A complete understanding of the brain requires an integrated description of the numerous scales and levels of neural organization. This means studying the interplay of genes and synapses, but also the relation between the structure and dynamics of the whole brain, which ultimately leads to different types of behavior, from perception to action, while asleep or awake. Yet multiscale brain modeling is challenging, in part because of the difficulty to simultaneously access information from multiple scales and levels. While some insight has been gained on the role of specific microcircuits on the generation of macroscale brain activity, a comprehensive characterization of how changes occurring at one scale or level can have an impact on other ones remains poorly understood. Recent efforts to address this gap include the development of new frameworks originating mostly from network science and complex systems theory. These theoretical contributions provide a powerful framework to analyze and model interconnected systems exhibiting interactions within and between different layers of information. Recent advances for the characterization of the multiscale brain organization in terms of structure-function, oscillation frequencies, and temporal evolution are presented. Efforts are reviewed on the multilayer network properties underlying the physics of higher-order organization of neuronal assemblies, as well as on the identification of multimodal network-based biomarkers of brain pathologies such as Alzheimer’s disease. This Colloquium concludes with a perspective discussion of how recent results from multilayer network theory, involving generative modeling, controllability, and machine learning, could be adopted to address new questions in modern physics and neuroscience.

中文翻译：

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研讨会：大脑网络组织的多尺度建模

对大脑的完整理解需要对神经组织的众多尺度和层次进行综合描述。这意味着研究基因和突触的相互作用，以及整个大脑的结构和动力学之间的关系，这最终导致不同类型的行为，从感知到行动，无论是睡着还是醒着。然而，多尺度大脑建模具有挑战性，部分原因是难以同时访问多个尺度和级别的信息。虽然人们对特定微电路在宏观大脑活动产生中的作用有了一些了解，但对在一个尺度或水平上发生的变化如何对其他尺度或水平产生影响的全面表征仍然知之甚少。最近为解决这一差距所做的努力包括开发主要源自网络科学和复杂系统理论的新框架。这些理论贡献提供了一个强大的框架来分析和建模互连系统，展示不同信息层内​​部和之间的交互。介绍了在结构功能、振荡频率和时间演化方面描述多尺度大脑组织的最新进展。回顾了神经元组件高阶组织物理基础的多层网络特性，以及识别阿尔茨海默氏病等脑部病理的基于多模式网络的生物标志物的努力。本次研讨会最后对如何采用涉及生成建模、可控性和机器学习的多层网络理论的最新成果来解决现代物理学和神经科学中的新问题进行了前瞻性讨论。