P161 Core-Peripheral Network Topology Facilitates Dynamic State Transitions in the Computational Modeling of Zebrafish Brain
Dongmyeong Lee*1,3,Yelim Lee1,2, Hae-Jeong Park1,2,3
1Yonsei University College of Medicine, Seoul, South of Korea
2BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, South of Korea
3Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South of Korea
Email: dmyeong@gmail.com
Introduction
Understanding how structural network topology shapes large-scale neural dynamics is a fundamental challenge in neuroscience. In particular, core-peripheral network topology is a crucial property, where highly connected "core" regions serve as hubs for integrating information across the brain, while sparsely connected "peripheral" regions support localized processing. Although many studies have explored the influence of core-peripheral topology on brain function at the macro-scale, the relationship between core-peripheral connectivity and dynamic information processing at the cellular level remains an open question. In this study, we investigate the impact of core-peripheral connectivity on whole-brain neural dynamics in zebrafish using computational modeling by integrating cellular-resolution structural connectivity data with a large-scale spiking neural network model.
Methods
To achieve this, we reconstructed a cellular-resolution structural connectivity network and extended it to develop a large-scale spiking neural network model consisting of 50,000 neurons across 72 distinct brain regions in the zebrafish brain. By systematically varying core-peripheral connection probabilities and coupling constants in the computational model, we examined their effects on neural activity fluctuations.
Results
Our results demonstrate that the zebrafish brain exhibits a distinct core-peripheral network structure, where core regions play a critical role in dynamic signal propagation and network reconfiguration by examining cellular connectivity data. Analysis of calcium imaging data revealed that the zebrafish brain dynamically transitions between multiple states, enabling adaptive and efficient information processing. Among four different connection types, i.e., peripheral-peripheral, core-peripheral, peripheral-core, and core-core, core-to-peripheral connections exhibited the highest functional fluctuations, closely mirroring experimentally observed calcium imaging data.
Discussion
These findings highlight that core-peripheral connectivity serves as a key structural mechanism regulating state transitions, optimizing the balance between network modularity and integration. This suggests that large-scale brain networks leverage core-peripheral topology to dynamically regulate state transitions and maintain optimal neural computation. By integrating experimental data with computational modeling, this study provides novel insights into how structural connectivity underlies large-scale neural computations and functional flexibility in the brain.
Acknowledgements
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (NO. 2023R1A2C200621711)
References
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