Biology subjects

Donald, K. A.

Publications and source records attributed to Donald, K. A..

10 recordsLinked to original sources

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↗

bioRxiv2025

Microbiome-behavior coupling shapes infant adaptation to early maternal unpredictability

We asked whether infant microbiome composition, together with infants behavioral responses to early maternal unpredictability, can offer novel mechanistic insights into behavioral phenotypic variation in human development. Maternal unpredictability was computed as the entropy rate of transitions among maternal sensory signals during naturalistic mother-infant interaction (n=256 dyads; 2-6 months). Infant visual orienting behavior (VOB) was indexed by infants simultaneous gaze shifts to and from the mother. Higher maternal unpredictability predicted more frequent VOB shifts, indicating a mature-for-age profile, and longitudinally forecast poorer inhibitory control at 19-28 months. A subgroup of infants with high maternal unpredictability and high VOB had inhibitory control outcomes closer to those of infants who experienced low maternal unpredictability, suggesting VOB strategy might buffer against the effects of high maternal unpredictability. In participants with metagenomic data (n=93), VOB was associated with taxonomic and functional gut microbial profiles along a Bifidobacterium breve to Bifidobacterium longum axis. Neuroactive gene-set enrichment analysis linked VOB to increase in tryptophan synthesis and glutamate synthesis genes. In contrast, maternal unpredictability showed depletions of GABA and tryptophan synthesis in the unique-effects model and, when VOB and the maternal unpredictability by VOB term were included, enrichment of acetate synthesis and quinolinic acid degradation. Notably, the maternal unpredictability by VOB interactions for tryptophan and glutamate were negative, indicating that the VOB-pathway coupling attenuates as unpredictability increases. Together, these findings support a framework in which maternal unpredictability is the environmental challenge, infant VOB expresses the behavioral strategy, and microbial metabolism modulates whether that strategy is biochemically feasible. TeaserInfant gut microbiome is linked to infant behavioral responses to maternal unpredictability, which in turn impacts how they learn to control their actions.

neuroscience↗

bioRxiv2025

Growth charts of infant visual neurodevelopment generalize across global contexts

Normative brain growth charts in early life hold great promise for furthering basic and clinical science. We leverage the rapid, substantial development of visual cortex function that is indexed by visual-evoked potentials (VEP) in electroencephalography to create longitudinal normative growth curves of task-related brain function with 1374 observations contributed by 802 infants (57 to 579 days old) from South Africa, Brazil, and the United States. Site-specific models were cross-validated and showed excellent fits to other sites samples, demonstrating functional growth curves generalize across contexts robustly. Deviations from the normative growth models associated with early environmental and behavioral measures such as prenatal exposures and postnatal cognition. These findings demonstrate the utility of using functional growth charts to understand and potentially act on individual neurodevelopmental trajectories. VEP brain function growth charts represent a new direction for EEG research to support healthy brain development globally.

neuroscience↗

bioRxiv2025

Maternal and child immune profiles are associated with neurometabolite measures of early-life neuroinflammation in children who are HIV-exposed and uninfected: a South African birth cohort

Children who are HIV-exposed and uninfected (HEU) are at risk of neurodevelopmental delays, which may be partially due to maternal immune dysregulation during pregnancy. This study investigates associations between maternal and child immune profiles and early neurometabolite profiles in HEU and HIV-unexposed (HU) children from a South African birth cohort. A subgroup of 156 children (66 HEU, 90 HU) from the Drakenstein Child Health Study underwent magnetic resonance spectroscopy at age 2-3 years, and maternal and child serum markers were measured at multiple timepoints via immunoassays. In HEU children, serum concentrations of maternal pro-inflammatory cytokines IL-5 ({beta}=0.79, p=0.005) and IL-8 ({beta}=0.64, p=0.02) were associated with myo-inositol ratios in parietal grey and white matter regions, respectively, while child serum MMP-9 at two years was associated with myo-inositol ratios in the midline parietal grey matter ({beta}=1.30, p=0.03). The association of maternal anti-inflammatory cytokine IL-13 with glutamate ratios in the midline parietal grey matter was negative in HEU ({beta}=-0.41, p=0.038) and positive in HU children ({beta}=0.42, p<0.0001). These findings suggest maternal immune activation may affect neurometabolite profiles in HEU children.

neuroscience↗

bioRxiv2024

Deep learning super-resolution of paediatric ultra-low-field MRI without paired high-field scans

Brain magnetic resonance imaging (MRI) is essential for diagnosis and neurodevelopmental research, but the high cost and infrastructure demands of high-field MRI limit its use to high-income settings. Ultra-low-field MRI scanners offer a more affordable and energy-efficient alternative, but their reduced resolution and signal-to-noise ratio restrict research and clinical utility, prompting the need for super-resolution techniques. Current super-resolution methods rely on either three anisotropic ultra-low-field scans acquired at different orientations (axial, coronal, sagittal) to reconstruct a higher-resolution image using multi-resolution registration (MRR) or the training of deep learning models using paired ultra-low- and high-field scans. Since acquiring three high-quality ultra-low-field scans is not always feasible, and paired high-field data may not be available, this study explores the efficacy of using a deep learning model to generate scans of MRR quality from a single ultra-low-field input scan. Results demonstrated significant enhancement in the quality of output scans, including improved image quality metrics, stronger tissue volume correlations, and greater Dice overlap of tissue segmentations. Generating higher-resolution brain scans from single ultra-low-field scans, without paired high-field data, reduces scanning time and further widens MRI accessibility in low- and middle-income countries. This approach also facilitates site-specific model training, which an exploratory external validation suggests may be necessary to address potential domain shifts across scanning sites.

