A High-Dimensional Quantification of Mouse Defensive Behaviors Reveals Enhanced Diversity and Stimulus Specificity

Abstract

Summary Instinctive defensive behaviors, consisting of stereotyped sequences of movements and postures, are an essential component of the mouse behavioral repertoire. Since defensive behaviors can be reliably triggered by threatening sensory stimuli, the selection of the most appropriate action depends on the stimulus property. However, since the mouse has a wide repertoire of motor actions, it is not clear which set of movements and postures represent the relevant action. So far, this has been empirically identified as a change in locomotion state. However, the extent to which locomotion alone captures the diversity of defensive behaviors and their sensory specificity is unknown. To tackle this problem, we developed a method to obtain a faithful 3D reconstruction of the mouse body that enabled to quantify a wide variety of motor actions. This higher dimensional description revealed that defensive behaviors are more stimulus specific than indicated by locomotion data. Thus, responses to distinct stimuli that were equivalent in terms of locomotion (e.g., freezing induced by looming and sound) could be discriminated along other dimensions. The enhanced stimulus specificity was explained by a surprising diversity. A clustering analysis revealed that distinct combinations of movements and postures, giving rise to at least 7 different behaviors, were required to account for stimulus specificity. Moreover, each stimulus evoked more than one behavior, revealing a robust one-to-many mapping between sensations and behaviors that was not apparent from locomotion data. Our results indicate that diversity and sensory specificity of mouse defensive behaviors unfold in a higher dimensional space, spanning multiple motor actions.

Measuring Behavior in the Home Cage: Study Design, Applications, Challenges, and Perspectives

“…The work by Berman et al (2014), the Datta lab (Wiltschko et al, 2015;Johnson et al, 2016) and Graving and Couzin (2020) are early examples that take a data-driven approach to unravel behavioral structure or ''grammar'' in data streams and relate so-called behavior ''syllables'' or words to behaviors relevant to animal behavior researchers. (Hsu and Yttri, 2019) and social behaviors (Nilsson et al, 2020), there are many potential applications of these tools, including defensive behavior measurement (Storchi et al, 2020), reaching movements (Parmiani et al, 2019), turning behavior (Mundorf et al, 2020). Multi-animal expansions of these tools will allow for more sophisticated behavior analysis between more than one individual, allowing for monitoring group-wide dynamics in various social patterns such as fighting, mating or parenting.…”

Pupillary reflex and behavioral masking responses to light as functional measures of retinal degeneration in mice

“…An alternative measure of retinal function should ideally be sensitive to a very limited degree of function, provide objective and quantitative measurement, and meaningfully represent vision. In mice, non-image-forming responses to light include: (1) circadian rhythm entrainment that aligns internal clock time with external solar time; (2) modification of sleep propensity; (3) suppression of pineal melatonin synthesis; (4) the PLR that adjusts pupil size to changing light conditions; (5) 'negative masking' which is a suppression of locomotor activity in bright light; and (6) transient increases in activity at lights-on [9,[14][15][16][17][18].…”

Look-up and look-down neurons in the mouse visual thalamus during freely moving exploration

“…An initial 3D reconstruction of the mouse body was obtained by triangulating body landmarks from the four cameras (see body landmarks in Fig.1A). The four-camera system was calibrated as previously described [24]. Tracking of body landmarks from individual cameras was performed with DeepLabCut [60].…”

“…Success of this endeavour is dependent upon a method to accurately quantify the wide repertoire of postures and movements available to freely moving mice. Computational methods to track body parts in 3D and use these to reconstruct pose at frame-by-frame resolution are increasingly available [21][22][23][24]. We have previously developed such an approach suitable for mice [24] and here extend it to measure a wide variety of 3D movements and postures.…”

“…Computational methods to track body parts in 3D and use these to reconstruct pose at frame-by-frame resolution are increasingly available [21][22][23][24]. We have previously developed such an approach suitable for mice [24] and here extend it to measure a wide variety of 3D movements and postures. We find that the higher dimensional description of behaviour enabled by this approach is key to understanding motor influences on the early visual system.…”