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Non-invasive biomarkers of fetal brain development reflecting prenatal stress: An integrative multi-scale multi-species perspective on data collection and analysis

dc.contributor.author Frasch, Martin G
dc.contributor.author Lobmaier, Silvia M
dc.contributor.author Stampalija, Tamara
dc.contributor.author Desplats, Paula
dc.contributor.author Pallarés, María Eugenia
dc.contributor.author Pastor, Verónica
dc.contributor.author Brocco, Marcela A
dc.contributor.author Wu, Hau-Tieng
dc.contributor.author Schulkin, Jay
dc.contributor.author Herry, Christophe L
dc.contributor.author Seely, Andrew JE
dc.contributor.author Metz, Gerlinde AS
dc.contributor.author Louzoun, Yoram
dc.contributor.author Antonelli, Marta C
dc.date.accessioned 2018-08-15T20:43:28Z
dc.date.available 2018-08-15T20:43:28Z
dc.date.issued 2018-05
dc.identifier.issn 0149-7634
dc.identifier.issn 1873-7528
dc.identifier.uri https://hdl.handle.net/10161/17339
dc.description.abstract © 2018 Elsevier Ltd Prenatal stress (PS) impacts early postnatal behavioural and cognitive development. This process of ‘fetal programming’ is mediated by the effects of the prenatal experience on the developing hypothalamic–pituitary–adrenal (HPA) axis and autonomic nervous system (ANS). We derive a multi-scale multi-species approach to devising preclinical and clinical studies to identify early non-invasively available pre- and postnatal biomarkers of PS. The multiple scales include brain epigenome, metabolome, microbiome and the ANS activity gauged via an array of advanced non-invasively obtainable properties of fetal heart rate fluctuations. The proposed framework has the potential to reveal mechanistic links between maternal stress during pregnancy and changes across these physiological scales. Such biomarkers may hence be useful as early and non-invasive predictors of neurodevelopmental trajectories influenced by the PS as well as follow-up indicators of success of therapeutic interventions to correct such altered neurodevelopmental trajectories. PS studies must be conducted on multiple scales derived from concerted observations in multiple animal models and human cohorts performed in an interactive and iterative manner and deploying machine learning for data synthesis, identification and validation of the best non-invasive detection and follow-up biomarkers, a prerequisite for designing effective therapeutic interventions.
dc.publisher Elsevier BV
dc.relation.ispartof Neuroscience & Biobehavioral Reviews
dc.relation.isversionof 10.1016/j.neubiorev.2018.05.026
dc.title Non-invasive biomarkers of fetal brain development reflecting prenatal stress: An integrative multi-scale multi-species perspective on data collection and analysis
dc.type Journal article
duke.contributor.id Wu, Hau-Tieng|0752916
dc.date.updated 2018-08-15T20:43:21Z
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group Duke
pubs.organisational-group Mathematics
pubs.organisational-group Statistical Science
pubs.publication-status Published
duke.contributor.orcid Wu, Hau-Tieng|0000-0002-0253-3156


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