Timothy Dunn Receives SFARI Award to Map Autism Models in Rodents

Timothy Dunn, an assistant professor of biomedical engineering at Duke University, received a 2025 Data Analysis Award from the Simons Foundation Autism Research Initiative (SFARI). SFARI’s mission is to advance the basic science of autism and related neurodevelopmental disorders by providing support to researchers as they pursue various projects using large, publicly available data resources. 

Dunn and his team will use the SFARI Data Analysis Award to more precisely model and explore expressions of autism in different animal models.

The Dunn lab specializes in creating technologies that can precisely track and map movement. Using videos of freely moving rats, the team trained machine-learning algorithms and neural networks to identify and map the precise 3D locations of the body joints on the animals. Researchers can then relate these measurements to data collected from brain recording technologies to examine links between neuronal activity and specific behaviors.

One such tool is DANNCE, short for 3-Dimensional Aligned Neural Network for Computational Ethology, which Dunn and his team developed in 2021. In 2025, Dunn and his team built social-DANNCE, which extended DANNCE measurements to socially interacting animals

“Many areas of neuroscience have been hamstrung by the lack of precise, objective, and reproducible descriptions of social behaviors, and social-DANNCE helps provide a solution to this longstanding problem,” said Dunn. “If you can’t quantify behavior precisely and comprehensively, you’re not going to get an accurate picture of how disease states or therapeutics affect behavior and movement.”

With the support from SFARI, Dunn and his team hope to use social-DANNCE measurements in rat and mouse autism models to determine which actions and behavioral patterns occur in both species and whether any shared behavioral patterns are affected in similar ways by autism. This knowledge, Dunn says, would help researchers determine the usefulness of such models for autism studies.

To accomplish this, the team will use a public dataset that includes over 140 million 3D full body poses in seven different rat models of autism and two different mice models. Any lone and social behaviors that are flagged as similar suggest potentially similar neural pathways, which could form the basis for future experiments.

 “This project will also establish a foundation for comparative behavioral phenotyping beyond just rodents, building towards a future where more rigorous links between animal models and human brain disorders transform translation from bench to bedside,” said Dunn.