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JoVE Journal
Neuroscience
Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal ...
Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal ...
JoVE Journal
Neuroscience
This content is Free Access.
JoVE Journal Neuroscience
Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury

Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury

Full Text
6,740 Views
06:31 min
October 6, 2020

DOI: 10.3791/61852-v

Johannes Heinzel1,2,3, Nicole Swiadek2,3, Mohamed Ashmwe2,3, Alexander Rührnößl2,3, Viola Oberhauser2,3, Jonas Kolbenschlag1, David Hercher2,3

1Department of Hand-, Plastic, Reconstructive and Burn Surgery, BG Trauma Center Tubingen,Eberhard Karls University, 2Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, 3Austrian Cluster for Tissue Regeneration

Overview

Automated gait analysis serves as an effective method for assessing functional recovery in rodent models of peripheral nerve and spinal cord injuries. This method allows for the examination of gait changes related to motor and sensory nerve function following injury. However, ensuring quality data acquisition necessitates thorough animal training and precise adjustments to hardware and software.

Key Study Components

Area of Science

  • Neuroscience
  • Locomotor function assessment
  • Peripheral and central nerve injury analysis

Background

  • Automated gait analysis measures locomotor function in rodents.
  • Details on gait changes post-injury highlight the importance of sensory and motor function assessment.
  • Challenges in data collection include animal training and equipment calibration.
  • Research on functional recovery in various injury models is significant for understanding nerve regeneration.

Purpose of Study

  • To evaluate the feasibility of automated gait analysis in studying functional recovery.
  • To investigate locomotor changes in response to peripheral nerve injury and spinal cord contusion.
  • To provide guidance for optimal data acquisition techniques.

Methods Used

  • Automated gait analysis platform utilized for real-time assessment.
  • Rodent models of peripheral and spinal cord injuries were employed.
  • Careful calibration of hardware and training protocols were outlined to ensure accurate measurements.
  • Steps included acclimatizing animals, setting up the recording environment, and specific training sessions over several days.
  • Data acquisition methods and classification processes were described for successful analysis.

Main Results

  • Gait analysis revealed significant alterations in locomotor patterns post-injury.
  • Changes included reduced print area and increased swing time, indicative of impaired movement.
  • Post-operative recovery processes were linked to the regeneration of nerves affecting gait mechanics.
  • Automated methods successfully classified and analyzed locomotor function changes, emphasizing the need for proper experimental conditions.

Conclusions

  • Automated gait analysis facilitates in-depth evaluation of nerve injury and recovery in rodent models.
  • The study highlights the necessity of meticulous training and equipment calibration for accurate data acquisition.
  • This methodology has broad implications for understanding nerve regeneration mechanisms and assessing treatments for nerve injuries.

Frequently Asked Questions

What are the advantages of using automated gait analysis?
Automated gait analysis provides objective and quantitative data on locomotor function, allowing for precise assessment of recovery after nerve injuries.
How is the rodent model prepared for gait analysis?
Rodents must undergo a series of training sessions to ensure they can navigate the walkway confidently and consistently before data acquisition begins.
What types of data are obtained from gait analysis?
Data obtained include measurements of print area, swing time, and coordination metrics, which reflect changes in motor and sensory function post-injury.
What are key considerations for successful data acquisition?
Ensuring proper environmental conditions, stable camera setup, and thorough calibration of equipment are crucial for high-quality data collection.
How can this method be adapted for different types of injuries?
The automated gait analysis approach can be tailored to various injury models by adjusting training protocols and calibration to accommodate specific nerve injuries.
What are some limitations of automated gait analysis?
Challenges may include the need for extensive training of animals and potential difficulties in ensuring accurate software classifications if calibration is not optimal.
How does this study contribute to understanding nerve regeneration?
It provides insights into the functional recovery processes in rodent models, enhancing the understanding of the effects of nerve injuries and potential therapeutic interventions.

Automated gait analysis is a feasible tool to evaluate functional recovery in rodent models of peripheral nerve injury and spinal cord contusion injury. While it requires only one setup to assess locomotor function in various experimental models, meticulous hard- and soft-ware adjustment and training of the animals is highly important.

