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

<h4>Objective</h4>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 hig...

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Main Authors: Bilal A Naved, Shuling Han, Kyle M Koss, Mary J Kando, Jiao-Jing Wang, Craig Weiss, Maya G Passman, Jason A Wertheim, Yuan Luo, Zheng J Zhang
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0312415
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author Bilal A Naved
Shuling Han
Kyle M Koss
Mary J Kando
Jiao-Jing Wang
Craig Weiss
Maya G Passman
Jason A Wertheim
Yuan Luo
Zheng J Zhang
author_facet Bilal A Naved
Shuling Han
Kyle M Koss
Mary J Kando
Jiao-Jing Wang
Craig Weiss
Maya G Passman
Jason A Wertheim
Yuan Luo
Zheng J Zhang
author_sort Bilal A Naved
collection DOAJ
description <h4>Objective</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.
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spelling doaj-art-647f284e5c8f44cda14a4b1c8f1f9f6c2025-01-17T05:31:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031241510.1371/journal.pone.0312415Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma.Bilal A NavedShuling HanKyle M KossMary J KandoJiao-Jing WangCraig WeissMaya G PassmanJason A WertheimYuan LuoZheng J Zhang<h4>Objective</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.https://doi.org/10.1371/journal.pone.0312415
spellingShingle Bilal A Naved
Shuling Han
Kyle M Koss
Mary J Kando
Jiao-Jing Wang
Craig Weiss
Maya G Passman
Jason A Wertheim
Yuan Luo
Zheng J Zhang
Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma.
PLoS ONE
title Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma.
title_full Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma.
title_fullStr Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma.
title_full_unstemmed Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma.
title_short Multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma.
title_sort multivariate description of gait changes in a mouse model of peripheral nerve injury and trauma
url https://doi.org/10.1371/journal.pone.0312415
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