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Birth, love, and fear: Physiological networks from pregnancy to parenthood
Azure D Grant1,2, Elise N Erickson3
1Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720, United States.
Pregnancy and childbirth are among the most dramatic physiological and emotional transformations of a lifetime. Despite their central importance to human survival, many gaps remain in our understanding of the temporal progression of and mechanisms underlying the transition to new parenthood. The goal of this paper is to outline the physiological and emotional development of the maternal-infant dyad from late pregnancy to the postpartum period, and to provide a framework to investigate this development using non-invasive timeseries. We focus on the interaction among neuroendocrine, emotional, and autonomic outputs in the context of late pregnancy, parturition, and post-partum. We then propose that coupled dynamics in these outputs can be leveraged to map both physiologic and pathologic pregnancy, parturition, and parenthood. This approach could address gaps in our knowledge and enable early detection or prediction of problems, with both personalized depth and broad population scale.
Pregnancy and childbirth are among the most dramatic physiological and emotional transformations of a lifetime. Despite their central importance to human survival, many gaps remain in our understanding of the temporal progression of and mechanisms underlying the transition to new parenthood. The goal of this paper is to outline the physiological and emotional development of the maternal-infant dyad from late pregnancy to the postpartum period, and to provide a framework to investigate this development using non-invasive timeseries. We focus on the interaction among neuroendocrine, emotional, and autonomic outputs in the context of late pregnancy, parturition, and post-partum. We then propose that coupled dynamics in these outputs can be leveraged to map both physiologic and pathologic pregnancy, parturition, and parenthood. This approach could address gaps in our knowledge and enable early detection or prediction of problems, with both personalized depth and broad population scale.
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