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- W4386816320 abstract "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 test errors of 14.39 pixels and 11.81 pixels for dam and pups respectively. Pose estimation data and human-annotated behavior labels from 30 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, with F1 scores above .997. 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 provides a basis for assessing home-cage dam-pup interactions 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." @default.
- W4386816320 created "2023-09-18" @default.
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- W4386816320 date "2023-09-16" @default.
- W4386816320 modified "2023-10-18" @default.
- W4386816320 title "Automated Maternal Behavior during Early life in Rodents (AMBER) pipeline" @default.
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- W4386816320 doi "https://doi.org/10.1101/2023.09.15.557946" @default.
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