Neuroscience Therapeutic Area, Janssen Research & Development, Titusville, NJ, USA.
Digital Phenotyping Group, Discovery Sciences, Janssen Research & Development, Spring House, PA, USA.
Mol Autism. 2020 May 11;11(1):31. doi: 10.1186/s13229-020-00327-4.
Reduction or differences in facial expression are a core diagnostic feature of autism spectrum disorder (ASD), yet evidence regarding the extent of this discrepancy is limited and inconsistent. Use of automated facial expression detection technology enables accurate and efficient tracking of facial expressions that has potential to identify individual response differences.
Children and adults with ASD (N = 124) and typically developing (TD, N = 41) were shown short clips of "funny videos." Using automated facial analysis software, we investigated differences between ASD and TD groups and within the ASD group in evidence of facial action unit (AU) activation related to the expression of positive facial expression, in particular, a smile.
Individuals with ASD on average showed less evidence of facial AUs (AU12, AU6) relating to positive facial expression, compared to the TD group (p < .05, r = - 0.17). Using Gaussian mixture model for clustering, we identified two distinct distributions within the ASD group, which were then compared to the TD group. One subgroup (n = 35), termed "over-responsive," expressed more intense positive facial expressions in response to the videos than the TD group (p < .001, r = 0.31). The second subgroup (n = 89), ("under-responsive"), displayed fewer, less intense positive facial expressions in response to videos than the TD group (p < .001; r = - 0.36). The over-responsive subgroup differed from the under-responsive subgroup in age and caregiver-reported impulsivity (p < .05, r = 0.21). Reduced expression in the under-responsive, but not the over-responsive group, was related to caregiver-reported social withdrawal (p < .01, r = - 0.3).
This exploratory study does not account for multiple comparisons, and future work will have to ascertain the strength and reproducibility of all results. Reduced displays of positive facial expressions do not mean individuals with ASD do not experience positive emotions.
Individuals with ASD differed from the TD group in their facial expressions of positive emotion in response to "funny videos." Identification of subgroups based on response may help in parsing heterogeneity in ASD and enable targeting of treatment based on subtypes.
ClinicalTrials.gov, NCT02299700. Registration date: November 24, 2014.
面部表情的减少或差异是自闭症谱系障碍（ASD）的核心诊断特征，但关于这种差异程度的证据有限且不一致。使用自动化面部表情检测技术可以准确、高效地跟踪面部表情，这有可能识别个体反应的差异。
我们向 124 名 ASD 儿童和成人（ASD 组）和 41 名典型发育（TD）儿童展示了短的“有趣视频”剪辑。使用自动化面部分析软件，我们研究了 ASD 组和 TD 组之间以及 ASD 组内与积极面部表情表达相关的面部动作单元（AU）激活的差异，特别是微笑。
与 TD 组相比，ASD 组的个体平均表现出较少的与积极面部表情相关的 AU12 和 AU6（p <.05，r = -0.17）。使用高斯混合模型进行聚类，我们在 ASD 组内识别出两个不同的分布，然后将其与 TD 组进行比较。一个亚组（n = 35），称为“过度反应”，对视频的积极面部表情表达比 TD 组更强烈（p <.001，r = 0.31）。第二个亚组（n = 89），“反应不足”，对视频的积极面部表情表达比 TD 组少且不强烈（p <.001；r = -0.36）。过度反应亚组与反应不足亚组在年龄和照顾者报告的冲动性方面存在差异（p <.05，r = 0.21）。反应不足亚组的表达减少与照顾者报告的社交退缩有关（p <.01，r = -0.3），但在过度反应亚组中没有。
本探索性研究没有考虑到多次比较，未来的工作必须确定所有结果的强度和可重复性。积极面部表情的表达减少并不意味着 ASD 个体没有体验到积极的情绪。
与 TD 组相比，ASD 个体在对“有趣视频”的积极情绪面部表情反应方面存在差异。基于反应的亚组识别可能有助于解析 ASD 的异质性，并能够根据亚型进行治疗靶向。
ClinicalTrials.gov，NCT02299700。注册日期：2014 年 11 月 24 日。
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