Automated maternal behavior during early life in rodents (AMBER) pipeline.
Automated maternal behavior during early life in rodents (AMBER) pipeline.
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DOI：
10.1038/s41598-023-45495-4
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发表时间：
2023-10-25
影响因子：
4.6
通讯作者：
Champagne, Frances A.
中科院分区：
文献类型：
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作者：
Lapp, Hannah E.;Salazar, Melissa G.;Champagne, Frances A.
出生后早期母婴互动对婴儿存活和婴儿发育至关重要。啮齿动物模型被广泛用于了解这些早期社会经验如何影响整个生命周期的神经生物学。然而，测量产后母鼠-幼鼠相互作用的方法通常涉及耗时的手动评分，在研究组之间差异很大，并且产生低密度数据，限制了下游分析应用。为了解决这些方法学问题，我们开发了啮齿动物早期生命期间的自动化母体行为（AMBER）管道，用于使用开源机器学习工具量化笼内母体和母鼠之间的相互作用。在出生后第1-10天的视频记录中，使用DeepLabCut跟踪大鼠母鼠（32个点）和个体幼仔（9个点/幼仔）的关键点。姿势估计模型达到关键点测试误差约为4.1-10 mm（14.39像素）和3.44-7.87 mm（11.81像素），这取决于母鼠和幼鼠的所有关键点上的平均帧中动物的深度。姿势估计数据和人类注释的行为标签，从38个视频使用简单行为分析（SimBA）生成行为分类器的大坝主动护理，被动护理，巢出席，舔和梳理，自我导向梳理，吃，喝使用随机森林算法。所有分类器在测试框架上都有很好的性能，F1得分超过0.886。在巢出勤（F1 = 0.990），主动护理（F1 = 0.828），舔和梳理（F1 = 0.766），但较低的吃，喝，和自我导向的梳理（F1 = 0.534-0.554）。一组242个视频与AMBER一起使用，并在出生后1-10个家庭笼子视频的预期范围内产生行为测量。这条管道是一个重大的进步，在评估家庭笼大坝幼鼠的相互作用的方式，减少实验人员的负担，同时增加可重复性，可靠性和细节的数据用于发展研究，而不需要特殊的住房系统或专有软件。
Mother-infant interactions during the early postnatal period are critical for infant survival and the scaffolding of infant development. Rodent models are used extensively to understand how these early social experiences influence neurobiology across the lifespan. However, methods for measuring postnatal dam-pup interactions typically involve time-consuming manual scoring, vary widely between research groups, and produce low density data that limits downstream analytical applications. To address these methodological issues, we developed the Automated Maternal Behavior during Early life in Rodents (AMBER) pipeline for quantifying home-cage maternal and mother–pup interactions using open-source machine learning tools. DeepLabCut was used to track key points on rat dams (32 points) and individual pups (9 points per pup) in postnatal day 1–10 video recordings. Pose estimation models reached key point test errors of approximately 4.1–10 mm (14.39 pixels) and 3.44–7.87 mm (11.81 pixels) depending on depth of animal in the frame averaged across all key points for dam and pups respectively. Pose estimation data and human-annotated behavior labels from 38 videos were used with Simple Behavioral Analysis (SimBA) to generate behavior classifiers for dam active nursing, passive nursing, nest attendance, licking and grooming, self-directed grooming, eating, and drinking using random forest algorithms. All classifiers had excellent performance on test frames, with F1 scores above 0.886. Performance on hold-out videos remained high for nest attendance (F1 = 0.990), active nursing (F1 = 0.828), and licking and grooming (F1 = 0.766) but was lower for eating, drinking, and self-directed grooming (F1 = 0.534–0.554). A set of 242 videos was used with AMBER and produced behavior measures in the expected range from postnatal 1–10 home-cage videos. This pipeline is a major advancement in assessing home-cage dam-pup interactions in a way that reduces experimenter burden while increasing reproducibility, reliability, and detail of data for use in developmental studies without the need for special housing systems or proprietary software.
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