ePoster

INFERRING MILK EJECTION TIMING FROM MATERNAL BEHAVIOR AND INFANT VOCALIZATIONS IN RATS

Viktoriya Tsayand 5 co-authors

Mannheim Central Institute of Mental Health

FENS Forum 2026 (2026)
Barcelona, Spain
Board PS04-08PM-330

Presentation

Date TBA

Board: PS04-08PM-330

Poster preview

INFERRING MILK EJECTION TIMING FROM MATERNAL BEHAVIOR AND INFANT VOCALIZATIONS IN RATS poster preview

Event Information

Poster Board

PS04-08PM-330

Abstract

Milk ejection reflex (MER) is a highly conserved mammalian mechanism that is essential for offspring survival and species propagation. During nursing, infant suckling induces burst firing in hypothalamic oxytocin (OT) neurons, leading to systemic OT release and consecutive increase in intramammary pressure. Beyond a simple reflex, however, MER requires integration of sensory inputs and maternal internal states. How and where this integration occurs in brain remains poorly understood, largely because precise identification of milk ejection events typically relies on invasive and technically demanding measurements. To address this limitation, we analyzed rat maternal behavior and infant vocalizations in parallel with hypothalamic OT neuron activity. We found that temporal relationship between maternal postural adaptations during nursing—specifically crouching behavior— and infant vocalizations contains substantial information about OT burst timing. The majority of crouches were preceded by infant vocalizations, but these occurred exclusively outside OT burst events. In contrast, a subset of crouches was not preceded by vocalizations and was time-locked to OT bursts. We termed these OT-associated events “silent crouches.” We further compared silent and non-silent crouches across different stages of lactation and observed a significant increase in silent crouches from the early- to mid-lactation period. This finding aligns with previous rodent studies reporting a higher frequency of OT bursts and milk ejections during mid lactation. Building on our ground-truth recordings, we are developing machine-learning approaches to reliably infer MER timing using video and audio data alone. Such non-invasive tools will provide improved experimental access to the neural mechanisms governing the MER.

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