skip to Main Content
[font_awesome icon="phone"] 1-800-987-654[font_awesome icon="envelope"] [email protected][font_awesome icon="user"][wp_login_url text="User Login" logout_text="Logout"]

Four tips for studying fine motor skills in mice

Toni Ahtoniemi, Ph.D. Charles River Discovery Research Services

Suggested guidelines on a new way to translate central nervous system disease from mice to humans.

Fine motor skills, such as the pace and frequency of finger tapping, are used for the early diagnosis of many diseases, such as Huntington’s disease. Likewise, functional motor recovery has been tested in patients with stroke to quantify the flexibility of movement in patients recovering from stroke (Rohrer et al., 2002).

The same readings cannot be used for mice, but what if there was a tool to analyze fine motor characteristics for a model of central nervous system (CNS) disease? Recently, researchers experimented with a novel, automated, high-precision kinematic motion analysis system that can be used to detect subtle phenotype changes, with earlier and more sensitive detection compared to traditional motion analysis ( Zörner et al., 2010). The system provides a more comprehensive study of fine motor performance and motor deficits than previous methods, which is necessary to fully model aspects of human CNS disease in preclinical models.

In kinematic analysis, movements of relevant parts of the body, such as the joints of the limbs, trunk and tail, are recorded using a high-speed camera simultaneously from the bottom and the sides. This allows the correlation of all parts of the body so that one can establish a complete profile of the motor skills of the animal. Kinematic analysis is not only applicable to the study of the development of fine motor defects in rodent models of the CNS and other motor diseases, but may also offer a sensitive tool to study the effectiveness of therapeutic approaches or to study subtle changes in motor skills, similar to the human CNS. the disease is studied.

If you plan to use this system …

  1. Know your model well. Kinematic motion can be used to detect subtle phenotype changes, with earlier and more sensitive detection compared to traditional motion analysis. However, choosing the right times will be essential to obtain the best result, which is why it is important to take into account the specifics of the model, especially the age and disease progression of the phenotype, as the parameters differ from ‘one model to another.
  2. Take note of the speed and direction. A kinematic analysis of the gait correlates all parts of the body in order to establish a complete profile of the animal’s motor capacities. By examining the speed and direction of movement, you add value to the simultaneous analysis of various limb joints, body parts and joint angles as well as coordination.
  3. Select your markers carefully. The higher sensitivity of these tests can detect even the most subtle changes in preclinical models that otherwise might not show a detectable phenotype using gross motor tests. However, the choice of markers will depend on the model and will vary from model to model.
  4. Pick the right time to test. Kinematic analysis offers advantages in behavioral pharmacology in that the higher sensitivity of the system widens the therapeutic window of opportunity for pharmacological testing. The higher throughput and automation also allow the system to be used as a sensitive screening tool. But choosing the right time to test after dosing is essential, as different compounds have different pharmacokinetic properties for absorption, distribution, metabolism, and excretion. It is therefore essential to know the properties of a compound and when the maximum concentrations are reached in the system or the brain in order to see the effects.

Toni Ahtoniemi, who obtained her doctorate. in 2008 on CNS-related disease models, is project manager at Charles River Discovery Research Services, based in Finland. This article was adapted from an article on Charles River’s science blog, Eureka.

The references:
Rohrer B, Fasoli S, Krebs HI, Hughes R, Volpe B, Frontera WR, Stein J, Hogan N. Fluidity of movement changes during recovery from stroke. The Journal of Neuroscience 2002 Sep 15; 22 (18): 8297-304.
Zörner B, Filli L, Starkey ML, Gonzenbach R, Kasper H, Röthlisberger M, Bolliger M, Schwab ME. Profiling of locomotor recovery: Complete quantification of impairments after CNS damage in rodents. Nature Methods 2010 Sep; 7 (9): 701-8.

Back To Top