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Background Despite advances in antenatal and neonatal care, preterm birth remains a leading cause of neurological disabilities in children. Infants born prematurely, particularly those delivered at the earliest gestational ages, commonly demonstrate increased rates of impairment across multiple neurodevelopmental domains. Indeed, the current literature establishes that preterm birth is a leading risk factor for cerebral palsy, is associated with executive function deficits, increases risk for impaired receptive and expressive language skills, and is linked with higher rates of co-occurring attention deficit hyperactivity disorder, anxiety, and autism spectrum disorders. These same infants also demonstrate elevated rates of aberrant cerebral structural and functional connectivity, with persistent changes evident across advanced magnetic resonance imaging modalities as early as the neonatal period. Emerging findings from cross-sectional and longitudinal investigations increasingly suggest that aberrant connectivity within key functional networks and white matter tracts may underlie the neurodevelopmental impairments common in this population. Main body This review begins by highlighting the elevated rates of neurodevelopmental disorders across domains in this clinical population, describes the patterns of aberrant structural and functional connectivity common in prematurely-born infants and children, and then reviews the increasingly established body of literature delineating the relationship between these brain abnormalities and adverse neurodevelopmental outcomes. We also detail important, typically understudied, clinical, and social variables that may influence these relationships among preterm children, including heritability and psychosocial risks. Conclusion Future work in this domain should continue to leverage longitudinal evaluations of preterm infants which include both neuroimaging and detailed serial neurodevelopmental assessments to further characterize relationships between imaging measures and impairment, information necessary for advancing our understanding of modifiable risk factors underlying these disorders and best practices for improving neurodevelopmental trajectories in this high-risk clinical population.

Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prematurely-born infants. Application of new analytical approaches can help translate the improved understanding of early functional connectivity provided through these studies into predictive models of neurodevelopmental outcome. One approach to achieving this goal is multivariate pattern analysis, a machine-learning, pattern classification approach well-suited for high-dimensional neuroimaging data. It has previously been adapted to predict brain maturity in children and adolescents using structural and resting state-functional MRI data. In this study, we evaluated resting state-functional MRI data from 50 preterm-born infants (born at 23–29weeks of gestation and without moderate–severe brain injury) scanned at term equivalent postmenstrual age compared with data from 50 term-born control infants studied within the first week of life. Using 214 regions of interest, binary support vector machines distinguished term from preterm infants with 84% accuracy (p<0.0001). Inter- and intra-hemispheric connections throughout the brain were important for group categorization, indicating that widespread changes in the brain's functional network architecture associated with preterm birth are detectable by term equivalent age. Support vector regression enabled quantitative estimation of birth gestational age in single subjects using only term equivalent resting state-functional MRI data, indicating that the present approach is sensitive to the degree of disruption of brain development associated with preterm birth (using gestational age as a surrogate for the extent of disruption). This suggests that support vector regression may provide a means for predicting neurodevelopmental outcome in individual infants. •Multivariate pattern analysis was applied to neonatal functional MRI data.•Support vector machines accurately distinguished term- from preterm-born infants.•Differences in inter- and intrahemispheric functional connectivity were widespread.•Support vector regression estimated birth gestational age of individual infants.•Results suggest potential for presymptomatic prediction of developmental outcomes.

Environmental influences on brain structure and function during early development have been well-characterized, but whether early environments are associated with the pace of brain development is not clear. In pre-registered analyses, we use flexible non-linear models to test the theory that prenatal disadvantage is associated with differences in trajectories of intrinsic brain network development from birth to three years ( n  = 261). Prenatal disadvantage was assessed using a latent factor of socioeconomic disadvantage that included measures of mother’s income-to-needs ratio, educational attainment, area deprivation index, insurance status, and nutrition. We find that prenatal disadvantage is associated with developmental increases in cortical network segregation, with neonates and toddlers with greater exposure to prenatal disadvantage showing a steeper increase in cortical network segregation with age, consistent with accelerated network development. Associations between prenatal disadvantage and cortical network segregation occur at the local scale and conform to a sensorimotor-association hierarchy of cortical organization. Disadvantage-associated differences in cortical network segregation are associated with language abilities at two years, such that lower segregation is associated with improved language abilities. These results shed light on associations between the early environment and trajectories of cortical development. Early environmental factors, like disadvantage, are associated with neurocognitive development. Here, the authors find that neonates and toddlers from economically disadvantaged backgrounds show accelerated brain development, with implications for language abilities in toddlerhood.

