Recognition of early stage thigmotaxis in Morris water maze test with convolutional neural network.
Journal:
PloS one
PMID:
29723266
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.