Journal
BRAIN BEHAVIOR AND IMMUNITY
Volume 52, Issue -, Pages 132-144Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.bbi.2015.10.013
Keywords
Morris water maze; Learning and memory; Spatial learning; Hippocampus; SVM; Machine learning; Strategy; Spatial resolution; Cognitive score
Categories
Funding
- Alzheimer's Association Foundation
- Feder Family Fund
Ask authors/readers for more resources
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. (C) 2015 The Authors. Published by Elsevier Inc.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available