Robustness and size-dependence of circadian rhythms in multiscale suprachiasmatic-nucleus networks

2026-01-30

Physical Review E

AI Summary

This study investigates how the multiscale network architecture of the suprachiasmatic nucleus (SCN) influences the robustness and synchrony of circadian rhythms. Leveraging empirical functional connectivity data from the mouse SCN, the authors construct self-similar multiscale networks using geometric branching growth (GBG) and geometric renormalization (GR) methods, systematically examining how network size, average degree, and clustering properties affect rhythm amplitude, period, and synchronization. For the first time in a real SCN network, they demonstrate robustness of circadian parameters across scales, showing that rhythm characteristics remain stable regardless of network size. Scale-dependent effects only reemerge when the average degree is artificially increased, revealing that average degree—not clustering structure—is the key driver of scale dependence in circadian dynamics, thereby overcoming limitations inherent in conventional synthetic models.

Technology Category

Cognitive Modeling & Cognitive Systems: Neural Spike CodingPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMultiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

Social Networks and Social Media: Computational social scienceGraph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphsEconomics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Understanding how multi-scale network structure influences circadian rhythms in the suprachiasmatic nucleus (SCN) is essential for uncovering the principles of rhythmic robustness and synchronization. Previous studies using synthetic SCN networks suggested a size-dependent phenomenon, in which rhythmic activity initially strengthens with network size and then saturates, but it remains unclear whether this occurs in real SCN networks. Here, we apply geometric branch growth (GBG) and geometric renormalization (GR) to generate self-similar scaled-up and scaled-down replicas from a single-scale functional mouse SCN network. Unlike synthetic models, these SCN replicas do not exhibit size-dependent rhythms: average period, amplitude, and synchronization remain stable across scales. By increasing the average degree with network size, we reproduce size-dependent rhythms and show that they arise from network connectivity, whereas low-degree networks fragment and fail to sustain oscillations. Disrupting clustering self-similarity slightly reduces synchronization, but circadian rhythms remain robust, indicating that average degree, rather than clustering, is the dominant structural driver. These results highlight the resilience of SCN rhythms to network scaling and provide a framework for linking multi-scale network structure to biological timekeeping.

Problem

• circadian rhythms
• suprachiasmatic nucleus
• network size-dependence
• multi-scale networks
• rhythmic robustness

Innovation

• geometric renormalization
• suprachiasmatic nucleus
• circadian rhythms
• network scaling
• average degree