Diffuse excessive high signal intensity in the preterm brain on advanced MRI represents widespread neuropathology

Early life brain network connectivity antecedents of executive function in children born preterm

Preterm birth is associated with an increased risk of executive function (EF) deficits, yet the underlying neural mechanisms remain unclear. We combine diffusion MRI, resting-state functional MRI, and graph theory analyses to examine how structural (SC) and functional connectivity (FC) at term-equivalent age (TEA) influence EF outcomes at 3 years corrected age in children born at or below 32 weeks' gestation. Here we show shorter average path length (a measure of efficient structural communication) in the insula is linked to better EF performance, implying that more direct structural pathways in this region facilitate critical cognitive processes. Additionally, higher betweenness centrality (a node-level metric of information flow) in parietal and superior temporal regions is associated with improved EF, reflecting these areas' prominent integrative roles in the whole-brain functional network. Importantly, our multimodal analyses reveal that regional structural efficiency helps shape functional organization, indicating a specific interplay between white-matter architecture and emergent functional hubs at TEA. These findings extend current knowledge by demonstrating how earlier disruptions in SC can alter subsequent FC patterns that support EF. By focusing on precise node-level metrics rather than broad within-network effects, our results clarify the contribution that SC has in guiding functional relationships essential for EF.

Brain MRI T2 hyperintensity and neurodevelopmental outcomes in neonatal encephalopathy

BACKGROUND

The objective of this study was to examine the association between early brain MRI T2 hyperintensity and neurodevelopmental outcomes in term infants with neonatal encephalopathy.

METHODS

A prospective cohort of neonates born ≥ 36 weeks postmenstrual age with neonatal encephalopathy underwent brain MRI in the early postnatal period. Scans were graded for T2 hyperintensity using Kidokoro scoring, and diffusion restriction using Barkovich scoring. The association between T2 hyperintensity (diffuse, mamillary body, pons) and Bayley-III cognitive, language, and motor composite scores at 3 years was examined using multivariable linear regression modeling.

RESULTS

The cohort included 102 term infants (63% males), with brain MRI at a median of 4 days of age (IQR: 1). T2 hyperintensity was present in 76% diffusely, 28% in the mamillary bodies, and 17% in the pons. Diffuse T2 hyperintensity score and mamillary body T2 hyperintensity were not associated with cognitive, language, and motor outcomes at 3 years of age when controlling for diffusion restriction.

CONCLUSIONS

T2 hyperintensity is a common neuroimaging finding on early brain MRI in neonatal encephalopathy. Diffuse, mamillary body, and pontine T2 hyperintensity were not associated with early neurodevelopmental outcomes and can help guide neuroprognostication in this population.

IMPACT STATEMENT

T2 hyperintensity on early brain MRI is a common finding in neonatal encephalopathy, however, it is not associated with neurodevelopmental outcomes at 3 years. These results can help with neuroprognostication in the neonatal intensive care unit. T2 hyperintensity in neonatal encephalopathy on early brain MRI is unlikely to influence future cognitive, language, and motor outcomes.

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment

The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning models has yielded promising outcomes in discerning phenotypic characteristics and enhancing disease diagnosis. The development of such a technique hinges on the efficient fusion of heterogeneous multimodal features, which initially reside within distinct representation spaces. Naively fusing the multimodal features does not adequately capture the complementary information and could even produce redundancy. In this work, we present a novel joint self-supervised and supervised contrastive learning method to learn the robust latent feature representation from multimodal MRI data, allowing the projection of heterogeneous features into a shared common space, and thereby amalgamating both complementary and analogous information across various modalities and among similar subjects. We performed a comparative analysis between our proposed method and alternative deep multimodal learning approaches. Through extensive experiments on two independent datasets, the results demonstrated that our method is significantly superior to several other deep multimodal learning methods in predicting abnormal neurodevelopment. Our method has the capability to facilitate computer-aided diagnosis within clinical practice, harnessing the power of multimodal data. The source code of the proposed model is publicly accessible on GitHub: https://github.com/leonzyzy/Contrastive-Network.

Comparative utility of MRI and EEG for early detection of cortical dysmaturation after postnatal systemic inflammation in the neonatal rat

BACKGROUND

Exposure to postnatal systemic inflammation is associated with increased risk of brain injury in preterm infants, leading to impaired maturation of the cerebral cortex and adverse neurodevelopmental outcomes. However, the optimal method for identifying cortical dysmaturation is unclear. Herein, we compared the utility of electroencephalography (EEG), diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI) at different recovery times after systemic inflammation in newborn rats.

METHODS

Sprague Dawley rat pups of both sexes received single-daily lipopolysaccharide (LPS; 0.3 mg/kg i.p.; n = 51) or saline (n = 55) injections on postnatal days (P)1, 2, and 3. A subset of these animals were implanted with EEG electrodes. Cortical EEG was recorded for 30 min from unanesthetized, unrestrained pups at P7, P14, and P21, and in separate groups, brain tissues were collected at these ages for ex-vivo MRI analysis (9.4 T) and Golgi-Cox staining (to assess neuronal morphology) in the motor cortex.

RESULTS

Postnatal inflammation was associated with reduced cortical pyramidal neuron arborization from P7, P14, and P21. These changes were associated with dysmature EEG features (e.g., persistence of delta waveforms, higher EEG amplitude, reduced spectral edge frequency) at P7 and P14, and higher EEG power in the theta and alpha ranges at P21. By contrast, there were no changes in cortical DTI or NODDI in LPS rats at P7 or P14, while there was an increase in cortical fractional anisotropy (FA) and decrease in orientation dispersion index (ODI) at P21.

