Over the last decade, there has been growing interest and important developments in fMRI functional connectivity analysis methods. These advances have been fostered by improvements in the research techniques that enable us to gather insights from different approaches, together with the need to better understand fMRI functional connectivity data.
In this Research Topic, we encourage researchers to summarize improvements and novel approaches to fMRI functional connectivity analysis methods. We also welcome contributions that look into the future and define the directions that methods will take in the coming years. What have been the key improvements so far? What are the most pressing matters that need to be addressed? Where will new fMRI functional connectivity analysis take us in the coming years?
Topics of interest to this Research Topic include, but are not limited to:
• Methods describing either new or existing functional connectivity analysis methods that are significantly improved or adapted for our understanding of brain networks and cognitive mechanisms. These manuscripts may include primary (original) data.
• Machine learning methods, including but not limited to deep learning algorithms, optimization methods and probabilistic graphical models for registration, processing and analyses of fMRI data investigating brain networks and cognitive mechanisms.
• Privacy-aware methods for fMRI such as federated learning and differential privacy for fMRI.
• Statistical, geometric and topological methods for modelling fMRI functional connectivity in brain networks and cognitive mechanisms.
• Application of machine learning models of functional connectivity such as graph neural networks.
• Applications of fMRI functional connectivity analyses for various tasks such as sentiment analysis, human-computer interaction, recognition and diagnosis of diseases and disorders, human brain mapping, neural encoding and decoding, image reconstruction and vision, that further our understanding of brain networks and cognitive mechanisms
• Original Research investigating brain networks and cognitive mechanisms using novel functional connectivity analysis methods.
• Protocols: Detailed descriptions, including pitfalls and troubleshooting, to benefit those who may evaluate or employ the techniques. The protocols must be proven to work.
Topic Editor Dr. Mete Ozay is employed by Samsung Research UK. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Over the last decade, there has been growing interest and important developments in fMRI functional connectivity analysis methods. These advances have been fostered by improvements in the research techniques that enable us to gather insights from different approaches, together with the need to better understand fMRI functional connectivity data.
In this Research Topic, we encourage researchers to summarize improvements and novel approaches to fMRI functional connectivity analysis methods. We also welcome contributions that look into the future and define the directions that methods will take in the coming years. What have been the key improvements so far? What are the most pressing matters that need to be addressed? Where will new fMRI functional connectivity analysis take us in the coming years?
Topics of interest to this Research Topic include, but are not limited to:
• Methods describing either new or existing functional connectivity analysis methods that are significantly improved or adapted for our understanding of brain networks and cognitive mechanisms. These manuscripts may include primary (original) data.
• Machine learning methods, including but not limited to deep learning algorithms, optimization methods and probabilistic graphical models for registration, processing and analyses of fMRI data investigating brain networks and cognitive mechanisms.
• Privacy-aware methods for fMRI such as federated learning and differential privacy for fMRI.
• Statistical, geometric and topological methods for modelling fMRI functional connectivity in brain networks and cognitive mechanisms.
• Application of machine learning models of functional connectivity such as graph neural networks.
• Applications of fMRI functional connectivity analyses for various tasks such as sentiment analysis, human-computer interaction, recognition and diagnosis of diseases and disorders, human brain mapping, neural encoding and decoding, image reconstruction and vision, that further our understanding of brain networks and cognitive mechanisms
• Original Research investigating brain networks and cognitive mechanisms using novel functional connectivity analysis methods.
• Protocols: Detailed descriptions, including pitfalls and troubleshooting, to benefit those who may evaluate or employ the techniques. The protocols must be proven to work.
Topic Editor Dr. Mete Ozay is employed by Samsung Research UK. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
