Complex systems are composed of a large number of non-trivially interacting components whose collective behavior cannot be determined from the behavior of the individual components. Many real-world systems can be modeled as complex, such as stock markets, the Internet, social networks and the brain. Particularly, in the brain, a massive number of microscopic components (neurons or cortical areas) are interacting with each other in nonlinear ways, where important information resides in the relationships between the components and not necessarily within their individual dynamics. Hence, studying the dynamics of these components without knowing how they are interconnected does not allow for the understanding of the brain’s behavior as a whole. Furthermore, connectivity is often unknown and difficult to infer due to large system-sizes and multiple time and spatial scales. This poses significant challenges and opens questions, forming the basis for 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.
Complex systems are composed of a large number of non-trivially interacting components whose collective behavior cannot be determined from the behavior of the individual components. Many real-world systems can be modeled as complex, such as stock markets, the Internet, social networks and the brain. Particularly, in the brain, a massive number of microscopic components (neurons or cortical areas) are interacting with each other in nonlinear ways, where important information resides in the relationships between the components and not necessarily within their individual dynamics. Hence, studying the dynamics of these components without knowing how they are interconnected does not allow for the understanding of the brain’s behavior as a whole. Furthermore, connectivity is often unknown and difficult to infer due to large system-sizes and multiple time and spatial scales. This poses significant challenges and opens questions, forming the basis for 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.
