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Control of coupled neural oscillations using near-periodic inputs
1Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee 37996, USA.
Chaos (Woodbury, N.Y.)
|April 2, 2022
Summary
Researchers developed a new control strategy for adaptive deep brain stimulation (aDBS) to disrupt neural synchronization. This method uses a reduced-order model to improve treatment efficacy for neurological disorders like Parkinson's disease.
Area of Science:
- Computational neuroscience
- Neurotechnology
- Dynamical systems theory
Background:
- Deep brain stimulation (DBS) is a key treatment for Parkinson's disease.
- Adaptive DBS (aDBS) aims to enhance efficacy and reduce side effects by timing stimulation to neural oscillations.
- Investigating the dynamics of neural populations under phase-specific aDBS is crucial for advancing therapeutic strategies.
Purpose of the Study:
- To investigate the dynamical behavior of large coupled neuron populations under near-periodic stimulation relevant to adaptive DBS.
- To develop and validate a closed-loop control strategy to disrupt synchronization in these neural populations.
- To explore real-time implementation of control strategies without full model knowledge.
Main Methods:
- Utilized an adaptive phase-amplitude reduction strategy to model neural population dynamics.
- Developed a reduced-order model with four state variables to capture phase distribution evolution.
- Devised and validated a closed-loop control strategy to counteract neural synchronization.
Main Results:
- Demonstrated that a reduced-order model accurately captures the phase distribution dynamics of large oscillatory neuron populations.
- Successfully devised and validated a closed-loop control strategy to disrupt coupling-induced synchronization.
- Identified methods for real-time control implementation by estimating model terms from observable data.
Conclusions:
- A four-state reduced-order model effectively describes neural population dynamics under near-periodic forcing for aDBS.
- Closed-loop control can successfully disrupt synchronization in neural networks, offering a potential therapeutic advancement.
- The proposed control strategy is adaptable for real-world implementation even when underlying neural dynamics are not fully known.
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