Abstract
The assessment of spatial cognitive learning in rodents is a central approach in neuroscience, as it enables one to assess and quantify the effects of treatments and genetic manipulations from a broad perspective. Although the Morris water maze (MWM) is a well-validated paradigm for testing spatial learning abilities, manual categorization of performance in the MWM into behavioral strategies is subject to individual interpretation, and thus to biases. Here we offer a support vector machine (SVM) - based, automated, MWM unbiased strategy classification (MUST-C) algorithm, as well as a cognitive score scale. This model was examined and validated by analyzing data obtained from five MWM experiments with changing platform sizes, revealing a limitation in the spatial capacity of the hippocampus. We have further employed this algorithm to extract novel mechanistic insights on the impact of members of the Toll-like receptor pathway on cognitive spatial learning and memory. The MUST-C algorithm can greatly benefit MWM users as it provides a standardized method of strategy classification as well as a cognitive scoring scale, which cannot be derived from typical analysis of MWM data.
Original language | English |
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Pages (from-to) | 132-144 |
Number of pages | 13 |
Journal | Brain, Behavior, and Immunity |
Volume | 52 |
DOIs | |
State | Published - 1 Feb 2016 |
Bibliographical note
Publisher Copyright:© 2015 The Authors.
Funding
We would like to thank the following students for conducting experiments that were analyzed in this manuscript: Tzlil Margan, Daniel Cohen, Ana Hutorskoy and Yaarit Ovadia. IRF3−/− mice were generously provided by Dr. Michael Diamond and Dr. Tada Taniguchi. This study was supported by the Alzheimer’s Association Foundation and the Feder Family Fund .
Funders | Funder number |
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Alzheimer’s Association Foundation | |
Feder Family Fund |
Keywords
- Cognitive score
- Hippocampus
- Learning and memory
- Machine learning
- Morris water maze
- SVM
- Spatial learning
- Spatial resolution
- Strategy