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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%.
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