The emergence of functional architecture during early brain development

Development of human visual cortical function: A scoping review of task- and naturalistic-fMRI studies through the interactive specialization and maturational frameworks

Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies.

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimer's disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning

The early stages of human development are increasingly acknowledged as pivotal in laying the groundwork for subsequent behavioral and cognitive development. Spatiotemporal (4D) brain functional atlases are important in elucidating the development of human brain functions. However, the scarcity of such atlases for early life stages stems from two primary challenges: (1) the significant noise in functional magnetic resonance imaging (fMRI) that complicates the generation of high-quality atlases for each age group, and (2) the rapid and complex changes in the early human brain that hinder the maintenance of temporal consistency in 4D atlases. This study tackles these challenges by integrating low-rank tensor learning with spectral embedding, thereby proposing a novel, data-driven 4D functional atlas generation framework based on spectral functional network learning (SFNL). This method utilizes low-rank tensor learning to capture common functional connectivity (FC) patterns across different ages, thus optimizing FCs for each age group to improve the temporal consistency of functional networks. Incorporating spectral embedding aids in mitigating potential noise in FC networks derived from fMRI data by reconstructing networks in the spectral space. Utilizing SFNL-generated functional networks enables the creation of consistent and highly qualified spatiotemporal functional atlases. The framework was applied to the developing Human Connectome Project (dHCP) dataset, generating the first neonatal 4D functional atlases with fine-grained temporal and spatial resolutions. Experimental evaluations focusing on functional homogeneity, reliability, and temporal consistency demonstrated the superiority of our framework compared to existing methods for constructing 4D atlases. Additionally, network analysis experiments, including individual identification, functional systems development, and local efficiency assessments, further corroborate the efficacy and robustness of the generated atlases. The 4D atlases and related codes will be made publicly accessible (https://github.com/zhaoyunxi/neonate-atlases).

Evaluation of the effect of intraventricular haemorrhage on cerebral perfusion in preterm neonates using three-dimensional pseudo-continuous arterial spin labelling

BACKGROUND

Intraventricular haemorrhage (IVH) often arises as a cerebral complication directly related to preterm birth. The impaired autoregulation of cerebral blood flow is closely associated with IVH in preterm neonates. Three-dimensional pseudo-continuous arterial spin labelling (3D-pCASL) is a noninvasive magnetic resonance imaging (MRI) technique used for evaluating cerebral perfusion.

OBJECTIVE

This study aimed to compare cerebral blood flow values among three distinct groups using 3D-pCASL: preterm neonates with and without IVH and preterm neonates at term-equivalent age.

MATERIALS AND METHODS

A total of 101 preterm neonates who underwent conventional MRI and 3D-pCASL were included in this study. These neonates were categorised into three groups: 12 preterm neonates with IVH, 52 preterm neonates without IVH, and 37 healthy neonates at term-equivalent age. Cerebral blood flow measurements were obtained from six brain regions of interest (ROIs)-the frontal lobe, temporal lobe, parietal lobe, occipital lobe, basal ganglia, and thalamus-in the right and left hemispheres.

RESULTS

The cerebral blood flow values measured in all ROIs of preterm neonates with IVH were significantly lower than those of neonates at term-equivalent age (all P<0.05). Additionally, the cerebral blood flow in the temporal lobe was lower in preterm neonates without IVH than in neonates at term-equivalent age (16.87±5.01 vs. 19.76±5.47 ml/100 g/min, P=0.012). Furthermore, a noteworthy positive correlation was observed between post-menstrual age and cerebral blood flow in the temporal lobe (P=0.037), basal ganglia (P=0.010), and thalamus (P=0.010).

CONCLUSION

The quantitative cerebral blood flow values, as measured by 3D-pCASL, highlighted that preterm neonates with IVH had decreased cerebral perfusion. This finding underscores the potential of 3D-pCASL as a technique for evaluating the developmental aspects of the brain in preterm neonates.

Functional parcellation of the neonatal cortical surface

The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. We next derive a set of 283 cortical surface parcels from a sample of n = 261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.

The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review

Machine learning and deep learning are two subsets of artificial intelligence that involve teaching computers to learn and make decisions from any sort of data. Most recent developments in artificial intelligence are coming from deep learning, which has proven revolutionary in almost all fields, from computer vision to health sciences. The effects of deep learning in medicine have changed the conventional ways of clinical application significantly. Although some sub-fields of medicine, such as pediatrics, have been relatively slow in receiving the critical benefits of deep learning, related research in pediatrics has started to accumulate to a significant level, too. Hence, in this paper, we review recently developed machine learning and deep learning-based solutions for neonatology applications. We systematically evaluate the roles of both classical machine learning and deep learning in neonatology applications, define the methodologies, including algorithmic developments, and describe the remaining challenges in the assessment of neonatal diseases by using PRISMA 2020 guidelines. To date, the primary areas of focus in neonatology regarding AI applications have included survival analysis, neuroimaging, analysis of vital parameters and biosignals, and retinopathy of prematurity diagnosis. We have categorically summarized 106 research articles from 1996 to 2022 and discussed their pros and cons, respectively. In this systematic review, we aimed to further enhance the comprehensiveness of the study. We also discuss possible directions for new AI models and the future of neonatology with the rising power of AI, suggesting roadmaps for the integration of AI into neonatal intensive care units.

