Puxeddu Maria Grazia, Pope Maria, Varley Thomas F, Faskowitz Joshua, Sporns Olaf
Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Rome, Italy.
Commun Biol. 2025 May 30;8(1):840. doi: 10.1038/s42003-025-08198-2.
Brain functioning relies on specialized systems whose integration enables cognition and behavior. Network science provides tools to model the brain as a set of interconnected brain regions wherein those segregated systems (modules) can be identified by optimizing the weights of pairwise connections within them. However, knowledge alone of these pairwise connections might not suffice: brain dynamics are also engendered by higher-order interactions that simultaneously involve multiple brain areas. Here, we propose a community detection algorithm that accounts for multivariate interactions and finds modules of brain regions whose activity is maximally redundant. We compared redundancy-dominated modules to those identified with conventional methods, uncovering a new organization of the transmodal cortex. Moreover, by identifying a spatial resolution where within-module redundancy and between-module synergy are maximally balanced, we captured a higher-order manifestation of the interplay between segregation and integration of information. Finally, we distinguish brain regions with high and low topological specialization based on their contribution to within- or between-module redundancy, and we observed how redundant modules reconfigure across the lifespan. Altogether, the results show a modular organization of the brain that accounts for higher-order interactions and pave the way for future investigations that might link it to cognition, behavior, or disease.
大脑功能依赖于专门的系统，这些系统的整合促成了认知和行为。网络科学提供了工具，可将大脑建模为一组相互连接的脑区，其中那些分离的系统（模块）可通过优化它们内部成对连接的权重来识别。然而，仅了解这些成对连接可能并不够：大脑动力学还由同时涉及多个脑区的高阶相互作用产生。在此，我们提出一种社区检测算法，该算法考虑多元相互作用，并找到其活动具有最大冗余度的脑区模块。我们将以冗余为主导的模块与用传统方法识别出的模块进行比较，发现了跨模态皮层的一种新组织方式。此外，通过确定模块内冗余和模块间协同作用达到最大平衡的空间分辨率，我们捕捉到了信息分离与整合之间相互作用的一种高阶表现形式。最后，我们根据脑区对模块内或模块间冗余的贡献，区分出具有高拓扑特异性和低拓扑特异性的脑区，并观察了冗余模块在整个生命周期中的重新配置情况。总体而言，结果显示了大脑的一种模块化组织方式，该方式考虑了高阶相互作用，并为未来可能将其与认知、行为或疾病联系起来的研究铺平了道路。