Automated gait analysis is a widely used method for evaluating functional recovery enrollment models of peripheral nerve injury, repair, and regeneration. The method can be used to study changes in gait after peripheral nerve injury, relating both to motor and sensory nerve function. Researchers new to the method might experience difficulties in acquiring high quality data.

Typical hurdles are adequate animal training and accurate hardware and software calibration. During training sessions and on the test day, turn off all of the light sources in the behavioral test room and face the computer screen for data acquisition away from the camera to prevent its light from interfering with the camera. Confirm that the device is installed in a stable position in a way that prevents any form of vibration, and bring the rats into the behavioral test room in their own home cage for at least 30 minutes before the test, On the first day of training, gently grasp an animal under its trunk and carefully place it in the walkway entrance.

Let the rat explore the opening of the corridor without any interference, and wait until the animal voluntarily crosses the walkway to reach its home cage without any external stimulus or motivation. On the second day of training, allow the animals to become accustomed to entering the walkway and returning to their home cage without hesitation. On the third day of training, confirm that the animals have learned to cross the walkway at a uniform speed without hesitation, sniffing, or other explorative movements.

On the fourth and fifth, repeat the training to reinforce the testing procedure. On the day of the experiment, use commercial glass cleaner and a squeegee to clean the top and bottom of the walkway taking care to remove all of the fluid from the ends of the walkway. Place both the lightest and heaviest rats onto the walkway and select a registered camera from the setup tab to allow the camera gain, red ceiling light, green walkway light, and green intensity threshold to be adjusted to ensure an optimal paw print detection for all of the animals in the analysis.

Next, click open acquisition and acquire a snapshot of the empty cleaned walkway to be used as a reference throughout the data acquisition procedure. The status will change from waiting for snapshot to ready for acquisition. Click start acquisition.

The status will change from ready for acquisition to waiting for run to start. When the camera is ready, place a rat onto the walkway and follow the animal's movement on the computer screen. Note, the status change from waiting for run to start to recording run.

At the end of the analysis, click classify in the experimental explorer tab of the trial to be classified, and play the acquired run at normal speed to determine whether the data conforms to the classification requirements. Click auto classify for automatic classification of the paw prints by the software. For correct calculation of the normal step sequence patterns, make sure that the classifying algorithm is not confused by non-visible paw prints, leading to flawed NSSP, and that only paw prints that are detectable while the contralateral paw is also visible for NSSP calculations are included.

For statistical analysis of the data, click view run statistics to obtain a comprehensive overview of the run statistics. Then select file and export, to export the run or trial statistics into a spreadsheet software. Following sciatic nerve injury, rats use the heel of the paw for weight support only and the limb is moved in a sweeping circumductory movement.

Therefore, locomotor changes assessed via automated gait analysis become apparent by a significantly reduced print area and a significantly increased swing time. Femoral nerve resection results in denervation of the quadriceps muscle of the thigh, impairing knee extension and inducing hyperflection of the ankle joint with consecutive lifting of the corresponding heel. Starting from post-operative week four, reenervation of the quadriceps by the regenerating femoral nerve leads to reversal of these changes.

As the quadriceps muscle of the thigh also plays a role in the swing phase of the respective paw, the swing time is greatly prolonged in rats with femoral nerve injury. Gait analysis reveals markedly altered paw prints after thoracic spinal cord contusion. Spinal cord contusion at the thoracic 11 level also results in a decrease of the print area ratio, an increment of the swing time ratio.

The coordination related regularity index also decreases at post-operative week two, while the base of support of the hind paws shows a marked increase. When using automated gate analysis to evaluate functional recovery in rodents, be sure to carefully train the animals and to be meticulous when making any software or hardware adjustments. By using automated gait analysis, injury and regeneration of the central and peripheral nervous system can be evaluated in various rodent models, including yet unstudied formal nerve models, such as the median nerve.

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Automated Gait AnalysisFunctional RecoveryPeripheral Nerve InjurySpinal Cord Contusion InjuryAnimal TrainingData AcquisitionBehavioral Test RoomCalibrationCamera SetupPaw Print DetectionRat TrainingTesting ProcedureMovement Analysis

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