Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, Basel, Switzerland.
Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Mol Autism. 2022 May 18;13(1):22. doi: 10.1186/s13229-022-00500-x.
BACKGROUND: Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed. METHODS: We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split). RESULTS: In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset. LIMITATIONS: The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects. CONCLUSIONS: This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects.
背景：了解自闭症谱系障碍（ASD）相关的神经元回路的发展对于揭示其病因和开发治疗方法至关重要。静息态 EEG 提供了一个观察自发性局部和长程神经元同步的窗口，已经在许多 ASD 研究中进行了探讨，但结果不一致。需要在大型和综合样本中进行无偏倚的研究，重点是可重复性。
方法：我们在 411 名儿童、青少年和成年人（n=212 名 ASD，n=199 名神经典型[NT]，所有 IQ>75）中量化了静息态 EEG 的阿尔法波峰指标、功率谱（PS，2-32 Hz）和功能连接（FC）。我们在源空间中使用来自参与者 MRI 的个体头部模型进行分析。我们使用线性混合效应模型，考虑年龄、性别、智商和地点效应，对 ASD 和 NT 组的 PS 和 FC 的均值和方差进行了差异检验。然后，我们使用机器学习来评估 EEG 特征的多元组合是否可以更好地区分 ASD 和 NT 参与者。所有分析都嵌入在训练-验证方法（70%-30% 分割）中。
结果：在训练数据集中，我们发现年龄和组之间存在对睁眼反应的交互作用（未校正 p=0.042），并且 PS 和 FC 存在显著但较弱的 ASD 与 NT 分类性能（敏感性 0.52-0.62，特异性 0.59-0.73）。这些发现都没有在验证数据集中显著重现，尽管验证数据集中的效应量与训练数据集的预测区间重叠。
局限性：在验证数据集中检测到训练数据集中发现的弱效应（与训练数据集的效应大小相当）的统计能力很小，我们不能完全得出训练数据集效应的可重复性结论。
结论：这表明 ASD 和 NT 中的 PS 和 FC 值有很强的重叠，并且两组之间的差异（无论是均值还是方差）都具有最小的效应量。需要更大的研究来调查和复制这种潜在的效应。
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