Mouse gait analysis is the quantitative study of how laboratory mice walk, serving as a behavioral assay to evaluate motor function, balance, and coordination. This method provides objective data on an animal’s neurological and musculoskeletal health. It has become a widespread tool in biomedical research for understanding how certain conditions or treatments affect movement.
Research Applications of Gait Analysis
The study of walking patterns in mice provides a window into a range of human diseases, as mice can be genetically modified to replicate these conditions. Subtle shifts in gait are often early indicators of disease progression or the effectiveness of a therapeutic intervention. This makes gait analysis a frequent choice for tracking neurodegenerative conditions like Parkinson’s, amyotrophic lateral sclerosis (ALS), and Huntington’s disease to monitor the gradual decline in motor control.
Beyond neurodegeneration, the technique is used to assess recovery after physical trauma, such as spinal cord injury, by quantifying improvements in coordination. It is also used to evaluate pain in arthritis models, as an animal in pain will alter its walking pattern to avoid discomfort on an affected limb.
Common Gait Analysis Systems
Researchers use several technologies to conduct mouse gait analysis. Treadmill-based systems like CatWalk XT and DigiGait feature a transparent, often motorized, walkway. Beneath this walkway, a high-speed camera captures video of the paws as they contact the surface. For example, the CatWalk XT system uses internally reflected light that illuminates the footprint only when a paw makes direct contact, allowing for automated data collection.
A more traditional method involves applying non-toxic ink to a mouse’s paws and having it walk across a strip of paper. This footprint analysis is a low-cost way to capture basic static measurements, such as the distance between steps and paw placement, but it cannot capture temporal or pressure details.
A third approach uses markerless tracking systems with artificial intelligence. These systems analyze standard video of mice moving freely in an open area. By identifying key points on the animal’s body, software can extract detailed gait information without specialized equipment, allowing for the study of movement in a less restrictive environment.
Core Gait Metrics
Gait analysis systems generate numerous metrics, which are grouped into categories to simplify interpretation. Spatial parameters relate to the physical dimensions of a mouse’s steps. These include stride length, the distance between two consecutive placements of the same paw, and the base of support, which is the distance between the left and right paws. Another spatial metric is the paw angle, describing the paw’s orientation relative to the direction of movement.
Temporal metrics focus on the timing of the gait cycle. The stance duration is the amount of time a paw is in contact with the ground, while the swing duration is the time it spends in the air. Together, these metrics help define the rhythm and speed of the animal’s walk.
Coordination metrics assess the relationship between the movements of all four limbs. The regularity index, for example, measures how consistent the step pattern is, with a perfectly regular pattern approaching 100%. Advanced systems also measure pressure metrics, such as the peak pressure exerted by each paw and the total surface area of paw contact.
Data Interpretation in Preclinical Models
The data from gait analysis allows researchers to link changes in metrics to underlying physiology. In a mouse model of Parkinson’s disease, a shortened stride length and increased paw ground time can point to motor deficits and postural instability, reflecting movement difficulties seen in human patients.
Similarly, if a mouse with arthritis applies less pressure with one paw or has a shorter stance time on that limb, it indicates pain and avoidance behavior. Gait asymmetry, where an animal favors one side of its body, is an indicator of a localized issue such as a nerve injury or unilateral brain damage from a stroke. Because velocity can be a confounding variable, it is often controlled or accounted for in statistical analyses to ensure observed changes are due to the condition being studied.