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.

识别对象是一种基本的认知能力，例如阅读或社会交流，其损失或障碍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）的个体在面部识别中受到损害，可能导致他们在社交互动方面的困难。社会认知的差异是自闭症的决定性特征之一。但是，在自闭症中看到的面部处理的神经机制差异的确切性质很难研究，其与社会行为的关系尚不清楚。尽管我们对人面部感知潜在的大脑区域网络的理解已经大大提高了，但当前的模型仍然是定性的，并且将特定大脑区域内的处理视为“黑匣子”。为了了解分级（而不是全或任何人）的识别缺陷并确定治疗干预的靶标，需要严格将视觉刺激与神经激活和行为联系起来的神经计算的定量和机械模型。拟议项目的目的是，基于基于综合方法的典型发展和自闭症个体的神经机制，基于基于人脸识别和模型驱动的行为和fMRI实验的大脑区域中神经加工的计算建模的紧密相互作用。特别是，该模型促使实验在典型发展和ASD受试者中探测面部表示，并将机械学上的行为和fMRI的差异与神经处理水平的差异联系起来 - 提供对观察到的面部识别缺陷的神经原因的洞察力，以及评估当前神经处理差异的机会。在研究的第二部分中，该模型将推动实验以研究典型和自闭症个体的高级可塑性和感知学习。这不仅将利用面部感知的见解来探索两个学科群体之间的更一般差异，而且还有望为修复工作提供基础。这项研究与公共卫生的相关性在于其直接应用该研究计划的结果，以更好地了解自闭症谱系内外发现的障碍。