RECORD: A high-throughput system for complex naturalistic decision-making in rodents

Abstract Translational studies benefit from experimental designs where laboratory organisms use human-relevant behaviors. One such behavior is decision-making, however studying complex decision-making in rodents poses many problems. We overcame these limitations by designing a fully automated, inexpensive, high-throughput framework to study decision-making across multiple levels of rewards and costs in rodents: the REward-COst in Rodent Decision-making (RECORD) system. RECORD integrates three components: 1) 3D-printed configurable arenas, 2) custom electronic hardware, and 3) software for databasing, analysis, and computational modeling. RECORD sequentially offers subjects single cost/reward trade-offs that they can approach or avoid. We validated four different behavioral protocols, highlighting the versatility of our system. RECORD enables the execution of a decision-making task battery without requiring food or water deprivation, thus allowing us to characterize a profound and systematic impact of food deprivation on decision-making. We also demonstrate how RECORD enables analysis of heterogeneity in decision-making both within and across individuals and show, for the first time, that this behavioral variability is quantifiably constrained. Using oxycodone self-administration as a test case, we also reveal how analytic approaches that incorporate behavioral heterogeneity are more sensitive to detecting perturbations in decision-making than conventional approaches that average behavior across all individuals. Overall, RECORD is a more powerful and sensitive approach to studying decision-making in animals, with features that facilitate translational studies of DM in psychiatric disorders.