neuroscience↗

bioRxiv2024

Early life microbial succession in the gut follows common patterns in humans across the globe

Characterizing the dynamics of microbial community succession in the infant gut microbiome is crucial for understanding child health and development, but no normative model currently exists. Here, we estimate child age using gut microbial taxonomic relative abundances from metagenomes, with high temporal resolution ({+/-}3 months) for the first 1.5 years of life. Using 3,154 samples from 1,827 infants across 12 countries, we trained a random forest model, achieving a root mean square error of 2.61 months. We identified key taxonomic predictors of age, including declines in Bifidobacterium spp. and increases in Faecalibacterium prausnitzii and Lachnospiraceae. Microbial succession patterns are conserved across infants from diverse human populations, suggesting universal developmental trajectories. Functional analysis confirmed trends in key microbial genes involved in feeding transitions and dietary exposures. This model provides a normative benchmark of "microbiome age" for assessing early gut maturation that can be used alongside other measures of child development.

microbiology↗

bioRxiv2024

Evaluating a Novel High-Density EEG Sensor Net Structure for Improving Inclusivity in Infants with Curly or Tightly Coiled Hair

Electroencephalography (EEG) is an important tool in the field of developmental cognitive neuroscience for indexing neural activity. However, racial biases persist in EEG research that limit the utility of this tool. One bias comes from the structure of EEG nets/caps that do not facilitate equitable data collection across hair textures and types. Recent efforts have improved EEG net/cap design, but these solutions can be time-intensive, reduce sensor density, and are more difficult to implement in younger populations. The present study focused on testing EEG sensor net designs over infancy. Specifically, we compared EEG data quality and retention between two high-density saline-based EEG sensor net designs from the same company (Magstim EGI, Whitland, UK) within the same infants during a baseline EEG paradigm. We found that within infants, the tall sensor nets resulted in lower impedances during collection, including lower impedances in the key online reference electrode for those with greater hair heights and resulted in a greater number of usable EEG channels and data segments retained during pre-processing. These results suggest that along with other best practices, the modified tall sensor net design is useful for improving data quality and retention in infant participants with curly or tightly-coiled hair.

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↗

bioRxiv2018

Maternal psychosocial risk factors and offspring gestational epigenetic age acceleration in a South African birth cohort study

Epigenetic age (EA) acceleration is associated with higher risk of chronic disease and mortality in adults. However, little is known about whether and how in utero exposures might shape gestational EA acceleration at birth. We aimed to explore associations between maternal psychosocial risk factors and offspring gestational EA acceleration at birth in a South African birth cohort study - the Drakenstein Child Health Study. Maternal psychosocial risk factors included trauma/stressor exposure; posttraumatic stress disorder (PTSD); depression, psychological distress; and alcohol/tobacco use. Offspring gestational EA acceleration at birth was calculated using an epigenetic clock previously devised for neonates. Bivariate linear regression was used to explore unadjusted associations between maternal risk factors and offspring gestational EA acceleration at birth. A stepwise regression method was then used to determine the best multivariable model for adjusted associations. Data from 272 maternal-offspring dyads were included in the current analysis. In the stepwise regression model, maternal trauma exposure ({beta}=7.92; p<0.01) or PTSD ({beta}=7.46; p<0.01) were significantly associated with offspring gestational EA acceleration at birth, controlling for ethnicity, offspring sex, head circumference at birth, maternal HIV status, and prenatal tobacco or alcohol use. In site-stratified models, these associations retained statistical significance and direction of effect. Maternal trauma exposure or PTSD may thus be associated with offspring gestational EA acceleration at birth. Given the novelty of this preliminary finding, and its potential translational relevance, further studies to delineate underlying biological pathways and to explore clinical implications of EA acceleration are warranted.

genomics↗

bioRxiv2017

Gene expression in cord blood links genetic risk for neurodevelopmental disorders with maternal psychological distress and adverse childhood outcomes

Prenatal exposure to maternal stress and depression has been identified as a risk factor for adverse behavioral and neurodevelopmental outcomes in early childhood. However, the molecular mechanisms through which maternal psychopathology shapes offspring development remain poorly understood. We applied transcriptome-wide screens to 149 umbilical cord blood samples from neonates born to mothers with posttraumatic stress disorder (PTSD; n=20), depression (n=31) and PTSD with comorbid depression (n=13), compared to carefully matched trauma exposed controls (n=23) and healthy mothers (n=62). Analyses by maternal diagnoses revealed a clear pattern of gene expression signatures distinguishing neonates born to mothers with a history of psychopathology from those without. Co-expression network analysis identified distinct gene expression perturbations across maternal diagnoses, including two depression-related modules implicated in axon-guidance and mRNA stability, as well as two PTSD-related modules implicated in TNF signaling and cellular response to stress. Notably, these disease-related modules were enriched with brain-expressed genes and genetic risk loci for autism spectrum disorder and schizophrenia, which may imply a causal role for impaired developmental outcomes. These molecular alterations preceded changes in clinical measures at twenty-four months, including reductions in cognitive and socio-emotional outcomes in affected infants. Collectively, these findings indicate that prenatal exposure to maternal psychological distress induces neuronal, immunological and behavioral abnormalities in affected offspring and support the search for early biomarkers of exposures to adverse in utero environments and the classification of children at risk for impaired development.

developmental biology↗