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. All analyses were embedded within a train-validation approach (70%–30% split). Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. 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. We performed analyses in source-space using individual head models derived from the participants’ MRIs. 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, all with IQ > 75). Unbiased investigation in large and comprehensive samples focusing on replicability is needed. 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. 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.
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