Pathfinder: open source software for analyzing spatial navigation search strategies

Abstract

Spatial navigation is a universal behavior that varies depending on goals, experience and available sensory stimuli. Spatial navigational tasks are routinely used to study learning, memory and goal-directed behavior, in both animals and humans. One popular paradigm for testing spatial memory is the Morris water maze, where subjects learn the location of a hidden platform that offers escape from a pool of water. Researchers typically express learning as a function of the latency to escape, though this reveals little about the underlying navigational strategies. Recently, a number of studies have begun to classify water maze search strategies in order to clarify the precise spatial and mnemonic functions of different brain regions, and to identify which aspects of spatial memory are disrupted in disease models. However, despite their usefulness, strategy analyses have not been widely adopted due to the lack of software to automate analyses. To address this need we developed Pathfinder, an open source application for analyzing spatial navigation behaviors. In a representative dataset, we show that Pathfinder effectively characterizes the development of highly-specific spatial search strategies as male and female mice learn a standard spatial water maze. Pathfinder can read data files from commercially- and freely-available software packages, is optimized for classifying search strategies in water maze paradigms, but can also be used to analyze 2D navigation by other species, and in other tasks, as long as timestamped xy coordinates are available. Pathfinder is simple to use, can automatically determine pool and platform geometry, generates heat maps, analyzes navigation with respect to multiple goal locations, and can be updated to accommodate future developments in spatial behavioral analyses. Given these features, Pathfinder may be a useful tool for studying how navigational strategies are regulated by the environment, depend on specific neural circuits, and are altered by pathology.

The effect of background strain on the behavioral phenotypes of the MDGA2+/− mouse model of autism spectrum disorder

“…However, this difference is most likely because of differences in visual ability in the background strains than to the MDGA2 genotype. Mice who have learned a spatial search strategy during acquisition spend more time in the correct quadrant during the probe trial than mice using a procedural search strategy 97,102,107 . In terms of sex differences, males had faster learning than females when measured by latency in the MWM, but not when measured by distance.…”

“…Search strategies used by the mice to locate the hidden platform were compared between groups on the last day of acquisition (A3) and reversal training (R3), and on the visible platform trials. Swim paths were visualized using the WaterMaze software and identified as spatial or nonspatial ("direct" or "indirect" in the visible platform test) based on the criteria described by Stover and Brown 97,102 …”

Inhibiting adult neurogenesis differentially affects spatial learning in females and males

“…The rats' trajectory was recorded with an Ethovision (Noldus) tracking system, and performance was assessed via latency to locate the escape platform and swim speed. Ideal path error (conceptually similar to cumulative search error / proximity metrics (51)), which can detect spatial performance differences between trials that have similar latencies and distances (52), was calculated with Pathfinder software as follows: the distance from the platform was summed over all samples to obtain a cumulative distance metric. To control for different release locations, the cumulative distance for the optimal path was also calculated based on a direct escape path from the release location and the average swim speed.…”

“…Rats were euthanized 60 minutes after the probe trial in order to capture experiencedependent Fos expression in activated neurons (see Immunohistochemistry, below). Search strategy analyses Navigational search strategies employed in the water maze were detected using Pathfinder software (52), with the following parameters: angular corridor width: 45°, chaining annulus width: 45cm, thigmotaxis zone width: 15cm, Direct Swim maximum ideal path error: 125, max heading error: 35°; Focal Search max distance to swim path centroid: 30, max distance to goal: 30, min distance covered: 100cm, max distance covered: 500cm; Directed Search min time in angular corridor: 70%, max distance covered: 400cm, max ideal path error: 1500; Indirect Search max ideal path error: 450, max average heading error: 70°; Semi-Focal Search max distance to swim path centroid: 60, max distance to goal: 60, min distance covered: 200cm, max distance covered: 5000cm; Chaining min time in annulus: 70%, min quadrants visited: 4, max area of maze traversed: 40%; Scanning max area of maze traversed: 20%, min area of maze traversed: 0%, max average distance to maze center: 60; Thigmotaxis time in full zone: 60%, time in smaller zone: 0%, min total distance covered: 400cm. Random search min area of maze traversed: 5%.…”

Novel measures of Morris water maze performance that use vector field maps to assess accuracy, uncertainty, and intention of navigational searches

“…Path strategy-based methods use semi-automated or machine learning algorithms to classify swim trajectories. Interestingly, such methods can detect multiple search strategies within a given MWM session (Cooke et al, 2019; Vouros et al, 2018). A parameter free machine learning based algorithm was also developed to classify MWM search strategies (Overall et al, 2020).…”