Despite ample behavioral evidence of atypical facial emotion processing in individuals with autism spectrum disorder (ASD), the neural underpinnings of such behavioral heterogeneities remain unclear. Here, I have used brain-tissue mapped artificial neural network (ANN) models of primate vision to probe candidate neural and behavior markers of atypical facial emotion recognition in ASD at an image-by-image level. Interestingly, the image-level behavioral patterns of the ANNs better matched the neurotypical subjects 'behavior than those measured in ASD. This behavioral mismatch was most remarkable when the ANN behavior was decoded from units that correspond to the primate inferior temporal (IT) cortex. ANN-IT responses also explained a significant fraction of the image-level behavioral predictivity associated with neural activity in the human amygdala (from epileptic patients without ASD), strongly suggesting that the previously reported facial emotion intensity encodes in the human amygdala could be primarily driven by projections from the IT cortex. In sum, these results identify primate IT activity as a candidate neural marker and demonstrate how ANN models of vision can be used to generate neural circuit-level hypotheses and guide future human and nonhuman primate studies in autism.

SIGNIFICANCE STATEMENT Moving beyond standard parametric approaches that predict behavior with high-level categorical descriptors of a stimulus (e.g., level of happiness/fear in a face image), in this study, I demonstrate how an image-level probe, using current deep-learning-based ANN models, allows identification of more diagnostic stimuli for autism spectrum disorder enabling the design of more powerful experiments. This study predicts that IT cortex activity is a key candidate neural marker of atypical facial emotion processing in people with ASD. Importantly, the results strongly suggest that ASD-related atypical facial emotion intensity encodes in the human amygdala could be primarily driven by projections from the IT cortex.

尽管有大量行为证据表明患有自闭症谱系障碍 (ASD) 的个体存在非典型面部情绪处理，但此类行为异质性的神经基础仍不清楚。在这里，我使用了灵长类动物视觉的脑组织映射人工神经网络 (ANN) 模型来逐个图像地探测 ASD 中非典型面部情绪识别的候选神经和行为标记。有趣的是，与自闭症谱系障碍中测量的行为相比，人工神经网络的图像级行为模式更符合神经典型受试者的行为。当从对应于灵长类下颞叶 (IT) 皮层的单元解码 ANN 行为时，这种行为不匹配最为显着。ANN-IT 响应还解释了与人类杏仁核（来自没有 ASD 的癫痫患者）神经活动相关的图像级行为预测的重要部分，强烈表明先前报道的人类杏仁核中的面部情绪强度编码可能主要由驱动通过 IT 皮层的投射。总之，这些结果将灵长类动物 IT 活动确定为候选神经标记，并展示了如何使用 ANN 视觉模型生成神经回路级假设并指导未来人类和非人类灵长类动物的自闭症研究。

重要性声明超越了标准参数方法，该方法使用刺激的高级分类描述符（例如，面部图像中的幸福/恐惧程度）预测行为，在这项研究中，我演示了图像级探测如何使用当前的深度 -基于学习的 ANN 模型，允许识别更多自闭症谱系障碍的诊断刺激，从而能够设计更强大的实验。这项研究预测，IT 皮层活动是 ASD 患者非典型面部情绪处理的关键候选神经标记。重要的是，结果强烈表明人类杏仁核中与 ASD 相关的非典型面部情绪强度编码可能主要是由 IT 皮层的投射驱动的。