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

TL;DR

This study investigates how the structure of the suprachiasmatic nucleus network affects circadian rhythm robustness and size-dependence, revealing that average degree influences rhythms more than clustering, and that rhythms are resilient across scales.

Contribution

The paper introduces a method to generate self-similar SCN network replicas and demonstrates that network connectivity, especially average degree, governs circadian rhythm stability across scales.

Findings

SCN network replicas do not show size-dependent rhythms.

Increasing average degree with network size reproduces size-dependent rhythms.

Rhythms remain robust despite disruptions to clustering self-similarity.

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.