Colloquium: Multiscale modeling of brain network organization

作     者：Charley Presigny Fabrizio De Vico Fallani 

作者机构：Sorbonne Université Institut du Cerveau—Paris Brain Institute—ICM CNRS Inria Inserm AP-HP Hôpital de la Pitié Salpêtrière F-75013 Paris France 

出 版 物：《Reviews of Modern Physics》 (现代物理学评论)

年 卷 期：2022年第94卷第3期

页      面：031002-031002页

核心收录：

学科分类：07[理学] 0702[理学-物理学] 

基　　金：Horizon 2020 Framework Programme, H2020, (864729) Horizon 2020 Framework Programme, H2020 European Research Council, ERC 

主　　题：Network phase transitions Random walks Brain network Coupled oscillators Multilayer & multiplex networks Community detection algorithms Neuroimaging Neuronal network models 

摘      要：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.