Autism spectrum disorders are heterogeneous neurodevelopmental syndromes characterized by repetitive behaviors and deficits in language development and social interaction. Studies in people with the disorder have implicated a number of candidate mutations, and researchers have engineered mice that harbor these genetic defects. Validation of these mouse models, however, requires detailed behavioral analyses that quantify both solitary and social behaviors.
Much of the promise of these mouse models lies in the possibility that they replicate specific circuit-level defects that lead to abnormal behaviors. Understanding these circuit defects requires moment-to-moment characterization of the motor patterns that emerge from the nervous system. Current methods for behavioral phenotyping have significant limitations, in both the way the data are acquired (often through the use of two-dimensional cameras set up in an enclosure) and how the data are analyzed (often through human-supervised or influenced classification).
To address these issues, Sandeep Datta and his colleagues at Harvard Medical School have developed a platform that couples high-resolution, three-dimensional imaging with analytic methods to classify mouse behaviors without human intervention or bias. Preliminary data indicate that, within a given context, mice seem to show stereotyped patterns of motor outputs that can be altered by specific sensory cues, such as scents from food or predators. These findings raise the possibility that a behavioral fingerprint can be identified for any given mouse strain, thereby enabling researchers to test the influence of genetics on behavior. Datta’s team plans to build on this preliminary work by asking whether individual mouse strains exhibit distinct patterns of stereotyped behaviors. The goal is to develop a quantitative classification scheme that can be used to effectively discriminate among common mouse strains, as well as capture the behavioral repertoire of mouse models for autism-related genes.
