多模态结构-功能MRI在孤独症谱系障碍大尺度脑网络层级异常中的研究进展OA

Autism spectrum disorder(ASD)is a neurodevelopmental condition that emerges in early childhood,characterized by social communication difficulties and restricted,repetitive behaviors.Despite their diverse symptoms,individuals with ASD may have shared neurobiological characteristics.Advances in MRI now allow researchers to study brain network abnormalities in ASD from multiple,noninvasive perspectives.This review highlights recent studies using diffusion and functional MRI,covering tract-based spatial statistics,graph theory analyses,functional gradient mapping,and sliding-window approaches.We focus on how these methods help reveal white matter integrity,functional coordination,and hierarchical brain organization in ASD.We also discuss the potential of multimodal image fusion and artificial intelligence(AI)for ASD subtype identification and auxiliary diagnosis.Overall,findings from multimodal MRI indicate cross-scale alterations of brain networks in ASD,providing new imaging evidence for understanding its neural mechanisms and for supporting early identification and individualized intervention.On this basis,we briefly outline the main limitations of current research and,from an integrated perspective spanning"microstructure-large-scale function-structure-function coupling-multimodal analysis",propose future research directions to inform radiologists and neuroscience researchers.