Network geometry breakdown, not entropy increase: a neurotopological interpretation of epileptic seizures

Epileptic pathology is commonly interpreted as a state of increased neural disorder or high entropy. We challenge this interpretation and propose that such conclusions arise from incomplete or unidimensional metric approaches. Using a Unified Neurotopological Framework, we characterize brain dynamics by integrating both informational content and network geometric structure. We quantify neural coherence through a multimodal metric set including Permutation Entropy, Persistent Homology, and Ricci Curvature. The framework is validated using EEG data by comparing status epilepticus with healthy resting-state activity (isostasis). Our results (p < 0.001) show that epileptic seizures do not reflect heightened disorder but instead constitute a catastrophic collapse of network geometry, resulting in disordered low-complexity coherence. Furthermore, we identify a paradox in which the resting healthy brain demonstrates low-ordered complexity, yet remains functionally coherent. These findings suggest that the critical biomarker differentiating health from pathology is not entropy magnitude but the dynamic geometry of neural coherence.

Keywords: Independent component analysis, EEG, Ricci curvature, Persistent homology, Epilepsy, Isostasis

How to Cite this Article:
Arturo Salazar Chon & Dhay Amer Kadhim (2025). Network geometry breakdown, not entropy increase: a neurotopological interpretation of epileptic seizures. Journal of Multiscale Neuroscience 4(4): 266-275.
DOI: https://doi.org/10.56280/1723377902

Authors Affiliation:
Arturo Salazar
Department of Psychology and Social Sciences, Universidad Autonoma de Occidente, Guamuchil, Sinaloa, Mexico 81470

Amer Kadhim
Department of Biomedical Engineering, College of Engineering, University of Babylon, Iraq 51002

Received: 5 November 2025
Accepted: 24 November 2025
Online Published: 9 December 2025

Conflict of Interest
The authors declare no conflict of interest

​Copyright: © 2025 The Author(s). Published by Neural Press.
This is an open access article distributed under the terms and conditions of the CC BY 4.0 license.

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