Mice behavioral patterns carry a lot of information about their health and disease. Current behavioral assessments of multiple sclerosis-like disease in mice revolve around human judgement of a degree of the paralysis of limbs. This assessment provides very limited information as to how the animal is reacting to a disease as paralysis is not the only behavioral change that can be observed. We are developing an automated approach to assess mice behavior which will analyze a wide variety of behaviors in an automated computerized fashion. Using Clever Sys software as the foundation we are developing methodology to test the behavioral patterns of mice with experimental autoimmune encephalomyelitis. Our preliminary data shows increased accuracy and reproducible results in this new approach to behavior assessment.
Quality of Life Video Assessment (QoLVA):
We are currently working with Clever Sys Behavioral Recognition Technology software, which reads-out for over 40 different actions of a mouse in its home cage environment. Our in-house hardware system of 8 video cameras provide 24hour video recordings for the analysis. We extract complete sets of data describing the behavioral patterns of the mice. Currently, we are working on collecting the data to describe the normal patterns of behavior of a healthy black mouse (C57/B6). Our next step will be to define the differences between the healthy and EAE model; and further, look at a number of potential treatments. QoLVA allows us not only to observe the current state of a mouse but to monitor the progression of a disease 24/7 for any length of time.
From the many benefits that QoLVA can offer the scientific community, the ability to connect behavioral patterns to the inflammation in the brain is something that intrigues our research group. We are looking for the behavioral signatures that are associated with multiple sclerosis model in mice (EAE).