Experience of early-life pain in premature infants is associated with atypical cerebellar development and later neurodevelopmental deficits

BACKGROUND

Infants born very and extremely premature (V/EPT) are at a significantly elevated risk for neurodevelopmental disorders and delays even in the absence of structural brain injuries. These risks may be due to earlier-than-typical exposure to the extrauterine environment, and its bright lights, loud noises, and exposures to painful procedures. Given the relative underdeveloped pain modulatory responses in these infants, frequent pain exposures may confer risk for later deficits.

METHODS

Resting-state fMRI scans were collected at term equivalent age from 148 (45% male) infants born V/EPT and 99 infants (56% male) born at term age. Functional connectivity analyses were performed between functional regions correlating connectivity to the number of painful skin break procedures in the NICU, including heel lances, venipunctures, and IV placements. Subsequently, preterm infants returned at 18 months, for neurodevelopmental follow-up and completed assessments for autism risk and general neurodevelopment.

RESULTS

We observed that V/EPT infants exhibit pronounced hyperconnectivity within the cerebellum and between the cerebellum and both limbic and paralimbic regions correlating with the number of skin break procedures. Moreover, skin breaks were strongly associated with autism risk, motor, and language scores at 18 months. Subsample analyses revealed that the same cerebellar connections strongly correlating with breaks at term age were associated with language dysfunction at 18 months.

CONCLUSIONS

These results have significant implications for the clinical care of preterm infants undergoing painful exposures during routine NICU care, which typically occurs without anesthesia. Repeated pain exposures appear to have an increasingly detrimental effect on brain development during a critical period, and effects continue to be seen even 18 months later.

Functional parcellation of the neonatal brain

The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that adult- and older infant-derived parcels are a poor fit with neonatal data, emphasizing the need for neonatal-specific parcels. We next derive a set of 283 cortical surface parcels from a sample of n=261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.

Longitudinal developmental trajectories of functional connectivity reveal regional distribution of distinct age effects in infancy

Prior work has shown that different functional brain networks exhibit different maturation rates, but little is known about whether and how different brain areas may differ in the exact shape of longitudinal functional connectivity growth trajectories during infancy. We used resting-state functional magnetic resonance imaging (fMRI) during natural sleep to characterize developmental trajectories of different regions using a longitudinal cohort of infants at 3 weeks (neonate), 1 year, and 2 years of age (n = 90; all with usable data at three time points). A novel whole brain heatmap analysis was performed with four mixed-effect models to determine the best fit of age-related changes for each functional connection: (i) growth effects: positive-linear-age, (ii) emergent effects: positive-log-age, (iii) pruning effects: negative-quadratic-age, and (iv) transient effects: positive-quadratic-age. Our results revealed that emergent (logarithmic) effects dominated developmental trajectory patterns, but significant pruning and transient effects were also observed, particularly in connections centered on inferior frontal and anterior cingulate areas that support social learning and conflict monitoring. Overall, unique global distribution patterns were observed for each growth model indicating that developmental trajectories for different connections are heterogeneous. All models showed significant effects concentrated in association areas, highlighting the dominance of higher-order social/cognitive development during the first 2 years of life.

Geodesic theory of long association fibers arrangement in the human fetal cortex

Association fibers connect different areas of the cerebral cortex over long distances and integrate information to achieve higher brain functions, particularly in humans. Prototyped association fibers are developed to the respective tangential direction throughout the cerebral hemispheres along the deepest border of the subplate during the fetal period. However, how guidance to remote areas is achieved is not known. Because the subplate is located below the cortical surface, the tangential direction of the fibers may be biased by the curved surface geometry due to Sylvian fissure and cortical poles. The fiber length can be minimized if the tracts follow the shortest paths (geodesics) of the curved surface. Here, we propose and examine a theory that geodesics guide the tangential direction of long association fibers by analyzing how geodesics are spatially distributed on the fetal human brains. We found that the geodesics were dense on the saddle-shaped surface of the perisylvian region and sparse on the dome-shaped cortical poles. The geodesics corresponded with the arrangement of five typical association fibers, supporting the theory. Thus, the geodesic theory provides directional guidance information for wiring remote areas and suggests that long association fibers emerge from minimizing their tangential length in fetal brains.

Fine-grained functional parcellation maps of the infant cerebral cortex

Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.

Functional neural network connectivity at 3 months predicts infant-mother dyadic flexibility during play at 6 months

Early functioning of neural networks likely underlies the flexible switching between internal and external orientation and may be key to the infant's ability to effectively engage in social interactions. To test this hypothesis, we examined the association between infants' neural networks at 3 months and infant-mother dyadic flexibility (denoting the structural variability of their interaction dynamics) at 3, 6, and 9 months. Participants included thirty-five infants (37% girls) and their mothers (87% White). At 3 months, infants participated in a resting-state functional magnetic resonance imaging session, and functional connectivity (FC) within the default mode (DMN) and salience (SN) networks, as well as DMN-SN internetwork FC, were derived using a seed-based approach. When infants were 3, 6, and 9 months, infant-mother dyads completed the Still-Face Paradigm where their individual engagement behaviors were observed and used to quantify dyadic flexibility using state space analysis. Results revealed that greater within-DMN FC, within-SN FC, and DMN-SN anticorrelation at 3 months predicted greater dyadic flexibility at 6 months, but not at 3 and 9 months. Findings suggest that early synchronization and interaction between neural networks underlying introspection and salience detection may support infants' flexible social interactions as they become increasingly active and engaged social partners.

