Automated assessment of pavlovian conditioned freezing and shock reactivity in mice using the video freeze system

Abstract

The Pavlovian conditioned freezing paradigm has become a prominent mouse and rat model of learning and memory, as well as of pathological fear. Due to its efficiency, reproducibility and well-defined neurobiology, the paradigm has become widely adopted in large-scale genetic and pharmacological screens. However, one major shortcoming of the use of freezing behavior has been that it has required the use of tedious hand scoring, or a variety of proprietary automated methods that are often poorly validated or difficult to obtain and implement. Here we report an extensive validation of the Video Freeze system in mice, a "turn-key" all-inclusive system for fear conditioning in small animals. Using digital video and near-infrared lighting, the system achieved outstanding performance in scoring both freezing and movement. Given the large-scale adoption of the conditioned freezing paradigm, we encourage similar validation of other automated systems for scoring freezing, or other behaviors.

Keywords: amygdala; anxiety; classical conditioning; fear; hippocampus; memory; phenotyping.

Figure 1

(A) Training and context test environment. Video still image (320 × 240, 8-bit grayscale) showing the environment with white and near-infrared light. (B) Tone Test Context. Video still showing the same chamber with environmental modifications, including a flat white acrylic sheet over the shock grids, a black triangular tee-pee (translucent only to NIR light, as shown), and NIR light only. The actual environment appeared to the unassisted eye as total darkness. The odor was also changed between the two environments.

Figure 2

Linear fit and correlation for various video parameters. (A) Correlation. The linear correlation between VideoFreeze-scored and human-scored freezing is compared with number of frames (minimum freeze duration) for various motion index thresholds. A larger number of frames yielded higher correlations. (B) Intercept. The linear fit between VideoFreeze-scored and human-scored freezing is compared for the y-intercept. The y-intercept is important because it reflects how much the system overestimates or underestimates freezing. Larger number of video frames and lower motion thresholds yielded lower y-intercepts. A threshold of 18 yielded the lowest nonnegative intercept. (C) Slope. The slope term from the linear fit is depicted compared with frames and motion threshold. Larger frame numbers yielded a slope closer to 1. A motion threshold of 18 and number of frames of 30 was chosen for having the best combination of high correlation, intercept close to 0, and slope close to 1. Au, arbitrary units.

Figure 3

Linear fit and correlation for (18,30). (A) White Light. The linear fit between computer (VideoFreeze, VF) and human (Handscore, HS) scored freezing is depicted for the white light condition. (B) NIR light. The linear fit is depicted for the NIR light condition. In both cases, a nearly perfect fit and correlation were observed. (C) Bland–Altman Plot. The difference between computer (VF) and human (HS) scores is plotted against the mean of those two scores. There was good agreement between human and computer scores across the range of freezing scores. Mean bias was 0.89%.

Figure 4

Comparison of mean freezing for (18,30). (A)Training. Freezing observed by humans and VideoFreeze is depicted for the baseline (first 2 min) during the training day and post-shock freezing (Context, last 3 min). (B) Tone Test. Freezing during the tone baseline (Tone BL, first 2 min) and during the tone (last 3 min) is depicted. In all cases, VideoFreeze estimated the means error nearly perfectly.

Figure 5

Motion Index. (A) Locomotor activity. The Motion Index can be used to estimate locomotor activity. Activity during the baseline from the training day (Context) and tone test (Tone) is depicted. Activity starts high (baseline, first 2 min) dramatically drops after conditioning (Test, last 3 min). Activity is higher in the NIR-only light during the tone test (Tone Baseline, first two min), and drops when the tone is played (Tone Test, last 3 min). (B) Activity suppression. Activity suppression scores can be used to correct for differences in baseline activity and can be used as an alternative measure of fear. (C) Shock Reactivity. The motion index during the 2-s shock is compared to true mouse speed. Shock reactivity could reliably be measured using the motion index and showed a good linear fit with true speed.