A robust DeepLabCut-based pipeline for the multi-dimensional quantification of vestibular dysfunction in mice

Area of Science:

• Neuroscience
• Behavioral Science
• Biomedical Engineering

Background:

• Assessing vestibular disorders requires precise behavioral quantification.
• Current methods often miss complex kinematic details like rotational asymmetry.

Purpose of the Study:

• To develop a cost-effective, high-precision automated analysis pipeline for quantifying behavioral phenotypes in a mouse model of Bilateral Vestibular Dysfunction (BVD).
• To utilize DeepLabCut for fine-grained behavioral analysis, including rotational metrics.

Main Methods:

• Established a BVD mouse model using bilateral intratympanic gentamicin injection.
• Monitored locomotor function via Open Field Test over 7 days post-operation.
• Employed DeepLabCut for tracking anatomical landmarks and a custom Python framework for analysis.

Main Results:

• Identified a biphasic behavioral progression: acute hypokinesia/thigmotaxis (Days 1-2) and chronic hyperactivity/circling (Days 5-7).
• The pipeline detected early rotational tendencies missed by traditional metrics.
• Demonstrated significant spatial disorientation and pathological circling in the BVD model.

Conclusions:

• The DeepLabCut-based pipeline offers a sensitive, objective, and accessible tool for multi-dimensional assessment of vestibular dysfunction.
• This methodology provides a robust paradigm for evaluating functional recovery and therapeutic interventions in BVD.
• Enables detailed analysis of fine-grained behavioral deficits in neurological models.