Functional MRI responses to naturalistic stimuli are increasingly typical across early childhood

While findings show that throughout development, there are child- and age-specific patterns of brain functioning, there is also evidence for significantly greater inter-individual response variability in young children relative to adults. It is currently unclear whether this increase in functional "typicality" (i.e., inter-individual similarity) is a developmental process that occurs across early childhood, and what changes in BOLD response may be driving changes in typicality. We collected fMRI data from 81 typically developing 4-8-year-old children during passive viewing of age-appropriate television clips and asked whether there is increasing typicality of brain response across this age range. We found that the "increasing typicality" hypothesis was supported across many regions engaged by passive viewing. Post hoc analyses showed that in a priori ROIs related to language and face processing, the strength of the group-average shared component of activity increased with age, with no concomitant decline in residual signal or change in spatial extent or variability. Together, this suggests that increasing inter-individual similarity of functional responses to audiovisual stimuli is an important feature of early childhood functional brain development.

Age-related differences in resting-state functional connectivity from childhood to adolescence

The human brain is active at rest, and spontaneous fluctuations in functional MRI BOLD signals reveal an intrinsic functional architecture. During childhood and adolescence, functional networks undergo varying patterns of maturation, and measures of functional connectivity within and between networks differ as a function of age. However, many aspects of these developmental patterns (e.g. trajectory shape and directionality) remain unresolved. In the present study, we characterised age-related differences in within- and between-network resting-state functional connectivity (rsFC) and integration (i.e. participation coefficient, PC) in a large cross-sectional sample of children and adolescents (n = 628) aged 8-21 years from the Lifespan Human Connectome Project in Development. We found evidence for both linear and non-linear differences in cortical, subcortical, and cerebellar rsFC, as well as integration, that varied by age. Additionally, we found that sex moderated the relationship between age and putamen integration where males displayed significant age-related increases in putamen PC compared with females. Taken together, these results provide evidence for complex, non-linear differences in some brain systems during development.

Development of neonatal brain functional centrality and alterations associated with preterm birth

Formation of the functional connectome in early life underpins future learning and behavior. However, our understanding of how the functional organization of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterized voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and a decrease within the motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.

Correlation of abnormal brain changes with perinatal factors in very preterm infants based on diffusion tensor imaging

Background

It remains unclear whether very preterm (VP) infants have the same level of brain structure and function as full-term (FT) infants. In addition, the relationship between potential differences in brain white matter microstructure and network connectivity and specific perinatal factors has not been well characterized.

Objective

This study aimed to investigate the existence of potential differences in brain white matter microstructure and network connectivity between VP and FT infants at term-equivalent age (TEA) and examine the potential association of these differences with perinatal factors.

Methods

A total of 83 infants were prospectively selected for this study: 43 VP infants (gestational age, or GA: 27–32 weeks) and 40 FT infants (GA: 37–44 weeks). All infants at TEA underwent both conventional magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Significant differences in white matter fractional anisotropy (FA) and mean diffusivity (MD) images between the VP and FT groups were observed using tract-based spatial statistics (TBSS). The fibers were tracked between each pair of regions in the individual space, using the automated anatomical labeling (AAL) atlas. Then, a structural brain network was constructed, where the connection between each pair of nodes was defined by the number of fibers. Network-based statistics (NBS) were used to examine differences in brain network connectivity between the VP and FT groups. Additionally, multivariate linear regression was conducted to investigate potential correlations between fiber bundle numbers and network metrics (global efficiency, local efficiency, and small-worldness) and perinatal factors.

Results

Significant differences in FA were observed between the VP and FT groups in several regions. These differences were found to be significantly associated with perinatal factors such as bronchopulmonary dysplasia (BPD), activity, pulse, grimace, appearance, respiratory (APGAR) score, gestational hypertension, and infection. Significant differences in network connectivity were observed between the VP and FT groups. Linear regression results showed significant correlations between maternal years of education, weight, the APGAR score, GA at birth, and network metrics in the VP group.

Conclusions

The findings of this study shed light on the influence of perinatal factors on brain development in VP infants. These results may serve as a basis for clinical intervention and treatment to improve the outcome of preterm infants.

Mapping Human Brain Pathways: Challenges and Opportunities in the Integration of Scales

The human brain is composed of a complex web of pathways. Diffusion magnetic resonance (MR) tractography is a neuroimaging technique that relies on the principle of diffusion to reconstruct brain pathways. Its tractography is broadly applicable to a range of problems as it is amenable for study in individuals of any age and from any species. However, it is well known that this technique can generate biologically implausible pathways, especially in regions of the brain where multiple fibers cross. This review highlights potential misconnections in two cortico-cortical association pathways with a focus on the aslant tract and inferior frontal occipital fasciculus. The lack of alternative methods to validate observations from diffusion MR tractography means there is a need to develop new integrative approaches to trace human brain pathways. This review discusses integrative approaches in neuroimaging, anatomical, and transcriptional variation as having much potential to trace the evolution of human brain pathways.

Robust sex differences in functional brain connectivity are present in utero

Sex-based differences in brain structure and function are observable throughout development and are thought to contribute to differences in behavior, cognition, and the presentation of neurodevelopmental disorders. Using multiple support vector machine (SVM) models as a data-driven approach to assess sex differences, we sought to identify regions exhibiting sex-dependent differences in functional connectivity and determine whether they were robust and sufficiently reliable to classify sex even prior to birth. To accomplish this, we used a sample of 110 human fetal resting state fMRI scans from 95 fetuses, performed between 19 and 40 gestational weeks. Functional brain connectivity patterns classified fetal sex with 73% accuracy. Across SVM models, we identified features (functional connections) that reliably differentiated fetal sex. Highly consistent predictors included connections in the somatomotor and frontal areas alongside the hippocampus, cerebellum, and basal ganglia. Moreover, high consistency features also implicated a greater magnitude of cross-region connections in females, while male weighted features were predominately within anatomically bounded regions. Our findings indicate that these differences, which have been observed later in childhood, are present and reliably detectable even before birth. These results show that sex differences arise before birth in a manner that is consistent and reliable enough to be highly identifiable.

