Interpretation of seizure dynamics using fuzzy-based neural computational modelling

Prathaban, Banu Priya; Rajendran, Subash; Ganeshkumar, N; Gayatri, M.; Chembian, W. T.

doi:10.1007/s00500-023-08545-7

Cited by 1 publication

(1 citation statement)

References 20 publications

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“…In comparison, the Wendling model can generate six common EEG signals, including background noise, alpha waves, epileptic spike waves, etc., thus enhancing the physiological and physical reality in certain aspects. The signals simulated in the Wendling model have rich dynamic characteristics and wider frequency bands and are closer to real human EEG features [ 38 , 39 , 40 , 41 ]. Ursino et al [ 22 ] proposed an expanded Wendling model to faithfully replicate actual brain EEG signals, they and obtained different rhythm combinations (β and γ, α and γ, or a wide spectrum).…”

Section: Discussionmentioning

confidence: 99%

Construction and Analysis of a New Resting-State Whole-Brain Network Model

Cui,

Li,

Shao

et al. 2024

Brain Sciences

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(1) Background: Mathematical modeling and computer simulation are important methods for understanding complex neural systems. The whole-brain network model can help people understand the neurophysiological mechanisms of brain cognition and functional diseases of the brain. (2) Methods: In this study, we constructed a resting-state whole-brain network model (WBNM) by using the Wendling neural mass model as the node and a real structural connectivity matrix as the edge of the network. By analyzing the correlation between the simulated functional connectivity matrix in the resting state and the empirical functional connectivity matrix, an optimal global coupling coefficient was obtained. Then, the waveforms and spectra of simulated EEG signals and four commonly used measures from graph theory and small-world network properties of simulated brain networks under different thresholds were analyzed. (3) Results: The results showed that the correlation coefficient of the functional connectivity matrix of the simulated WBNM and empirical brain networks could reach a maximum value of 0.676 when the global coupling coefficient was set to 20.3. The simulated EEG signals showed rich waveform and frequency-band characteristics. The commonly used graph-theoretical measures and small-world properties of the constructed WBNM were similar to those of empirical brain networks. When the threshold was set to 0.22, the maximum correlation between the simulated WBNM and empirical brain networks was 0.709. (4) Conclusions: The constructed resting-state WBNM is similar to a real brain network to a certain extent and can be used to study the neurophysiological mechanisms of complex brain networks.

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