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
Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States of America.
Comprehensive Transplant Center, Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America.
PLoS One. 2025 Jan 7;20(1):e0312415. doi: 10.1371/journal.pone.0312415. eCollection 2025.
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 gait analysis in animals. It is also the first study to use multivariate statistics to characterize and distinguish among different gradations of gait deficit in animals. This study contributes a comprehensive, multivariate characterization pipeline for application in the study of any pathologies in which gait is a quantitative translational outcome metric.
神经损伤动物模型对于研究神经损伤与修复至关重要，特别是对于那些无法在人体中进行研究的干预措施。然而，尽管步态数据具有高度的多维性，但动物步态分析绝大多数仅限于单变量分析。因此，对于周围神经损伤和创伤情况下步态的各种时空成分之间如何相互关联，我们知之甚少。我们假设，步态的多变量特征将揭示与周围神经损伤和创伤具有生物学相关性的步态时空成分之间的关系。我们进一步假设，与仅使用单变量分析相比，这些成分之间的合理关系将允许在不同步态表型之间进行更准确的分类。
收集代表步态神经肌肉骨骼序列损伤程度不断增加的各组小鼠的DigiGait数据；即(a)健康对照组，(b)仅通过股神经和坐骨神经完全切断+重新连接造成的神经损伤，以及(c)通过全后肢移植造成的神经、肌肉和骨骼损伤。使用探索性因子分析和前向特征选择评估30多个时空测量值之间的多变量关系，以识别最能描述步态表型的特征和潜在因子。然后使用识别出的特征训练分类器模型，并与仅使用单变量分析识别出的特征训练的模型进行比较。
确定了10 - 15个与描述创伤性周围神经损伤程度增加情况下的步态相关的特征。因子分析揭示了所识别特征之间的关系，并能够推断出一组进一步描述不同步态表型的潜在因子。这些潜在因子与各组之间的生物学差异相关（例如，由于移植导致肢体解剖结构改变或由于周围神经损伤导致精细运动功能异常）。与仅使用单变量分析相比，使用所识别特征训练的模型生成的值可用于以高统计学显著性（p < .001）和准确性（>80%）区分病理生理状态。
这是对步态分析多变量方法的首次性能评估，也是与动物单变量步态分析相比首次证明其卓越性能。这也是第一项使用多变量统计来表征和区分动物不同程度步态缺陷的研究。本研究贡献了一个全面的多变量特征分析流程，可应用于任何以步态作为定量转化结果指标的病理学研究。
