TY - JOUR T1 - An Analysis of Variability in “CatWalk” Locomotor Measurements to Aid Experimental Design and Interpretation JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0092-20.2020 VL - 7 IS - 4 SP - ENEURO.0092-20.2020 AU - Miriam Aceves AU - Valerie A. Dietz AU - Jennifer N. Dulin AU - Unity Jeffery AU - Nicholas D. Jeffery Y1 - 2020/07/01 UR - http://www.eneuro.org/content/7/4/ENEURO.0092-20.2020.abstract N2 - Preclinical studies in models of neurologic injury and disease rely on behavioral outcomes to measure intervention efficacy. For spinal cord injury, the CatWalk system provides unbiased quantitative assessment of subtle aspects of locomotor function in rodents and so can powerfully detect significant differences between experimental and control groups. Although clearly of key importance, summary group-level data can obscure the variability within and between individual subjects and therefore make it difficult to understand the magnitude of effect in individual animals and the proportion of a group that may show benefit. Here, we calculate reference change intervals (RCIs) that define boundaries of normal variability for measures of rat locomotion on the CatWalk. Our results indicate that many commonly-used outcome measures are highly variable, such that differences of up to 70% from baseline value must be considered normal variation. Many CatWalk outcome variables are also highly correlated and dependent on run speed. Application of calculated RCIs to open access data (https://scicrunch.org/odc-sci) on hindlimb stride length in spinal cord-injured rats illustrates the complementarity between group-level (16 mm change; p = 0.0009) and individual-level (5/32 animals show change outside RCI boundaries) analysis between week 3 and week 6 after injury. We also conclude that interdependence among CatWalk variables implies that test “batteries” require careful composition to ensure that different aspects of defective gait are analyzed. Calculation of RCIs aids in experimental design by quantifying variability and enriches overall data analysis by providing details of change at an individual level that complement group-level analysis. ER -