Automated phenotyping of mouse social behavior
Year: 2011
Type: dissertation
Abstract: Inspired by the connections between social behaviorand intelligence, I have developed a trainable system to phenotype
mouse social behavior. This system is of immediate interest to
researchers studying mouse models of social disorders such as
depression or autism. Mice studies provide a controlled environment
to begin exploring the questions of how to best quantify social
behavior. For the purposes of evaluating this system and to
encourage further research, I introduce a new video dataset
annotated with five social behaviors: nose-to-nose sniffing,
nose-to-head sniffing, nose-to-anogenital sniffing, crawl under /
crawl over, and upright head contact. These four behaviors are of
particular importance to researchers characterizing mouse social
avoidance [9]. To effectively phenotype mouse social behavior, the
system incorporates a novel mice tracker, and modules to represent
and to classify social behavior. The mice tracker addresses the
challenging computer vision problem of tracking two identical,
highly deformable mice through complex occlusions. The tracker
maintains an ellipse model of both mice and leverages motion cues
and shape priors to maintain tracks during occlusions. Using these
tracks, the classification system represents behavior with 14
spatial features characterizing relative position, relative motion,
and shape. A regularized least squares (RLS) classifier, trained
over representative instances of each behavior, classifies the
behavior present in each frame. This system demonstrates the
enormous potential for building automated systems to quantitatively
study mouse social behavior.
Author: Nicholas Edelman
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1494107862