# Model-Based Investigation of Aberrant Neural Face Representation in Autism > **NIH NIH R01** · WASHINGTON UNIVERSITY · 2022 · $404,933 ## Abstract 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. ## Key facts - **NIH application ID:** 10422001 - **Project number:** 1R01MH129426-01 - **Recipient organization:** WASHINGTON UNIVERSITY - **Principal Investigator:** Shuo Wang - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $404,933 - **Award type:** 1 - **Project period:** 2022-05-12 → 2027-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10422001 ## Citation > US National Institutes of Health, RePORTER application 10422001, Model-Based Investigation of Aberrant Neural Face Representation in Autism (1R01MH129426-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10422001. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*