Altered development of structural MRI connectome hubs at near-term age in very and moderately preterm infants.

Preterm infants may exhibit altered developmental patterns of the brain structural network by endogenous and exogenous stimuli, which are quantifiable through hub and modular network topologies that develop in the third trimester. Although preterm brain networks can compensate for white matter microstructural abnormalities of core connections, less is known about how the network developmental characteristics of preterm infants differ from those of full-term infants. We identified 13 hubs and 4 modules and revealed subtle differences in edgewise connectivity and local network properties between 134 preterm and 76 full-term infants, identifying specific developmental patterns of the brain structural network in preterm infants. The modules of preterm infants showed an imbalanced composition. The edgewise connectivity in preterm infants showed significantly decreased long- and short-range connections and local network properties in the dorsal superior frontal gyrus. In contrast, the fusiform gyrus and several nonhub regions showed significantly increased wiring of short-range connections and local network properties. Our results suggested that decreased local network in the frontal lobe and excessive development in the occipital lobe may contribute to the understanding of brain developmental deviances in preterm infants.

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity.

The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.

Measuring Optokinetic Reflex and Vestibulo-Ocular Reflex in Unilateral Vestibular Organ Damage Model of Zebrafish.

One-sided vestibular disorders are common in clinical practice; however, their models have not been fully established. We investigated the effect of unilateral or bilateral deficits in the vestibular organs on the vestibulo-ocular reflex (VOR) and optokinetic reflex (OKR) of zebrafish using in-house equipment. For physical dislodgement of the otoliths in the utricles of zebrafish larvae, one or both utricles were separated from the surrounding tissue using glass capillaries. The video data from VOR and OKR tests with the larvae was collected and processed using digital signal processing techniques such as fast Fourier transform and low-pass filters. The results showed that unilateral and bilateral damage to the vestibular system significantly reduced VOR and OKR. In contrast, no significant difference was observed between unilateral and bilateral damage. This study confirmed that VOR and OKR were significantly reduced in zebrafish with unilateral and bilateral vestibular damage. Follow-up studies on unilateral vestibular disorders can be conducted using this tool.

Impact of COVID-19 pandemic on neurodevelopmental outcome in very low birth weight infants: a nationwide cohort study.

Introduction: Children who have experienced the coronavirus disease 2019 (COVID-19) pandemic are at an increased risk of adverse neurologic developmental outcomes. Limited data exist on the environmental influences of during the COVID-19 pandemic on preterm infant development. This study aimed to investigate whether COVID-19 exposure affects the neurodevelopmental outcomes in preterm children up to 3 years of age. Methods: This prospective cohort study included all very low birth weight (VLBW) infants from the Korean Neonatal Network who had undergone a neurodevelopmental assessment between January 2015, and May 2022. The neurodevelopmental outcomes along with the scores on the Bayley Scales of Infant and Toddler Development (BSID) and the Korean Developmental Screening Test for Infants and Children of pediatric patients aged 18-24 and 33-39 months who were exposed to COVID-19 were compared with those of VLBW children born and tested before the pandemic. Results: The cohort included 1,683 VLBW infants. The pandemic group had significantly lower language scores on the BSID-III at ages 18-24 months (p = 0.021) and 33-39 months (p = 0.023) than the pre-pandemic group after adjusting for gestational age, morbidity, and environmental factors. At 2nd follow-up period, the pandemic group showed significantly lower scores in the cognitive (p = 0.026) domains with a mean difference of 7 points and had a significantly higher percentage of ≤-1SD in the gross motor domain (p < 0.001) compared with the pre-pandemic group. Conclusion: Preterm children who experienced the COVID-19 pandemic are at higher risk of abnormal neurodevelopmental outcomes in the first 3 years of life than preterm infants born before the COVID-19 pandemic.

Maternal pre-pregnancy obesity affects the uncinate fasciculus white matter tract in preterm infants.

Background: A growing body of evidence suggests an association between a higher maternal pre-pregnancy body mass index (BMI) and adverse long-term neurodevelopmental outcomes for their offspring. Despite recent attention to the effects of maternal obesity on fetal and neonatal brain development, changes in the brain microstructure of preterm infants born to mothers with pre-pregnancy obesity are still not well understood. This study aimed to detect the changes in the brain microstructure of obese mothers in pre-pregnancy and their offspring born as preterm infants using diffusion tensor imaging (DTI). Methods: A total of 32 preterm infants (born to 16 mothers with normal BMI and 16 mothers with a high BMI) at <32 weeks of gestation without brain injury underwent brain magnetic resonance imaging at term-equivalent age (TEA). The BMI of all pregnant women was measured within approximately 12 weeks before pregnancy or the first 2 weeks of gestation. We analyzed the brain volume using a morphologically adaptive neonatal tissue segmentation toolbox and calculated the major white matter (WM) tracts using probabilistic maps of the Johns Hopkins University neonatal atlas. We investigated the differences in brain volume and WM microstructure between preterm infants of mothers with normal and high BMI. The DTI parameters were compared among groups using analysis of covariance adjusted for postmenstrual age at scan and multiple comparisons. Results: Preterm infants born to mothers with a high BMI showed significantly increased cortical gray matter volume (p = 0.001) and decreased WM volume (p = 0.003) after controlling for postmenstrual age and multiple comparisons. We found a significantly lower axial diffusivity in the uncinate fasciculus (UNC) in mothers with high BMI than that in mothers with normal BMI (1.690 ± 0.066 vs. 1.762 ± 0.101, respectively; p = 0.005). Conclusion: Our study is the first to demonstrate that maternal obesity impacts perinatal brain development patterns in preterm infants at TEA, even in the absence of apparent brain injury. These findings provide evidence for the detrimental effects of maternal obesity on brain developmental trajectories in offspring and suggest potential neurodevelopmental outcomes based on an altered UNC WM microstructure, which is known to be critical for language and social-emotional functions.

