Development and Validation of a Machine Learning-Derived Kinematic Deviation Index for the Evaluation of Forelimb in Rodents

Abstract

Background:

Rodent models are widely used to study neurological conditions and assess forelimb movement to measure functional performance, deficit, recovery, and treatment effectiveness. Traditional assessment methods based on endpoints such as whether the task is successfully accomplished, while easy to implement, provide limited information on movement patterns critical for assessing different functional strategies. On the other side, detailed kinematic analysis provides granular information on the movement patterns but is difficult to compare across laboratories and may not translate to clinical metrics of upper limb function.

Methods:

To address these limitations, we developed and validated a machine learning derived kinematic deviation index (KDI) for rodents that mimics current trends in clinical neurological research. The KDI is a unitless summary score that quantifies the difference between an animal movement during a task and its optimal performance derived from spatiotemporal marker sequences without pre-specifying movements.

Results:

We demonstrate the utility of KDI in assessing reaching and grasping in mice and validate its discrimination between trial endpoints in healthy animals. Furthermore, we show KDI sensitivity to interventions disrupting neurological function, including acute and chronic spinal cord injury and optogenetic disruption of sensorimotor circuits.

Conclusion:

The KDI provides a comprehensive measure of motor function that bridges the gap between detailed kinematic analysis and simple success/failure metrics, offering a valuable tool for assessing recovery and compensation in rodent models of neurological disorders.