Multiscale modeling of brain network organization

Abstract

A complete understanding of the brain requires an integrated description of the numerous scales of neural organization. It means studying the interplay of genes, synapses, and even whole brain regions 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 access simultaneously to information from multiple spatiotemporal scales. While some insights have been gained on the role of specific microcircuits (e.g., thalamocortical), a comprehensive characterization of how changes occurring at one scale can have an impact on other ones, remains poorly understood. Recent efforts to address this gap include the development of new analytical tools mostly adapted from network science and dynamical systems theory. These theoretical contributions provide a powerful framework to analyze and model interconnected complex systems exhibiting interactions within and between different scales, or layers. Here, we present recent 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 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 neuroscience.

References in corpus (15)

• Fast unfolding of communities in large networks
• Modularity and community structure in networks
• Emergent complex neural dynamics
• Diffusion dynamics on multiplex networks
• Layer aggregation and reducibility of multilayer interconnected networks
• Untangling cross-frequency coupling in neuroscience
• Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences
• Graph analysis of functional brain networks: practical issues in translational neuroscience
• The multilayer connectome of Caenorhabditis elegans
• Identification of core-periphery structure in networks
• Self-similar correlation function in brain resting-state fMRI
• Can we neglect the multi-layer structure of functional networks?
• Spreading of infectious diseases on heterogeneous populations: multi-type network approach
• Immunization Dynamics on a 2-layer Network Model
• Static and dynamic measures of human brain connectivity predict complementary aspects of human cognitive performance