The older adult brain is less modular, more integrated, and less efficient at rest: A systematic review of large-scale resting-state functional brain networks in aging

The literature on large-scale resting-state functional brain networks across the adult lifespan was systematically reviewed. Studies published between 1986 and July 2021 were retrieved from PubMed. After reviewing 2938 records, 144 studies were included. Results on 11 network measures were summarized and assessed for certainty of the evidence using a modified GRADE method. The evidence provides high certainty that older adults display reduced within-network and increased between-network functional connectivity. Older adults also show lower segregation, modularity, efficiency and hub function, and decreased lateralization and a posterior to anterior shift at rest. Higher-order functional networks reliably showed age differences, whereas primary sensory and motor networks showed more variable results. The inflection point for network changes is often the third or fourth decade of life. Age effects were found with moderate certainty for within- and between-network altered patterns and speed of dynamic connectivity. Research on within-subject bold variability and connectivity using glucose uptake provides low certainty of age differences but warrants further study. Taken together, these age-related changes may contribute to the cognitive decline often seen in older adults.

Associations between maternal prenatal depression and neonatal behavior and brain function - Evidence from the functional near-infrared spectroscopy

BACKGROUND

Maternal prenatal depression is a significant public health issue associated with mental disorders of offspring. This study aimed to determine if maternal prenatal depressive symptoms are associated with changes in neonatal behaviors and brain function at the resting state.

METHODS

A total of 204 pregnant women were recruited during the third trimester and were evaluated by Edinburgh Postpartum Depression Scale (EPDS). The mother-infant pairs were divided into the depressed group (n = 75) and control group (n = 129) based on the EPDS, using a cut-off value of 10. Cortisol levels in the cord blood and maternal blood collected on admission for delivery were measured. On day three of life, all study newborns were evaluated by the Neonatal Behavior Assessment Scale (NBAS) and 165 infants were evaluated by resting-state functional near-infrared spectroscopy (rs-fNIRS). To minimize the influences of potential bias on the rs-fNIRS results, we used a binary logistic regression model to carry out propensity score matching between the depressed group and the control group. Rs-fNIRS data from 21 pairs of propensity score-matched newborns were used for analysis. The associations between maternal EPDS scores, neonatal NBAS scores, and cortisol levels were analyzed using linear regressions and the mediation analysis models.

RESULTS

Compared to the control group, the newborns in the depressed group had lower scores in the social-interaction and autonomic system dimensions of NBAS (P < 0.01). Maternal and umbilical cord plasma cortisol levels in the depressed group were higher (P < 0.01) than in the control group. However, only umbilical cord plasma cortisol played a negative mediating role in the relationship between maternal EPDS and NBAS in the social-interaction and autonomic system (β med = -0.054 [-0.115,-0.018] and -0.052 [-0.105,-0.019]. Proportional mediation was 13.57 % and 12.33 for social-interaction and autonomic systems, respectively. The newborns in the depressed group showed decreases in the strength of rs-fNIRS functional connections, primarily the connectivity of the left frontal-parietal and temporal-parietal regions. However, infants in the depressed and control groups showed no differences in topological characteristics of the brain network, including standardized clustering coefficient, characteristic path length, small-world property, global efficiency, and local efficiency (P > 0.05). The social-interaction Z-scores had positive correlations with functional connectivity strength of left prefrontal cortex-left parietal lobe (r = 0.57, p < 0.01)，prefrontal cortex-left parietal lobe - left temporal lobe (r = 0.593, p < 0.01) and left parietal lobe - left temporal lobe (r = 0.498, p < 0.01). Autonomic system Z-scores were also significantly positive correlation with prefrontal cortex-left parietal lobe (r = 0.509, p < 0.01)，prefrontal cortex-left parietal lobe - left temporal lobe (r = 0.464, p < 0.01), left parietal lobe - left temporal lobe (r = 0.381, p < 0.05), and right temporal lobe and left temporal lobe (r = 0.310, p < 0.05).

CONCLUSION

This study shows that maternal prenatal depression may affect the development of neonatal social-interaction and autonomic system and the strength of neonatal brain functional connectivity. The fetal cortisol may play a role in behavioral development in infants exposed to maternal prenatal depression. Our findings highlight the importance of prenatal screening for maternal depression and early postnatal behavioral evaluation that provide the opportunity for early diagnosis and intervention to improve neurodevelopmental outcomes.

Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome

Consisting of distributed and interconnected structures that interact through cortico-cortical connections and cortico-subcortical loops, the sensorimotor (SM) network undergoes rapid maturation during the perinatal period and is thus particularly vulnerable to preterm birth. However, the impact of prematurity on the development and integrity of the emerging SM connections and their relationship to later motor and global impairments are still poorly understood. In this study we aimed to explore to which extent the early microstructural maturation of SM white matter (WM) connections at term-equivalent age (TEA) is modulated by prematurity and related with neurodevelopmental outcome at 18 months corrected age. We analyzed 118 diffusion MRI datasets from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA and a control group of full-term (FT) neonates paired for age at MRI and sex. We delineated WM connections between the primary SM cortices (S1, M1 and paracentral region) and subcortical structures using probabilistic tractography, and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. To go beyond tract-specific univariate analyses, we computed a maturational distance related to prematurity based on the multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Our results confirmed the presence of microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Maturational distance analyses highlighted that prematurity has a differential effect on SM tracts with higher distances and thus impact on (i) cortico-cortical than cortico-subcortical connections; (ii) projections involving S1 than M1 and paracentral region; and (iii) the most rostral cortico-subcortical tracts, involving the lenticular nucleus. These different alterations at TEA suggested that vulnerability follows a specific pattern coherent with the established WM caudo-rostral progression of maturation. Finally, we highlighted some relationships between NODDI-derived maturational distances of specific tracts and fine motor and cognitive outcomes at 18 months. As a whole, our results expand understanding of the significant impact of premature birth and early alterations on the emerging SM network even in low-risk infants, with possible relationship with neurodevelopmental outcomes. This encourages further exploration of these potential neuroimaging markers for prediction of neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.

Functional brain activity is highly associated with cortical myelination in neonates

Functional organization of the human cerebral cortex is highly constrained by underlying brain structures, but how functional activity is associated with different brain structures during development is not clear, especially at the neonatal stage. Since long-range functional connectivity is far from mature in the dynamically developing neonatal brain, it is of great scientific significance to investigate the relationship between different structural and functional features at the local level. To this end, for the first time, correlation and regression analyses were performed to examine the relationship between cortical morphology, cortical myelination, age, and local brain functional activity, as well as functional connectivity strength using high-resolution structural and resting-state functional MRI data of 177 neonates (29-44 postmenopausal weeks, 98 male and 79 female) from both static and dynamic perspectives. We found that cortical myelination was most strongly associated with local brain functional activity across the cerebral cortex than other cortical structural features while controlling the age effect. These findings suggest the crucial role of cortical myelination in local brain functional development at birth, providing valuable insights into the fundamental biological basis of functional activity at this early developmental stage.

The Effects of Prenatal Exposure to Neighborhood Crime on Neonatal Functional Connectivity

BACKGROUND

Maternal exposure to adversity during pregnancy has been found to affect infant brain development; however, the specific effect of prenatal crime exposure on neonatal brain connectivity remains unclear. Based on existing research, we hypothesized that living in a high-crime neighborhood during pregnancy would affect neonatal frontolimbic connectivity over and above other individual- and neighborhood-level adversity and that these associations would be mediated by maternal psychosocial stress.

METHODS

Participants included 399 pregnant women, recruited as part of the eLABE (Early Life Adversity, Biological Embedding, and Risk for Developmental Precursors of Mental Disorders) study. In the neonatal period, 319 healthy, nonsedated infants were scanned using resting-state functional magnetic resonance imaging (repetition time = 800 ms; echo time = 37 ms; voxel size = 2.0 × 2.0 × 2.0 mm3; multiband = 8) on a Prisma 3T scanner and had at least 10 minutes of high-quality data. Crime data at the block group level were obtained from Applied Geographic Solution. Linear regressions and mediation models tested associations between crime, frontolimbic connectivity, and psychosocial stress.

RESULTS

Living in a neighborhood with high property crime during pregnancy was related to weaker neonatal functional connectivity between the thalamus-anterior default mode network (aDMN) (β = -0.15, 95% CI = -0.25 to -0.04, p = .008). Similarly, high neighborhood violent crime was related to weaker functional connectivity between the thalamus-aDMN (β = -0.16, 95% CI = -0.29 to -0.04, p = .01) and amygdala-hippocampus (β = -0.16, 95% CI = -0.29 to -0.03, p = .02), controlling for other types of adversity. Psychosocial stress partially mediated relationships between the thalamus-aDMN and both violent and property crime.

CONCLUSIONS

These findings suggest that prenatal exposure to crime is associated with weaker neonatal limbic and frontal functional brain connections, providing another reason for targeted public policy interventions to reduce crime.

Sensory-based interventions in the NICU: systematic review of effects on preterm brain development

BACKGROUND

Infants born preterm are known to be at risk for abnormal brain development and adverse neurobehavioral outcomes. To improve early neurodevelopment, several non-pharmacological interventions have been developed and implemented in the neonatal intensive care unit (NICU). Sensory-based interventions seem to improve short-term neurodevelopmental outcomes in the inherently stressful NICU environment. However, how this type of intervention affects brain development in the preterm population remains unclear.

METHODS

A systematic review of the literature was conducted for published studies in the past 20 years reporting the effects of early, non-pharmacological, sensory-based interventions on the neonatal brain after preterm birth.

RESULTS

Twelve randomized controlled trials (RCT) reporting short-term effects of auditory, tactile, and multisensory interventions were included after the screening of 1202 articles. Large heterogeneity was identified among studies in relation to both types of intervention and outcomes. Three areas of focus for sensory interventions were identified: auditory-based, tactile-based, and multisensory interventions.

CONCLUSIONS

Diversity in interventions and outcome measures challenges the possibility to perform an integrative synthesis of results and to translate these for evidence-based clinical practice. This review identifies gaps in the literature and methodological challenges for the implementation of RCTs of sensory interventions in the NICU.

