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Poster B in Poster Session B: Tuesday, August 4, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms

Predicting Individualized Functional Topographies in Autism Spectrum Disorder

Ian Abenes¹, Yue Wang², Runnan Cao², Anila D'Mello³, Shuo Wang², Guo Jiahui¹; ¹University of Texas at Dallas, ²Washington University in St. Louis, ³University of Texas Southwestern Medical Center

Presenter: Ian Abenes

Functional localizers have been widely used to localize individualized functional areas. However, given constraints on scanning efficiency, ecological validity, and the practical challenges of collecting localizer data in clinical populations, alternative approaches are needed. Previous work has found that hyperalignment can estimate category-selective topographies with high fidelity, even in individuals with perceptual deficits such as face blindness. Here, we extend hyperalignment to individuals with autism spectrum disorder (ASD), a developmental disorder with deficits in face perception and category-selective cortex. Using a dataset consisting of 24 individuals with ASD and 24 controls, we predicted their topographies for faces and objects using connectivity hyperalignment (CHA), and compared the predicted maps with estimations from their own functional localizer data. On average, the CHA-predicted maps were highly correlated with participants’ own topographies at both the whole-brain and fine-grained searchlight level. Hence, we show that category-selective topographies can be estimated with high fidelity using hyperalignment in clinical populations with widespread atypical neural responses.

Topic Area: Development, Individual Differences & Clinical Populations