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自闭症患者非典型面部表情加工的行为和神经标记的计算研究

A Computational Probe into the Behavioral and Neural Markers of Atypical Facial Emotion Processing in Autism.

机构信息

McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 01239

Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, Massachusetts 01239.

出版信息

J Neurosci. 2022 Jun 22;42(25):5115-5126. doi: 10.1523/JNEUROSCI.2229-21.2022. Epub 2022 Jun 15.

Abstract

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. 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.

摘要

尽管自闭症谱系障碍(ASD)个体在行为上有大量异常的面部表情处理证据,但这种行为异质性的神经基础仍不清楚。在这里,我使用了基于灵长类动物视觉的大脑组织映射人工神经网络(ANN)模型,在图像层面上探究 ASD 中异常面部表情识别的候选神经和行为标记物。有趣的是,ANN 的图像级行为模式比 ASD 中测量的更符合神经典型个体的行为。当从对应于灵长类动物下颞叶(IT)皮层的单元中解码 ANN 行为时,这种行为不匹配最为显著。ANN-IT 反应也解释了与人类杏仁核(来自无 ASD 的癫痫患者)神经活动相关的图像级行为预测性的很大一部分,强烈表明先前报道的面部情绪强度在人类杏仁核中的编码可能主要由 IT 皮层的投射驱动。总之,这些结果确定了灵长类动物 IT 活动作为候选神经标记物,并展示了如何使用视觉 ANN 模型生成神经回路级假设,并指导未来自闭症的人类和非人类灵长类动物研究。本研究超越了使用刺激的高级类别描述符(例如,面部图像中的快乐/恐惧程度)来预测行为的标准参数方法,展示了如何使用当前基于深度学习的 ANN 模型进行图像级探测,从而识别出更具诊断性的自闭症谱系障碍刺激,从而设计出更强大的实验。该研究预测 IT 皮层活动是 ASD 患者异常面部表情处理的关键候选神经标记物。重要的是,结果强烈表明,人类杏仁核中与 ASD 相关的异常面部情绪强度编码可能主要由 IT 皮层的投射驱动。

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Sensory perception in autism.自闭症的感觉知觉。
Nat Rev Neurosci. 2017 Nov;18(11):671-684. doi: 10.1038/nrn.2017.112. Epub 2017 Sep 29.

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