IMPACT

This paper represents the first systematic review to investigate the effect of non-pharmacological, sensory-based interventions in the NICU on neonatal brain development. Although reviewed RCTs present evidence on the impact of such interventions on the neonatal brain following preterm birth, it is not yet possible to formulate clear guidelines for clinical practice. This review integrates existing literature on the effect of sensory-based interventions on the brain after preterm birth and identifies methodological challenges for the conduction of high-quality RCTs.

Functional individual variability development of the neonatal brain

Individual variability in cognition and behavior results from the differences in brain structure and function that have already emerged before birth. However, little is known about individual variability in brain functional architecture at local level in neonates which is of great significance to explore owing to largely undeveloped long-range functional connectivity and segregated functions in early brain development. To address this, resting-state fMRI data of 163 neonates ranged from 32 to 45 postconceptional weeks (PCW) were used in this study, and various functional features including functional parcellation similarity, local brain activity and local functional connectivity were used to characterize individual functional variability. We observed significantly higher local functional individual variability in superior parietal, sensorimotor, and visual cortex, and lower variability in the frontal, insula and cingulate cortex relative to other regions within each hemisphere. The mean local functional individual variability significantly increased with age, and the age effect was found larger in brain regions such as the occipital, temporal, prefrontal and parietal cortex. Our findings promote the understanding of brain plasticity and regional differential maturation in the early stage.

NeoRS: A Neonatal Resting State fMRI Data Preprocessing Pipeline

Resting state functional MRI (rsfMRI) has been shown to be a promising tool to study intrinsic brain functional connectivity and assess its integrity in cerebral development. In neonates, where functional MRI is limited to very few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed to reduce artifacts compromising the interpretation of results. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults due to myelination, neonates can’t be processed with the existing adult pipelines, as they are not adapted. Therefore, we developed NeoRS, a rsfMRI pipeline for neonates. The pipeline relies on popular neuroimaging tools (FSL, AFNI, and SPM) and is optimized for the neonatal brain. The main processing steps include image registration to an atlas, skull stripping, tissue segmentation, slice timing and head motion correction and regression of confounds which compromise functional data interpretation. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized, and motion management optimized, as it is a major issue when processing neonatal rsfMRI data. The pipeline includes quality control using visual assessment checkpoints. To assess the effectiveness of NeoRS processing steps we used the neonatal data from the Baby Connectome Project dataset including a total of 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. NeoRS also includes popular functional connectivity analysis features such as seed-to-seed or seed-to-voxel correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto-parietal networks were evaluated. Topology found the different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab and allows parallel computing to reduce computational times; it is open-source and available on GitHub (https://github.com/venguix/NeoRS). NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.

Developmental Neuropathology and Neurodegeneration of Down Syndrome: Current Knowledge in Humans

Individuals with Down syndrome (DS) suffer from developmental delay, intellectual disability, and an early-onset of neurodegeneration, Alzheimer’s-like disease, or precocious dementia due to an extra chromosome 21. Studying the changes in anatomical, cellular, and molecular levels involved may help to understand the pathogenesis and develop target treatments, not just medical, but also surgical, cell and gene therapy, etc., for individuals with DS. Here we aim to identify key neurodevelopmental manifestations, locate knowledge gaps, and try to build molecular networks to better understand the mechanisms and clinical importance. We summarize current information about the neuropathology and neurodegeneration of the brain from conception to adulthood of foetuses and individuals with DS at anatomical, cellular, and molecular levels in humans. Understanding the alterations and characteristics of developing Down syndrome will help target treatment to improve the clinical outcomes. Early targeted intervention/therapy for the manifestations associated with DS in either the prenatal or postnatal period may be useful to rescue the neuropathology and neurodegeneration in DS.

Predicting age and clinical risk from the neonatal connectome

The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why individual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p<0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.

An atlas of genetic correlations between gestational age and common psychiatric disorders

We aim to systematically explore the potential genetic correlations between five major psychiatric disorders and gestational ages. Genome-wide association study (GWAS) summary datasets of attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) in discovery were downloaded from the Psychiatric GWAS Consortium (PGC) website. Suggestive (Raw p < 0.05) genetic associations in the discovery phrase were further replicated in independent GWASs which downloaded from PGC, the FinnGen study or Integrative Psychiatric Research (iPSYCH) website. GWASs of gestational duration, preterm and post-term birth were derived from previous studies of infants from the Early Growth Genetics (EGG) Consortium, the iPSYCH study, and the Genomic and Proteomic Network for Preterm Birth Research (GPN). We calculated genetic correlations using linkage disequilibrium score (LDSC) regression. Mendelian randomization (MR) analyses were performed to investigate the causal effects. We identified four suggestive genetic correlations between psychiatric disorders and gestational age factors in discovery LDSC and two replicated in a confirmation LDSC: gestational duration and ADHD (r_g  = -0.1405, FDR p = 0.0406), post-term birth and SCZ (r_g  = -0.2003, FDR p = 0.0042). We also observed causal effect of post-term birth on SCZ by MR (P_{Weighted median}  = 0.037, P_{Inverse variance weighted}  = 0.007). Our analysis suggested no significant evidence of horizontal pleiotropy and heterogeneity. This study showed the genetic correlation evidences between gestational age phenotypes and psychiatric disorders, providing novel clues for understanding the pathogenic factors of common psychiatric disorders. LAY SUMMARY: Whereas gestational age factors were reported to be associated with psychiatric disorders, the genetic relationship and causality remain to be revealed. The present study reported the first large-scale genetic correlations investigation of the associations between gestational age phenotypes and psychiatric disorders. Results indicate causal relationships between post-term birth and schizophrenia (SCZ), as well as suggestive genetic correlations between gestational duration and attention deficit/hyperactivity disorder (ADHD). This study provided novel clues for understanding the pathogenic factors of common psychiatric disorders.