The importance of motion correction when processing resting state functional magnetic resonance imaging (rs-fMRI) data is well-established in adult cohorts. This includes adjustments based on self-limited, large amplitude subject head motion, as well as factitious rhythmic motion induced by respiration. In adults, such respiration artifact can be effectively removed by applying a notch filter to the motion trace, resulting in higher amounts of data retained after frame censoring (e.g., “scrubbing”) and more reliable correlation values. Due to the unique physiological and behavioral characteristics of infants and toddlers, rs-fMRI processing pipelines, including methods to identify and remove colored noise due to subject motion, must be appropriately modified to accurately reflect true neuronal signal. These younger cohorts are characterized by higher respiration rates and lower-amplitude head movements than adults; thus, the presence and significance of comparable respiratory artifact and the subsequent necessity of applying similar techniques remain unknown. Herein, we identify and characterize the consistent presence of respiratory artifact in rs-fMRI data collected during natural sleep in infants and toddlers across two independent cohorts (aged 8–24 months) analyzed using different pipelines. We further demonstrate how removing this artifact using an age-specific notch filter allows for both improved data quality and data retention in measured results. Importantly, this work reveals the critical need to identify and address respiratory-driven head motion in fMRI data acquired in young populations through the use of age-specific motion filters as a mechanism to optimize the accuracy of measured results in this population.

Health disparities are driven by underlying social disadvantage and psychosocial stressors. However, how social disadvantage and psychosocial stressors lead to adverse health outcomes is unclear, particularly when exposure begins prenatally. Variations in the gut microbiome and circulating proinflammatory cytokines offer potential mechanistic pathways. Here, we interrogate the gut microbiome of mother-child dyads to compare high-versus-low prenatal social disadvantage, psychosocial stressors and maternal circulating cytokine cohorts (prospective case-control study design using gut microbiomes from 121 dyads profiled with 16 S rRNA sequencing and 89 dyads with shotgun metagenomic sequencing). Gut microbiome characteristics significantly predictive of social disadvantage and psychosocial stressors in the mothers and children indicate that different discriminatory taxa and related pathways are involved, including many species of Bifidobacterium and related pathways across several comparisons. The lowest inter-individual gut microbiome similarity was observed among high-social disadvantage/high-psychosocial stressors mothers, suggesting distinct environmental exposures driving a diverging gut microbiome assembly compared to low-social disadvantage/low-psychosocial stressors controls ( P  = 3.5 × 10 −5 for social disadvantage, P  = 2.7 × 10 −15 for psychosocial stressors). Children’s gut metagenome profiles at 4 months also significantly predicted high/low maternal prenatal IL-6 ( P  = 0.029), with many bacterial species overlapping those identified by social disadvantage and psychosocial stressors. These differences, based on maternal social and psychological status during a critical developmental window early in life, offer potentially modifiable targets to mitigate health inequities. Here, in a cohort of mother-child dyads, the authors show that maternal prenatal social disadvantage and psychosocial stressors associate with distinct gut microbiome taxonomic and functional diversity in both the mothers and their four-month children.

T1- and T2-weighted (T1w and T2w) images are essential for tissue classification and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However, these anatomical data can be challenging to acquire in non-sedated neonatal cohorts, which are prone to high amplitude movement and display lower tissue contrast than adults. As a result, one of these modalities may be missing or of such poor quality that they cannot be used for accurate image processing, resulting in subject loss. While recent literature attempts to overcome these issues in adult populations using synthetic imaging approaches, evaluation of the efficacy of these methods in pediatric populations and the impact of these techniques in conventional MR analyses has not been performed. In this work, we present two novel methods to generate pseudo-T2w images: the first is based in deep learning and expands upon previous models to 3D imaging without the requirement of paired data, the second is based in nonlinear multi-atlas registration providing a computationally lightweight alternative. We demonstrate the anatomical accuracy of pseudo-T2w images and their efficacy in existing MR processing pipelines in two independent neonatal cohorts. Critically, we show that implementing these pseudo-T2w methods in resting-state functional MRI analyses produces virtually identical functional connectivity results when compared to those resulting from T2w images, confirming their utility in infant MRI studies for salvaging otherwise lost subject data.

Prenatal exposure to heightened maternal inflammation has been associated with adverse neurodevelopmental outcomes, including atypical brain maturation and psychiatric illness. In mothers experiencing socioeconomic disadvantage, immune activation can be a product of the chronic stress inherent to such environmental hardship. While growing preclinical and clinical evidence has shown links between altered neonatal brain development and increased inflammatory states in utero, the potential mechanism by which socioeconomic disadvantage differentially impacts neural-immune crosstalk remains unclear. In the current study, we investigated associations between socioeconomic disadvantage, gestational inflammation, and neonatal white matter microstructure in 320 mother-infant dyads over-sampled for poverty. We analyzed maternal serum levels of four cytokines (IL-6, IL-8, IL-10, TNF-α) over the course of pregnancy in relation to offspring white matter microstructure and socioeconomic disadvantage. Higher average maternal IL-6 was associated with very low socioeconomic status (SES; INR < 200% poverty line) and lower neonatal corticospinal fractional anisotropy (FA) and lower uncinate axial diffusivity (AD). No other cytokine was associated with SES. Higher average maternal IL-10 was associated with lower FA and higher radial diffusivity (RD) in corpus callosum and corticospinal tracts, higher optic radiation RD, lower uncinate AD, and lower FA in inferior fronto-occipital fasciculus and anterior limb of internal capsule tracts. SES moderated the relationship between average maternal TNF-α levels during gestation and neonatal white matter diffusivity. When these interactions were decomposed, the patterns indicated that this association was significant and positive among very low SES neonates, whereby TNF-α was inversely and significantly associated with inferior cingulum AD. By contrast, among the more advantaged neonates (lower-to-higher SES [INR ≥ 200% poverty line]), TNF-α was positively and significantly associated with superior cingulum AD. Taken together, these findings suggest that the relationship between prenatal cytokine exposure and white matter microstructure differs as a function of SES. These patterns are consistent with a scenario where gestational inflammation’s effects on white matter development diverge depending on the availability of foundational resources in utero.

