About this Research Topic
Functional magnetic resonance imaging (fMRI) is a non-invasive neuroimaging technique that can detect brain signals and convert them into a spatial representation of the brain according to signal intensities.
Resting-state fMRI (rs-fMRI) is a powerful tool, offering the ability to simultaneously identify multiple brain networks to understand brain organization. rs-fMRI can be adopted for clinical applications with little concern on the design of the fMRI paradigm and its compliance by the patients.
Recent advances in fMRI techniques enable the acquisition of data with a submillimeter voxel size, which can lead to the mapping of functional signals with a better spatial resolution and allow the characterization of functional profiles with respect to cortical depth. Future advances in the field will significantly contribute to the understanding of brain function and organization while being used as a tool to improve clinical diagnosis.
The importance of Rs-fMRI has been successfully demonstrated in many previous studies. However, its use for clinical applications is usually performed using standard 2D echo planar imaging with a relatively small voxel size (2~3 mm). Although this voxel size is able to identify the between-group effects (i.e. patient vs control), it is still not precise enough in identifying and localizing the target brain areas in pre-surgical settings.
For a more accurate delineation of functional areas, the use of advanced, high-resolution imaging methods combined with new analytic methods may represent a great advance for their clinical applications. A novel analysis scheme using an artificial intelligence-based (AI) technique has recently been able to effectively extract the features of functional data. This approach can be facilitated by high-resolution fMRI techniques to improve the accuracy of the feature extraction.
This Research Topic focuses on the development of novel methods in fMRI enhancing our understanding of brain organization at the systems level. We welcome authors to address the following, but not limited to, subtopics, aiming to provide further insights into novel methodologies, paradigms, and protocols:
1) development of high-resolution fMRI techniques that can contribute to improved clinical diagnosis;
2) development of imaging methodologies and dedicated protocols for clinical applications;
3) investigation of the brain function and connectivity in patients using an advanced fMRI method;
4) development of data analysis methods for high-resolution fMRI data sets and;
5) use of an artificial intelligence-based technique for big data analysis in clinical applications.
The corresponding types of manuscripts can be Original Research, Methods, Clinical Trial, Case Report, Data Report, Brief Research Report, Technology and Code.
Resting-state fMRI (rs-fMRI) is a powerful tool, offering the ability to simultaneously identify multiple brain networks to understand brain organization. rs-fMRI can be adopted for clinical applications with little concern on the design of the fMRI paradigm and its compliance by the patients.
Recent advances in fMRI techniques enable the acquisition of data with a submillimeter voxel size, which can lead to the mapping of functional signals with a better spatial resolution and allow the characterization of functional profiles with respect to cortical depth. Future advances in the field will significantly contribute to the understanding of brain function and organization while being used as a tool to improve clinical diagnosis.
The importance of Rs-fMRI has been successfully demonstrated in many previous studies. However, its use for clinical applications is usually performed using standard 2D echo planar imaging with a relatively small voxel size (2~3 mm). Although this voxel size is able to identify the between-group effects (i.e. patient vs control), it is still not precise enough in identifying and localizing the target brain areas in pre-surgical settings.
For a more accurate delineation of functional areas, the use of advanced, high-resolution imaging methods combined with new analytic methods may represent a great advance for their clinical applications. A novel analysis scheme using an artificial intelligence-based (AI) technique has recently been able to effectively extract the features of functional data. This approach can be facilitated by high-resolution fMRI techniques to improve the accuracy of the feature extraction.
This Research Topic focuses on the development of novel methods in fMRI enhancing our understanding of brain organization at the systems level. We welcome authors to address the following, but not limited to, subtopics, aiming to provide further insights into novel methodologies, paradigms, and protocols:
1) development of high-resolution fMRI techniques that can contribute to improved clinical diagnosis;
2) development of imaging methodologies and dedicated protocols for clinical applications;
3) investigation of the brain function and connectivity in patients using an advanced fMRI method;
4) development of data analysis methods for high-resolution fMRI data sets and;
5) use of an artificial intelligence-based technique for big data analysis in clinical applications.
The corresponding types of manuscripts can be Original Research, Methods, Clinical Trial, Case Report, Data Report, Brief Research Report, Technology and Code.
Keywords: Resting-state, Data Analysis, Artificial Intelligence (AI), Functional magnetic resonance imaging (fMRI), Feature extraction, Layer-specific fMRI, High-resolution fMRI
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
