Resting State fMRI

Resting-state fMRI (rs-fMRI) studies brain activity at rest to understand functional connectivity between brain regions, aiming to identify biomarkers for neurological and psychiatric disorders. Current research heavily utilizes graph neural networks, convolutional neural networks, and other deep learning architectures, often incorporating techniques like wavelet transforms and multi-scale atlases to analyze the complex, dynamic nature of brain networks. These advanced methods are improving diagnostic accuracy for conditions like Alzheimer's disease, autism spectrum disorder, and post-stroke aphasia, offering potential for earlier and more precise diagnoses and personalized treatment strategies.