Development of functional organization within the sensorimotor network across the perinatal period

In the mature human brain, the neural processing related to different body parts is reflected in patterns of functional connectivity, which is strongest between functional homologs in opposite cortical hemispheres. To understand how this organization is first established, we investigated functional connectivity between limb regions in the sensorimotor cortex in 400 preterm and term infants aged across the equivalent period to the third trimester of gestation (32–45 weeks postmenstrual age). Masks were obtained from empirically derived functional responses in neonates from an independent data set. We demonstrate the early presence of a crude but spatially organized functional connectivity, that rapidly matures across the preterm period to achieve an adult‐like configuration by the normal time of birth. Specifically, connectivity was strongest between homolog regions, followed by connectivity between adjacent regions (different limbs but same hemisphere) already in the preterm brain, and increased with age. These changes were specific to the sensorimotor network. Crucially, these trajectories were strongly dependent on age more than age of birth. This demonstrates that during the perinatal period the sensorimotor cortex undergoes preprogrammed changes determining the functional movement organization that are not altered by preterm birth in absence of brain injury.

Towards a model of language neurobiology in early development

Understanding language neurobiology in early childhood is essential for characterizing the developmental structural and functional changes that lead to the mature adult language network. In the last two decades, the field of language neurodevelopment has received increasing attention, particularly given the rapid advances in the implementation of neuroimaging techniques and analytic approaches that allow detailed investigations into the developing brain across a variety of cognitive domains. These methodological and analytical advances hold the promise of developing early markers of language outcomes that allow diagnosis and clinical interventions at the earliest stages of development. Here, we argue that findings in language neurobiology need to be integrated within an approach that captures the dynamic nature and inherent variability that characterizes the developing brain and the interplay between behavior and (structural and functional) neural patterns. Accordingly, we describe a framework for understanding language neurobiology in early development, which minimally requires an explicit characterization of the following core domains: i) computations underlying language learning mechanisms, ii) developmental patterns of change across neural and behavioral measures, iii) environmental variables that reinforce language learning (e.g., the social context), and iv) brain maturational constraints for optimal neural plasticity, which determine the infant's sensitivity to learning from the environment. We discuss each of these domains in the context of recent behavioral and neuroimaging findings and consider the need for quantitatively modeling two main sources of variation: individual differences or trait-like patterns of variation and within-subject differences or state-like patterns of variation. The goal is to enable models that allow prediction of language outcomes from neural measures that take into account these two types of variation. Finally, we examine how future methodological approaches would benefit from the inclusion of more ecologically valid paradigms that complement and allow generalization of traditional controlled laboratory methods.

OUP accepted manuscript

One of the great frontiers of consciousness science is understanding how early consciousness arises in the development of the human infant. The reciprocal relationship between the default mode network and fronto-parietal networks—the dorsal attention and executive control network—is thought to facilitate integration of information across the brain and its availability for a wide set of conscious mental operations. It remains unknown whether the brain mechanism of conscious awareness is instantiated in infants from birth. To address this gap, we investigated the development of the default mode and fronto-parietal networks and of their reciprocal relationship in neonates. To understand the effect of early neonate age on these networks, we also assessed neonates born prematurely or before term-equivalent age. We used the Developing Human Connectome Project, a unique Open Science dataset which provides a large sample of neonatal functional MRI data with high temporal and spatial resolution. Resting state functional MRI data for full-term neonates (n = 282, age 41.2 weeks ± 12 days) and preterm neonates scanned at term-equivalent age (n = 73, 40.9 weeks ± 14.5 days), or before term-equivalent age (n = 73, 34.6 weeks ± 13.4 days), were obtained from the Developing Human Connectome Project, and for a reference adult group (n = 176, 22–36 years), from the Human Connectome Project. For the first time, we show that the reciprocal relationship between the default mode and dorsal attention network was present at full-term birth or term-equivalent age. Although different from the adult networks, the default mode, dorsal attention and executive control networks were present as distinct networks at full-term birth or term-equivalent age, but premature birth was associated with network disruption. By contrast, neonates before term-equivalent age showed dramatic underdevelopment of high-order networks. Only the dorsal attention network was present as a distinct network and the reciprocal network relationship was not yet formed. Our results suggest that, at full-term birth or by term-equivalent age, infants possess key features of the neural circuitry that enables integration of information across diverse sensory and high-order functional modules, giving rise to conscious awareness. Conversely, they suggest that this brain infrastructure is not present before infants reach term-equivalent age. These findings improve understanding of the ontogeny of high-order network dynamics that support conscious awareness and of their disruption by premature birth.

Patterns of Fetal and Infant Growth and Brain Morphology at Age 10 Years

IMPORTANCE

Preterm birth and low birth weight are associated with brain developmental and neurocognitive outcomes in childhood; however, not much is known about the specific critical periods in fetal life and infancy for these outcomes.

OBJECTIVE

To examine the associations of fetal and infant growth patterns with brain morphology in children at school age.

DESIGN, SETTING, AND PARTICIPANTS

This population-based, prospective cohort study was conducted from February 1 to April 16, 2021, as a part of the Generation R Study in Rotterdam, the Netherlands. The study included 3098 singleton children born between April 1, 2002, and January 31, 2006.

