Multiscale modeling of brain network organization
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by
Charley Presigny, Fabrizio De Vico Fallani
2022
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.
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