De Asis-Cruz, J. | Search bioRxiv

bioRxiv2025

MRI-Based Structural Development of the Human Newborn Hypothalamus

BackgroundPreclinical evidence suggests that intrauterine exposures can impact hypothalamic structure at birth and future disease risk, yet early human data are limited. MethodsHypothalamic volumes were measured from 699 T1-weighted MRI scans from 631 newborns (54% female; 27-45 weeks postmenstrual age/PMA) in the Developing Human Connectome Project. Linear mixed-effects models tested associations with prenatal exposures: gestational age (GA) at birth, PMA at scan, sex, maternal body mass index (BMI), and smoking. Findings were partially replicated in the Adolescent Brain and Child Development (ABCD) Study (release 5.1) data (16,934 observations from 11,207 participants). ResultsAbsolute hypothalamus volume increased with PMA (+5.5%/week, t=39.9, p<10-{superscript 1}), but not after adjusting for brain volume (t=1.2, p=0.24). Males showed larger absolute (+3.3%, t=3.2, p=0.002) but smaller relative hypothalamus volume (t=-2.8, p=0.005). Lower GA was linked to larger relative hypothalamus volume (t=-6.5, p<10-), with evidence for sex moderation (t=-2.4, p=0.019). Smoking during pregnancy was associated with smaller hypothalamus volume in newborns (t=-2.05, p=0.04; dose dependence: t=-2.9, p<0.01). Smoking remained associated with reduced hypothalamus volume in adolescents (t=-2.8, p=0.005). ConclusionsThe findings suggest that the hypothalamus is a crucial and underexplored target of perinatal influences for understanding the origins of long-term health and disease. Impact- This study highlights the hypothalamus as a critical and underexplored target for understanding how prenatal exposures in human newborns could influence long-term health and disease. - Gestational age (GA) at birth, postmenstrual age at scan, and smoking during pregnancy are associated with hypothalamic volume in newborns. - The effects of GA on adjusted hypothalamic volume appear to be transient, while the effects of smoking seem to last throughout adolescence. - Our findings suggested sex-specific effects on the associations between volume and age measures across development.

neuroscience↗

bioRxiv2022

Towards A More Informative Representation of the Fetal-Neonatal Brain Connectome using Variational Autoencoder

Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal-neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the lifespan. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal-neonatal brain development, including non-linear growth, for the first time, we developed quantitative, systems-wide representations of neuronal circuitry in a large sample (>700) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called Variational Autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting state data in healthy adults. Here, we demonstrated that non-linear brain features, i.e., latent variables, derived with the VAE, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, non-linear fetal-neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life.

neuroscience↗