Background Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity. Methods The developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6-25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity. Results Machine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = - 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity. Limitations This work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable. Conclusions Findings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.

中文翻译：

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自闭症谱系障碍的症状严重程度与规范性脑发育的偏离有关。

背景自闭症谱系障碍（ASD）是一种异质性神经发育疾病。ASD的大脑发育偏离典型大脑发育的程度，以及这种偏差与个人水平上观察到的行为结果之间的关系尚未得到很好的研究。我们假设与ASD患者典型脑发育的偏离程度与观察到的症状严重程度有关。方法比较了ASD样本（n = 247）和典型发育儿童（TDC）（n = 220）之间的解剖学变化（皮质厚度，表面积和体积）和扩散指标（分数各向异性和表观扩散系数）。 ）6-25岁。机器学习用于根据TDC样本中的这些指标预测年龄（大脑年龄），从而定义大脑发育的规范模型。然后，该模型用于计算ASD样本中的大脑年龄。时间年龄和脑年龄之间的差异被认为是发育偏差指数（DDI），然后与ASD症状严重程度相关。结果在所有五个指标上训练的机器学习模型可以准确地预测TDC（r = 0.88）和ASD（r = 0.85）样本中的年龄，并且扩散指标对模型具有显着贡献。在ASD组中，源自分数各向异性的DDI与ASD症状严重程度相关（r =-0.2），因此，脑龄最大的个体严重程度最低，而脑部延迟最大的个体严重程度最高。 。局限性这项研究仅研究了五个特定的大脑指标与一个在有限的年龄范围内的ASD症状严重程度的指标之间的线性关系。报告的效果大小适中。在结果可临床应用之前，需要做进一步的工作来调查其他年龄范围，行为其他方面，其他神经生物学措施以及独立样本中的发育差异。结论结论表明，与典型脑发育的偏离程度与ASD症状严重程度有关，部分解释了ASD中观察到的异质性。我们的方法能够参考正常的大脑发育和鉴定不同的发育亚型来表征每个人，从而有助于更好地理解ASD中的发育异质性。