Frontiers in Network Physiology is delighted to present the ‘Reviews in Networks in the Brain System’ article collection, part of a series of Research Topics reviewing the current status of the field.
Reviews in Networks in the Brain System will publish high-quality scholarly review papers on key topics in Networks in the Brain System. It aims to highlight recent advances in the field, whilst emphasizing important directions and new possibilities for future inquiries. We anticipate the research presented will promote discussion in the Network Physiology community and translate to best practice applications in public and policy settings, as well as stimulating understanding and future research.
Reviews in Networks in the Brain System welcomes full-length or mini review articles, perspectives and opinion pieces across the full Networks in the Brain System
section scope . Article type guidelines can be found
here and new articles will be added to this collection as they are published.
Potential topics of interest include but are not limited to the following:
• data-driven contruction of networks (assessing pairwiese to higher-order interactions, statistical validation, methods from sync-theory, nonlinear dynamics, ...)
• experimental assessment of brain networks (functional and structural)
• novel graph-theoretical approaches (e.g. visibility graphs, multiplex and multilayer networks, centralities, ...)
• network-based modelling of brain dynamics (physiology - pathophysiology, from simple oscillators to complex neuron models)
• brain - other-organs interactions (e.g. heart, gut, ...)
• new measures for quantifying synchronization in different scales (temporal, spatial, amplitude-frequency, etc)
• novel data analysis tools for characterizing structural or functional networks (eg topological data analysis)
• novel distance/dissimilarity measures for comparing temporal networks, brain networks, etc.
• machine learning methods for predicting / classifying brain states from data
• network-based modelling of the interactions of symptoms to understand mental disorders