What is the Most Sensitive Measure of Water Maze Probe Test Performance?

The water maze is commonly used to assay spatial cognition, or, more generally, learning and memory in experimental rodent models. In the water maze, mice or rats are trained to navigate to a platform located below the water's surface. Spatial learning is then typically assessed in a probe test, where the platform is removed from the pool and the mouse or rat is allowed to search for it. Performance in the probe test may then be evaluated using either occupancy-based (percent time in a virtual quadrant [Q] or zone [Z] centered on former platform location), error-based (mean proximity to former platform location [P]) or counting-based (platform crossings [X]) measures. While these measures differ in their popularity, whether they differ in their ability to detect group differences is not known. To address this question we compiled five separate databases, containing more than 1600 mouse probe tests. Random selection of individual trials from respective databases then allowed us to simulate experiments with varying sample and effect sizes. Using this Monte Carlo-based method, we found that the P measure consistently outperformed the Q, Z and X measures in its ability to detect group differences. This was the case regardless of sample or effect size, and using both parametric and non-parametric statistical analyses. The relative superiority of P over other commonly used measures suggests that it is the most appropriate measure to employ in both low- and high-throughput water maze screens

Correlated quantum percolation in the lowest Landau level

Our understanding of localization in the integer quantum Hall effect is informed by a combination of semi-classical models and percolation theory. Motivated by the effect of correlations on classical percolation we study numerically electron localization in the lowest Landau level in the presence of a power-law correlated disorder potential. Careful comparisons between classical and quantum dynamics suggest that the extended Harris criterion is applicable in the quantum case. This leads to a prediction of new localization quantum critical points in integer quantum Hall systems with power-law correlated disorder potentials. We demonstrate the stability of these critical points to addition of competing short-range disorder potentials, and discuss possible experimental realizations.Comment: 15 pages, 12 figure

Development and Validation of a Sensitive Entropy-Based Measure for the Water Maze

In the water maze, mice are trained to navigate to an escape platform located below the water's surface, and spatial learning is most commonly evaluated in a probe test in which the platform is removed from the pool. While contemporary tracking software provides precise positional information of mice for the duration of the probe test, existing performance measures (e.g., percent quadrant time, platform crossings) fail to exploit fully the richness of this positional data. Using the concept of entropy (H), here we develop a new measure that considers both how focused the search is and the degree to which searching is centered on the former platform location. To evaluate how H performs compared to existing measures of water maze performance we compiled five separate databases, containing more than 1600 mouse probe tests. Random selection of individual trials from respective databases then allowed us to simulate experiments with varying sample and effect sizes. Using this Monte Carlo-based method, we found that H outperformed existing measures in its ability to detect group differences over a range of sample or effect sizes. Additionally, we validated the new measure using three models of experimentally induced hippocampal dysfunction: (1) complete hippocampal lesions, (2) genetic deletion of αCaMKII, a gene implicated in hippocampal behavioral and synaptic plasticity, and (3) a mouse model of Alzheimer's disease. Together, these data indicate that H offers greater sensitivity than existing measures, most likely because it exploits the richness of the precise positional information of the mouse throughout the probe test

What is the most sensitive measure of water maze probe test performance?

The water maze is commonly used to assay spatial cognition, or, more generally, learning and memory in experimental rodent models. In the water maze, mice or rats are trained to navigate to a platform located below the water’s surface. Spatial learning is then typically assessed in a probe test, where the platform is removed from the pool and the mouse or rat is allowed to search for it. Performance in the probe test may then be evaluated using either occupancy-based (percent time in virtual quadrant [Q] or zone [Z] centered on former platform location), error-based (mean proximity to former platform location [P]) or counting-based (platform crossings [X]) measures. While these measures differ in their popularity, whether they differ in their ability to detect group differences is not known. To address this question we compiled 5 separate databases, containing more than 1600 mouse probe tests. Random selection of individual trials from respective databases then allowed us to simulate experiments with varying sample and effect sizes. Using this Monte Carlo-based method, we found that the P measure consistently outperformed the Q, Z and X measures in its ability to detect group differences. This was the case regardless of sample or effect size, and using both parametric and non-parametric statistical analyses. The relative superiority of P over other commonly used measures suggests that it is the most appropriate measure to employ in both low- and high-throughput water maze screens