AI 中文总结
该研究基于有向网络的癫痫发作动力学模型，发现营养相干性等结构特性与癫痫发作倾向密切相关，其模拟结果随网络规模增大而增强，提示大脑信息处理的整体方向性或与癫痫发作倾向有关。
AI 中文摘要
癫痫被广泛认为是由大脑连接驱动的疾病。我们使用有向网络上的癫痫发作动力学模型，研究结构特性如何影响癫痫发作倾向。我们发现营养相干性、谱半径、强连通性和非正规性等特性与癫痫发作倾向密切相关，并给出谱半径与循环结构之间理论关系的证明。我们的模拟结果对模型中使用的耦合类型具有鲁棒性，且随网络规模增大而增强。这些结果表明，大脑信息处理的整体方向性可能与癫痫发作倾向有关。
英文摘要
Epilepsy is widely regarded as a disorder driven by connectivity in the brain. We use a model of seizure dynamics on directed networks to investigate how structural properties affect seizure propensity. We find that properties such as trophic coherence, spectral radius, strong connectivity and non-normality are closely related to seizure propensity, and present a proof of a theoretical relationship between spectral radius and cycle structure. Our simulated results are robust to the kind of coupling used in the model and become stronger as network size is increased. They suggest that the overall directionality of information processing in the brain may be related to a propensity for epileptic seizures.
Comments20 pages, 11 figures (including supplementaries). Associated code is available at https://github.com/pjkissack/TrophicAnalysis.jl
