2016
DOI: 10.1016/j.bbi.2015.10.013
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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, autom… Show more

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Cited by 47 publications
(50 citation statements)
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“…Starch was mixed in water to make it opaque. Rats were trained individually, before start of study, to reach submerged platform [22]. Time to reach the platform was noted for each rat at interval of 15 days for two months.…”
Section: Water Maze Testmentioning
confidence: 99%
“…Starch was mixed in water to make it opaque. Rats were trained individually, before start of study, to reach submerged platform [22]. Time to reach the platform was noted for each rat at interval of 15 days for two months.…”
Section: Water Maze Testmentioning
confidence: 99%
“…While control mice were able to learn the task as indicated by a reduced escape latency throughout the 10 days of training, Setd1b cKO mice were severely impaired ( Fig 1F). We also performed a more sensitive analysis using a modified version of the MUST-C algorithm to measure the different spatial strategies that represent either hippocampus-dependent or independent abilities (Illouz et al, 2016). Our results indicate that Setd1b cKO mice fail to adapt hippocampus-dependent search strategies such as "direct", "corrected" and "short-chaining" (Fig 1G).…”
Section: Loss Of Setd1b In Adult Forebrain Neurons Impairs Hippocampumentioning
confidence: 91%
“…The behavioral experiments were performed as described previously (Kerimoglu et al, 2017b). For in depth feature analysis from water maze data, a modified version of MUST-C algorithm was used (Illouz et al, 2016).…”
Section: Behavior Experimentsmentioning
confidence: 99%
“…Using previous rodent based methods developed for Morris water maze (Wolfer & Lipp, 2000;Graziano et al, 2003;Gehring et al, 2015;Illouz et al, 2016;Vouros et al, 2018), we quantified four different features from the swimming trajectories of zebrafish. Coordinates of the swimming trajectories were extracted from the Viewpoint-…”
Section: Automated Locomotion Analysismentioning
confidence: 99%