Dynamic Brain

Research on dynamic brain networks focuses on understanding how brain connectivity patterns change over time, moving beyond static representations to capture the complexity of brain function. Current investigations leverage advanced machine learning techniques, such as graph neural networks and transformers, to analyze high-dimensional neuroimaging data (EEG and fMRI) and identify dynamic functional connectivity patterns associated with neurological and psychiatric conditions. These analyses aim to improve diagnostic accuracy, personalize treatments, and provide deeper insights into brain organization and its relationship to behavior and cognition. The development of robust and explainable models for analyzing dynamic brain networks is crucial for advancing neuroscience and clinical applications.