Sparse Hierarchical Representation Learning on Functional Brain Networks for Prediction of Autism Severity Levels.

Machine learning algorithms have been widely applied in diagnostic tools for autism spectrum disorder (ASD), revealing an altered brain connectivity. However, little is known about whether an magnetic resonance imaging (MRI)-based brain network is related to the severity of ASD symptoms in a large-scale cohort. We propose a graph convolution neural network-based framework that can generate sparse hierarchical graph representations for functional brain connectivity. Instead of assigning initial features for each node, we utilized a feature extractor to derive node features and the extracted representations can be fed to a hierarchical graph self-attention framework to effectively represent the entire graph. By incorporating connectivity embeddings in the feature extractor, we propose adjacency embedding networks to characterize the heterogeneous representations of the brain connectivity. Our proposed model variants outperform the benchmarking model with different configurations of adjacency embedding networks and types of functional connectivity matrices. Using this approach with the best configuration (SHEN atlas for node definition, Tikhonov correlation for connectivity estimation, and identity-adjacency embedding), we were able to predict individual ASD severity levels with a meaningful accuracy: the mean absolute error (MAE) and correlation between predicted and observed ASD severity scores resulted in 0.96, and r = 0.61 (P < 0.0001), respectively. To obtain a better understanding on how to generate better representations, we investigate the relationships between the extracted feature embeddings and the graph theory-based nodal measurements using canonical correlation analysis. Finally, we visualized the model to identify the most contributive functional connections for predicting ASD severity scores.

Altered Cerebral Curvature in Preterm Infants Is Associated with the Common Genetic Variation Related to Autism Spectrum Disorder and Lipid Metabolism.

Preterm births are often associated with neurodevelopmental impairment. In the critical developmental period of the fetal brain, preterm birth disrupts cortical maturation. Notably, preterm birth leads to alterations in the fronto-striatal and temporal lobes and the limbic region. Recent advances in MRI acquisition and analysis methods have revealed an integrated approach to the genetic influence on brain structure. Based on imaging studies, we hypothesized that the altered cortical structure observed after preterm birth is associated with common genetic variations. We found that the presence of the minor allele at rs1042778 in OXTR was associated with reduced curvature in the right medial orbitofrontal gyrus (p < 0.001). The presence of the minor allele at rs174576 in FADS2 (p < 0.001) or rs740603 in COMT (p < 0.001) was related to reduced curvature in the left posterior cingulate gyrus. This study provides biological insight into altered cortical curvature at term-equivalent age, suggesting that the common genetic variations related to autism spectrum disorder (ASD) and lipid metabolism may mediate vulnerability to early cortical dysmaturation in preterm infants.

Abnormal thalamocortical connectivity of preterm infants with elevated thyroid stimulating hormone identified with diffusion tensor imaging.

While thyroid disturbances during perinatal and postnatal periods in preterm infants with congenital hypothyroidism reportedly disrupt neuronal development, no study has considered the effect of thyroid disturbances in premature infants with subclinical hypothyroidism with elevations of thyroid stimulating hormone. We aimed to identify altered fiber integrity from the thalamus to cortices in preterm infants with subclinical hypothyroidism. All preterm infants born were categorized according to thyroid stimulating hormone levels through serial thyroid function tests (36 preterm controls and 29 preterm infants with subclinical hypothyroidism). Diffusion tensor images were acquired to determine differences in thalamocortical fiber lengths between the groups, and cerebral asymmetries were investigated to observe neurodevelopmental changes. Thalamocortical fiber lengths in the subclinical hypothyroidism group were significantly reduced in the bilateral superior temporal gyrus, heschl's gyrus, lingual gyrus, and calcarine cortex (all p < 0.05). According to the asymmetric value in the orbitofrontal regions, there is a left dominance in the subclinical hypothyroidism group contrary to the controls (p = 0.012), and that of the cuneus areas showed significant decreases in the subclinical hypothyroidism group (p = 0.035). These findings could reflect altered neurodevelopment, which could help treatment plans using biomarkers for subclinical hypothyroidism.

Altered microstructure of the splenium of corpus callosum is associated with neurodevelopmental impairment in preterm infants with necrotizing enterocolitis.

BACKGROUND: Necrotizing enterocolitis (NEC) is a devastating disease in preterm infants with significant morbidities, including neurodevelopmental impairment (NDI). This study aimed to investigate whether NEC is associated with (1) brain volume expansion and white matter maturation using diffusion tensor imaging analysis and (2) NDI compared with preterm infants without NEC. METHODS: We included 86 preterm infants (20 with NEC and 66 without NEC) with no evidence of brain abnormalities on trans-fontanelle ultrasonography and magnetic resonance imaging at term-equivalent age (TEA). Regional brain volume analysis and white matter tractography were performed to study brain microstructure alterations. NDI was assessed using the Bayley Scales of Infant and Toddler Development-III (BSID-III) at 18 months of corrected age (CA). RESULTS: Preterm infants with NEC showed significantly high risk of motor impairment (odds ratio 58.26, 95% confidence interval 7.80-435.12, p < 0.001). We found significantly increased mean diffusivity (MD) in the splenium of corpus callosum (sCC) (p = 0.001) and the left corticospinal tract (p = 0.001) in preterm infants with NEC. The sCC with increased MD showed a negative association with the BSID-III language (p = 0.025) and motor scores (p = 0.002) at 18 months of CA, implying the relevance of sCC integrity with later NDI. CONCLUSION: The white matter microstructure differed between preterm infants with and without NEC. The prognostic value of network parameters of sCC at TEA may provide better information for the early detection of NDI in preterm infants.