Computer vision analysis of mother-infant interaction identified efficient pup retrieval in V1b vasopressin receptor knockout mice

Abstract

Close contact between lactating rodent mothers and their infants is essential for effective nursing. Whether the mothers effort to retrieve the infants to their nest requires the vasopressin-V1b vasopressin receptor axis has not been fully defined. To address this question, V1b receptor knockout (V1bKO) and control mice were analyzed in pup retrieval test. Because an exploring mother in a new test cage randomly accessed to multiple infants in changing backgrounds over time, a computer vision-based deep learning analysis was applied to continuously calculate the distances between the mother and the infants as a parameter of their relationship. In an open-field, a virgin female V1bKO mice entered fewer times into the center area and moved shorter distances than wild-type (WT). While this behavioral pattern persisted in V1bKO mother, the pup retrieval test demonstrated that total distances between a V1bKO mother and infants came closer in a shorter time than with a WT mother. Moreover, in the medial preoptic area, parts of the V1b receptor transcripts were detected in galanin- and c-fos-positive neurons following maternal stimulation by infants. This research highlights the effectiveness of deep learning analysis in evaluating the mother-infant relationship and the critical role of V1b receptor in pup retrieval during the early lactation phase. Highlights(1) Mother-infant relationship was calculated as distances by computer vision and object detection analysis. (2) V1b knockout female mice tended to avoid entering centerfield in a new cage compared to control female. (3) Lack of V1b vasopressin receptor in mice facilitated the pup retrieval in early infancy, even with shorter distances of total movements.