Objectives To assess glymphatic function and white matter integrity in children with autism spectrum disorder (ASD) using multi-parametric MRI, combined with machine learning to evaluate ASD detection performance. Materials and methods This retrospective study collected data from 110 children with ASD (80 exploratory, 43 validation) and 68 typically developing children (50 exploratory, 18 validation) from two centers. The automated diffusion tensor imaging along the perivascular space (aDTI-ALPS), fractional anisotropy (FA), cerebrospinal fluid volume, and perivascular space (PVS) volume indices were extracted from DTI, three-dimensional T1-weighted, and T2-weighted images. Intergroup comparisons were conducted using t-tests, Mann–Whitney U-test, and tract-based spatial statistics. Correlation analysis assessed the relationship between glymphatic function, white matter integrity, and clinical scales. Machine learning models based on MRI indices were developed using the AutoGluon framework. Results The PVS volume (p < 0.001) was larger, and aDTI-ALPS index (p < 0.001) was lower in children with ASD compared to typically developing children. FA values were reduced in the ASD group and positively correlated with aDTI-ALPS index. The aDTI-ALPS index correlated with ASD severity (r = −0.27, p = 0.02) and developmental delays (r = 0.63, p < 0.001). Mediation analysis indicated the aDTI-ALPS index partially mediated the relationship between white matter integrity and developmental delay. The MRI-based model achieved an area under the curve of 0.84 for ASD diagnosis. Conclusion Analyzing glymphatic function and white matter integrity enhances understanding of ASD’s neurobiological underpinnings. The multi-parametric MRI, combined with machine learning, can facilitate the early detection of ASD. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> How can multi-parametric MRI based on the glymphatic system improve early diagnosis of autism spectrum disorder (ASD) beyond the limitations of current behavioral assessments? <Emphasis Type="BoldItalic">Findings</Emphasis> Glymphatic dysfunction and disruptions in white matter integrity were associated with clinical symptoms of ASD. Multi-parametric MRI with machine learning can improve early ASD detection. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> Multi-parametric MRI, focusing on glymphatic function and white matter integrity, enhances the diagnostic accuracy of ASD by serving as an objective complement to clinical scales.

Assessment of glymphatic function and white matter integrity in children with autism using multi-parametric MRI and machine learning

期刊论文
发表日期：

2025年1月23日

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Miaoyan Wang,
Keyi He,
Lili Zhang,
Dandan Xu,
Xianjun Li,
Lei Wang,
Bo Peng,
Anqi Qiu,
Yakang Dai,
Cailei Zhao,
Haoxiang Jiang

摘要

Objectives

To assess glymphatic function and white matter integrity in children with autism spectrum disorder (ASD) using multi-parametric MRI, combined with machine learning to evaluate ASD detection performance.

Materials and methods

This retrospective study collected data from 110 children with ASD (80 exploratory, 43 validation) and 68 typically developing children (50 exploratory, 18 validation) from two centers. The automated diffusion tensor imaging along the perivascular space (aDTI-ALPS), fractional anisotropy (FA), cerebrospinal fluid volume, and perivascular space (PVS) volume indices were extracted from DTI, three-dimensional T1-weighted, and T2-weighted images. Intergroup comparisons were conducted using t-tests, Mann–Whitney U-test, and tract-based spatial statistics. Correlation analysis assessed the relationship between glymphatic function, white matter integrity, and clinical scales. Machine learning models based on MRI indices were developed using the AutoGluon framework.

Results

The PVS volume (p < 0.001) was larger, and aDTI-ALPS index (p < 0.001) was lower in children with ASD compared to typically developing children. FA values were reduced in the ASD group and positively correlated with aDTI-ALPS index. The aDTI-ALPS index correlated with ASD severity (r = −0.27, p = 0.02) and developmental delays (r = 0.63, p < 0.001). Mediation analysis indicated the aDTI-ALPS index partially mediated the relationship between white matter integrity and developmental delay. The MRI-based model achieved an area under the curve of 0.84 for ASD diagnosis.

Conclusion

Analyzing glymphatic function and white matter integrity enhances understanding of ASD’s neurobiological underpinnings. The multi-parametric MRI, combined with machine learning, can facilitate the early detection of ASD.

Key Points

Question How can multi-parametric MRI based on the glymphatic system improve early diagnosis of autism spectrum disorder (ASD) beyond the limitations of current behavioral assessments?

Findings Glymphatic dysfunction and disruptions in white matter integrity were associated with clinical symptoms of ASD. Multi-parametric MRI with machine learning can improve early ASD detection.

Clinical relevance Multi-parametric MRI, focusing on glymphatic function and white matter integrity, enhances the diagnostic accuracy of ASD by serving as an objective complement to clinical scales.