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Spatial learning and memory are used by all individuals who need to move in a space. Morris water maze (MWM) is an accepted method for its evaluation in murine models and has many protocols, ranging from the classic parameters of latency, distance, and number of crossings to the platform zone, to other more complex methods involving computerized trajectory analysis. Algorithm-based SS analysis is an alternative that enriches traditional classic parameters. We developed a non-computerized parameter-based Search Strategy Algorithm (SSA), to classify strategies and detect changes in spatial memory and learning. For this, our algorithm was validated using young and aged rats, evaluated by two observers who classified the trajectories of the rats based on the effectiveness, localization, and precision to reach the platform. SSA is classified into 10 categories, classified by effectiveness, initial direction, and precision. Traditional measurements were unable to show significant differences in the learning process. However, significant differences were identified in SSA. Young rats used a direct search strategy (SS), while aged rats preferred indirect ones. The number of platform crossings was the only variable to show the difference in the intermediate probe trial. The parameter-based algorithm represents an alternative to the computerized SS methods to analyze the spatial memory and learning process in young and age rats. We validate the use of SSA as an alternative to computerized SS analysis spatial learning acquisition. We demonstrated that aged rats had the ability to learn spatial memory tasks using different search strategies. The use of SSA resulted in a reliable and reproducible method to analyze MWM protocols.

Spatial learning and memory are used by all individuals who need to move in a space. Morris water maze (MWM) is an accepted method for its evaluation in murine models and has many protocols, ranging from the classic parameters of latency, distance, and number of crossings to the platform zone, to other more complex methods involving computerized trajectory analysis. Algorithm-based SS analysis is an alternative that enriches traditional classic parameters. We developed a non-computerized parameter-based Search Strategy Algorithm (SSA), to classify strategies and detect changes in spatial memory and learning. For this, our algorithm was validated using young and aged rats, evaluated by two observers who classified the trajectories of the rats based on the effectiveness, localization, and precision to reach the platform. SSA is classified into 10 categories, classified by effectiveness, initial direction, and precision. Traditional measurements were unable to show significant differences in the learning process. However, significant differences were identified in SSA. Young rats used a direct search strategy (SS), while aged rats preferred indirect ones. The number of platform crossings was the only variable to show the difference in the intermediate probe trial. The parameter-based algorithm represents an alternative to the computerized SS methods to analyze the spatial memory and learning process in young and age rats. We validate the use of SSA as an alternative to computerized SS analysis spatial learning acquisition. We demonstrated that aged rats had the ability to learn spatial memory tasks using different search strategies. The use of SSA resulted in a reliable and reproducible method to analyze MWM protocols. [ABSTRACT FROM AUTHOR]