Automatic recognition of explorative strategies in the Morris Water Maze

Notwithstanding the development of reliable tracking systems, the quantification methodology of the Morris water maze (MWM) has witnessed an operational mismatch between the indexes used to quantify MWM performance and the cognitive concepts derived from these indexes. Indeed, escape latency is the main, and often unique, performance measure used for the quantification of behavior. Aim of the present work was to overcome this limitation by presenting a methodology that allows for automatic categorical pattern recognition of the behavioral strategies performed in the MWM. By selecting few a priori and user-defined behavioral categories, many quantitative variables and regions of interest (ROIs), we used discriminant analysis (DA) to obtain 97.9% of correct automatic recognition of categories. The developed discriminant model (DM) also allowed to predict category membership of newly recorded swim paths with the same statistical efficacy (96%), and to identify the variables that better discriminate between adjacent categories. The combination of DA with a tracking system, a selection of many variables, different ROIs and qualitative categorization, reduces the gap between the measurement process and the categories used to describe a given behavior, and offers a methodology to computationally reproduce the human categorization of behaviors in the MWM.