The ability to study learning and memory behavior in living organisms has significantly increased our understanding of what genes affect this behavior, allowing for the rational design of therapeutics in diseases that affect cognition. The fruit fly, Drosophila melanogaster, is a well established model organism used to study the mechanisms of both learning and memory in vivo. The techniques used to assess this behavior in flies, while powerful, suffer from a lack of speed and quantification. The technical goal of this project is to create an automated method for characterizing this behavior in fruit flies by analyzing video of their movements. A method is developed to replace and improve a labor-intensive, subjective evaluation process with one that is automated, consistent and reproducible; thus allowing for robust, high-throughput analysis of large quantities of video data.
The method includes identifying individual flies in a video, quantifying their size (which is correlated with their gender), and tracking their motion. Once the flies are identified and tracked, various geometric measures may be computed, for example distance between flies, their relative orientation, velocities and percentage of time the flies are in contact with each other. This data is computed for numerous experimental videos and produces high-dimensional feature vectors that quantify the behavior of the flies. Clustering techniques, e.g., k-means clustering, may then be applied to the feature vectors in order to computationally group each specimen by genotype. Our results show that we are able to automatically differentiate between normal and defective flies. We also generated a Computed Courtship Index (CCI), a computational equivalent of the existing Courtship Index (CI), and compared CCI with CI. These results demonstrate that our automated analysis provides a numerical scoring of fly behavior that is similar to the scoring produced by human observers.