RT Journal Article SR Electronic T1 An analysis of variability in ‘CatWalk’ locomotor measurements to aid experimental design and interpretation JF eneuro JO eNeuro FD Society for Neuroscience SP ENEURO.0092-20.2020 DO 10.1523/ENEURO.0092-20.2020 A1 Miriam Aceves A1 Valerie A Dietz A1 Jennifer N Dulin A1 Unity Jeffery A1 Nicholas D Jeffery YR 2020 UL http://www.eneuro.org/content/early/2020/07/09/ENEURO.0092-20.2020.abstract AB Preclinical studies in models of neurological injury and disease rely upon 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’ 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 upon run speed. Application of calculated reference change intervals to open access data (odc-sci.org) on hindlimb stride length in spinal cord-injured rats illustrates the complementarity between group-level (16mm change; P=0.0009) and individual-level (5/32 animals show change outside reference change interval boundaries) analysis between week 3 and week 6 after injury. We also conclude that interdependence amongst CatWalk variables implies that test ‘batteries’ require careful composition to ensure that different aspects of defective gait are analyzed. Calculation of reference change intervals 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.Significance statement Selection of robust candidate interventions for translation from experimental animals into the neurology clinic requires meticulous examination of behavioral effects observed in the laboratory. Although analysis of group-level data, the current mainstay, is critically important, analysis of individual-level data provides a complementary viewpoint that, bearing in mind the immense variability in neurological deficits in people with spinal cord injury, has high relevance to the interpretation of studies on putative therapies. Here we describe the derivation of specific ‘reference change intervals’ and, using example data, show how these augment interpretation of overall effect and can aid in effective experimental design. The combination of group-level and individual-level analysis will provide more stringent analysis of intervention effects in neurological injury and disease research.