Model-Based Investigation of Aberrant Neural Face Representation in Autism

基于模型的自闭症神经面部表征异常研究

• 批准号: 10621347
• 负责人: Shuo Wang
• 金额: $ 39.05万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-12 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10621347
• 关键词: Adult; Age; Amygdaloid structure; Anterior; Anxiety; Appearance; Area; Automobile Driving; Back; Behavioral; Biological; Brain; Brain region; Child; Code; Collaborations; Complex; Cues; Data; Development; Disease; Emotional; Engineering; Eye; Eye Movements; Face; Face Processing; Facial Expression; Facial Expression Recognition; Functional Magnetic Resonance Imaging; Gender; Goals; Human; Individual; Intervention; Investigation; Judgment; Life; Literature; Modeling; Moods; Neurologic; Outcome; Participant; Perception; Persons; Population; Prefrontal Cortex; Protocols documentation; Quality of life; Race; Research; Shapes; Skin; Social Interaction; Social isolation; Spatial Distribution; Stimulus; Techniques; Testing; Texture; Time; Training; Variant; Visualization; Work; adolescent with autism spectrum disorder; adult with autism spectrum disorder; autism spectrum disorder; autistic children; cognitive neuroscience; comparison control; computer science; deep neural network; face perception; fusiform face area; grasp; improved; individuals with autism spectrum disorder; inferotemporal cortex; insight; intervention refinement; neural; neural model; neuroimaging; neuromechanism; novel; predictive modeling; response; screening; showing emotion; skills; social; social deficits; social implication; trait; trustworthiness; vector; visual stimulus; visual tracking

Project Summary Faces often convey a wealth of information and processing the human face is at the focal point of most social interactions. When we see a person's face, we can easily recognize their unique identity and general features such as race, gender, and age. The gestalt of facial processing enables us to make judgments about a person's mood or other aspects such as their level of trustworthiness. Yet, this simple perceptual task is difficult for individuals with autism spectrum disorder (ASD), a population that spends limited amounts of time engaged in face-to-face eye contact or social interactions in general. Although there is a large body of literature on face perception and many studies have documented abnormal face processing in people with ASD, most existing studies focus on the recognition of faces and emotional expressions or on perception of a particular social attribute (e.g., trustworthiness). It remains largely unclear how the brain represents and evaluates faces in general, and whether/how this mechanism differs in ASD. The study of face processing in ASD is very important because it will not only help us understand the social deficits of this disorder but also provide a unique opportunity to study the factors related to the functional specialization of normal face processing. In this project, we propose to conduct one of the very first studies to investigate neural face representation in individuals with ASD and delineate those brain regions involved in coding facial features. Importantly, by using concurrent functional magnetic resonance imaging (fMRI) and eye tracking and taking advantage of recent advances in deep neural networks (DNNs), we are able to extract association-based features from any face and synthesize new faces for validating model predictions. The primary objectives of this research are two-fold: (1) to establish a general neural representation of faces by constructing and validating neural face models, and (2) to compare neural representations of faces between people with ASD and controls. The collaboration between cognitive neuroscience and computer science in this project provides a unique opportunity to better understand how individuals with ASD perceive human faces, specifically what brain mechanisms are involved in representing faces in general. Obtaining this level of understanding of the neural computational underpinnings of face representation will be unique to our understanding of face processing in controls without ASD as well as those with ASD. In turn, this research may provide insights into the developmental trajectory of this pervasive deficit in autism and potential targets for intervention.

项目概要 面部通常传达丰富的信息，处理人脸是大多数社交的焦点 互动。当我们看到一个人的脸时，我们可以很容易地识别出他独特的身份和一般特征 例如种族、性别和年龄。面部处理的格式塔使我们能够对一个人做出判断 人的情绪或其他方面，例如他们的可信度。然而，这个简单的感知任务却很困难 对于患有自闭症谱系障碍 (ASD) 的人来说，这是一个花有限时间参与活动的人群 一般而言，在面对面的目光接触或社交互动中。虽然脸上有大量文献 感知和许多研究都记录了自闭症患者的面部处理异常，大多数现有的 研究重点是面部和情绪表达的识别或特定社会的感知 属性（例如，可信度）。目前仍不清楚大脑如何表示和评估面孔 一般情况，以及这种机制在 ASD 中是否/如何不同。自闭症谱系障碍（ASD）中人脸处理的研究非常多 很重要，因为它不仅可以帮助我们了解这种疾病的社会缺陷，还可以提供一种 研究与正常面部处理的功能专业化相关的因素的独特机会。 在这个项目中，我们建议进行第一个研究来调查神经人脸 描绘自闭症谱系障碍患者的大脑区域，并描绘出参与编码面部特征的大脑区域。 重要的是，通过使用并发功能磁共振成像 (fMRI) 和眼球追踪和拍摄 利用深度神经网络（DNN）的最新进展，我们能够提取基于关联的数据 来自任何面部的特征并合成新面部以验证模型预测。主要目标 这项研究有两个方面：（1）通过构建和 验证神经面部模型，以及 (2) 比较 ASD 患者面部的神经表征 和控制。该项目中认知神经科学和计算机科学之间的合作提供了 这是一个独特的机会，可以更好地了解患有自闭症谱系障碍的人如何感知人脸，特别是 大脑机制通常涉及代表面孔。获得这种程度的理解 面部表征的神经计算基础对于我们对面部的理解将是独一无二的 在没有 ASD 和有 ASD 的控件中进行处理。反过来，这项研究可能会提供一些见解 自闭症普遍存在的缺陷的发展轨迹和潜在的干预目标。