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Predicting the effects of deep brain stimulation using a reduced coupled oscillator model
Gihan Weerasinghe1, Benoit Duchet1, Hayriye Cagnan1
1MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
Plos Computational Biology
|August 9, 2019
Summary
This study models brain oscillations for closed-loop deep brain stimulation (DBS). Mathematical analysis suggests optimal DBS timing depends on neural oscillation phase and amplitude, potentially improving treatment for essential tremor (ET).
Area of Science:
- Neuroscience
- Computational Biology
- Biomedical Engineering
Background:
- Deep brain stimulation (DBS) is a key treatment for Parkinson's disease and essential tremor (ET).
- Current DBS uses constant frequency pulses; novel closed-loop DBS aims for improved efficacy and reduced side effects by adapting to neural activity.
- Minimizing pathological neural oscillations is crucial for successful closed-loop DBS.
Purpose of the Study:
- To develop a mathematical model predicting brain oscillation responses to DBS.
- To identify optimal stimulation strategies for closed-loop DBS based on neural oscillation phase and amplitude.
- To propose a novel hybrid closed-loop DBS strategy.
Main Methods:
- Utilized a reduced Kuramoto model of coupled oscillators to represent neurons.
- Analyzed how stimulation affects neural oscillations based on their phase and amplitude.
- Compared model predictions with existing data from essential tremor (ET) patients.
Main Results:
- Predicted that optimal DBS stimulation is phase-specific.
- Found that stimulation is more effective when applied during lower amplitudes of brain oscillations.
- Validated predictions against ET patient data.
Conclusions:
- Mathematical modeling provides insights into optimizing closed-loop DBS.
- A phase-specific and amplitude-dependent stimulation strategy may enhance DBS efficacy.
- A proposed hybrid strategy combines phase-locked and adaptive DBS approaches for potential clinical benefit.
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