CONCLUSIONS

EEG may be useful for identifying the early evolution of impaired cortical development after early life postnatal systemic inflammation, while DTI and NODDI seem to be more suited to assessing established cortical changes.

Structural connectivity at term equivalent age and language in preterm children at 2 years corrected

We previously reported interhemispheric structural hyperconnectivity bypassing the corpus callosum in children born extremely preterm (<28 weeks) versus term children. This increased connectivity was positively associated with language performance at 4-6 years of age in our prior work. In the present study, we aim to investigate whether this extracallosal connectivity develops in extremely preterm infants at term equivalent age by leveraging a prospective cohort study of 350 very and extremely preterm infants followed longitudinally in the Cincinnati Infant Neurodevelopment Early Prediction Study. For this secondary analysis, we included only children born extremely preterm and without significant brain injury (n = 95). We use higher-order diffusion modelling to assess the degree to which extracallosal pathways are present in extremely preterm infants and predictive of later language scores at 22-26 months corrected age. We compare results obtained from two higher-order diffusion models: generalized q-sampling imaging and constrained spherical deconvolution. Advanced MRI was obtained at term equivalent age (39-44 weeks post-menstrual age). For structural connectometry analysis, we assessed the level of correlation between white matter connectivity at the whole-brain level at term equivalent age and language scores at 2 years corrected age, controlling for post-menstrual age, sex, brain abnormality score and social risk. For our constrained spherical deconvolution analyses, we performed connectivity-based fixel enhancement, using probabilistic tractography to inform statistical testing of the hypothesis that fibre metrics at term equivalent age relate to language scores at 2 years corrected age after adjusting for covariates. Ninety-five infants were extremely preterm with no significant brain injury. Of these, 53 had complete neurodevelopmental and imaging data sets that passed quality control. In the connectometry analyses adjusted for covariates and multiple comparisons (P < 0.05), the following tracks were inversely correlated with language: bilateral cerebellar white matter and middle cerebellar peduncles, bilateral corticospinal tracks, posterior commissure and the posterior inferior fronto-occipital fasciculus. No tracks from the constrained spherical deconvolution/connectivity-based fixel enhancement analyses remained significant after correction for multiple comparisons. Our findings provide critical information about the ontogeny of structural brain networks supporting language in extremely preterm children. Greater connectivity in more posterior tracks that include the cerebellum and connections to the regions of the temporal lobes at term equivalent age appears to be disadvantageous for language development.

A Semi-Supervised Graph Convolutional Network for Early Prediction of Motor Abnormalities in Very Preterm Infants

Approximately 32-42% of very preterm infants develop minor motor abnormalities. Earlier diagnosis soon after birth is urgently needed because the first two years of life represent a critical window of opportunity for early neuroplasticity in infants. In this study, we developed a semi-supervised graph convolutional network (GCN) model that is able to simultaneously learn the neuroimaging features of subjects and consider the pairwise similarity between them. The semi-supervised GCN model also allows us to combine labeled data with additional unlabeled data to facilitate model training. We conducted our experiments on a multisite regional cohort of 224 preterm infants (119 labeled subjects and 105 unlabeled subjects) who were born at 32 weeks or earlier from the Cincinnati Infant Neurodevelopment Early Prediction Study. A weighted loss function was applied to mitigate the impact of an imbalanced positive:negative (~1:2) subject ratio in our cohort. With only labeled data, our GCN model achieved an accuracy of 66.4% and an AUC of 0.67 in the early prediction of motor abnormalities, outperforming prior supervised learning models. By taking advantage of additional unlabeled data, the GCN model had significantly better accuracy (68.0%, p = 0.016) and a higher AUC (0.69, p = 0.029). This pilot work suggests that the semi-supervised GCN model can be utilized to aid early prediction of neurodevelopmental deficits in preterm infants.

Aberrant claustrum structure in preterm-born neonates: an MRI study

The human claustrum is a gray matter structure in the white matter between insula and striatum. Previous analysis found altered claustrum microstructure in very preterm-born adults associated with lower cognitive performance. As the claustrum development is related to hypoxia-ischemia sensitive transient cell populations being at-risk in premature birth, we hypothesized that claustrum structure is already altered in preterm-born neonates. We studied anatomical and diffusion-weighted MRIs of 83 preterm- and 83 term-born neonates at term-equivalent age. Additionally, claustrum development was analyzed both in a spectrum of 377 term-born neonates and longitudinally in 53 preterm-born subjects. Data was provided by the developing Human Connectome Project. Claustrum development showed increasing volume, increasing fractional anisotropy (FA), and decreasing mean diffusivity (MD) around term both across term- and preterm-born neonates. Relative to term-born ones, preterm-born neonates had (i) increased absolute and relative claustrum volumes, both indicating increased cellular and/or extracellular matter and being in contrast to other subcortical gray matter regions of decreased volumes such as thalamus; (ii) lower claustrum FA and higher claustrum MD, pointing at increased extracellular matrix and impaired axonal integrity; and (iii) aberrant covariance between claustrum FA and MD, respectively, and that of distributed gray matter regions, hinting at relatively altered claustrum microstructure. Results together demonstrate specifically aberrant claustrum structure in preterm-born neonates, suggesting altered claustrum development in prematurity, potentially relevant for later cognitive performance.