Dynamic Functional Connectivity

Dynamic functional connectivity (dFC) analyzes the time-varying patterns of interactions between different brain regions, aiming to understand the brain's dynamic functional organization and its relationship to cognition and behavior. Current research heavily utilizes machine learning, particularly deep learning architectures like graph neural networks and transformers, along with novel methods like masked autoencoders and mode decomposition techniques, to analyze fMRI data and extract meaningful dFC features. These advancements are improving the accuracy of disease diagnosis (e.g., autism spectrum disorder, schizophrenia) and enabling the discovery of novel biomarkers, ultimately leading to a more nuanced understanding of brain function and dysfunction.