Machine-learning-based video analysis of grasping behavior during recovery from cervical spinal cord injury

Duque, Daniela Hernandez; Racca, Jordan M.; Esteve, Isaac V. Manzanera; Yang, Pai‐Feng; Gore, John C.; Chen, Li Min

doi:10.1016/j.bbr.2022.114150

Cited by 3 publications

(1 citation statement)

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“…Alternative assessments, such as the string test, coat hanger test, and horizontal bar test only evaluate forelimb strength and coordination, not locomotion. Additionally, machine-learning algorithms for automated unbiased analyses of these behaviors have been implemented only on a limited scale, with observations specifically focusing on hindlimb behaviors (Eisdorfer et al, 2022; Sato et al, 2022) or grasping behaviors (Duque et al, 2023; O’Neill et al, 2022). Unlike these previous applications, our study comprehensively investigated various unexplored parameters such as forelimb and tail poses, compensatory mechanisms, alterations in head position, lift and stance profiles, anxiety, and fatigue-like behaviors.…”

Section: Discussionmentioning

confidence: 99%

A deep learning-based approach for unbiased kinematic analysis in CNS injury

Ascona,

Tieu,

Gonzalez-Vega

et al. 2024

Preprint

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Traumatic spinal cord injury (SCI) is a devastating condition that impacts over 300,000 individuals in the US alone. Depending on the severity of the injury, SCI can lead to varying degrees of sensorimotor deficits and paralysis. Despite advances in our understanding of the underlying pathological mechanisms of SCI and the identification of promising molecular targets for repair and functional restoration, few therapies have made it into clinical use. To improve the success rate of clinical translation, more robust, sensitive, and reproducible means of functional assessment are required. The gold standards for the evaluation of locomotion in rodents with SCI are the Basso Beattie Bresnahan (BBB) and Basso Mouse Scale (BMS) tests. To overcome the shortcomings of current methods, we developed two separate marker-less kinematic analysis paradigms in mice, MotorBox and MotoRater, based on deep-learning algorithms generated with the DeepLabCut open-source toolbox. The MotorBox system uses an originally designed, custom-made chamber, and the MotoRater system was implemented on a commercially available MotoRater device. We validated the MotorBox and MotoRater systems by comparing them with the traditional BMS test and extracted metrics of movement and gait that can provide an accurate and sensitive representation of mouse locomotor function post-injury, while eliminating investigator bias and variability. The integration of MotorBox and/or MotoRater assessments with BMS scoring will provide a much wider range of information on specific aspects of locomotion, ensuring the accuracy, rigor, and reproducibility of behavioral outcomes after SCI.

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