کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7280584 | 1473915 | 2016 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Unraveling cognitive traits using the Morris water maze unbiased strategy classification (MUST-C) algorithm
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
ایمنی شناسی و میکروب شناسی
ایمونولوژی
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Brain, Behavior, and Immunity - Volume 52, February 2016, Pages 132-144
Journal: Brain, Behavior, and Immunity - Volume 52, February 2016, Pages 132-144
نویسندگان
Tomer Illouz, Ravit Madar, Yoram Louzon, Kathleen J. Griffioen, Eitan Okun,