A modification to the Kuramoto model to simulate epileptic seizures as synchronization

Area of Science:

• Computational Neuroscience
• Mathematical Biology
• Epilepsy Research

Background:

• The Kuramoto model describes coupled oscillator synchronization, relevant to natural phenomena.
• Epileptic seizures can be viewed as synchronized neuronal activity, suggesting model applicability.
• Existing models may not fully capture seizure onset and progression dynamics.

Purpose of the Study:

• To modify the Kuramoto model for simulating epileptic seizures.
• To incorporate logistic growth in coupling strength to represent seizure dynamics.
• To use Fast Fourier Transform (FFT) derived parameters for realistic neuronal frequency and amplitude.

Main Methods:

• Modified the Kuramoto model with a logistic growth coupling function.
• Extracted basal state neuronal frequencies and amplitudes using FFT from EEG signals.
• Simulated seizure emergence by increasing synchronization in the modified Kuramoto model.
• Employed Dynamic Time Warping (DTW) for signal comparison.

Main Results:

• The modified Kuramoto model successfully simulated seizure onset and progression.
• Numerical simulations demonstrated the emergence of epileptic seizure dynamics.
• Comparison using DTW showed good agreement between simulated and approximated seizure signals.

Conclusions:

• The modified Kuramoto model provides a viable computational tool for epilepsy research.
• Neuronal synchronization, modeled via logistic growth, is crucial for seizure dynamics.
• This approach enhances understanding of seizure mechanisms and potential interventions.