Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma

• Published in: PloS one 2025-01, Vol.20 (1), p.e0312415
• Main Authors: Naved, Bilal A, Han, Shuling, Koss, Kyle M, Kando, Mary J, Wang, Jiao-Jing, Weiss, Craig, Passman, Maya G, Wertheim, Jason A, Luo, Yuan, Zhang, Zheng J
• Format: Article
• Language: English
• Subjects: Animal models; Animals; Biological effects; Biology and Life Sciences; Bupivacaine; Context; Damage detection; Data analysis; Diabetic neuropathy; Dimensional analysis; Disease Models, Animal; Factor analysis; Feature selection; Gait; Gait - physiology; Hypotheses; Hypothesis testing; Injury analysis; Injury prevention; Laboratory animals; Male; Medical research; Medicine and Health Sciences; Medicine, Experimental; Mice; Multidimensional methods; Multivariate Analysis; Multivariate statistical analysis; Ostomy; Performance evaluation; Peripheral Nerve Injuries - physiopathology; Peripheral nerves; Phenotypes; Physical Sciences; Research and Analysis Methods; Sciatic Nerve - injuries; Spatiotemporal data; Statistical analysis; Statistical models; Surgery; Surgical outcomes; Sutures; Transplantation; Transplants & implants; Trauma
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0312415

Animal models of nerve injury are important for studying nerve injury and repair, particularly for interventions that cannot be studied in humans. However, the vast majority of gait analysis in animals has been limited to univariate analysis even though gait data is highly multi-dimensional. As a result, little is known about how various spatiotemporal components of the gait relate to each other in the context of peripheral nerve injury and trauma. We hypothesize that a multivariate characterization of gait will reveal relationships among spatiotemporal components of gait with biological relevance to peripheral nerve injury and trauma. We further hypothesize that legitimate relationships among said components will allow for more accurate classification among distinct gait phenotypes than if attempted with univariate analysis alone. DigiGait data was collected of mice across groups representing increasing degrees of damage to the neuromusculoskeletal sequence of gait; that is (a) healthy controls, (b) nerve damage only via total nerve transection + reconnection of the femoral and sciatic nerves, and (c) nerve, muscle, and bone damage via total hind-limb transplantation. Multivariate relationships among the 30+ spatiotemporal measures were evaluated using exploratory factor analysis and forward feature selection to identify the features and latent factors that best described gait phenotypes. The identified features were then used to train classifier models and compared to a model trained with features identified using only univariate analysis. 10-15 features relevant to describing gait in the context of increasing degrees of traumatic peripheral nerve injury were identified. Factor analysis uncovered relationships among the identified features and enabled the extrapolation of a set of latent factors that further described the distinct gait phenotypes. The latent factors tied to biological differences among the groups (e.g. alterations to the anatomical configuration of the limb due to transplantation or aberrant fine motor function due to peripheral nerve injury). Models trained using the identified features generated values that could be used to distinguish among pathophysiological states with high statistical significance (p < .001) and accuracy (>80%) as compared to univariate analysis alone. This is the first performance evaluation of a multivariate approach to gait analysis and the first demonstration of superior performance as compared to univariate ga