Background Among presenting conditions in pediatric acute care settings, conduct disorder (CD) is a potentially stigmatizing yet common diagnosis in the setting of behavioral dysregulation requiring psychiatric admission. Concerns exist about over‐diagnosis of CD in non‐Hispanic Black children relative to White peers and the potential for the CD diagnosis to obfuscate manifestations of co‐occurring psychiatric conditions. Methods We evaluated the number of manuscripts on CD diagnoses that report race and ethnicity and co‐occurring mental health characteristics (i.e., history of trauma, attention‐deficit hyperactivity disorder [ADHD], or substance use disorders [SUDs]) that are often undertreated in racially minoritized children. Using the keywords “conduct disorder” or “externalizing disorder,” we screened peer‐reviewed articles in PubMed published from January 2013 to November 2023. Results We screened 2791 manuscripts for potential inclusion, of which 136 were original research articles with data on children with CD diagnoses. Thirteen articles contained racial and ethnic data for people with CD diagnoses, with the majority illustrating that Black children were overrepresented among those with CD diagnoses. No studies provided details on how race and ethnicity were ascertained and few mentioned racism as a potential explanation for racial disparities in CD diagnoses. No studies contained data collected after 2016, with three studies using data collected as early as 2001–2002. Across all articles, data on co‐occurring trauma, ADHD, and SUDs were scarce. Conclusions The overwhelming majority of studies on CD diagnoses did not provide comprehensive data on race and ethnicity and co‐occurring psychiatric disorders in children receiving diagnoses of CD. Highlights Conduct disorder (CD) is a potentially stigmatizing diagnosis that is overrepresented in Black children relative to White peers in the US and is commonly diagnosed during emergency room visits for behavioral problems. We conducted a rapid review to evaluate the number of manuscripts on CD diagnoses that account for race and ethnicity and co‐occurring mental health characteristics that can manifest in symptoms overlapping with CD's behavioral manifestations. Race and ethnicity was not documented in most clinical studies of CD diagnoses, illustrating a significant area for improvement in research.

During the third trimester of human brain development, the cerebral cortex undergoes dramatic surface expansion and folding. Physical models suggest that relatively rapid growth of the cortical gray matter helps drive this folding, and structural data suggest that growth may vary in both space (by region on the cortical surface) and time. In this study, we propose a unique method to estimate local growth from sequential cortical reconstructions. Using anatomically constrained multimodal surface matching (aMSM), we obtain accurate, physically guided point correspondence between younger and older cortical reconstructions of the same individual. From each pair of surfaces, we calculate continuous, smooth maps of cortical expansion with unprecedented precision. By considering 30 preterm infants scanned two to four times during the period of rapid cortical expansion (28–38 wk postmenstrual age), we observe significant regional differences in growth across the cortical surface that are consistent with the emergence of new folds. Furthermore, these growth patterns shift over the course of development, with noninjured subjects following a highly consistent trajectory. This information provides a detailed picture of dynamic changes in cortical growth, connecting what is known about patterns of development at the microscopic (cellular) and macroscopic (folding) scales. Since our method provides specific growth maps for individual brains, we are also able to detect alterations due to injury. This fully automated surface analysis, based on tools freely available to the brain-mapping community, may also serve as a useful approach for future studies of abnormal growth due to genetic disorders, injury, or other environmental variables.

Background: Preterm infants are at risk for white matter (WM) injury and adverse neurodevelopmental outcomes. Methods: Serial diffusion tensor magnetic resonance imaging data were obtained from very preterm infants ( N = 78) born <30 wk gestation imaged up to four times from 26–42 wk postmenstrual age. Slopes were calculated for fractional anisotropy (FA) and mean diffusivity (MD) within regions of interest for infants with ≥2 scans ( N = 50). Sixty-five children underwent neurodevelopmental testing at 2 y of age. Results: FA slope for the posterior limb of the internal capsule was greater than other regions. The anterior limb of the internal capsule (ALIC), corpus callosum, and optic radiations demonstrated greater FA slope with increasing gestational age. Infants with patent ductus arteriosus had lower FA slope in the ALIC. MD slope was lower with prolonged ventilation or lack of antenatal steroids. At 2 y of age, lower motor scores were associated with lower FA in the left but higher FA in the right inferior temporal lobe at term-equivalent age. Better social–emotional competence was related to lower FA in the left cingulum bundle. Conclusion: This study demonstrates regional variability in the susceptibility/sensitivity of WM maturation to perinatal factors and relationships between altered diffusion measures and developmental outcomes in preterm neonates.