R fMRI

Resting-state fMRI (rs-fMRI) analysis aims to understand brain function by studying spontaneous brain activity patterns, primarily focusing on identifying biomarkers for neurological and psychiatric disorders. Current research heavily utilizes deep learning, employing architectures like transformers, convolutional neural networks, and graph neural networks, often coupled with techniques like variational autoencoders and graph learning algorithms to analyze functional connectivity and time-series data. These advancements improve diagnostic accuracy and offer insights into disease mechanisms, potentially leading to more effective early detection and personalized treatment strategies. Furthermore, incorporating expert knowledge into AI models enhances performance and interpretability, bridging the gap between computational analysis and clinical practice.