Autism spectrum disorder (henceforth autism) is a complex neurodevelopmental condition associated with differences in gray matter (GM) volume covariations, as reported in our previous study of the Longitudinal European Autism Project (LEAP) data. To make progress on the identification of potential neural markers and to validate the robustness of our previous findings, we aimed to replicate our results using data from the Enhancing Neuroimaging Genetics Through Meta-Analysis (ENIGMA) autism working group. We studied 781 autistic and 927 non-autistic individuals (6–30 years, IQ ≥ 50), across 37 sites. Voxel-based morphometry was used to quantify GM volume as before. Subsequently, we used spatial maps of the two autism-related independent components (ICs) previously identified in the LEAP sample as templates for regression analyses to separately estimate the ENIGMA-participant loadings to each of these two ICs. Between-group differences in participants’ loadings on each component were examined, and we additionally investigated the relation between participant loadings and autistic behaviors within the autism group. The two components of interest, previously identified in the LEAP dataset, showed significant between-group differences upon regressions into the ENIGMA cohort. The associated brain patterns were consistent with those found in the initial identification study. The first IC was primarily associated with increased volumes of bilateral insula, inferior frontal gyrus, orbitofrontal cortex, and caudate in the autism group relative to the control group (β = 0.129, p = 0.013). The second IC was related to increased volumes of the bilateral amygdala, hippocampus, and parahippocampal gyrus in the autism group relative to non-autistic individuals (β = 0.116, p = 0.024). However, when accounting for the site-by-group interaction effect, no significant main effect of the group can be identified (p > 0.590). We did not find significant univariate association between the brain measures and behavior in autism (p > 0.085). The distributions of age, IQ, and sex between LEAP and ENIGMA are statistically different from each other. Owing to limited access to the behavioral data of the autism group, we were unable to further our understanding of the neural basis of behavioral dimensions of the sample. The current study is unable to fully replicate the autism-related brain patterns from LEAP in the ENIGMA cohort. The diverse group effects across ENIGMA sites demonstrate the challenges of generalizing the average findings of the GM covariation patterns to a large-scale cohort integrated retrospectively from multiple studies. Further analyses need to be conducted to gain additional insights into the generalizability of these two GM covariation patterns.

中文翻译：

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自闭症的灰质协变：使用 ENIGMA 自闭症队列进行样本外复制

正如我们之前对欧洲纵向自闭症项目 (LEAP) 数据的研究所报道的，自闭症谱系障碍（以下简称自闭症）是一种复杂的神经发育疾病，与灰质 (GM) 体积协变差异相关。为了在潜在神经标记物的识别方面取得进展并验证我们之前发现的稳健性，我们旨在使用通过荟萃分析增强神经影像遗传学（ENIGMA）自闭症工作组的数据来复制我们的结果。我们研究了 37 个地点的 781 名自闭症患者和 927 名非自闭症患者（6-30 岁，智商 ≥ 50）。像以前一样，使用基于体素的形态测量来量化 GM 体积。随后，我们使用先前在 LEAP 样本中识别的两个与自闭症相关的独立成分 (IC) 的空间图作为回归分析的模板，以分别估计这两个 IC 中每一个的 ENIGMA 参与者负荷。我们检查了参与者在每个组件上的负荷的组间差异，并且我们还研究了参与者负荷与自闭症组内自闭症行为之间的关系。先前在 LEAP 数据集中确定的两个感兴趣的组成部分在回归 ENIGMA 队列后显示出显着的组间差异。相关的大脑模式与最初的识别研究中发现的一致。第一个 IC 主要与自闭症组相对于对照组的双侧岛叶、额下回、眶额皮质和尾状核体积增加有关（β = 0.129，p = 0.013）。第二个 IC 与自闭症组相对于非自闭症个体的双侧杏仁核、海马和海马旁回体积增加有关（β = 0.116，p = 0.024）。然而，在考虑逐组交互效应时，无法识别该组的显着主效应（p > 0.590）。我们没有发现自闭症患者的大脑测量和行为之间存在显着的单变量关联（p > 0.085）。LEAP 和 ENIGMA 之间的年龄、智商和性别分布在统计上彼此不同。由于自闭症群体的行为数据有限，我们无法进一步了解样本行为维度的神经基础。目前的研究无法在 ENIGMA 队列中完全复制 LEAP 中与自闭症相关的大脑模式。ENIGMA 站点的不同群体效应表明，将 GM 协变模式的平均结果推广到从多项研究中回顾性整合的大规模队列中存在挑战。需要进行进一步的分析，以获得对这两种 GM 协变模式的普遍性的更多见解。