Undergraduate Research Project
Smart Cage: Automatic Behavior Assessment Using Statistical and Data Mining Techniques
Project Description
Mouse models have been widely used in laboratory to evaluate novel candidate drugs or investigate new
medical treatments. The whole procedure requires extensive preclinical behavioral evaluation. However,
behavioral assessment of mouse models is still a slow and labor-intensive process, and thus results in a low
rate of throughput and significant impediment to medical discovery [1]. The significant bottleneck factors
include 1) the need for human observation and 2) the inability of an experimenter to simultaneously assess
multiple animals. Despite these drawbacks, behavioral data remain the main drivers of new drug discovery.
The limitations in the procedure create a bottleneck in most new drug and pre-clinical studies. Relieving this
bottleneck could substantially facilitate the procedure for new drug and disease treatment discovery. In
recent years, several companies have attempted to develop automated solutions to mouse behavioral
evaluation. Most of them are based on video analysis to recognize a small number of behaviors with limited
capability. In this project, we will work with a newly developed Smart Cage of Mouse developed from
Behavioral Instruments, Hillsborough, NJ [2]. The Smart Cage system is capable of automatically
identifying 23 unique behaviors and providing a complete, real-time profile of mouse behavior with an
integration of the state-of-the-art video analysis and advanced vibration analysis.
In this project, we will investigate the mouse
behavior data collected from the newly developed
Smart Cage, and evaluate the effectiveness of the
designed behavioral spectrometer to assess
behavioral differences for mouse under different
conditions. The students will work on the dataset
with 38 Male C57BL/6 mice (Harlan Laboratories,
USA) 10 weeks of age (~25g). Mice were
habituated for 1 week to the animal colony and had
ad-lib access to food and water. The mice were
assigned into two groups, each with 19 mice. One
group received two injections of 40mg/kg MPTP
injected subcutaneously in a volume of 10ml/kg mouse weight separated by 24 hours. The other group
received saline injections as a control procedure. The mice were individually placed in one Smart Cage with
behavioral spectrometer for data collection of 45 minutes. The student will make statistical analysis of the
collected multivariate behavioral data, and investigate if the behavioral differences of the mice from the two
groups can be discriminated by the monitored behavioral data. The study in this project will contribute to an
intelligent decision-making system to achieve automated behavioral assessments of mouse models, which
can be useful to relieve the bottleneck of mouse experiments in various new drug and pre-clinical studies.
Project Advisor:
Shouyi Wang, Ph.D.
Assistant Professor
Department of Industrial and Manufacturing Systems Engineering
University of Texas at Arlington
500 West First Street, Arlington, TX 76019
Office: Woolf Hall 420H
Tel: 817-272-2921
Fax: 817-272-3406
Email: shouyiw@uta.edu
References
[1] Tecott LH, Nestler EJ. Neurobehavioral assessment in the information age. Nat Neu-rosci, 7: 462–6, 2004.
[2] http://behavioralinstruments.com/