Name: MouseWalker (MW) Video Tracking and Kinematic Data Analysis
Uploaded: 2023-03-24T14:50:00
Description: Watch this Scientific Journal Video about MouseWalker MW Video Tracking and Kinematic Data Analysis at JoVE.com

The video demonstrates using MATLAB to analyze motion data from MouseWalker software, focusing on automated tracking and principal component analysis (PCA) of mice with spinal cord injuries. Key findings include changes in gait and stance characteristics, supported by quantitative measurements and visual data outputs saved for further analysis.

mousewalker (mw) video tracking and kinematic data analysis

MouseWalkerを用いた脊髄損傷マウスの自発運動機能障害の定量化

The effective coordination of four limbs is not unique to quadruped animals. Forelimb-hindlimb coordination in humans remains important to accomplish several tasks, such as swimming and alterations of speed while walking1. Various limb kinematic2 and motor program1,3,4, as well as proprioceptive feedback circuits5, are conserved between humans and other mammals&#160;and should be considered when analyzing therapeutic options for motor disorders, such as spinal cord injury (SCI)6,7,8.
In order to walk, several spinal connections from the forelimbs and hindlimbs need to be properly wired and rhythmically activated, which requires inputs from the brain and feedback from the somatosensory system2,9,10. These connections culminate in the central pattern generators (CPGs), which are situated at a cervical and lumbar level for the forelimbs and hindlimbs, respectively1,9,10. Often, after SCI, the disruption of neuronal connectivity and the formation of an inhibitory glial scar12 limit the recovery of locomotor function, with outcomes varying from total paralysis to restricted function of a group of limbs depending on the injury severity. Tools to precisely quantify locomotor function after SCI are critical for monitoring recovery and evaluating the effects of treatments or other clinical interventions6.
The standard metric assay for mouse contusion models of SCI is the Basso mouse scale (BMS)13,14, a non-parametric score that considers trunk stability, tail position, plantar stepping, and forelimb-hindlimb coordination in an open field arena. Even though the BMS is extremely reliable for most cases, it requires at least two experienced raters to observe all the angles of animal movement in order to account for natural variability and reduce bias.
Other assays have also been developed to assess motor performance after SCI quantitatively. These include the rotarod test, which measures time spent on a rotating cylinder15; the horizontal ladder, which measures the number of missed railings and positive ladder grabs16,17; and the beam walking test, which measures the time an animal takes and the number of failures it makes when crossing a narrow beam18. Despite reflecting a combination of motor deficits, none of these tests produce direct locomotor information about forelimb-hindlimb coordination.
To specifically and more thoroughly analyze walking behavior, other assays have been developed to reconstruct step cycles and gaiting strategies. One example is the footprint test, where the inked paws of an animal draw a pattern over a sheet of white paper19. Although simple in its execution, extracting kinematic parameters such as stride length is cumbersome and inaccurate. Moreover, the lack of dynamic parameters, such as the duration of the step cycle or leg-timed coordination, limits its applications; indeed, these dynamic parameters can only be acquired by analyzing frame-by-frame videos of rodents walking through a transparent surface. For SCI studies, researchers have analyzed walking behavior from a lateral view using a treadmill, including reconstructing the step cycle and measuring the angular variations of each leg joint4,20,21. Even though this approach can be extremely informative6, it remains focused on a specific set of limbs and lacks additional gait features, such as coordination.
To fill these gaps, Hamers and colleagues developed a quantitative test based on an optical touch sensor using frustrated total internal reflection (fTIR)22. In this method, light propagates through glass via internal reflection, becomes scattered upon paw pressing, and, finally, is captured by a high-speed camera. More recently, an open-source version of this method, called MouseWalker, was made available, and this approach combines an fTIR walkway with a tracking and quantification software package23. Using this method, the user can extract a large set of quantitative parameters, including step, spatial, and gait patterns, footprint positioning, and forelimb-hindlimb coordination, as well as visual outputs, such as footprint patterns (mimicking the inked paw assay6) or stance phases relative to the body axis. Importantly, due to its open-source nature, new parameters can be extracted by updating the MATLAB script package.
Here, the previously published assembly of the MouseWalker23 system is updated. A description of how to set it up is provided, with all the steps required to achieve the best video quality, tracking conditions, and parameter acquisition. Additional post-quantification tools are also shared to enhance the analysis of the MouseWalker (MW) output dataset. Finally, the usefulness of this tool is demonstrated by obtaining quantifiable values for general locomotor performance, specifically step cycles and forelimb-hindlimb coordination, in a spinal cord injury (SCI) context.

