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      Functional Gait Assessment Using Manual, Semi-Automated and Deep Learning Approaches Following Standardized Models of Peripheral Nerve Injury in Mice.

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          Abstract

          Objective: To develop a standardized model of stretch−crush sciatic nerve injury in mice, and to compare outcomes of crush and novel stretch−crush injuries using standard manual gait and sensory assays, and compare them to both semi-automated as well as deep-learning gait analysis methods. Methods: Initial studies in C57/Bl6 mice were used to develop crush and stretch−crush injury models followed by histologic analysis. In total, 12 eight-week-old 129S6/SvEvTac mice were used in a six-week behavioural study. Behavioral assessments using the von Frey monofilament test and gait analysis recorded on a DigiGait platform and analyzed through both Visual Gait Lab (VGL) deep learning and standardized sciatic functional index (SFI) measurements were evaluated weekly. At the termination of the study, neurophysiological nerve conduction velocities were recorded, calf muscle weight ratios measured and histological analyses performed. Results: Histological evidence confirmed more severe histomorphological injury in the stretch−crush injured group compared to the crush-only injured group at one week post-injury. Von Frey monofilament paw withdrawal was significant for both groups at week one compared to baseline (p < 0.05), but not between groups with return to baseline at week five. SFI showed hindered gait at week one and two for both groups, compared to baseline (p < 0.0001), with return to baseline at week five. Hind stance width (HSW) showed similar trends as von Frey monofilament test as well as SFI measurements, yet hind paw angle (HPA) peaked at week two. Nerve conduction velocity (NCV), measured six weeks post-injury, at the termination of the study, did not show any significant difference between the two groups; yet, calf muscle weight measurements were significantly different between the two, with the stretch−crush group demonstrating a lower (poorer) weight ratio relative to uninjured contralateral legs (p < 0.05). Conclusion: Stretch−crush injury achieved a more reproducible and constant injury compared to crush-only injuries, with at least a Sunderland grade 3 injury (perineurial interruption) in histological samples one week post-injury in the former. However, serial behavioral outcomes were comparable between the two crush groups, with similar kinetics of recovery by von Frey testing, SFI and certain VGL parameters, the latter reported for the first time in rodent peripheral nerve injury. Semi-automated and deep learning-based approaches for gait analysis are promising, but require further validation for evaluation in murine hind-limb nerve injuries.

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          Author and article information

          Journal
          Biomolecules
          Biomolecules
          MDPI AG
          2218-273X
          2218-273X
          Sep 23 2022
          : 12
          : 10
          Affiliations
          [1 ] Department of Clinical Neurosciences, Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
          [2 ] Department of Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
          [3 ] Department of Clinical Neurosciences and Cell Biology and Anatomy, Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
          Article
          biom12101355
          10.3390/biom12101355
          9599622
          36291564
          f14e6eee-340c-459a-aec2-7e821733ff08
          History

          SFI,VGL,Visual Gait Lab,convolutional neural network,crush injury,deep learning,deep neural networks,gait analysis,nerve conduction velocity,neuroma in continuity,peripheral nerve injury,sciatic functional index,sciatic nerve,semi-automated,stretch–crush injury,von Frey monofilament test,NCV,NIC,PNI,CNN,DNNs

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