Resting State fMRI Data

Resting-state fMRI (rsfMRI) data analysis aims to understand brain function by studying brain activity patterns when individuals are not performing specific tasks. Current research heavily utilizes graph neural networks (GNNs) and other machine learning models, such as transformers and dynamic mode decomposition (DMD) algorithms, to analyze the complex connectivity patterns within rsfMRI data for applications like disease classification (e.g., Alzheimer's, Parkinson's, depression, autism) and cognitive ability prediction. These analyses reveal crucial information about brain network organization and dynamics, offering valuable insights into neurological and psychiatric disorders and potentially improving diagnostic accuracy and personalized treatment strategies.