Kleefstra syndrome is a disorder caused by a mutation in the EHMT1 gene characterized in humans by general developmental delay, mild to severe intellectual disability and autism. Here, we characterized cumulative memory in the Ehmt1^+/− mouse model using the Object Space Task. We combined conventional behavioral analysis with automated analysis by deep-learning networks, a session-based computational learning model, and a trial-based classifier. Ehmt1^+/− mice showed more anxiety-like features and generally explored objects less, but the difference decreased over time. Interestingly, when analyzing memory-specific exploration, Ehmt1^+/− show increased expression of cumulative memory, but a deficit in a more simple, control memory condition. Using our automatic classifier to differentiate between genotypes, we found that cumulative memory features are better suited for classification than general exploration differences. Thus, detailed behavioral classification with the Object Space Task produced a more detailed behavioral phenotype of the Ehmt1^+/− mouse model.

Kleefstra综合征是一种由EHMT1基因突变引起的疾病，其特征是人类的总体发育延迟，轻度至重度智力障碍和自闭症。在这里，我们使用对象空间任务来表征Ehmt1 ^+/-模型中的累积内存。我们将传统的行为分析与通过深度学习网络，基于会话的计算学习模型以及基于试验的分类器的自动分析相结合。Ehmt1 ^+/-小鼠表现出更多的焦虑样特征，通常探索的对象更少，但是随着时间的推移，差异有所减小。有趣的是，在分析特定于内存的探索时，Ehmt1 ^+/-显示出增加的累积记忆表达，但在更简单的控制记忆条件下缺乏。使用我们的自动分类器来区分基因型，我们发现累积记忆特征比常规探索差异更适合分类。因此，使用对象空间任务进行详细的行为分类会产生Ehmt1 ^+/-鼠标模型的更详细的行为表型。