Abnormality of Early White Matter Development in Tuberous Sclerosis Complex and Autism Spectrum Disorder: Longitudinal Analysis of Diffusion Tensor Imaging Measures.

IF 2 4区 医学 Q3 CLINICAL NEUROLOGY Journal of Child Neurology Pub Date : 2024-05-01 Epub Date: 2024-05-15 DOI:10.1177/08830738241248685
Siddharth Srivastava, Fanghan Yang, Anna K Prohl, Peter E Davis, Jamie K Capal, Rajna Filip-Dhima, E Martina Bebin, Darcy A Krueger, Hope Northrup, Joyce Y Wu, Simon K Warfield, Mustafa Sahin, Bo Zhang
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Abstract

Background: Abnormalities in white matter development may influence development of autism spectrum disorder in tuberous sclerosis complex (TSC). Our goals for this study were as follows: (1) use data from a longitudinal neuroimaging study of tuberous sclerosis complex (TACERN) to develop optimized linear mixed effects models for analyzing longitudinal, repeated diffusion tensor imaging metrics (fractional anisotropy, mean diffusivity) pertaining to select white matter tracts, in relation to positive Autism Diagnostic Observation Schedule-Second Edition classification at 36 months, and (2) perform an exploratory analysis using optimized models applied to all white matter tracts from these data. Methods: Eligible participants (3-12 months) underwent brain magnetic resonance imaging (MRI) at repeated time points from ages 3 to 36 months. Positive Autism Diagnostic Observation Schedule-Second Edition classification at 36 months was used. Linear mixed effects models were fine-tuned separately for fractional anisotropy values (using fractional anisotropy corpus callosum as test outcome) and mean diffusivity values (using mean diffusivity right posterior limb internal capsule as test outcome). Fixed effects included participant age, within-participant longitudinal age, and autism spectrum disorder diagnosis. Results: Analysis included data from n = 78. After selecting separate optimal models for fractional anisotropy and mean diffusivity values, we applied these models to fractional anisotropy and mean diffusivity of all 27 white matter tracts. Fractional anisotropy corpus callosum was related to positive Autism Diagnostic Observation Schedule-Second Edition classification (coefficient = 0.0093, P = .0612), and mean diffusivity right inferior cerebellar peduncle was related to positive Autism Diagnostic Observation Schedule-Second Edition classification (coefficient = -0.00002071, P = .0445), though these findings were not statistically significant after multiple comparisons correction. Conclusion: These optimized linear mixed effects models possibly implicate corpus callosum and cerebellar pathology in development of autism spectrum disorder in tuberous sclerosis complex, but future studies are needed to replicate these findings and explore contributors of heterogeneity in these models.

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结节性硬化综合症和自闭症谱系障碍的早期白质发育异常:弥散张量成像测量的纵向分析
背景:白质发育异常可能会影响结节性硬化症复合体(TSC)自闭症谱系障碍的发展。我们的研究目标如下(1)利用结节性硬化综合征(TACERN)纵向神经影像学研究的数据,建立优化的线性混合效应模型,用于分析特定白质束的纵向、重复扩散张量成像指标(分数各向异性、平均扩散率)与 36 个月时自闭症诊断观察表第二版分类的正相关性;以及(2)利用这些数据中应用于所有白质束的优化模型进行探索性分析。研究方法符合条件的参与者(3-12 个月)在 3 到 36 个月的重复时间点接受了脑磁共振成像(MRI)检查。采用 36 个月时自闭症诊断观察表第二版的阳性分类。线性混合效应模型分别对分数各向异性值(使用分数各向异性胼胝体作为测试结果)和平均扩散值(使用平均扩散右后肢内囊作为测试结果)进行了微调。固定效应包括参与者年龄、参与者纵向年龄和自闭症谱系障碍诊断。分析结果分析包括来自 n = 78 的数据。在为分数各向异性和平均扩散值分别选择了最佳模型后,我们将这些模型应用于所有 27 个白质束的分数各向异性和平均扩散值。胼胝体的分数各向异性与自闭症诊断观察表(第二版)的阳性分类有关(系数 = 0.0093,P = .0612),平均扩散率右小脑下梗与自闭症诊断观察表(第二版)的阳性分类有关(系数 = -0.00002071,P = .0445),但这些发现在多重比较校正后并无统计学意义。结论这些优化的线性混合效应模型可能表明,胼胝体和小脑病理学与结节性硬化症复合体自闭症谱系障碍的发展有关,但还需要未来的研究来复制这些发现并探索这些模型中的异质性因素。
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来源期刊
Journal of Child Neurology
Journal of Child Neurology 医学-临床神经学
CiteScore
4.20
自引率
5.30%
发文量
111
审稿时长
3-6 weeks
期刊介绍: The Journal of Child Neurology (JCN) embraces peer-reviewed clinical and investigative studies from a wide-variety of neuroscience disciplines. Focusing on the needs of neurologic patients from birth to age 18 years, JCN covers topics ranging from assessment of new and changing therapies and procedures; diagnosis, evaluation, and management of neurologic, neuropsychiatric, and neurodevelopmental disorders; and pathophysiology of central nervous system diseases.
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