Detailed classification of swimming paths in the Morris Water Maze: multiple strategies within one trial

Abstract

The Morris Water Maze is a widely used task in studies of spatial learning with rodents. Classical performance measures of animals in the Morris Water Maze include the escape latency, and the cumulative distance to the platform. Other methods focus on classifying trajectory patterns to stereotypical classes representing different animal strategies. However, these approaches typically consider trajectories as a whole, and as a consequence they assign one full trajectory to one class, whereas animals often switch between these strategies, and their corresponding classes, within a single trial. To this end, we take a different approach: we look for segments of diverse animal behaviour within one trial and employ a semi-automated classification method for identifying the various strategies exhibited by the animals within a trial. Our method allows us to reveal significant and systematic differences in the exploration strategies of two animal groups (stressed, non-stressed), that would be unobserved by earlier methods.

Rtrack: a software package for reproducible automated water maze analysis

“…2 The simple measures of goal-finding latency during learning and re-learning trials have since been improved upon by the discovery that certain search patterns are associated with more subtle hippocampal deficits. [3][4][5][6][7] Swim paths can thus be classified into different strategies reflecting the type of learning being employed.…”

Sodium nitroprusside prevents the detrimental effects of glucose on the neurovascular unit and behaviour in zebrafish

“…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-…”

A generalised framework for detailed classification of swimming paths inside the Morris Water Maze

“…To assign one trajectory to multiple classes, we earlier proposed the division of the full animal swimming paths into segments [17]. In our method each segment overlaps significantly with its previous one (percentages of 70% and 90% have been performed on this analysis) to make sure that important information is not lost due to an unfavourable segmentation.…”

“…In contrast to the classifiers, the ensembles have high agreements among them (more than 80%) and nearly nullify the cross validation error of the classifiers 2. However, since in our method the cross validation was used for both tuning and testing [17], additionally we manually assess the error of the ensembles on two out of the four segmentations (see the appendix section).…”

“…In our previous work [17] we argued that animals employ several strategies during each trial in order to find the platform and by assigning whole animal trajectories to single behavioural classes results in the loss of important information. For this reason, we proposed a more sophisticated automatic quantification methodology capable of classifying and presenting the various animal behaviours in much more detail during each trial.…”