Abstract

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 Chon, Department of Psychology and Social Sciences, Universidad Autonoma de Occidente, Guamuchil, Sinaloa, Mexico 81470 

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

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-NC-ND 4.0 license. 

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