Journal of psychiatric research · 2026
Alterations in white matter integrity and asymmetry in children with autism spectrum disorder: an automated fiber quantification tractography study.
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Luo Dan, Zhao Chunfeng, Nie Lisha, Yin Yu, Liu Xiaoqing, Yu Haoyue, Wang Fuqin, Chen Heng, Liu Heng
Аннотация
OBJECTIVE: To investigate microstructural and lateralization abnormalities of white matter (WM) in children with autism spectrum disorder (ASD) using automated fiber quantification tractography, assess their relationship with behavioral symptoms, and analyze developmental trajectories of WM tracts with age.
MATERIALS AND METHODS: This study analyzed diffusion tensor imaging data of 36 children with ASD (mean age, 5.44 ± 0.95 years; male:female, 28:8) and 27 healthy controls (mean age, 5.06 ± 1.83 years; male:female, 17:10) using automated fiber quantification. A support vector machine (SVM) classifier was applied to distinguish ASD from healthy controls (HC) based on WM metrics.
RESULTS: ASD children exhibited local WM abnormalities, atypical lateralization, and impaired age-related development of WM. The callosum forceps major (CF_major) and left inferior longitudinal fasciculus impairments correlated with social impairments, while CF_major and right thalamic radiation impairments correlated with repetitive behaviors. The cingulum cingulate was atypically lateralized and negatively correlated with the Childhood Autism Rating Scale. SVM classification based on WM metrics achieved 75 % accuracy (AUC = 0.81) in distinguishing ASD from HC.
CONCLUSIONS: Children with ASD show subtle microstructural abnormalities, atypical lateralization patterns, and delayed development of WM tracts, correlated with behavioral symptoms. SVM classification supports the discriminative utility of WM metrics as potential ASD biomarkers.
MATERIALS AND METHODS: This study analyzed diffusion tensor imaging data of 36 children with ASD (mean age, 5.44 ± 0.95 years; male:female, 28:8) and 27 healthy controls (mean age, 5.06 ± 1.83 years; male:female, 17:10) using automated fiber quantification. A support vector machine (SVM) classifier was applied to distinguish ASD from healthy controls (HC) based on WM metrics.
RESULTS: ASD children exhibited local WM abnormalities, atypical lateralization, and impaired age-related development of WM. The callosum forceps major (CF_major) and left inferior longitudinal fasciculus impairments correlated with social impairments, while CF_major and right thalamic radiation impairments correlated with repetitive behaviors. The cingulum cingulate was atypically lateralized and negatively correlated with the Childhood Autism Rating Scale. SVM classification based on WM metrics achieved 75 % accuracy (AUC = 0.81) in distinguishing ASD from HC.
CONCLUSIONS: Children with ASD show subtle microstructural abnormalities, atypical lateralization patterns, and delayed development of WM tracts, correlated with behavioral symptoms. SVM classification supports the discriminative utility of WM metrics as potential ASD biomarkers.