ArticlePloS one2018
Recognition of early stage thigmotaxis in Morris water maze test with convolutional neural network.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
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- Cognitive comorbidities in the rat pilocarpine model of epilepsy.Frontiers in neurology · 2024Article
- Particle-environment interactions in arbitrary dimensions: A unifying analytic framework to model diffusion with inert spatial heterogeneities.Physical review research · 2023Article
- A murine model of hnRNPH2-related neurodevelopmental disorder reveals a mechanism for genetic compensation by Hnrnph1.The Journal of clinical investigation · 2023Article
- Search Strategy Analysis of 5xFAD Alzheimer Mice in the Morris Water Maze Reveals Sex- and Age-Specific Spatial Navigation Deficits.Biomedicines · 2023Article
- Comparison of the effect of postweaning social isolation, enriched environment, and exercise training on learning and memory functions in rats.Turkish journal of medical sciences · 2023Article
- White Button Mushroom (Brain sciences · 2022Article
- ACancers · 2022Article
- Morris water maze: a versatile and pertinent tool for assessing spatial learning and memory.Experimental animals · 2022Review
Corrections and comments
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10 authors.
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Abstract
The Morris water maze test (MWM) is a useful tool to evaluate rodents' spatial learning and memory, but the outcome is susceptible to various experimental conditions. Thigmotaxis is a commonly observed behavioral pattern which is thought to be related to anxiety or fear. This behavior is associated with prolonged escape latency, but the impact of its frequency in the early stage on the final outcome is not clearly understood. We analyzed swim path trajectories in male C57BL/6 mice with or without bilateral common carotid artery stenosis (BCAS) treatment. There was no significant difference in the frequencies of particular types of trajectories according to ischemic brain surgery. The mouse groups with thigmotaxis showed significantly prolonged escape latency and lower cognitive score on day 5 compared to those without thigmotaxis. As the next step, we made a convolutional neural network (CNN) model to recognize the swim path trajectories. Our model could distinguish thigmotaxis from other trajectories with 96% accuracy and specificity as high as 0.98. These results suggest that thigmotaxis in the early training stage is a predictive factor for impaired performance in MWM, and machine learning can detect such behavior easily and automatically.
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