Non-invasive biomarkers of fetal brain development reflecting prenatal stress: An integrative multi-scale multi-species perspective on data collection and analysis
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.
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https://hdl.handle.net/10161/17339Published Version (Please cite this version)
10.1016/j.neubiorev.2018.05.026Publication Info
Frasch, Martin G; Lobmaier, Silvia M; Stampalija, Tamara; Desplats, Paula; Pallarés,
María Eugenia; Pastor, Verónica; ... Antonelli, Marta C (2018). Non-invasive biomarkers of fetal brain development reflecting prenatal stress: An
integrative multi-scale multi-species perspective on data collection and analysis.
Neuroscience & Biobehavioral Reviews. 10.1016/j.neubiorev.2018.05.026. Retrieved from https://hdl.handle.net/10161/17339.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Hau-Tieng Wu
Research Professor of Mathematics

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