Spike-Time Stimulation (FTSTS) strategy. This automated approach tunes stimulation amplitude to control neuronal network synchrony, improving upon manual parameter adjustments.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Deep brain stimulation (DBS) shows promise for neurological disorders by correcting abnormal neural firing patterns.
  • Current DBS systems necessitate manual adjustment of stimulation parameters, posing a clinical challenge.
  • Previous research introduced Forced Temporal Spike-Time Stimulation (FTSTS) to desynchronize neural networks via synaptic plasticity, but it operated in an open-loop manner.

Purpose of the Study:

  • To develop and validate a closed-loop DBS strategy by integrating a proportional-integral (PI) controller with FTSTS.
  • To automate the tuning of stimulation amplitude for controlling neuronal network synchrony.
  • To investigate the effects of stimulation parameters on neural firing rates and network synchrony.

Main Methods:

  • Utilized a computational model of an excitatory-inhibitory (E-I) neural network (400 excitatory, 100 inhibitory neurons).
  • Performed spectral analysis on spiking data to correlate network synchrony with mean firing rates of E and I neurons.
  • Designed and implemented a PI controller to dynamically adjust FTSTS amplitude based on inhibitory neuron firing rate feedback.

Main Results:

  • Established a correlation between network synchrony and mean population firing rates.
  • Systematically analyzed the impact of stimulation amplitude and frequency on E and I neuron firing rates.
  • Demonstrated the successful control of neuronal synchronization in the E-I network model using the closed-loop PI-controlled FTSTS strategy.

Conclusions:

  • A closed-loop FTSTS strategy using a PI controller can effectively automate DBS parameter tuning.
  • This automated approach offers a promising method for managing neuronal synchrony in brain disorders.
  • The developed computational model provides a platform for further research into adaptive DBS therapies.