Exploring social interactions in ASD model mice

Our lab seeks to understand how animals actively sample sensory information from the world, how the brain organizes sensory information, and how that information is transformed into action. All three of these features are disrupted in individuals with autism spectrum disorders (ASDs).

The brain allows animals to successfully interact with a natural world that is rich with opportunity and rife with danger. These interactions are mediated by sensation and movement, which are used by animals to learn about their environment and to make useful predictions about the future.

We have established a machine learning algorithm based on motion sequencing that reveals the timescales and structure of mouse behaviors.

We are using a new, specialized form of this method, called So Mo-Seq (Social Motion Sequencing) to study social interactions in ASD mouse models. SoSeq can identify behavioral coupling at a distance in mice and when used in combination with neural recordings in the prefrontal cortex (a brain region important for social interactions), we can identify specialized neural codes that mediate social interactions.

Through this powerful tool, we have made substantial progress on understanding the relationship between ASD genes and behavior, and have developed important techniques and preliminary data that promise to further open our understanding of sensory biology in the context of ASD.