Brain imaging, providing a way to non-invasively map the structure and function of the brain, has developed significantly in recent years. Take functional brain imaging as an example: the advent of functional brain imaging, such as functional magnetic resonance imaging (fMRI), has sparked excitement over its potential to revolutionize researchers' understanding of the brain’s physical basis. Additionally, it offers a powerful tool that assists in understanding how the brain adapts to various cognitive activities and tasks. Besides, structural brain imaging, such as diffusion tensor imaging (DTI), enables researchers to model brain structural connections by computation and reveal the underlying mechanisms in various neurological and psychiatric disorders.
As the global representation of brain functional/structural connectivity, brain networks are essential in the neural basis of cognition, neuroanatomy, functional brain imaging, neurodevelopment, etc. Brain network computing includes the construction and reconstruction of brain networks, brain network analysis, and brain network optimization. While brain imaging is used to study the anatomical structure and working mechanisms of the brain in two or three dimensions with qualitative and quantitative analysis, brain network computing enables the study of brain topological features and covariant features. There are various tools for constructing brain networks, such as PANDA, GRETNA, et al. However, brain networks deriving from existing tools are subjective and time-consuming and it largely depends on the experience of the operator. This Research Topic aims to develop AI-based algorithms for constructing brain networks automatically.
Generative artificial intelligence refers to new technologies that employ existing data including images, text, and audio files to create new content. This new content has a similar underlying pattern of real-world data and has great potential applications in many areas. Synthetic data from generative AI can train machine learning models to be less biased and help robots to learn more abstract concepts both in the real and virtual world. In the area of brain science, generative artificial intelligence offers a powerful tool for brain imaging and brain network computing. It enables extracting brain spatio-temporal features and reconstructing the topological connectivity of brain networks. By reconstructing brain networks, generative AI can detect new neurological biomarkers for various brain disorders.
This Research Topic explores the advances, challenges, and prospects of brain imaging and brain network computing techniques. This Research Topic is focused on novel methodological approaches or applications of related new methods including validation studies.
Topics include but are not limited to the following:
-New methods and new techniques for constructing brain structural/functional networks
-Generative artificial intelligence for constructing brain networks
-Generative artificial intelligence for developing end-to-end brain network computing and analyzing pipelines
-Generative artificial intelligence for brain network analysis and brain network optimization
-Generative artificial intelligence for reconstructing brain images and brain networks
-New techniques for learning brain network spatial/temporal representations
-Network neuroscience and graph theory
-Nonlinear feature selection and cross-frequency coupling in neuroscience
-Multi-modal brain imaging
-New machine learning and algorithms for brain disorders analysis
-AI-aided diagnosis of brain disorders
-Brain-inspired intelligence
-Multi-modal brain imaging
Brain imaging, providing a way to non-invasively map the structure and function of the brain, has developed significantly in recent years. Take functional brain imaging as an example: the advent of functional brain imaging, such as functional magnetic resonance imaging (fMRI), has sparked excitement over its potential to revolutionize researchers' understanding of the brain’s physical basis. Additionally, it offers a powerful tool that assists in understanding how the brain adapts to various cognitive activities and tasks. Besides, structural brain imaging, such as diffusion tensor imaging (DTI), enables researchers to model brain structural connections by computation and reveal the underlying mechanisms in various neurological and psychiatric disorders.
As the global representation of brain functional/structural connectivity, brain networks are essential in the neural basis of cognition, neuroanatomy, functional brain imaging, neurodevelopment, etc. Brain network computing includes the construction and reconstruction of brain networks, brain network analysis, and brain network optimization. While brain imaging is used to study the anatomical structure and working mechanisms of the brain in two or three dimensions with qualitative and quantitative analysis, brain network computing enables the study of brain topological features and covariant features. There are various tools for constructing brain networks, such as PANDA, GRETNA, et al. However, brain networks deriving from existing tools are subjective and time-consuming and it largely depends on the experience of the operator. This Research Topic aims to develop AI-based algorithms for constructing brain networks automatically.
Generative artificial intelligence refers to new technologies that employ existing data including images, text, and audio files to create new content. This new content has a similar underlying pattern of real-world data and has great potential applications in many areas. Synthetic data from generative AI can train machine learning models to be less biased and help robots to learn more abstract concepts both in the real and virtual world. In the area of brain science, generative artificial intelligence offers a powerful tool for brain imaging and brain network computing. It enables extracting brain spatio-temporal features and reconstructing the topological connectivity of brain networks. By reconstructing brain networks, generative AI can detect new neurological biomarkers for various brain disorders.
This Research Topic explores the advances, challenges, and prospects of brain imaging and brain network computing techniques. This Research Topic is focused on novel methodological approaches or applications of related new methods including validation studies.
Topics include but are not limited to the following:
-New methods and new techniques for constructing brain structural/functional networks
-Generative artificial intelligence for constructing brain networks
-Generative artificial intelligence for developing end-to-end brain network computing and analyzing pipelines
-Generative artificial intelligence for brain network analysis and brain network optimization
-Generative artificial intelligence for reconstructing brain images and brain networks
-New techniques for learning brain network spatial/temporal representations
-Network neuroscience and graph theory
-Nonlinear feature selection and cross-frequency coupling in neuroscience
-Multi-modal brain imaging
-New machine learning and algorithms for brain disorders analysis
-AI-aided diagnosis of brain disorders
-Brain-inspired intelligence
-Multi-modal brain imaging
