Advances in fetal and neonatal neuroimaging and everyday exposures

Abstract

The complex, tightly regulated process of prenatal brain development may be adversely affected by "everyday exposures" such as stress and environmental pollutants. Researchers are only just beginning to understand the neural sequelae of such exposures, with advances in fetal and neonatal neuroimaging elucidating structural, microstructural, and functional correlates in the developing brain. This narrative review discusses the wide-ranging literature investigating the influence of parental stress on fetal and neonatal brain development as well as emerging literature assessing the impact of exposure to environmental toxicants such as lead and air pollution. These 'everyday exposures' can co-occur with other stressors such as social and financial deprivation, and therefore we include a brief discussion of neuroimaging studies assessing the effect of social disadvantage. Increased exposure to prenatal stressors is associated with alterations in the brain structure, microstructure and function, with some evidence these associations are moderated by factors such as infant sex. However, most studies examine only single exposures and the literature on the relationship between in utero exposure to pollutants and fetal or neonatal brain development is sparse. Large cohort studies are required that include evaluation of multiple co-occurring exposures in order to fully characterize their impact on early brain development. IMPACT: Increased prenatal exposure to parental stress and is associated with altered functional, macro and microstructural fetal and neonatal brain development. Exposure to air pollution and lead may also alter brain development in the fetal and neonatal period. Further research is needed to investigate the effect of multiple co-occurring exposures, including stress, environmental toxicants, and socioeconomic deprivation on early brain development.

Fig. 1. Advanced neuroimaging approaches.

a Structural MRI: Left and Center, Brain segmentation and cortical morphometry of a neonate from dHCP data release 3; Right, Tensor-based morphometry t-statistic map adapted from Lautarescu and colleagues. b Diffusion MRI: top left, diagram of DTI, CSD, and NODDI models; Bottom left, structural connectomics network construction and edge-wise analysis, adapted from Bonthrone and colleagues; Right, tractography reconstruction of the uncinate fasciculus with an illustration of fiber orientation distributions in a region of crossing fibers and glass brain illustration of uncinate fasciculus, dorsal and ventral cingulum tractography reconstructions, adapted from Lautarescu and colleagues. c Functional MRI: Top left, Illustration of time-series from the sensorimotor network adapted from Fitzgibbon and colleagues; bottom left, MELODIC-derived resting-state functional networks adapted from Ciarrusta and colleagues; right, Functional connectivity analysis including a neonatal adaptation of the AAL atlas, and seed connectivity and voxel-wise connectivity adapted from Ciarrusta and colleagues; figure based on Kwon and colleagues. All images adapted from papers published under Creative Commons Attribution Licence CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).

Fig. 2. The relationship between maternal depressive symptoms and fibre diffusivity.

Plots showing the relationship between maternal depressive symptoms (EPDS) and mean fiber diffusivity (FD) in the left and right uncinate fasciculus, controlling for infant gestational age at birth, postmenstrual age at scan, sex, maternal socioeconomic status, and maternal history of poor mental health. Adapted from Lautarescu and colleagues^published under Creative Commons Attribution Licence CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).

Fig. 3. The relationship between stressful life events and diffusion MRI measures.

Partial regression scatterplots showing the relationships between stressful life events and mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in the left uncinate fasciculus (L-UF; top row) and AD in the right uncinate fasciculus (R-UF; bottom row). These results were obtained controlling for infant gestational age at birth, postmenstrual age at scan, socioeconomic status, total days on parenteral nutrition, sex, and maternal age. Bottom left: Glass brain” illustrations showing the skeletonized versions of the uncinate fasciculus (blue) and inferior longitudinal fasciculus (green) medial surface overlaid on the template radial diffusivity image, presented in coronal and sagittal planes (left to right). Adapted from Lautarescu and colleagues published under Creative Commons Attribution Licence CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).

Fig. 4. Association of maternal depression and hippocampal connectivity.

Higher depressive symptoms in the 3rd trimester as measured by the Reynolds Adolescent Depression Scale (RADS) were associated with weaker infant connectivity between the a left and b right hippocampus and the posterior cingulate cortex (PCC). When dichotomized based on clinical cutoffs, neonates from mothers with prenatal depression exhibited greater connectivity c between the left hippocampus and the right temporal lobe and d between the right hippocampus and the right sensory-motor cortex. Scatterplots below the images visualize the distribution of the observed data points for average infant connectivity in the detected regions plotted against maternal depressive symptoms or clinical group. Image from Scheinost and colleagues published under Creative Commons Attribution Licence CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).

Fig. 5. Canonical correlations between air pollutants and relative brain volumes.

Canonical correlations of air pollutants: NO₂, 2. PM₁₀, 3. PM_2.5, canonical correlations of the relative brain volumes: White Matter, Cortical Gray matter, Ventricles, Cerebellum, Deep Gray Nuclei, Brainstem, Amygdala & Hippocampus, extracerebral CSF. A Scatterplot of the canonical correlation. Adapted from Bos and colleagues, published under Creative Commons Attribution Licence CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).