Structural and functional brain networks have been becoming an increasingly useful tool in understanding the interactions among the separated brain regions, and the pathogenesis of specific neurological disease. In the past decade, there has been an increasing interest in modelling the brain networks based on various mode data (e.g., fMRI, EEG, PET and DTI) and capturing feature representations of brain networks (e.g., connection, graph topology, and graph neural networks) for understanding pathogenesis. Due to the complexity of the brain being far beyond our imagination, revealing the mystery of the brain is still facing many challenges. Thus, there is still debate over the many ways to construct brain networks, how to effectively utilize the multi-modal data, and how to best reveal information about brain health and disorder.
The application of network science in the brain has promoted our understanding of structure and functional organization of the brain. Furthermore, studying the brain within this framework effectively reveals how neurological diseases affect brain organization. In this Research Topic, we seek to gather new findings on brain network construction, multimodal fusion, representation of network learning, and making inferences and predictions via brain networks. More specifically, the goal of this research topic is to promote the current understanding of the brain connectome via mathematical modelling, develop new and advanced methods to capture the graphical relationship between function and structure, effectively utilize the multi-modal data, and accurately learn the representation of the network in brain disorders, thereby promoting our understanding of the underlying configuration and dynamics of the brain.
Both original research and review articles are welcome. Studies should focus on major trends and challenges in this field. Potential subtopics include but are not limited to the following:
1. Machine/deep learning for brain network analysis.
2. Machine/deep learning for multiple network integration.
3. Neurological diseases or disorders mechanisms via brain network.
4. Artificial intelligence applications: graph neural network, graph kernel, etc.
5. Identification of neurological diseases or disorders.
6. Graphical relationship between function and structure of the brain.
7. Data-driven based brain network construction.
Structural and functional brain networks have been becoming an increasingly useful tool in understanding the interactions among the separated brain regions, and the pathogenesis of specific neurological disease. In the past decade, there has been an increasing interest in modelling the brain networks based on various mode data (e.g., fMRI, EEG, PET and DTI) and capturing feature representations of brain networks (e.g., connection, graph topology, and graph neural networks) for understanding pathogenesis. Due to the complexity of the brain being far beyond our imagination, revealing the mystery of the brain is still facing many challenges. Thus, there is still debate over the many ways to construct brain networks, how to effectively utilize the multi-modal data, and how to best reveal information about brain health and disorder.
The application of network science in the brain has promoted our understanding of structure and functional organization of the brain. Furthermore, studying the brain within this framework effectively reveals how neurological diseases affect brain organization. In this Research Topic, we seek to gather new findings on brain network construction, multimodal fusion, representation of network learning, and making inferences and predictions via brain networks. More specifically, the goal of this research topic is to promote the current understanding of the brain connectome via mathematical modelling, develop new and advanced methods to capture the graphical relationship between function and structure, effectively utilize the multi-modal data, and accurately learn the representation of the network in brain disorders, thereby promoting our understanding of the underlying configuration and dynamics of the brain.
Both original research and review articles are welcome. Studies should focus on major trends and challenges in this field. Potential subtopics include but are not limited to the following:
1. Machine/deep learning for brain network analysis.
2. Machine/deep learning for multiple network integration.
3. Neurological diseases or disorders mechanisms via brain network.
4. Artificial intelligence applications: graph neural network, graph kernel, etc.
5. Identification of neurological diseases or disorders.
6. Graphical relationship between function and structure of the brain.
7. Data-driven based brain network construction.
