Bermudez Contreras Edgar, Sutherland Robert J, Mohajerani Majid H, Whishaw Ian Q
Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Canada.
Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Canada.
Neurosci Biobehav Rev. 2022 May;136:104621. doi: 10.1016/j.neubiorev.2022.104621. Epub 2022 Mar 17.
Documenting a mouse's "real world" behavior in the "small world" of a laboratory cage with continuous video recordings offers insights into phenotypical expression of mouse genotypes, development and aging, and neurological disease. Nevertheless, there are challenges in the design of a small world, the behavior selected for analysis, and the form of the analysis used. Here we offer insights into small world analyses by describing how acute behavioral procedures can guide continuous behavioral methodology. We show how algorithms can identify behavioral acts including walking and rearing, circadian patterns of action including sleep duration and waking activity, and the organization of patterns of movement into home base activity and excursions, and how they are altered with aging. We additionally describe how specific tests can be incorporated within a mouse's living arrangement. We emphasize how machine learning can condense and organize continuous activity that extends over extended periods of time.
通过连续视频记录在实验室笼子的“小世界”中记录小鼠的“现实世界”行为，有助于深入了解小鼠基因型的表型表达、发育与衰老以及神经疾病。然而，在小世界的设计、选择用于分析的行为以及所采用的分析形式方面存在挑战。在此，我们通过描述急性行为程序如何指导连续行为方法，来深入探讨小世界分析。我们展示了算法如何识别包括行走和站立在内的行为动作、包括睡眠时间和清醒活动在内的昼夜活动模式，以及运动模式如何组织成巢穴活动和外出活动，以及它们如何随衰老而改变。我们还描述了如何将特定测试纳入小鼠的生活环境中。我们强调机器学习如何能够浓缩和组织长时间的连续活动。
