Kinematic Analysis of Fine Motor Skills with the Montoya Staircase and Supervised Machine Learning: A framework from Video Acquisition to Behavioral Analysis Using DeepLabCut for Markerless Pose Estimation

Fine motor assessment is essential for evaluating recovery and compensatory mechanisms following cortical lesions or neurodegenerative disorders affecting the motor cortex. Among the most widely used behavioral paradigms, the Montoya Staircase Test assesses forelimb dexterity in rodents, yet conventional scoring based on pellet retrieval success provides limited information regarding the specific movement components underlying task performance. In this paper, we introduce a markerless pose-estimation pipeline combining DeepLabCut with quantitative kinematic analysis to characterize reaching behavior during the Staircase Test. Our approach integrates trajectory analysis including Normalized Jerk Score (NJS) and synergy indices, providing complementary quantitative metrics that reflect movement fluidity, stability and intersegmental coordination. To evaluate the applicability of this framework, we analyzed forelimb reaching behavior of mice of either sex in a mouse model of traumatic brain injury (TBI) induced by controlled cortical impact of the primary motor cortex. TBI animals exhibited altered inter-segmental coordination patterns and increased trajectory variability, revealing changes in movement organization that complement conventional pellet retrieval measures. Importantly, these quantitative descriptors provided information on movement execution and coordination that cannot be directly inferred from endpoint behavioral scores alone. This pipeline has been successfully applied to recordings made with relatively modest imaging parameters (384 × 288 pixels, 25 frames per second), thus supporting the feasibility of quantitative kinematic analysis without specialized motion capture equipment. Together, these findings demonstrate that quantitative kinematic analysis complements conventional endpoint measures by providing additional insight into the mechanisms underlying motor impairment. This framework can be readily adapted to other motor tasks and neurological models, offering a practical tool for the functional characterization of motor deficits and recovery in preclinical studies. Significance Statement Accurate assessment of fine motor function in preclinical models remains limited, as conventional behavioral scores provide limited information regarding movement execution and coordination. This study presents a standardized framework that integrates markerless tracking based on DeepLabCut, movement fluidity analysis and kinematic synergy quantification, all within a single Montoya Staircase Test workflow. While these approaches have been used separately to assess motor performance in animal models, this is the first time they have been combined within a reproducible analysis pipeline specifically designed for assessing coordinated grasping movements following traumatic brain injury. By linking behavioral performance to quantitative descriptors of movement execution and inter-segmental coordination, the proposed framework provides a more comprehensive characterization of motor impairments than outcome criteria measure alone.