Location
University of Nevada, Las Vegas
Start Date
16-4-2011 2:00 PM
End Date
16-4-2011 3:30 PM
Description
Neuroscientists commonly use a Morris Water Maze to assess learning in rodents. In his kind of a maze, the subjects learn to swim toward a platform hidden in opaque water as they orient themselves according to the cues on the walls. This protocol presents a challenge to statistical analysis, because an artificial cut-off must be set for those experimental subjects that do not reach the platform so as they do not drown from exhaustion. This fact leads to the data being right censored. In our experimental data, which compares learning in rodents that have chemically induced symptoms of schizophrenia to a control group of rodents a cut-off of 60 seconds was used, and is the mode of the distribution. Utilizing Bayesian inferential procedures, we account for the censoring in the data and compare the results of learning between the treatment and control groups
Keywords
Learning; Bayesian statistical decision theory; Research — Methodology; Rodents
Disciplines
Neuroscience and Neurobiology | Psychology | Statistical Methodology
Language
English
Included in
Neuroscience and Neurobiology Commons, Psychology Commons, Statistical Methodology Commons
Analysis of Morris Water Maze data with Bayesian statistical methods
University of Nevada, Las Vegas
Neuroscientists commonly use a Morris Water Maze to assess learning in rodents. In his kind of a maze, the subjects learn to swim toward a platform hidden in opaque water as they orient themselves according to the cues on the walls. This protocol presents a challenge to statistical analysis, because an artificial cut-off must be set for those experimental subjects that do not reach the platform so as they do not drown from exhaustion. This fact leads to the data being right censored. In our experimental data, which compares learning in rodents that have chemically induced symptoms of schizophrenia to a control group of rodents a cut-off of 60 seconds was used, and is the mode of the distribution. Utilizing Bayesian inferential procedures, we account for the censoring in the data and compare the results of learning between the treatment and control groups