challenging synchronization-based network reconstruction from time series: a critical evaluation using
Bahareh Karimi Rahjerdi1, Shivakumar Rajagopal2, Fahimeh Nazarimehr3
1Department of Biomedical Engineering, Amirkabir University of Technology (Tehran polytechnic), Tehran, Iran.
Biological Cybernetics
|March 24, 2026
Summary
Network reconstruction from time series data is unreliable. Synchronization does not always imply direct coupling, challenging methods used in neuroscience and dynamical systems research.
Area of Science:
- Dynamical systems
- Computational neuroscience
- Network science
Background:
- Reconstructing complex networks from time series data is common in neuroscience.
- Synchronization-based measures like phase locking value (PLV) are frequently used.
- The assumption that synchronization implies direct coupling is often questionable.
Purpose of the Study:
- To critically investigate the relationship between structural connectivity and functional synchronization.
- To assess the validity of synchronization-based network inference methods.
- To evaluate network reconstruction in coupled FitzHugh-Nagumo (FHN) neuron models.
Main Methods:
- Generated synthetic networks with known topologies (regular, small-world, scale-free).
- Computed pairwise synchronization from time series data of coupled FHN neurons.
- Reconstructed functional networks based on synchronization strength and compared them to structural networks using RMSE.
Main Results:
- High synchronization between nodes does not necessarily indicate a direct structural link.
- Structurally coupled nodes can remain desynchronized.
- Synchronization-based network inference can be unreliable.
Conclusions:
- Synchronization-based methods may misrepresent structural connectivity.
- Caution is needed when interpreting functional connectivity as structural connectivity, especially in brain network studies.
- Findings challenge the reliability of current network inference techniques.
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