CRCNS: A model-based investigation of face processing in autism

CRCNS：基于模型的自闭症面部处理研究

• 批准号: 7047404
• 负责人: Maximilian K Riesenhuber
• 金额: $ 25.99万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2008-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7047404
• 关键词: autism; behavior test; behavioral /social science research tag; clinical research; cognition; computational neuroscience; computer simulation; developmental psychology; face; functional magnetic resonance imaging; human subject; learning; model design /development; neural information processing; neural plasticity; neuropsychology; perception disorder; psychological models; psychological tests; social perception; visual perception

Recognizing objects is a fundamental cognitive ability, e.g., for reading or social communication, and its loss or impairment is associated with a number of neural disorders. For example, many studies have shown that individuals with Autism Spectrum Disorder (ASD) are impaired in face recognition, possibly contributing to their difficulty in social interaction. Differences in social cognition are one of the defining features of autism. However, the precise nature of the differences in the neural mechanisms of face processing seen in autism has been difficult to study and its relationship to social behavior is unknown. While our understanding of the network of brain areas underlying human face perception has advanced considerably, current models are still qualitative and treat processing within specific brain areas as "black boxes". To understand the graded (rather than all-or-none) recognition deficits and to identify targets for therapeutic intervention, a quantitative and mechanistic model of the neural computations underlying face perception is required that rigorously links visual stimuli to neural activation and behavior. The purpose of the proposed project is to explore the neural mechanisms of face processing in typically developing and autistic individuals using an integrated approach based on a tight interaction of computational modeling of neural processing in the brain areas underlying human face recognition and of model-driven behavioral and fMRI experiments. In particular, the model motivates experiments to probe the face representation in typically developing and ASD subjects, and to mechanistically link differences in behavior and fMRI to differences at the level of neural processing -providing insight into the neural causes of the observed face recognition deficits along with an opportunity to evaluate current theories of neural processing differences in ASD. In a second part of the study, the model will drive experiments to study high-level plasticity and perceptual learning in typically developing and autistic individuals. This will not only leverage the insights from face perception to explore more general differences between the two subject groups, but is also expected to provide a foundation for remediation efforts. The relevance of this research to public health lies in its direct application of the results of this research program to achieve a better understanding of impairments found within and outside the autism spectrum.

识别物体是一种基本的认知能力，例如阅读或社交沟通的能力，其丧失或损害与许多神经疾病有关。例如，许多研究表明，患有自闭症谱系障碍（ASD）的人的面部识别能力受损，这可能导致他们在社交互动中遇到困难。社会认知的差异是自闭症的定义特征之一。然而，自闭症患者面部处理神经机制差异的确切性质一直难以研究，其与社会行为的关系尚不清楚。虽然我们对人类面部感知背后的大脑区域网络的理解已经有了相当大的进步，但当前的模型仍然是定性的，并将特定大脑区域内的处理视为“黑匣子”。为了理解分级（而不是全有或全无）的识别缺陷并确定治疗干预的目标，需要一个面部感知基础神经计算的定量和机械模型，将视觉刺激与神经激活和行为严格联系起来。拟议项目的目的是使用基于人脸识别背后的大脑区域神经处理计算模型和模型驱动行为的紧密相互作用的综合方法，探索典型发育和自闭症个体的面部处理的神经机制。和功能磁共振成像实验。特别是，该模型激发了实验探索典型发育中和自闭症谱系障碍受试者的面部表征，并从机制上将行为和功能磁共振成像的差异与神经处理水平的差异联系起来，从而深入了解观察到的面部识别缺陷的神经原因有机会评估当前自闭症谱系障碍神经处理差异的理论。在研究的第二部分中，该模型将推动实验来研究典型发育和自闭症个体的高水平可塑性和感知学习。这不仅将利用面部感知的洞察来探索两个主题组之间更普遍的差异，而且有望为补救工作提供基础。这项研究与公共卫生的相关性在于它直接应用该研究项目的结果，以更好地了解自闭症谱系内外发现的损伤。