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

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.