Nartallo-Kaluarachchi Ramón, Bonetti Leonardo, Fernández-Rubio Gemma, Vuust Peter, Deco Gustavo, Kringelbach Morten L, Lambiotte Renaud, Goriely Alain
Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom.
Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford OX3 9BX, United Kingdom.
Proc Natl Acad Sci U S A. 2025 Mar 11;122(10):e2408791122. doi: 10.1073/pnas.2408791122. Epub 2025 Mar 7.
Information processing in the human brain can be modeled as a complex dynamical system operating out of equilibrium with multiple regions interacting nonlinearly. Yet, despite extensive study of the global level of nonequilibrium in the brain, quantifying the irreversibility of interactions among brain regions at multiple levels remains an unresolved challenge. Here, we present the Directed Multiplex Visibility Graph Irreversibility framework, a method for analyzing neural recordings using network analysis of time-series. Our approach constructs directed multilayer graphs from multivariate time-series where information about irreversibility can be decoded from the marginal degree distributions across the layers, which each represents a variable. This framework is able to quantify the irreversibility of every interaction in the complex system. Applying the method to magnetoencephalography recordings during a long-term memory recognition task, we quantify the multivariate irreversibility of interactions between brain regions and identify the combinations of regions which showed higher levels of nonequilibrium in their interactions. For individual regions, we find higher irreversibility in cognitive versus sensorial brain regions while for pairs, strong relationships are uncovered between cognitive and sensorial pairs in the same hemisphere. For triplets and quadruplets, the most nonequilibrium interactions are between cognitive-sensorial pairs alongside medial regions. Combining these results, we show that multilevel irreversibility offers unique insights into the higher-order, hierarchical organization of neural dynamics from the perspective of brain network dynamics.
人类大脑中的信息处理可以被建模为一个复杂的动态系统，该系统在非平衡状态下运行，多个区域进行非线性相互作用。然而，尽管对大脑非平衡的全局水平进行了广泛研究，但在多个层面上量化大脑区域间相互作用的不可逆性仍然是一个未解决的挑战。在此，我们提出了有向多重可见性图不可逆性框架，这是一种使用时间序列网络分析来分析神经记录的方法。我们的方法从多变量时间序列构建有向多层图，其中不可逆性信息可以从各层的边际度分布中解码出来，每层代表一个变量。该框架能够量化复杂系统中每次相互作用的不可逆性。将该方法应用于长期记忆识别任务期间的脑磁图记录，我们量化了大脑区域间相互作用的多变量不可逆性，并确定了其相互作用中表现出更高非平衡水平的区域组合。对于单个区域，我们发现在认知脑区与感觉脑区中不可逆性更高，而对于成对区域，在同一半球的认知与感觉成对区域之间发现了很强的关系。对于三联体和四联体，最不平衡的相互作用发生在认知 - 感觉成对区域与内侧区域之间。综合这些结果，我们表明，从脑网络动力学的角度来看，多级不可逆性为神经动力学的高阶层次组织提供了独特的见解。
