Resting State

Resting-state research investigates spontaneous brain activity patterns measured via functional magnetic resonance imaging (fMRI) to understand brain organization and function, often focusing on identifying biomarkers for neurological and psychiatric disorders. Current research heavily utilizes deep learning, employing graph neural networks, convolutional neural networks, transformers, and variational autoencoders to analyze complex fMRI data, often incorporating multimodal data (e.g., fMRI and sMRI) and leveraging techniques like graph representation learning and domain adaptation to improve model robustness and generalizability. These advancements hold significant promise for improving the diagnosis and understanding of various brain disorders, offering more accurate and efficient clinical tools and potentially revealing novel insights into brain connectivity and its relationship to cognition and behavior.