Autism spectrum disorder is (ASD) characterized by a persisting triad of impairments of social interaction, language as well as inflexible, stereotyped and ritualistic behaviors. Increasingly, scientific evidence suggests a neurobiological basis of these emotional, social and cognitive deficits in individuals with ASD.

The aim of this randomized controlled brain self-regulation intervention study was to investigate whether the core symptomatology of ASD could be reduced via an electroencephalography (EEG) based brain self-regulation training of Slow Cortical Potentials (SCP). 41 male adolescents with ASD were recruited and allocated to a) an experimental group undergoing 24 sessions of EEG-based brain training (n₁ = 21), or to b) an active control group undergoing conventional treatment (n₂ = 20), that is, clinical counseling during a 3-months intervention period. We employed real-time neurofeedback training recorded from a fronto-central electrode intended to enable participants to volitionally regulate their brain activity.

Core autistic symptomatology was measured at six time points during the intervention and analyzed with Bayesian multilevel approach to characterize changes in core symptomatology. Additional Bayesian models were formulated to describe the neural dynamics of the training process as indexed by SCP (time-domain) and power density (PSD, frequency-domain) measures.

The analysis revealed a substantial improvement in the core symptomatology of ASD in the experimental group (reduction of 21.38 points on the Social Responsiveness Scale, SD = 5.29), which was slightly superior to that observed in the control group (evidence Ratio = 5.79). Changes in SCP manifested themselves as different trajectories depending on the different feedback conditions and tasks. Further, the model of PSD revealed a continuous decrease in delta power, parallel to an increase in alpha power. Most notably, a non-linear (quadratic) model turned out to be better at predicting the data than a linear model across all analyses. Taken together, our analyses suggest that behavioral and neural processes of change related to neurofeedback training are complex and non-linear. Moreover, they have implications for the design of future trials and training protocols.

自闭症谱系障碍 (ASD) 的特点是持续存在社交互动、语言障碍以及僵化、刻板和仪式化的行为障碍。越来越多的科学证据表明，自闭症谱系障碍患者的这些情绪、社交和认知缺陷具有神经生物学基础。

这项随机对照大脑自我调节干预研究的目的是调查是否可以通过基于脑电图 (EEG) 的慢皮质电位 (SCP) 大脑自我调节训练来减少 ASD 的核心症状。招募了 41 名患有自闭症谱系障碍 (ASD) 的男性青少年，并将其分配到 a) 实验组，接受 24 次基于脑电图的大脑训练 (n ₁  = 21)，或 b) 积极对照组，接受常规治疗 (n ₂  = 20)，即，为期 3 个月的干预期间的临床咨询。我们采用从额中央电极记录的实时神经反馈训练，旨在使参与者能够有意识地调节他们的大脑活动。

在干预期间的六个时间点测量核心自闭症症状，并使用贝叶斯多层次方法进行分析，以表征核心症状的变化。制定了额外的贝叶斯模型来描述训练过程的神经动力学，如 SCP（时域）和功率密度（PSD，频域）测量索引。

分析显示，实验组 ASD 核心症状有显着改善（社会反应量表降低 21.38 分，SD  = 5.29），略优于对照组（证据比 = 5.79）。根据不同的反馈条件和任务，SCP的变化表现为不同的轨迹。此外，PSD 模型显示 delta 功率持续下降，同时 alpha 功率增加。最值得注意的是，在所有分析中，非线性（二次）模型在预测数据方面都比线性模型更好。综上所述，我们的分析表明，与神经反馈训练相关的行为和神经变化过程是复杂且非线性的。此外，它们对未来试验和培训方案的设计也有影响。