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This study combined in vivo calcium imaging and intramammary pressure recording to uncover the temporal connections between episodic activity of oxytocin neurons and ME in conscious lactating rats and developed a supervised machine learning framework (ME Decoder) to enable automated analyses of ME.
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Oxytocin-mediated milk ejection (ME) is pivotal to effective breastfeeding and productive health, yet behaviorally decoding and revealing neural mechanisms of ME remains challenging. Here, we combined in vivo calcium imaging and intramammary pressure recording to uncover the temporal connections between episodic activity of oxytocin neurons and ME in conscious lactating rats. Leveraging the association and behavioral responses in dam and pup, we developed a supervised machine learning framework (ME Decoder) to enable automated analyses of ME. Inspired by its interpretable features, we defined the activity-coupled dam-pup interactions (ADPI), manifested by high kyphosis of the dam followed by pup treading and stretch, as the behavioral signatures of ME. By ME Decoder and ADPI analyses, we detected reduced ME but unaffected activity of oxytocinergic neurons after systemic blockade of oxytocin receptor. Our study uncovers the oxytocinergic and behavioral signatures of ME and provides a generalizable approach for further investigation.
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@article{Xiao2026Decoding,
title = {Decoding the oxytocinergic and behavioral signatures of milk ejection},
author = {Wei Xiao and Q Zheng and Yang Wang and Ying Yuan and Yuge Chen and Tianxing Zheng and Yuzhu Chen and Gao Y and Bingrui Song and B Y Zhang and Liyao Qiu and L Zeng and Ma Huan and Colin H. Brown and Shumin Duan and Gang Pan and Zhihua Gao},
journal = {bioRxiv (Cold Spring Harbor Laboratory)},
year = {2026},
doi = {10.64898/2026.07.12.738011},
url = {https://doi.org/10.64898/2026.07.12.738011}
}
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