About this Research Topic
Despite the inherent complexities of the brain, if we consider its components as nodes, and the underlying physical interaction among them as links in a network, we simplify the problem and at the same time harvest useful information. This simplified version of the system has proven successful, allowing to shift the focus from the emergent behaviors to the resultant connectivity, where characterization analysis can rely on powerful tools from the emerging area of Network Neuroscience. Thus, it is important to infer connectivity that represents the physical interaction among data collected from the dynamics of the nodes, such as data from EEG, fMRI, PET, MEG or other brain imaging techniques. Although network inference from brain data has been studied extensively in recent years using cross-correlation, mutual information, mutual information rate, recurrences, functional dynamics, and Granger causality to name a few, it still presents major challenges. The inferred network is always an approximation and the measured signals suffer from several factors, for example, interference, volume conduction, noise, and damping. How representative is the inferred network to the axonal, anatomical or functional connectivity? How reliable are the inference methods in describing a connectivity? How are the brain’s multi-scales reflected on the inferred connectivity? We think modeling the brain from its inferred structure will provide understanding on how emergence sets in different scales, thus, also providing answers for the posed challenges.
This Research Topic requires multi- and inter-disciplinary efforts, thus, we welcome contributions from researchers working on several related fields, not only those on inference methods, but also those working on Complex Systems, Biophysics, and Network Neuroscience. We expect this Research Topic will advance our understanding on complex systems in general, and in particular, on the inner workings of the brain and its states. We seek contributions that will shed light on the fundamental aspects and novel approaches on network inference, on structural and functional properties of the brain, and on emergent and synchronization phenomena. The manuscripts may include, but are not limited to, analytical and numerical approaches, development of mathematical modeling and computational methods related to complex systems and network neuroscience.
Keywords: Biophysics, network inference, complex systems, emergence, synchronisation
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.