Understanding the functional mechanism of human brain is crucial in basic and clinical neuroscience. The functional system of the human brain involves a complex network, which is characterized by its community architecture. Community detection in functional brain networks facilitates the understanding of the underlying brain organization and its related cognitive function. Most neurological diseases present themselves as a disorder of modularity. Community detection techniques have been widely used in real networks. Novel deep learning techniques such as graph convolutional network due to its success on supervised and simi-supervised classification of nodes in a graph represent a promising tool because they will detect important theory-driven biomarkers for disease progression and prediction. These quantitative methods are believed to be of clinical importance when it comes to explore the functional mechanism of patients with disorders. Although several classical community detection algorithms have been introduced in recent neuroimaging studies, it's unknown whether the novel approaches can capture new diagnostic characteristics.
The aim of this Research Topic is to present the current state of the art in the theory and application of advanced community detection approaches in neuroimaging to study neural basis of the neurological and mental disorders.
This Research Topic welcomes Original Research, Review, and Meta-analysis articles. All contributions addressing the community characteristic of the brain networks using neuroimaging techniques, methods, and techniques to solve community-related problems, state of the art of this hot and relevant Research Topic, will be welcome.
Understanding the functional mechanism of human brain is crucial in basic and clinical neuroscience. The functional system of the human brain involves a complex network, which is characterized by its community architecture. Community detection in functional brain networks facilitates the understanding of the underlying brain organization and its related cognitive function. Most neurological diseases present themselves as a disorder of modularity. Community detection techniques have been widely used in real networks. Novel deep learning techniques such as graph convolutional network due to its success on supervised and simi-supervised classification of nodes in a graph represent a promising tool because they will detect important theory-driven biomarkers for disease progression and prediction. These quantitative methods are believed to be of clinical importance when it comes to explore the functional mechanism of patients with disorders. Although several classical community detection algorithms have been introduced in recent neuroimaging studies, it's unknown whether the novel approaches can capture new diagnostic characteristics.
The aim of this Research Topic is to present the current state of the art in the theory and application of advanced community detection approaches in neuroimaging to study neural basis of the neurological and mental disorders.
This Research Topic welcomes Original Research, Review, and Meta-analysis articles. All contributions addressing the community characteristic of the brain networks using neuroimaging techniques, methods, and techniques to solve community-related problems, state of the art of this hot and relevant Research Topic, will be welcome.
