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Updated: Jul 12, 2026

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
Published on: September 12, 2011
Unraveling the complex interplay between glymphatic function, age, and brain structure in school-aged children with
Zhongfeng Xie1, Di Zhou1, Shuchao Wang1
1Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Insights
Children with Autism Spectrum Disorder (ASD) show impaired glymphatic function, which is linked to brain structure changes and varies with age. The ALPS index may help detect these neurobiological differences early.
Area of Science:
- Neuroscience
- Developmental Biology
- Medical Imaging
Background:
- Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with poorly understood mechanisms linking brain structure and function.
- Glymphatic system dysfunction is implicated in various neurological disorders, but its role in ASD, particularly concerning age-related brain changes, remains unclear.
Purpose of the Study:
- To investigate the relationship between glymphatic function, brain structure (gray matter volume), and age in children with ASD.
- To explore the potential of glymphatic function indices as biomarkers for early ASD detection.
Main Methods:
- Diffusion tensor image analysis along the perivascular space (ALPS) was used to assess glymphatic function in 130 children (67 ASD, 63 typically developing).
- Voxel-based morphometry (VBM) measured gray matter volume (GMV).
- Statistical analyses, including moderation and ROC curve analyses, examined correlations between ALPS, age, and GMV.
Main Results:
- Children with ASD exhibited significantly reduced glymphatic function compared to typically developing controls, indicated by lower ALPS_L and ALPS_Bi indices.
- The ALPS index was positively correlated with age and negatively correlated with GMV in regions critical for social and cognitive processing.
- Age moderated the relationship between ALPS and GMV, with the negative association weakening as age increased. ALPS indices showed good diagnostic potential (AUC > 0.7).
Conclusions:
- Glymphatic dysfunction is present in children with ASD and appears to be age-dependent, influencing brain structure.
- The ALPS index shows promise as a potential biomarker for identifying neurobiological changes associated with ASD, aiding early detection and targeted interventions.
Abstract:
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition. The relationship between glymphatic dysfunction, brain structure, and age in ASD children is poorly understood, hindering targeted interventions. A total of 130 participants, including 67 children with ASD and 63 typically developing (TD) children, were enrolled in this research. Glymphatic function was assessed using diffusion tensor image analysis along the perivascular space (ALPS). Voxel-based morphometry was employed to measure gray matter volume (GMV). Statistical analyses were conducted to explore correlations between age, ALPS indices, and GMV, and to assess whether age moderates these relationships. Our results showed that children with ASD exhibited reduced glymphatic function, with significant differences in the ALPS_L index (P = 0.024) and ALPS_Bi index (P = 0.025) indices compared to TD children. The ALPS index was positively correlated with age (P < 0.05) and negatively correlated with GMV, particularly in regions linked to social and cognitive processing. The moderation analysis revealed that age moderated the relationship between the ALPS index and GMV, showing that the negative association between them weakened with increasing age. Receiver operating characteristic (ROC) curve analysis indicated that the ALPS index effectively distinguishes ASD from TD children (ALPS_L index area under the curve (AUC) = 0.710, ALPS_Bi index AUC = 0.712). Our study suggests that glymphatic dysfunction in children with ASD may be age-dependent, influencing brain structure, particularly GMV. The ALPS index holds potential as a diagnostic biomarker for early detection of ASD-related neurobiological changes, with implications for targeted therapeutic interventions.
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