Delabays Robin, De Pasquale Giulia, Dörfler Florian, Zhang Yuanzhao
School of Engineering, University of Applied Sciences of Western Switzerland HES-SO, Sion, Switzerland.
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Nat Commun. 2025 Mar 19;16(1):2691. doi: 10.1038/s41467-025-57664-2.
A plethora of methods have been developed in the past two decades to infer the underlying network structure of an interconnected system from its collective dynamics. However, methods capable of inferring nonpairwise interactions are only starting to appear. Here, we develop an inference algorithm based on sparse identification of nonlinear dynamics (SINDy) to reconstruct hypergraphs and simplicial complexes from time-series data. Our model-free method does not require information about node dynamics or coupling functions, making it applicable to complex systems that do not have a reliable mathematical description. We first benchmark the new method on synthetic data generated from Kuramoto and Lorenz dynamics. We then use it to infer the effective connectivity in the brain from resting-state EEG data, which reveals significant contributions from non-pairwise interactions in shaping the macroscopic brain dynamics.
在过去二十年里，人们开发了大量方法，用于从互联系统的集体动力学中推断其潜在的网络结构。然而，能够推断非成对相互作用的方法才刚刚开始出现。在这里，我们开发了一种基于非线性动力学稀疏识别（SINDy）的推理算法，用于从时间序列数据中重建超图和单纯复形。我们的无模型方法不需要关于节点动力学或耦合函数的信息，使其适用于没有可靠数学描述的复杂系统。我们首先在由Kuramoto和Lorenz动力学生成的合成数据上对新方法进行基准测试。然后，我们使用它从静息态脑电图数据中推断大脑中的有效连通性，这揭示了非成对相互作用在塑造宏观大脑动力学方面的重要贡献。
