Antenatal maternal anxiety predicts variations in neural structures implicated in anxiety disorders in newborns.

OBJECTIVE: Antenatal maternal anxiety predicts offspring neurodevelopment and psychopathology, although the degree to which these associations reflect postnatal influences is unclear. To limit this possibility, we assessed newborn neuronal microstructures using diffusion tensor imaging (DTI) and assessed neonatal microstructure variation in relation to antenatal anxiety and in prediction of infant socio-emotional behavior at age 1 year. METHOD: Dyads were drawn from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO) cohort, and included mothers who completed the Speilberger State-Trait Anxiety Inventory (STAI) at 26 weeks gestation (scoring >90, n = 20; scoring <70, n = 34) and their neonates (5-17 days postnatal) who took part in DTI. RESULTS: Antenatal anxiety predicted variation in fractional anisotropy (FA) of regions important to cognitive-emotional responses to stress (i.e., the right insula and dorsolateral prefrontal cortex), sensory processing (e.g., right middle occipital), and socio-emotional function (e.g., the right angular gyrus, uncinate fasciculus, posterior cingulate, and parahippocampus). In a subset of infants with Infant Toddler Socio-Emotional Assessment (ITSEA) data, some of these right lateralized clusters predicted infant internalizing (e.g., insula: ß = 0.511, p = .03) but not externalizing behavior 1 year later, although these analyses failed to withstand the correction for multiple comparisons. CONCLUSION: These findings suggest the need for larger-scale investigations of the role that corticolimbic structures play in regulating cognitive-emotional responses to threat, and potentially in mediating the cross-generational transmission of anxiety, as well as in underscoring the importance of early mother-infant intervention programs.

Prenatal maternal depression associates with microstructure of right amygdala in neonates at birth.

BACKGROUND: Antenatal maternal cortisol levels associate with alterations in the amygdala, a structure associated with emotion regulation, in the offspring. However, because offspring brain and behavior are commonly assessed years after birth, the timing of such maternal influences is unclear. This study aimed to examine the association between antenatal maternal depressive symptomatology and neonatal amygdala volume and microstructure and thus establish evidence for the transgenerational transmission of vulnerability for affective disorders during prenatal development. METHODS: Our study recruited Asian mothers at 10 to 13 weeks pregnancy and assessed maternal depression at 26 weeks gestation using the Edinburgh Postnatal Depression Scale. Structural magnetic resonance imaging and diffusion tensor imaging were performed with 157 nonsedated, 6- to 14-day-old newborns and then analyzed to extract the volume, fractional anisotropy, and axial diffusivity values of the amygdala. RESULTS: Adjusting for household income, maternal age, and smoking exposure, postconceptual age at magnetic resonance imaging, and birth weight, we found significantly lower fractional anisotropy (p = .009) and axial diffusivity (p = .028), but not volume (p = .993), in the right amygdala in the infants of mothers with high compared with those with low-normal Edinburgh Postnatal Depression Scale scores. CONCLUSIONS: The results reveal a significant relation between antenatal maternal depression and the neonatal microstructure of the right amygdala, a brain region closely associated with stress reactivity and vulnerability for mood anxiety disorders. These findings suggest the prenatal transmission of vulnerability for depression from mother to child and that interventions targeting maternal depression should begin early in pregnancy.

Morphology and microstructure of subcortical structures at birth: a large-scale Asian neonatal neuroimaging study.

This paper presents the growth pattern and sexual dimorphism of the thalamus and basal ganglia in a large-scale Asian neonatal cohort using both T2-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Our study observed a robust growth of the thalamus and basal ganglia (caudate, putamen, globus pallidus, and anterior limb of internal capsule) beyond the overall brain growth in the early postnatal period (36-43 weeks of the gestational age). Additionally, the microstructure of the two structures was integrated as reflected by an increase in fractional anisotropy (FA) and a decrease in axial and radial water diffusivities in the first few weeks of life. Sexual dimorphism was only observed in the whole brain growth and the left thalamic volume but not in the other volumes or DTI measures of the basal ganglia and thalamus at birth. Even though the pattern of sexual dimorphism in the total brain volume is present at birth and persists throughout postnatal brain development, sexual dimorphisms of the basal ganglia and thalamus differ from those found in later stages of brain development, indicating that regionally distinct patterns of postnatal brain development between males and females arise after birth.

Population differences in brain morphology and microstructure among Chinese, Malay, and Indian neonates.

We studied a sample of 75 Chinese, 73 Malay, and 29 Indian healthy neonates taking part in a cohort study to examine potential differences in neonatal brain morphology and white matter microstructure as a function of ethnicity using both structural T2-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We first examined the differences in global size and morphology of the brain among the three groups. We then constructed the T2-weighted MRI and DTI atlases and employed voxel-based analysis to investigate ethnic differences in morphological shape of the brain from the T2-weighted MRI, and white matter microstructure measured by fractional anisotropy derived from DTI. Compared with Malay neonates, the brains of Indian neonates' tended to be more elongated in anterior and posterior axis relative to the superior-inferior axis of the brain even though the total brain volume was similar among the three groups. Although most anatomical regions of the brain were similar among Chinese, Malay, and Indian neonates, there were anatomical variations in the spinal-cerebellar and cortical-striatal-thalamic neural circuits among the three populations. The population-related brain regions highlighted in our study are key anatomical substrates associated with sensorimotor functions.

Atlas-based automatic mouse brain image segmentation revisited: model complexity vs. image registration.

Although many atlas-based segmentation methods have been developed and validated for the human brain, limited work has been done for the mouse brain. This paper investigated roles of image registration and segmentation model complexity in the mouse brain segmentation. We employed four segmentation models [single atlas, multiatlas, simultaneous truth and performance level estimation (STAPLE) and Markov random field (MRF) via four different image registration algorithms (affine, B-spline free-form deformation (FFD), Demons and large deformation diffeomorphic metric mapping (LDDMM)] for delineating 19 structures from in vivo magnetic resonance microscopy images. We validated their accuracies against manual segmentation. Our results revealed that LDDMM outperformed Demons, FFD and affine in any of the segmentation models. Under the same registration, increasing segmentation model complexity from single atlas to multiatlas, STAPLE or MRF significantly improved the segmentation accuracy. Interestingly, the multiatlas-based segmentation using nonlinear registrations (FFD, Demons and LDDMM) had similar performance to their STAPLE counterparts, while they both outperformed their MRF counterparts. Furthermore, when the single-atlas affine segmentation was used as reference, the improvement due to nonlinear registrations (FFD, Demons and LDDMM) in the single-atlas segmentation model was greater than that due to increasing model complexity (multiatlas, STAPLE and MRF affine segmentation). Hence, we concluded that image registration plays a more crucial role in the atlas-based automatic mouse brain segmentation as compared to model complexity. Multiple atlases with LDDMM can best improve the segmentation accuracy in the mouse brain among all segmentation models tested in this study.