<i>StrIPETrack</i> : a real-time, ROI-flexible tracking platform for high-throughput zebrafish behavior

Quantitative phenotyping is essential to studies of animal behavior, enabling systematic analysis of variation arising from natural diversity or experimental manipulation. High-throughput behavioral assays that can simultaneously test multiple animals support sufficiently powered studies of behavioral variation, but accurate tracking of each animal is critical. Furthermore, behavioral tasks and experimental arenas span a wide range of complexity, from the reaction of a single larval zebrafish to an acoustic stimulus to associative conditioning in cue-rich environments. Here, we developed and validated <i>StrIPETrack</i> (Structural similarity-based Image Processing for Estimation and Tracking), a Python-based, modular animal tracking software designed for flexible region-of-interest (ROI) definitions and extensibility across assays. We show that <i>StrIPETrack</i> measures activity comparably to our previous LabVIEW-based zebrafish tracking software and detects similar behavioral differences between wild-type clutches. In addition, <i>StrIPETrack</i> accurately captures behavior in a complex arena: the Y-maze. Our approach for analyzing Y-maze navigation yields an expanded set of metrics beyond turn count and direction, revealing more subtle behavioral variation. Overall, this versatile software can be applied to monitor the activity of multiple animals in parallel in both simple high-throughput and more complex assays, and can be readily adapted to new paradigms.