Ringshaw, J. E. | Search bioRxiv

bioRxiv2026

Persistent neuroimmune alterations in children who are HIV-exposed but uninfected at age 6-7 years: Associations with language development in a South African birth cohort

BackgroundChildren who are HIV-exposed but uninfected (HEU) are at increased risk of neurodevelopmental delays, yet neuroimmune pathways linking perinatal HIV exposure to school readiness remain unclear. MethodsIn the Drakenstein Child Health Study, 268 children (94 HEU, 174 HIV-unexposed [HU]) underwent magnetic resonance spectroscopy in midline parietal grey and left parietal white matter regions at 6-7 years. Peripheral blood serum immune markers were measured in pregnancy and in children at 6 weeks, 2, 3, and 5 years. Linear mixed-effects models characterised child immune trajectories and linear regressions tested associations with creatine-referenced neurometabolite ratios and school readiness scores. ResultsMothers living with HIV had higher sCD14 and lower MMP-9, NGAL, and GM-CSF than mothers without HIV (p<0.05). Perinatal HIV exposure was associated with altered trajectories of child sCD14, GM-CSF, IL-1{beta}, IL-5, IL-10, and YKL-40. At 6-7 years, children who were HEU had lower parietal grey matter glutamate ratios and lower left parietal white matter choline ratios. By school entry, immune-neurometabolite associations were predominantly driven by child serum markers; IL-8 emerged as a consistent correlate across developmental stages. Children who were HEU had lower language scores than HU peers. Left parietal white matter choline ratios were positively associated with language and overall school readiness in HU children, but not HEU. ConclusionsPerinatal HIV exposure was associated with alterations in immune development, neurometabolites reflecting both white matter maturation and neuronal health, and school readiness. Our findings highlight potential neuroimmune pathways contributing to neurodevelopmental risks in children who are HEU.

neuroscience↗

bioRxiv2024

Multi-orientation U-Net for Super-Resolution of Ultra-Low-Field Paediatric MRI

Owing to the high cost of modern MRI systems, their use in clinical care and neurodevelopmental research is limited to hospitals and universities in high income countries. Ultra-low-field systems with significantly lower scanning costs present a promising avenue towards global MRI accessibility, however their reduced SNR compared to 1.5 or 3T systems limits their applicability for research and clinical use. In this paper, we describe a deep learning-based super-resolution approach to generate high-resolution isotropic T2-weighted scans from low-resolution paediatric input scans. We train a multi-orientation U-Net, which uses multiple low-resolution anisotropic images acquired in orthogonal orientations to construct a super-resolved output. Our approach exhibits improved quality of outputs compared to current state-of-the-art methods for super-resolution of ultra-low-field scans in paediatric populations. Crucially for paediatric development, our approach improves reconstruction of deep brain structures with the greatest improvement in volume estimates of the caudate, where our model improves upon the state-of-the-art in: linear correlation (r = 0.94 vs 0.84 using existing methods), exact agreement (Lins concordance correlation = 0.94 vs 0.80) and mean error (0.05 cm3 vs 0.36 cm3). Our research serves as proof-of-principle of the viability of training deep-learning based super-resolution models for use in neurodevelopmental research and presents the first model trained exclusively on paired ultra-low-field and high-field data from infants.

neuroscience↗