Improving behavioral assessment in genetically modified mice.

我已经建立，通过降维统计分析，小鼠的自发活动和焦虑样行为，到目前为止，在很大程度上是两个不可区分的结构。这一发现非常有趣。简而言之，这一结果表明，几乎所有与精神疾病小鼠模型中的焦虑样行为相关的结论，至少在很大程度上都是没有意义的。因此，未来的研究将是必要的，以提高老鼠行为研究的可信度。具体而言，为了可靠地区分上述两种结构，应该在小鼠模型研究中开发和引入一些与小鼠运动活动无关的测量方法。第二项研究成果涉及如何在时间序列设置中正确地建模小鼠运动活动。该领域的大多数研究人员采用简单的ANOVA模型。令人遗憾的是，这种模型存在严重的缺陷，包括不切实际的统计假设（例如，线性和高斯分布）。我设计了一个广义加性模型位置形状和规模（GAMLSS）模型，显着提高了结果的准确性。这些结果已在几个国际会议上传播。

I have established, via dimensionality reduction statistical analyses, that locomotor activity and anxiety-like behaviour in mice are, to date, two indistinguishable constructs to a large extent. This finding is of extreme interest. In a nutshell, this outcome leads to the ascertainment that pretty much all conclusions relative to anxiety-like behaviour in mice models of psychiatric disease are, at least to a large extent, moot. Consequently, future research will be necessary in order to improve the trustworthiness of mouse behaviour research. Specifically, some non-locomotor-activity-related measurements of mouse anxiety should be developed and introduced in mouse model research in order to reliably dissociate between the two above constructs.The second research achievement refers to how correctly model mouse locomotor activity in time series settings. Most researchers in the field employ simple ANOVA models. Regrettably, such models suffer from serious shortcomings including unrealistic statistical assumptions (e.g., linearity and Gaussian distribution). I have designed a Generalized Additive Model Location Shape and Scale (GAMLSS) model that significantly improve the accuracy of the results.These results have been disseminated in several international conferences.