• DOI:10.1103/RevModPhys.94.031002
• Corpus ID: 251291168

Colloquium : Multiscale modeling of brain network organization

@article{Presigny2021ColloquiumM,
  title={Colloquium
: Multiscale modeling of brain network organization},
  author={Charley Presigny and Fabrizio De Vico Fallani},
  journal={Reviews of Modern Physics},
  year={2021}
}

• Charley Presigny, F. De Vico Fallani
• Published 26 November 2021
• Biology
• Reviews of Modern Physics

A complete understanding of the brain requires an integrated description of the numer- ous scales and levels of neural organization. That 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 insights have 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/level, can have an impact on other ones, remains poorly understood. Recent efforts to address this gap include the development of new frameworks mostly originat-ing 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. Here, we present re- cent advances for the characterization of the multiscale brain organization in terms of structure-function, oscillation frequencies and temporal evolution. 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. We conclude this Colloquium with a perspective discussion on 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.