Volume 144, Number 3, November 2023
|Number of page(s)
|Mathematical and interdisciplinary physics
|07 December 2023
Novel perturbation mechanism underlying the network fragility evolution
1 Department of Dynamics and Control, Beihang University - Beijing, 100191, China
2 School of Mathematics and Physics, University of Science and Technology Beijing - Beijing, 100083, China
Received: 18 May 2023
Accepted: 14 November 2023
Studies have shown that fragility is an effective marker for seizures and seizure onset zone (SOZ). Through analysis and simulation of a probabilistic neural network under different inputs, the regularization mechanism of external input perturbations on the fragility is explored. It is theoretically found that the fragility of a perturbed node within seizure network is inversely associated with the received perturbation input, while the fragility of the other unperturbed nodes always oppositely changes with this perturbed node. By terming the node with high fragility as the fragile node (FN), it is interestingly shown that the FN would evolve to the node with the smallest input. Then, the network fragility is further investigated. Results show that the non-uniform perturbation inputs can more easily impact the network fragility. In addition, noise-induced variations of network connection can degrade the network fragility to some extent. Finally, the real data from a patient with epilepsy have verified the universality of the above obtained findings. These results may provide possible insights into stimulation strategies for seizure control in clinic.
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