A latest study presents a novel deep learning method for diagnosing autism spectrum disorder (ASD) by modeling dynamic functional connectivity (FC) with multi-head attention. This approach captures intricate brain connectivity patterns, enhancing diagnostic accuracy by up to 3.7%.
