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Sensitivity-analysis-guided Bayesian parameter estimation for neural mass models: Applications in epilepsy.

Narayan Puthanmadam Subramaniyam1, Jari Hyttinen1

  • 1Faculty of Medicine and Health Technology, <a href="https://ror.org/033003e23">Tampere University</a>, 33520 Tampere, Finland.

Physical Review. E
|November 20, 2024
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Summary
This summary is machine-generated.

This study identifies key parameters in neural mass models for accurate electroencephalography (EEG) analysis. The findings enable reliable estimation of these parameters, crucial for understanding brain dynamics and developing seizure control strategies.

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Area of Science:

  • Computational Neuroscience
  • Biophysics
  • Signal Processing

Background:

  • Neural mass models (NMMs) simulate brain activity like electroencephalography (EEG).
  • Estimating parameters in high-dimensional NMMs for EEG analysis is challenging.
  • Identifying sensitive NMM parameters is crucial for reliable brain dynamics insights.

Purpose of the Study:

  • To identify the most sensitive parameters of the Jansen and Rit NMM (JR-NMM) for reliable EEG data parameter estimation.
  • To develop and validate a Bayesian approach for estimating JR-NMM states and parameters.
  • To investigate changes in these parameters during epileptic seizures.

Main Methods:

  • Parameter sensitivity analysis using Morris and Sobol methods on the JR-NMM.
  • A Bayesian estimation framework combining expectation-maximization (EM) with the unscented Kalman smoother (UKS-EM).
  • Validation with simulated EEG data and application to intracranial EEG from epileptic patients.

Main Results:

  • The average inhibitory synaptic gain (B) and reciprocal time constant (b) were identified as the most sensitive JR-NMM parameters.
  • The UKS-EM method accurately estimated B and b under varying noise levels.
  • Significant changes in B and b were observed between pre-seizure, seizure, and post-seizure periods in epileptic patients.

Conclusions:

  • Sensitivity analysis effectively reduces NMM parameter space for reliable estimation.
  • The UKS-EM algorithm provides a powerful tool for estimating sensitive NMM parameters from EEG.
  • Parameter changes in B and b offer insights into seizure dynamics and potential for real-time tracking.