Analysis of locomotor behavior in the German Mouse Clinic
Analysis of locomotor behavior in the German Mouse Clinic
复制标题
德国小鼠诊所运动行为分析
DOI：
10.1016/j.jneumeth.2017.05.005
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发表时间：
2018
影响因子：
3
通讯作者：
Hölter SM
中科院分区：
文献类型：
--
作者：
Zimprich A;Östereicher MA;Becker L;Dirscherl P;Ernst L;Fuchs H;Gailus-Durner V;Garrett L;Giesert F;Glasl L;Hummel A;Rozman J;de Angelis MH;Vogt-Weisenhorn D;Wurst W;Hölter SM
背景突变小鼠模型的产生和表型分析随着对最有效的表型分析测试的研究而继续沿着增加。在这里，我们问，如果一个不同的运动测试的组合是必要的综合运动表型，或者如果一个大的数据集，从自动步态分析与CatWalk systems.New methodFirst的数据集，我们endeetly有意义地减少大的CatWalk数据集的主成分分析（PCA），以决定最相关的参数。分析了性别、体重、遗传背景和年龄等因素的影响。然后结合不同的运动测试进行了分析，调查之间的冗余tests.ResultThe提取的10个组件描述80%的总方差在猫步，表征步态的不同方面的可能性。在此基础上，检测了猫步的版本、性别、体重、年龄和遗传背景的影响。此外，PCA的组合运动测试表明，这些是独立的，没有显着的冗余，在他们的运动措施。与现有的方法相比，PCA允许细化的高维猫步（和其他测试）数据集，用于提取单个成分得分和随后的分析。以及在未来的实验中检测表型差异的一种协调措施。此外，虽然猫步对于检测与步态有关的运动表型是敏感的，但是有必要包括用于综合运动表型的其他测试。
BackgroundGeneration and phenotyping of mutant mouse models continues to increase along with the search for the most efficient phenotyping tests. Here we asked if a combination of different locomotor tests is necessary for comprehensive locomotor phenotyping, or if a large data set from an automated gait analysis with the CatWalk system would suffice.New methodFirst we endeavored to meaningfully reduce the large CatWalk data set by Principal Component Analysis (PCA) to decide on the most relevant parameters. We analyzed the influence of sex, body weight, genetic background and age. Then a combination of different locomotor tests was analyzed to investigate the possibility of redundancy between tests.ResultThe extracted 10 components describe 80% of the total variance in the CatWalk, characterizing different aspects of gait. With these, effects of CatWalk version, sex, body weight, age and genetic background were detected. In addition, the PCA on a combination of locomotor tests suggests that these are independent without significant redundancy in their locomotor measures.Comparison with existing methodsThe PCA has permitted the refinement of the highly dimensional CatWalk (and other tests) data set for the extraction of individual component scores and subsequent analysis.ConclusionThe outcome of the PCA suggests the possibility to focus on measures of the front and hind paws, and one measure of coordination in future experiments to detect phenotypic differences. Furthermore, although the CatWalk is sensitive for detecting locomotor phenotypes pertaining to gait, it is necessary to include other tests for comprehensive locomotor phenotyping.
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影响因子：
5.1
作者：
Chen YJ;Cheng FC;Sheu ML;Su HL;Chen CJ;Sheehan J;Pan HC
通讯作者：
Pan HC
社交笼中的自发行为可区分小鼠的品系、病变和突变
影响因子：
3
作者：
Elisabetta Vannoni;V. Võikar;G. Colacicco;Maria A. Sanchez;H. Lipp;D. Wolfer
通讯作者：
D. Wolfer
影响因子：
14.9
作者：
INFRAFRONTIER Consortium
通讯作者：
INFRAFRONTIER Consortium
影响因子：
2.6
作者：
Bronikowski, AM;Carter, PA;Garland, T
通讯作者：
Garland, T
影响因子：
4.4
作者：
Gonik M;Frank E;Keßler MS;Czamara D;Bunck M;Yen YC;Pütz B;Holsboer F;Bettecken T;Landgraf R;Müller-Myhsok B;Touma C;Czibere L
通讯作者：
Czibere L