The neurophysiology of face processing has been studied extensively in the context of social impairments associated with autism spectrum disorder (ASD), but the existing studies have concentrated mainly on univariate analyses of responses to upright faces, and, less frequently, inverted faces. The small number of existing studies on neurophysiological responses to inverted face in ASD have used univariate approaches, with divergent results. Here, we used a data-driven, classification-based, multivariate machine learning decoding approach to investigate the temporal and spatial properties of the neurophysiological evoked response for upright and inverted faces, relative to the neurophysiological evoked response for houses, a neutral stimulus. 21 (2 females) ASD and 29 (4 females) TD participants ages 7 to 19 took part in this study. Group level classification accuracies were obtained for each condition, using first the temporal domain of the evoked responses, and then the spatial distribution of the evoked responses on the cortical surface, each separately. We found that classification of responses to inverted neutral faces vs. houses was less accurate in ASD compared to TD, in both the temporal and spatial domains. In contrast, there were no group differences in the classification of evoked responses to upright neutral faces relative to houses. Using the classification in the temporal domain, lower decoding accuracies in ASD were found around 120 ms and 170 ms, corresponding the known components of the evoked responses to faces. Using the classification in the spatial domain, lower decoding accuracies in ASD were found in the right superior marginal gyrus (SMG), intra-parietal sulcus (IPS) and posterior superior temporal sulcus (pSTS), but not in core face processing areas. Importantly, individual classification accuracies from both the temporal and spatial classifiers correlated with ASD severity, confirming the relevance of the results to the ASD phenotype.

面部处理的神经生理学在与自闭症谱系障碍（ASD）相关的社交障碍的背景下得到了广泛的研究，但现有的研究主要集中在对直立面部反应的单变量分析，以及较少见的倒立面部反应。现有的少数关于自闭症谱系障碍患者对倒立脸的神经生理反应的研究使用了单变量方法，但结果不同。在这里，我们使用数据驱动、基于分类的多元机器学习解码方法来研究直立和倒立面孔的神经生理学诱发反应相对于房屋（中性刺激）的神经生理学诱发反应的时间和空间特性。21 名（2 名女性）ASD 和 29 名（4 名女性）TD 参与者（年龄 7 至 19 岁）参加了这项研究。首先使用诱发反应的时间域，然后分别使用诱发反应在皮质表面上的空间分布，获得每种情况的组级分类准确性。我们发现，与 TD 相比，ASD 中对倒立的中性脸与房屋的反应分类在时空域上都不太准确。相比之下，相对于房屋，对直立中性面孔的诱发反应分类不存在群体差异。使用时域中的分类，在 120 毫秒和 170 毫秒左右发现 ASD 解码精度较低，对应于面部诱发反应的已知成分。使用空间域中的分类，发现 ASD 中的解码精度较低的是右上边缘回 (SMG)、顶内沟 (IPS) 和后上颞沟 (pSTS)，但核心面部处理区域则不然。重要的是，时间和空间分类器的个体分类准确性与 ASD 严重程度相关，证实了结果与 ASD 表型的相关性。