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ORIGINAL RESEARCH article

Front. Netw. Physiol.

Sec. Networks of Dynamical Systems

This article is part of the Research TopicSelf-Organization of Complex Physiological Networks: Synergetic Principles and Applications — In Memory of Hermann HakenView all 16 articles

Modelling brain metabolism with interacting nonautonomous phase oscillators

Provisionally accepted
Samuel  J.K. BarnesSamuel J.K. BarnesAna´ı  Echeverr´ıaAna´ı Echeverr´ıaJoshua  HawleyJoshua HawleyYevhen  F. SuprunenkoYevhen F. SuprunenkoAneta  StefanovskaAneta Stefanovska*
  • Lancaster University, Lancaster, United Kingdom

The final, formatted version of the article will be published soon.

Traditional brain models have focused primarily on electrical signalling, offering valuable insights but often overlooking the crucial role of metabolism within the neurovascular unit. Existing metabolic models tend to be highly detailed and mass-based, relying on strict conservation laws that limit their applicability to the brain's thermodynamically open environment. In this study, we present a novel, phenomenological model of neuronal energy metabolism using a network of coupled Kuramoto oscillators. This non-autonomous phase dynamics framework captures complex, time-dependent interactions and allows for multiple synchronization states among metabolic processes. Our model captures key features consistent with healthy neurovascular dynamics, despite not being directly fitted to empirical data from resting-state brains successfully replicates healthy neurovascular dynamics and reveals how disruptions in metabolic synchrony may contribute to dementia-related pathology. By emphasizing the importance of metabolic coordination in the neurovascular unit, this work opens new avenues for therapeutic strategies targeting neurodegenerative diseases and provides a versatile methodological foundation for future brain modelling efforts.

Keywords: astrocyte, Brain, Coupled oscillators, Metabolism, Network physiology, neurovascular unit, Nonautonomous systems, phase

Received: 07 Oct 2025; Accepted: 05 Jan 2026.

Copyright: © 2026 Barnes, Echeverr´ıa, Hawley, Suprunenko and Stefanovska. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Aneta Stefanovska

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