Multimodal magnetic resonance imaging pattern recognition in autism spectrum disorder

Abstract: Autism spectrum disorder (ASD) is a highly complex neurodevelopmental disorder characterized by high prevalence, heterogeneity, and lifelong impact. The underlying mechanisms of ASD remain largely unknown. Multimodal magnetic resonance imaging (MRI) has emerged as a novel tool to unveil the neuroimaging mechanisms of ASD. Studies based on single-modal MRI have already revealed widespread abnormalities in brain structure, function, and network connectivity in individuals with ASD. The affected regions encompass the amygdala, fusiform gyrus, orbitofrontal cortex, medial prefrontal cortex, anterior cingulate cortex, superior temporal sulcus, and insula, many of which are implicated in the social brain network. While frameworks for multimodal brain imaging analysis, involving image-level fusion, feature-level fusion, and decision-level fusion, offer multidimensional and multilevel information for understanding neural mechanisms in participants, research on ASD based on multimodal MRI fusion is still in its early stages. Moreover, ASD-assisted diagnosis and subtype classification models based on MRI features hold promise for providing objective evidence for clinical diagnosis and treatment. Future research should aim to construct an integrated analysis framework that fuses multimodal brain imaging, incorporating information from various dimensions such as brain function, structure, and networks. This approach will comprehensively delineate the developmental patterns of ASD and reveal its atypical neurodevelopmental mechanisms. Additionally, future studies need to delve into the abnormal mechanisms of the social brain network in ASD, explore social impairment circuits, and identify potential precision neural regulatory targets, thereby assisting clinical efforts in achieving precise diagnosis and treatment for ASD.