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Updated: Aug 7, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Aberrant white matter microstructure detected by automatic fiber quantification in pediatric myelin oligodendrocyte
Shuang Ding1, Zhuowei Shi2, Kaiping Huang1
1Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing 400014, China.
Background And Objectives:
Myelin oligodendrocyte glycoprotein antibody-associated diseases (MOGAD) is an idiopathic inflammatory demyelinating disorder in children, for which the precise damage patterns of the white matter (WM) fibers remain unclear. Herein, we utilized diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ) to identify patterns of fiber damage and to investigate the clinical significance of MOGAD-affected fiber tracts.
Methods:
A total of 28 children with MOGAD and 31 healthy controls were included in this study. The AFQ approach was employed to track WM fiber with 100 equidistant nodes defined along each tract for statistical analysis of DTI metrics in both the entire and nodal manner. The feature selection method was used to further screen significantly aberrant DTI metrics of the affected fiber tracts or segments for eight common machine learning (ML) to evaluate their potential in identifying MOGAD. These metrics were then correlated with clinical scales to assess their potential as imaging biomarkers.
Results:
In the entire manner, significantly reduced fractional anisotropy (FA) was shown in the left anterior thalamic radiation, arcuate fasciculus, and the posterior and anterior forceps of corpus callosum in MOGAD (all p < 0.05). In the nodal manner, significant DTI metrics alterations were widely observed across 37 segments in 10 fiber tracts (all p < 0.05), mainly characterized by decreased FA and increased radial diffusivity (RD). Among them, 14 DTI metrics in seven fiber tracts were selected as important features to establish ML models, and satisfactory discrimination of MOGAD was obtained in all models (all AUC > 0.85), with the best performance in the logistic regression model (AUC = 0.952). For those features, the FA of left cingulum cingulate and the RD of right inferior frontal-occipital fasciculus were negatively and positively correlated with the expanded disability status scale (r = -0.54, p = 0.014; r = 0.43, p = 0.03), respectively.
Conclusion:
Pediatric MOGAD exhibits extensive WM fiber tract aberration detected by AFQ. Certain fiber tracts exhibit specific patterns of DTI metrics that hold promising potential as biomarkers.
Insights
Automated fiber quantification revealed widespread white matter damage in pediatric Myelin oligodendrocyte glycoprotein antibody-associated diseases (MOGAD). These findings highlight potential imaging biomarkers for MOGAD diagnosis and monitoring.
Area of Science:
- Neuroimaging
- Neurology
- Pediatric Demyelinating Diseases
Background:
- Myelin oligodendrocyte glycoprotein antibody-associated diseases (MOGAD) is a pediatric inflammatory demyelinating disorder.
- The precise patterns of white matter (WM) fiber damage in MOGAD remain unclear.
Purpose of the Study:
- To identify white matter fiber damage patterns in pediatric MOGAD using diffusion tensor imaging (DTI) and automated fiber quantification (AFQ).
- To investigate the clinical significance of affected fiber tracts in MOGAD.
Main Methods:
- The study included 28 children with MOGAD and 31 healthy controls.
- AFQ was used to track WM fibers, analyzing DTI metrics at 100 nodes per tract.
- Machine learning models were trained using selected DTI metrics to identify MOGAD and correlated with clinical scales.
Main Results:
- Significant reductions in fractional anisotropy (FA) were observed in specific tracts, including the left anterior thalamic radiation and corpus callosum.
- Widespread DTI metric alterations, primarily decreased FA and increased radial diffusivity (RD), were found across 37 segments in 10 fiber tracts.
- Machine learning models achieved high discrimination for MOGAD (AUC > 0.85), with logistic regression showing the best performance (AUC = 0.952).
- FA and RD in specific tracts correlated with the expanded disability status scale.
Conclusions:
- Pediatric MOGAD demonstrates extensive white matter fiber tract abnormalities detectable by AFQ.
- Specific fiber tract DTI metric patterns show promise as potential imaging biomarkers for MOGAD.
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