Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural–functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.

Purpose: Preterm children with cerebral palsy (CP) often have varying hand dysfunction, while the specific brain injury with periventricular leukomalacia (PVL) cannot quite explain its mechanism. We aimed to investigate glymphatic activity using diffusion tensor image analysis along the perivascular space (DTI-ALPS) method and evaluate its association with brain lesion burden and hand dysfunction in children with CP secondary to PVL. Methods: We retrospectively enrolled 18 children with bilateral spastic CP due to PVL and 29 age- and sex-matched typically developing controls. The Manual Ability Classification System (MACS) was used to assess severity of hand dysfunction in CP. A mediation model was performed to explore the relationship among the DTI-ALPS index, brain lesion burden, and the MACS level in children with CP. Results: There were significant differences in the DTI-ALPS index between children with CP and their typically developing peers. The DTI-ALPS index of the children with CP was lower than that of the controls (1.448 vs. 1.625, P = 0.003). The mediation analysis showed that the DTI-ALPS index fully mediated the relationship between brain lesion burden and the MACS level (c′ = 0.061, P = 0.665), explaining 80% of the effect. Conclusion: This study provides new insights into the neural basis of hand dysfunction in children with CP, demonstrating an important role of glymphatic impairment in such patients. These results suggest that PVL might affect hand function in children with CP by disrupting glymphatic drainage.

Qiu L, et al. Korean J Radiol. 2025 May;26(5):485-497. https://doi.org/10.3348/kjr.2024.0718

Introduction: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in motor skills, communication, emotional expression, and social interaction. Accurate diagnosis of ASD remains challenging due to the reliance on subjective behavioral observations and assessment scales, lacking objective diagnostic indicators.Methods: In this study, we introduced a novel approach for diagnosing ASD, leveraging T1-based gray matter and ASL-based cerebral blood flow network metrics. Thirty preschool-aged patients with ASD and twenty-two typically developing (TD) individuals were enrolled. Brain network features, including gray matter and cerebral blood flow metrics, were extracted from both T1-weighted magnetic resonance imaging (MRI) and ASL images. Feature selection was performed using statistical t-tests and Minimum Redundancy Maximum Relevance (mRMR). A machine learning model based on random vector functional link network was constructed for diagnosis.Results: The proposed approach demonstrated a classification accuracy of 84.91% in distinguishing ASD from TD. Key discriminating network features were identified in the inferior frontal gyrus and superior occipital gyrus, regions critical for social and executive functions in ASD patients.Discussion: Our study presents an objective and effective approach to the clinical diagnosis of ASD, overcoming the limitations of subjective behavioral observations. The identified brain network features provide insights into the neurobiological mechanisms underlying ASD, potentially leading to more targeted interventions.