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  • Open Access

Detecting remote synchronization from empirical data of brain networks

Dehua Chen1, Zhiyin Yang1, Zhaohua Lin2, Guoshen Liang1, Wenbin Mao1, and Zonghua Liu1,*

  • *Contact author: zhliu@phy.ecnu.edu.cn
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Phys. Rev. Research 8, 033307 – Published 14 September, 2026

DOI: https://doi.org/10.1103/tmkq-33c4

Abstract

The emergence of functional connectivity between distant brain regions that lack a direct structural link remains a long-standing question in neuroscience. Remote synchronization (RS) offers a promising framework for uncovering the underlying mechanisms. So far, RS has been well studied in artificial network topologies such as star or starlike graphs but little attention has been paid to realistic brain networks with complicated topologies, due to their completely different synchronization pathways. In this study, we address this gap by introducing an efficient method, termed the noncompletely overlapping path algorithm, to detect RS using empirical structural and functional brain network data. Under this approach, two nodes are considered to exhibit RS if they are functionally synchronized despite being structurally disconnected. That is, they share neither a direct structural link nor an indirect path composed of synchronized nodes. We demonstrate that the proportion of RS nodes in a brain network strongly depends on the thresholds used to define both structural and functional connectivity, with optimal threshold values existing for each. Furthermore, we show that the method can be generalized: It applies not only to empirical time series but also to oscillator-based network models, and it admits a theoretical interpretation. This indicates that the algorithm offers a general framework to detect RS in complex networks.

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Phys. Rev. Research 8, 033307– Published 14 September, 2026
  • Received 24 May 2026
  • Accepted 30 August 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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