著者スポットライト:MouseWalkerを使用して脊髄損傷のマウスモデルにおける自発運動機能障害の定量化  

MouseWalkerを用いた脊髄損傷マウスモデルにおける自発運動機能障害の定量化

In this study, we use a mouse model of spinal cord injury to uncover ways of promoting repair of the spinal cord that could lead to effective locomotor and functional sensory recovery. With a mouse walker, we can move a step forward into a more quantitative analysis of spinal cord recovery by combining several graphical outputs and kinematic parameters with a set of post-quantification tools. High quality video cameras and sophisticated software packages allow a more direct and quantitative description of motor activity.The mouse walker is a good example of this. Currently two main challenges exist. First, the ability of laboratories to implement these technologies in their experimental pipeline.And second, to reduce the time and steps from video recordings to data generation. We realized that standard protocols were insufficient to describe all locomotive deficits we see after spinal cord injury. We also found that the mouse walker helped us measure coordination, one of the hardest parameters to assess in standard tests.Here, not only is the software free and open source, but the hardware is also easy to assemble with uncomplicated materials. We also provide open source tools to analyze the mouse walker data. The mouse walker is a valuable method for studying other motor related or motor dysfunctions, not just spinal cord injury.It can also be used in combination with already established behavior protocols.

Watch this Scientific Journal Video about MouseWalker MW Video Tracking and Kinematic Data Analysis at JoVE.com

MouseWalker (MW) Video Tracking and Kinematic Data Analysis  

マウスウォーカー(MW)ビデオトラッキングとキネマティックデータ解析

コンピューターで MATLAB を開きます。マウスウォーカースクリプトを含むフォルダを作業ディレクトリに追加し、マウスウォーカーを実行します。メインコマンドラインのM。ビデオフォルダを入力ディレクトリとしてロードします。すべてのキャリブレーションとしきい値パラメータが配置されている[設定]ウィンドウに移動します。一部のパラメーターを変更した場合の効果をテストするには、[プレビュー] ボタンをクリックします。しきい値パラメータを調整した後、ビデオが自動追跡の準備ができていることを確認します。最初のフレームに移動し、[自動]をクリックして追跡を開始します。トラッキングが完了したら、必要に応じて、適切なフットプリントを選択して手動で修正を実行します。[保存] ボタンを押して変更を保存します。次に、[評価]をクリックして、追跡されたビデオから出力ファイルを生成します。すべてのグラフィカル出力データプロットが結果フォルダに保存されていることを確認します。次に、MouseWalkerソフトウェアによって生成されたすべての定量測定値がExcelスプレッドシートに保存され、1.Information_Sheetに要約されていることを確認します。マウスマルチ評価を使用します。すべての実行の測定値を分析用の新しいファイルに集約するMスクリプト。主成分分析(PCA)を実行するには、pcaplotgeneratorを開きます。Spyderでpydをクリックし、[再生]ボタンをクリックしてコードを実行します。自動ウィンドウで分析するExcelファイルとシート名を選択します。シート名が変更されていない場合は、シート 1 と記述します。デジタルインクアッセイでは、後足のサポートの欠如が検出されました。左右の後足の両方のフットプリント面積が減少します。全体的なスタンストレースには、いくつかのユニークな特徴が表示されました。脊髄損傷後、後足のスタンストレースは短くなり、損傷後15日以降、タッチダウンとリフトオフの両方でランダムな毛穴の位置が高まりました。すべての運動学的運動パラメータの主成分分析は、最初の成分のデータに40%の分散を示し、すべての時点で脊髄損傷を受けた動物のグループを残りの動物から分離しました。ヒートマップスクリプトなどの他のスクリプトを使用して、脊髄損傷動物はすべての時点で歩行戦略の変化を示すことがわかりました。脊髄損傷マウスはまた、前肢と後肢の両方でより低いスタンス真直度指数を示しました。

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