An H-index for rodents: development of entropy-based measure for evaluation of spatial performance in the water maze
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1
Hospital for Sick Children, Neurosciences and Mental Health, Canada
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2
University of Alberta, Canada
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3
University of Toronto, Canada
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4
University of Barcelona, Spain
Spatial navigation is one of the most important aspects of behaviour mediated by the hippocampal system and has been extensively studied in rodent models. Development of testing tools that capture the richness of behaviour generated during navigation is essential for elucidating the principles by which the brain integrates, interprets, and responds to its surroundings. One of the most widely used tests for this purpose has been the Morris water maze. First introduced in 1984, it challenges mice to learn the location of a submerged platform in a pool of water. Over the course of training, mice learn to search more focally and, as a result, their escape-to-platform latencies decline. The shift towards more focal searching is most commonly evaluated by measuring where mice search in a probe test, when the platform is removed from the pool. Performance in the probe test has been traditionally computed by 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.
However, as recently revealed by our group, these measures have several flaws. First and foremost, they are measures of features of spatial learning (such as distance to the platform in the case of P) and ignore other aspects of spatial performance that could be important for characterization of cognitive processes in navigation. Second, these measures can be problematic computationally, as they produce non-normal distributions not suitable for widely used hypothetical tests, such as Student's t-test.
To address these concerns, we introduce an entropy-based measure of spatial performance in the water maze - H. We operate from the assumption that the entropy of the searching behaviour decreases as the mouse learns the location of the platform. H consists of two components that are summated together: the entropy with relation to platform location (how accurate the search is) and the entropy with relation to the animal's path (how focused the search is).
To evaluate how H performs in comparison to existing measures, we compiled 5 separate databases containing more than 1600 probe tests. Random selection of trials from respective databases allowed us to simulate experiments with varying sample and effect sizes. Using this Monte Carlo-based method, we found that H consistently outperformed the P, Q, Z, and X measures in detecting group differences over a range of sample or effect sizes, and using both parametric and non-parametric tests. Finally, we validated the new measure in three separate models of experimentally-induced hippocampal dysfunction: 1) complete hippocampal lesions 2) genetic deletion of αCaMKII, a gene important for hippocampal plasticity 3) a mouse model of Alzheimer's disease. Together, these data indicate that H offers greater sensitivity than existing measures, perhaps because it exploits the richness of precise positional information of the mouse throughout the probe test. Furthermore, the component entropies of H could also be considered individually to provide a more focused assessment. Such versatility can help elucidate facets of spatial learning that existing measures could not.
Conference:
Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009.
Presentation Type:
Poster Presentation
Topic:
General neuroinformatics
Citation:
Zaslavsky
K,
Reza
HR,
Wang
AH,
Yiu
AP,
Teixeira
CM,
Josselyn
SA and
Frankland
PW
(2019). An H-index for rodents: development of entropy-based measure for evaluation of spatial performance in the water maze.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2009.
doi: 10.3389/conf.neuro.11.2009.08.084
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Received:
22 May 2009;
Published Online:
09 May 2019.
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Correspondence:
Kirill Zaslavsky, Hospital for Sick Children, Neurosciences and Mental Health, Ontario, Canada, kirillpetrovuoft@gmail.com