Deng Jia-Rui, Tong Meiqinzi, Zhang Xiao-Tong, Lin Zhen-Ping, Wang Zhuo, Long Jinyi, Chen Zhuo-Ming
Department of Rehabilitation Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, People's Republic of China.
Wellness Technology Research Center, Hefei Intelligent Robot Institute, Hefei, Anhui Province, People's Republic of China.
Neuropsychiatr Dis Treat. 2025 Apr 15;21:903-916. doi: 10.2147/NDT.S517704. eCollection 2025.
Facial recognition is very primary and important in individuals' development and the event-related potential based on face recognition such as N170 is considered as the most potential objective marker of autism, the hot and difficult point of current research. We will explore the electrophysiological basis of facial recognition with autism and without autism. Given the link between facial recognition and social impairments, the core symptom of autism, it is also necessary to study the correlation between the P1 and N170 components and the severity of social functioning in autism.
In this study, autism and age-matched typically developing children were asked to examine photographs of faces, objects and butterflies and event-related potentials were recorded. The parents or caregivers of the participants were asked to fill out the Vineland Adaptive Behavior Scale. Finally, thirteen children with autism (6.60±2.12years) and ten typically developing (6.65±1.64years) children were included in the experiment.
Children with autism showed slower P1 and N170 latencies than typically developing children. The N170 amplitude for faces was larger than that for objects. Considering age as a covariant, the results primarily remained unchanged and the effect size of age was significant for the P1 and N170 latencies. As for the correlation between ERPs and the severity of social impairment, there were some significant correlations between the P1 and N170 latencies and social functioning.
This result not only suggests the electrophysiological basis of facial recognition but also indicates that the P1 and N170 components could assist in the diagnosis and assessment of autism. Moreover, the results suggest that age should be considered in analyses of the P1 and N170 latencies. Due to a limited number of participants, conducting a multi-center and large-sample study in the future is necessary.
面部识别在个体发展中非常基础且重要，基于面部识别的事件相关电位，如N170，被认为是自闭症最具潜力的客观标志物，也是当前研究的热点和难点。我们将探究有自闭症和无自闭症个体面部识别的电生理基础。鉴于面部识别与自闭症的核心症状社交障碍之间的联系，研究自闭症患者P1和N170成分与社交功能严重程度之间的相关性也很有必要。
在本研究中，要求自闭症儿童和年龄匹配的发育正常儿童观看面部、物体和蝴蝶的照片，并记录事件相关电位。要求参与者的父母或照顾者填写《文兰适应行为量表》。最终，13名自闭症儿童（6.60±2.12岁）和10名发育正常儿童（6.65±1.64岁）被纳入实验。
自闭症儿童的P1和N170潜伏期比发育正常儿童更长。面部的N170波幅大于物体的N170波幅。将年龄作为协变量考虑，结果基本保持不变，年龄对P1和N170潜伏期的效应量显著。至于事件相关电位与社交障碍严重程度之间的相关性，P1和N170潜伏期与社交功能之间存在一些显著相关性。
该结果不仅提示了面部识别的电生理基础，还表明P1和N170成分可辅助自闭症的诊断和评估。此外，结果表明在分析P1和N170潜伏期时应考虑年龄因素。由于参与者数量有限，未来有必要进行多中心、大样本研究。
