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  Telecommunication-inspired network models of healthy and diseased brains

Fazio, P., Mannone, M., Marwan, N., Ribino, P., Mehic, M., Swikir, A., Amendola, D., Riello, P., Voznak, M. (2026 online): Telecommunication-inspired network models of healthy and diseased brains. - Scientific Reports.
https://doi.org/10.1038/s41598-026-50758-x

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Fazio_2026_s41598-026-50758-x_reference.pdf (Publisher version), 22MB
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Fazio_2026_s41598-026-50758-x_reference.pdf
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 Creators:
Fazio, Peppino1, Author
Mannone, Maria2, Author           
Marwan, Norbert2, Author                 
Ribino, Patrizia1, Author
Mehic, Miralem1, Author
Swikir, Abdalla1, Author
Amendola, Danilo1, Author
Riello, Pietro1, Author
Voznak, Miroslav1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Recent advances in nanoelectronics have spurred increased interest in the human brain and its complex functions. Numerous studies have explored brain behavior in varying levels of detail, from individual neurons to entire lobes. Intricately structured, the brain is a complex organ susceptible to diseases that may disrupt the connectivity between its internal regions. Investigating this phenomenon, the present study applies a discrete finite-state model to map the behavior of neurons within a neuronal agglomerate and examine the effect of disease on these behaviors. Each agglomerate is then compared to a wireless clustered network and modeled as a finite-state system, with inter-cluster communications analyzed under conditions of temporal variations and degradation. This work represents one of the most advanced applications of discrete finite-state processes and routing theory in brain modeling.

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Language(s): eng - English
 Dates: 2026-04-232026-04-28
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41598-026-50758-x
MDB-ID: No MDB - stored outside PIK (see locators/paper)
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Working Group: Development of advanced time series analysis techniques
Research topic keyword: Health
Research topic keyword: Nonlinear Dynamics
Regional keyword: Europe
Model / method: Quantitative Methods
Model / method: Qualitative Methods
Model / method: Nonlinear Data Analysis
Regional keyword: Global
OATYPE: Gold Open Access
 Degree: -

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Title: Scientific Reports
Source Genre: Journal, SCI, Scopus, p3, OA
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals2_395
Publisher: Nature