Rapid, Open-Source, and Automated Quantification of the Head Twitch Response in C57BL/6J Mice Using DeepLabCut and Simple Behavioral Analysis

Abstract

Serotonergic psychedelics induce the head twitch response (HTR) in mice, an index of serotonin (5-HT) 2A receptor (5-HT_2A) agonism and a behavioral proxy for psychedelic effects in humans. Existing methods for detecting HTRs include time-consuming visual scoring, magnetometer-based approaches, and analysis of videos using semi-automated commercial software. Here, we present a new automated approach for quantifying HTRs from experimental videos using the open-source machine learning-based toolkits, DeepLabCut (DLC) and Simple Behavioral Analysis (SimBA). Pose estimation DLC models were trained to predict X,Y coordinates of 13 body parts of C57BL/6J mice using historical experimental videos of HTRs induced by various psychedelic drugs. Next, a nonoverlapping set of historical experimental videos was analyzed and used to train SimBA random forest behavioral classifiers to predict the presence of the HTR. The DLC + SimBA approach was then validated using a separate subset of visually scored videos. DLC + SimBA model performance was assessed at different video resolutions (50%, 25%, 12.5%) and frame rates (120, 60, 30 frames per second or fps). Our results indicate that HTRs can be quantified accurately at 50% resolution and 120 fps (precision = 95.45, recall = 95.56, F₁ = 95.51) or at lower frame rates and resolutions (i.e., 50% resolution and 60 fps). The best performing DLC + SimBA model combination was deployed to evaluate the effects of bufotenine, a tryptamine derivative with uncharacterized potency and efficacy in the modern HTR paradigm. Interestingly, bufotenine only induced elevated HTRs (ED₅₀ = 0.99 mg/kg, max counts = 24) when serotonin 1A receptors (5-HT_1A) were pharmacologically blocked and activity at other sites of action may also impact its pharmacological effects (e.g., serotonin transporter). HTR counts for a subset of 21 videos from bufotenine experiments were strongly correlated for DLC + SimBA vs visual scoring and semi-automated software detection methods (r = 0.98 and 0.99). Finally, the DLC + SimBA approach displayed high accuracy when compared to visual scoring of HTRs for three serotonergic psychedelic drugs with variable HTR frequencies (r = 0.99 vs mean visual scores from 3 blinded raters). In summary, the DLC + SimBA approach represents a modular, noninvasive, and open-source method of HTR detection from experimental videos with accuracy comparable to magnetometer-based approaches and greater speed than visual scoring.