Exploring the relationship between maternal prenatal stress and brain structure in premature neonates

Abstract

Background Exposure to maternal stress in utero is associated with a range of adverse outcomes. We previously observed an association between maternal stress and white matter microstructure in a sample of infants born prematurely. In this study, we aimed to investigate the relationship between maternal trait anxiety, stressful life events and brain volumes. Methods 221 infants (114 males, 107 females) born prematurely (median gestational age = 30.43 weeks [range 23.57–32.86]) underwent magnetic resonance imaging around term-equivalent age (mean = 42.20 weeks, SD = 1.60). Brain volumes were extracted for the following regions of interest: frontal lobe, temporal lobe, amygdala, hippocampus, thalamus and normalized to total brain volume. Multiple linear regressions were conducted to investigate the relationship between maternal anxiety/stress and brain volumes, controlling for gestational age at birth, postmenstrual age at scan, socioeconomic status, sex, days on total parenteral nutrition. Additional exploratory Tensor Based Morphometry analyses were performed to obtain voxel-wise brain volume changes from Jacobian determinant maps. Results and conclusion In this large prospective study, we did not find evidence of a relationship between maternal prenatal stress or trait anxiety and brain volumes. This was the case for both the main analysis using a region-of-interest approach, and for the exploratory analysis using Jacobian determinant maps. We discuss these results in the context of conflicting evidence from previous studies and highlight the need for further research on premature infants, particularly including term-born controls.

Publication
PLOS ONE
Laila Hadaya
Laila Hadaya
PhD Student

I have a Neuroscience (BSc) and Psychiatric Research (MSc) background. I am particularly interested in identifying predictive biomarkers of mental health outcomes and trajectories using neuroimaging and machine learning approaches.

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