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

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.

Electrospun Composite PLLA‐PPSB Nanofiber Nerve Conduits for Peripheral Nerve Defects Repair and Regeneration

“…Functional Recovery Evaluation: The functional recovery of the regenerated nerves was evaluated by gait analysis at eight weeks after implantation using a DigiGait apparatus (Mouse Specific, USA). [83] Briefly, the paws of the rats were captured by video during treadmill locomotion at a speed of 10 cm −1 s for 60 s of proper gait. Paw length (PL), the toe-spread distance between toes 1 and 5 (TS), and toe spread distance between toes 2 and 4 (IT) were recorded from the normal (N) and experimental (E) hind limbs.…”

Evaluation of the regeneration process of the sciatic nerve of a mouse animal model after application of freezing, crushing or electrocoagulation damage

“…Podobne wyniki uzyskano analizując parametry po uszkodzeniu nerwu klipsem naczyniowym. Model zmiażdżenia nerwu kulszowego jest w literaturze najczęściej pojawiającym się modelem uszkodzenia [13][14][15][16]. Z obserwacji uprzednio wykonywanych podobnych zabiegów chirurgicznych wnioskuje się, że tego typu uszkodzenie nie powoduje znacznej dotkliwości zwierzęcia, przy tym nie ograniczając jego lokomocji [17].…”

Transforming medicine: artificial intelligence integration in the peripheral nervous system

“…This method is promising in predicting chemotherapy-induced peripheral neuropathy and understanding the mechanisms of action of different drugs. In the study by Umansky et al ( 19 ), deep learning models were employed as a part of their innovative approach to analyze gait in mice following sciatic nerve injuries. Specifically, they utilized the Visual Gait Lab (VGL) deep learning system for gait analysis.…”