Characterizing spontaneous movements during early development of mice

Authors

• LF Cook
• JA Krynitsky
• L Zhang
• AR Inacio
• TJ Pohida
• SH Lee

Abstract

During a critical period of development in mouse pups, cortical connectivity and excitability undergo major reorganization. Little is known about the factors and developmental mechanisms that drive the specificity of corticocortical projections, especially cortical top-down (feedback) projections to the primary sensory cortex and the related functional changes. The overarching goal of this project is to investigate how the distinct subset of neurons in the neocortex that encode a behavioral state emerges during this cortical development.

To determine the developmental emergence of behavior state-dependent neurons in the neocortex, experiments involve longitudinal chronic calcium imaging in the primary somatosensory cortex. We track the same population of pyramidal neurons over time in mouse pups aged from postnatal day 8 to 30, while infrared cameras record the pups' behavior. Correlating populations of neurons to specific spontaneous kinetic behaviors first requires comprehensive tracking of movement during calcium imaging.

Here, we investigate techniques to characterize movement for identifying behavior state-dependent neurons. We compared the performance of body part pose estimation between two popular open-source deep-learning packages, DeepLabCut and SLEAP. We found that these packages mostly detect large movements but cannot reliably capture smaller facial features that are also indicative of behavioral states. Therefore, we described the movement of facial features by computing the motion energy within five regions (eyes, nose, mouth, whisker pad, and overall face) across video frames. Our results demonstrate that facial motion energy provides additional information that cannot be extracted from body part tracking alone, thereby providing more comprehensive data for behavior description.

Scientific Focus Area: Biomedical Engineering and Biophysics

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