The concepts of graph theory were first proposed in the eighteenth century, but only recently have they found widespread usage in the field of network science. Over the last decade, the field has grown significantly; this growth is often attributed to the small-world network model proposed by Watts & Strogatz which described a system that provides regional specialization with efficient global information transfer. The brain is one such system; accordingly, network science has become increasingly popular in the field of neuroimaging. Understanding the brain as a network is appealing as it can be viewed as a system with various interacting regions that produce complex behaviors. The application of network science to the brain has facilitated our understanding of how the brain is structurally and functionally organized. Furthermore, studying the brain within this framework has already shed light on how many diseases and disorders affect the brain.
Many network studies utilize graph metrics and centrality measures to identify important nodes (or vertices) in a network; in neuroimaging, these metrics are often averaged to find group differences between populations. In addition, algorithms that deduce network community structure have made it possible to understand how nodes are organized into different groups. Most network analysis tools are designed to analyze a static representation of an individual network. Expanding these analytic methods to groups of networks and networks with nodes that change dynamically presents a daunting challenge for the field.
The relative dearth of tools for analyzing and comparing brain networks precludes gleaning deeper insights into normal and abnormal brain function. More specifically, tools are needed to better assess group data and network dynamics. How does one deduce the properties from a group of networks? How does one incorporate dynamic changes in a network? As the field continues to grow, it is becoming increasingly important to develop methods for understanding the complexity of these data. While attempts have been made to tackle this issue, more work is needed. The future of network science in neuroimaging remains promising as long as we take a conscientious approach to developing methods that appropriately account for the complexity in the data.
The goal of this Research Topic is to explore the complexity in brain networks, with an emphasis on methods for analyzing groups of complex networks and dynamics of information spread on networks. The field of network science is expansive; thus complex network studies in other disciplines like physics and the social sciences are also welcome. Nonetheless, while we solicit papers across different fields, we would like to emphasize studies that focus on neuroimaging or methods that can be utilized for analyzing neuroimaging data.
The concepts of graph theory were first proposed in the eighteenth century, but only recently have they found widespread usage in the field of network science. Over the last decade, the field has grown significantly; this growth is often attributed to the small-world network model proposed by Watts & Strogatz which described a system that provides regional specialization with efficient global information transfer. The brain is one such system; accordingly, network science has become increasingly popular in the field of neuroimaging. Understanding the brain as a network is appealing as it can be viewed as a system with various interacting regions that produce complex behaviors. The application of network science to the brain has facilitated our understanding of how the brain is structurally and functionally organized. Furthermore, studying the brain within this framework has already shed light on how many diseases and disorders affect the brain.
Many network studies utilize graph metrics and centrality measures to identify important nodes (or vertices) in a network; in neuroimaging, these metrics are often averaged to find group differences between populations. In addition, algorithms that deduce network community structure have made it possible to understand how nodes are organized into different groups. Most network analysis tools are designed to analyze a static representation of an individual network. Expanding these analytic methods to groups of networks and networks with nodes that change dynamically presents a daunting challenge for the field.
The relative dearth of tools for analyzing and comparing brain networks precludes gleaning deeper insights into normal and abnormal brain function. More specifically, tools are needed to better assess group data and network dynamics. How does one deduce the properties from a group of networks? How does one incorporate dynamic changes in a network? As the field continues to grow, it is becoming increasingly important to develop methods for understanding the complexity of these data. While attempts have been made to tackle this issue, more work is needed. The future of network science in neuroimaging remains promising as long as we take a conscientious approach to developing methods that appropriately account for the complexity in the data.
The goal of this Research Topic is to explore the complexity in brain networks, with an emphasis on methods for analyzing groups of complex networks and dynamics of information spread on networks. The field of network science is expansive; thus complex network studies in other disciplines like physics and the social sciences are also welcome. Nonetheless, while we solicit papers across different fields, we would like to emphasize studies that focus on neuroimaging or methods that can be utilized for analyzing neuroimaging data.
