4.6 Article

An integrated workflow for 2D and 3D posture analysis during vestibular system testing in mice

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FRONTIERS IN NEUROLOGY
卷 14, 期 -, 页码 -

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FRONTIERS MEDIA SA
DOI: 10.3389/fneur.2023.1281790

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balance; inner ear; coordination; multisensory; spatial orientation; AI; ML

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This article presents a workflow for analyzing the postures of mice walking on a balance beam. By using new tools and scripts, posture can be tracked and subtle yet significant differences can be detected.
IntroductionPosture extraction from videos is fundamental to many real-world applications, including health screenings. In this study, we extend the utility and specificity of a well-established protocol, the balance beam, for examining balance and active motor coordination in adult mice of both sexes.ObjectivesThe primary objective of this study is to design a workflow for analyzing the postures of mice walking on a balance beam.MethodsWe developed new tools and scripts based on the FluoRender architecture, which can interact with DeepLabCut (DLC) through Python code. Notably, twenty input videos were divided into four feature point groups (head, body, tail, and feet), based on camera positions relative to the balance beam (left and right), and viewing angles (90 degrees and 45 degrees from the beam). We determined key feature points on the mouse to track posture in a still video frame. We extracted a standard walk cycle (SWC) by focusing on foot movements, which were computed by a weighted average of the extracted walk cycles. The correlation of each walk cycle to the SWC was used as the weight.ResultsWe learned that positions of the camera angles significantly improved the performance of 2D pose estimation (90 degrees) and 3D (45 degrees). Comparing the SWCs from age-matched mice, we found a consistent pattern of supporting feet on the beam. Two feet were consistently on the beam followed by three feet and another three feet in a 2-3-3 pattern. However, this pattern can be mirrored among individual subjects. A subtle phase shift of foot movement was also observed from the SWCs. Furthermore, we compared the SWCs with speed values to reveal anomalies in mouse walk postures. Some anomalies can be explained as the start or finish of the traversal, while others may be correlated to the distractions of the test environment, which will need further investigation.ConclusionOur posture analysis workflow improves the classical behavioral testing and analysis, allowing the detection of subtle, but significant differences in vestibular function and motor coordination.

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