Statistical analysis of behavioral data

Optimization and validation of a modified radial-arm water maze protocol using a murine model of mild closed head traumatic brain injury

Cognitive impairments can be a significant problem after a traumatic brain injury (TBI), which affects millions worldwide each year. There is a need for establish reproducible cognitive assays in rodents to better understand disease mechanisms and to develop therapeutic interventions towards treating TBI-induced impairments. Our goal was to validate and standardize the radial arm water maze (RAWM) test as an assay to screen for cognitive impairments caused by TBI. RAWM is a visuo-spatial learning test, originally designed for use with rats, and later adapted for mice. The present study investigates whether test procedures, such us the presence of extra-maze cues influences learning and memory performance. C57BL/6 mice were tested in an 8-arm RAWM using a four-day protocol. We demonstrated that two days of training, exposing the mice to extra-maze cues and a visible platform, influenced learning and memory performance. Mice that did not receive training performed poorer compared to mice trained. To further validate our RAWM protocol, we used scopolamine. We, also, demonstrated that a single mild closed head injury (CHI) caused deficits in this task at two weeks post-CHI. Our data supported the use of 7 trials per day and a spaced training protocol as key factor to unmask memory impairment following CHI. Here, we provide a detailed standard operating procedure for RAWM test, which can be applied to a variety of mouse models including neurodegenerative diseases and pathology, as well as when pharmacological approaches are used.

Analysis of longitudinal data from animals with missing values using SPSS

Testing of therapies for disease or injury often involves the analysis of longitudinal data from animals. Modern analytical methods have advantages over conventional methods (particularly when some data are missing), yet they are not used widely by preclinical researchers. Here we provide an easy-to-use protocol for the analysis of longitudinal data from animals, and we present a click-by-click guide for performing suitable analyses using the statistical package IBM SPSS Statistics software (SPSS). We guide readers through the analysis of a real-life data set obtained when testing a therapy for brain injury (stroke) in elderly rats. If a few data points are missing, as in this example data set (for example, because of animal dropout), repeated-measures analysis of covariance may fail to detect a treatment effect. An alternative analysis method, such as the use of linear models (with various covariance structures), and analysis using restricted maximum likelihood estimation (to include all available data) can be used to better detect treatment effects. This protocol takes 2 h to carry out.