title: Unraveling cognitive traits using the Morris water maze unbiased strategy classification (MUST-C) algorithm.
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.
authors.affiliation: The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan 5290002, Israel; The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel.
authors.forename: Tomer
authors.initials: T
authors.lastname: Illouz
authors.affiliation: The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan 5290002, Israel; The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel.
authors.forename: Ravit
authors.initials: R
authors.lastname: Madar
authors.affiliation: The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel; Department of Mathematics, Bar-Ilan University, Ramat Gan 5290002, Israel.
authors.forename: Yoram
authors.initials: Y
authors.lastname: Louzoun
authors.affiliation: Department of Biology and Chemistry, Liberty University, Lynchburg, VA 24515, USA.
authors.forename: Kathleen J
authors.initials: KJ
authors.lastname: Griffioen
authors.affiliation: The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan 5290002, Israel; The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan 5290002, Israel. Electronic address: eitan.okun@biu.ac.il.
authors.forename: Eitan
authors.initials: E
authors.lastname: Okun
mesh_terms: D000465:Algorithms; D000818:Animals; D003071:Cognition / Q000502:physiology*; D006624:Hippocampus / Q000502:physiology; D008297:Male; D018782:Maze Learning / Q000502:physiology*; D008568:Memory / Q000187:drug effects*; D051379:Mice; D008810:Mice, Inbred C57BL; D008822:Mice, Transgenic; D013028:Space Perception / Q000502:physiology; D065853:Spatial Learning / Q000502:physiology*; D060388:Support Vector Machine; D013550:Swimming / Q000502:physiology
keywords: Cognitive score; Hippocampus; Learning and memory; Machine learning; Morris water maze; SVM; Spatial learning; Spatial resolution; Strategy