Quantitative descriptions of animal behavior are essential to study the neural substrates of cognitive and emotional processes. Analyses of naturalistic behaviors are often performed by hand or with expensive, inflexible commercial software. Recently, machine learning methods for markerless pose estimation enabled automated tracking of freely moving animals, including in labs with limited coding expertise. However, classifying specific behaviors based on pose data requires additional computational analyses and remains a significant challenge for many groups. We developed BehaviorDEPOT (DEcoding behavior based on POsitional Tracking), a simple, flexible software program that can detect behavior from video timeseries and can analyze the results of experimental assays. BehaviorDEPOT calculates kinematic and postural statistics from keypoint tracking data and creates heuristics that reliably detect behaviors. It requires no programming experience and is applicable to a wide range of behaviors and experimental designs. We provide several hard-coded heuristics. Our freezing detection heuristic achieves above 90% accuracy in videos of mice and rats, including those wearing tethered head-mounts. BehaviorDEPOT also helps researchers develop their own heuristics and incorporate them into the software’s graphical interface. Behavioral data is stored framewise for easy alignment with neural data. We demonstrate the immediate utility and flexibility of BehaviorDEPOT using popular assays including fear conditioning, decision-making in a T-maze, open field, elevated plus maze, and novel object exploration.

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Adaptive decision making requires the evaluation of cost-benefit tradeoffs to guide action selection. Effort-based decision making involves weighing predicted gains against effort costs and is disrupted in several neuropsychiatric disorders. The ACC is postulated to control effort-base choice via its role in encoding the value of overcoming effort costs in rodent effort-based decision making assays. However, temporally precise methods of manipulating neural activity have rarely been applied to effort-based decision making. We developed and validated a mouse version of the barrier T-maze, and used optogenetics to inhibit ACC excitatory neurons at specific times during this task. Bilateral inhibition of ACC rapidly and reversibly impaired preference to exert greater effort for a larger reward when a less rewarded, low effort alternative was available. Equalizing effort for potential choice options led mice to choose the high reward arm of the maze regardless of whether the ACC was inhibited or not. The mechanics of choice behavior were altered during trials where the ACC was inhibited, but there were no effects on overall mobility or tendency to exert effort in an unrelated assay. These findings establish causality between ACC neural activity during a choice trial and effortful action selection during cost-benefit decision making.SIGNIFICANCE STATEMENTDisturbances in evaluating effort-based costs during decision making occur in depression, schizophrenia, addiction and Parkinson’s disease. Precisely resolving the function of prefrontal brain regions in mediating these processes will reveal key loci of dysfunction and potential therapeutic intervention in these disorders.