Rapid, open-source, and automated quantification of the head twitch response in C57BL/6J mice using DeepLabCut and Simple Behavioral Analysis

A new automated method using open-source machine learning toolkits, DeepLabCut and SimBA, accurately quantifies the head twitch response (HTR) in mice from experimental videos. The approach, trained and validated on videos of C57BL/6J mice given various psychedelic drugs, performed best at 50% video resolution and 120 frames per second (precision 95.45%, recall 95.56%, F1 score 95.51%) and also worked well at lower frame rates. When applied to bufotenine, a tryptamine derivative, elevated HTRs occurred only after blocking serotonin 1A receptors (ED50 = 0.99 mg/kg, max counts = 24). HTR counts from the automated method strongly correlated with visual scoring and semi-automated software (r = 0.98–0.99). The method offers a modular, noninvasive, open-source alternative to existing techniques.

Study at a glance

Abstract

Serotonergic psychedelics induce the head twitch response (HTR) in mice, an index of serotonin (5-HT) 2A receptor (5-HT2A) 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 non-overlapping 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 HTR paradigm. Interestingly, bufotenine only induced elevated HTRs (ED50 = 0.99 mg/kg, max counts = 24) when serotonin 1A receptors (5-HT1A) 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.