EXPOSURES

Fetal weight was estimated in the second and third trimesters of pregnancy by ultrasonography. Infant weight was measured at birth and at 6, 12, and 24 months. Fetal and infant weight acceleration or deceleration were defined as a change in SD scores greater than 0.67 between time points. Infant measurements also included peak weight velocity, and age and body mass index reached at adiposity peak.

MAIN OUTCOMES AND MEASURES

Brain structure, including global and regional brain volumes, was quantified by magnetic resonance imaging at age 10 years.

RESULTS

The study evaluated 3098 children (mean [SD] age at follow-up, 10.1 [0.6] years; 1557 girls [50.3%]; and 1753 Dutch [57.8%]). One SD score-higher weight gain until the second and third trimesters, birth, and 6, 12, and 24 months was associated with larger total brain volume independently of growth during any other age windows (second trimester: 5.7 cm3; 95% CI, 1.2-10.2 cm3; third trimester: 15.3 cm3; 95% CI, 11.0-19.6 cm3; birth: 20.8 cm3; 95% CI, 16.4-25.1 cm3; 6 months: 15.6 cm3; 95% CI, 11.2-19.9 cm3; 12 months: 11.3 cm3; 95% CI, 7.0-15.6 cm3; and 24 months: 11.1 cm3; 95% CI, 6.8-15.4 cm3). Compared with children with normal fetal and infant growth, those with fetal and infant growth deceleration had the smallest total brain volume (-32.5 cm3; 95% CI, -53.2 to -11.9 cm3). Children with fetal weight deceleration followed by infant catch-up growth had similar brain volumes as children with normal growth. Higher peak weight velocity and body mass index reached at adiposity peak were associated with larger brain volumes. Similar results were observed for cerebral and cerebellar gray and white matter volumes.

CONCLUSIONS AND RELEVANCE

This cohort study's findings suggest that both fetal and infant weight growth might be critical for cerebral and cerebellar brain volumes during childhood. Whether these associations link to neurocognitive outcomes should be further studied.

Structural and functional connectivity in premature neonates

Advances in neuroimaging have increasingly enabled researchers to investigate whether alterations in brain development commonly identified in preterm infants underlie their high risk of long-term neurodevelopmental impairment, including sensory, motor, cognitive, and psychiatric deficits. This review begins by examining the growing body of literature utilizing advanced magnetic resonance imaging (MRI) techniques to probe structural (via diffusion MRI) and functional (via resting state-functional MRI) connectivity development in the preterm brain during the neonatal period, both in the presence and absence of brain injury. It then details the recent work linking neonatal brain connectivity measures to neurodevelopmental and psychiatric outcomes in prematurely-born cohorts. Finally, building upon the recent substantive growth in the utilization of these neuroimaging modalities, it concludes by highlighting areas in which continued optimization of age-specific acquisition and analysis techniques for these data remains necessary, efforts fundamental to advancing the field toward establishing individual-level predictive capabilities in this high-risk population.

Reliability of an automated gaze-controlled paradigm for capturing neural responses during visual and face processing in toddlerhood

Electroencephalography (EEG) has substantial potential value for examining individual differences during early development. Current challenges in developmental EEG research include high dropout rates and low trial numbers, which may in part be due to passive stimulus presentation. Comparability is challenged by idiosyncratic processing pipelines. We present a novel toolbox (“Braintools”) that uses gaze‐contingent stimulus presentation and an automated processing pipeline suitable for measuring visual processing through low‐density EEG recordings in the field. We tested the feasibility of this toolbox in 61 2.5‐ to 4‐year olds, and computed test–retest reliability (1‐ to 2‐week interval) of event‐related potentials (ERP) associated with visual (P1) and face processing (N290, P400). Feasibility was good, with 52 toddlers providing some EEG data at the first session. Reliability values for ERP features were moderate when derived from 20 trials; this would allow inclusion of 79% of the 61 toddlers for the P1 and 82% for the N290 and P400. P1 amplitude/latency were more reliable across sessions than for the N290 and P400. Amplitudes were generally more reliable than latencies. Automated and standardized solutions to collection and analysis of event‐related EEG data would allow efficient application in large‐scale global health studies, opening significant potential for examining individual differences in development.

Prenatal maternal and cord blood vitamin D concentrations and negative affectivity in infancy

Higher maternal vitamin D concentration during pregnancy is associated with better child mental health. Negative affectivity, an early-emerging temperamental trait, indicates an increased risk of psychopathology. We investigated if maternal early/mid-pregnancy 25-hydroxyvitamin D (25(OH)D) and neonatal cord blood 25(OH)D concentrations are associated with Negative affectivity in infancy. We studied term-born infants from the vitamin D Intervention in Infants study (VIDI, n = 777, follow-up rate 80%, Finland), and the Generation R Study (n = 1505, follow-up rate 40%, Netherlands). We measured maternal serum 25(OH)D at 6-27 weeks (VIDI) or 18-25 weeks (Generation R) of pregnancy, and cord blood 25(OH)D at birth (both cohorts). Caregivers rated infant Negative affectivity at 11.7 months (VIDI) or 6.5 months (Generation R) using the Revised Infant Behavior Questionnaire. Using linear regression, we tested associations between 25(OH)D and Negative affectivity adjusted for infant age, sex, season of 25(OH)D measurement, maternal age, education, smoking, and body-mass-index. Per 10 nmol/l increase in maternal early/mid-pregnancy 25(OH)D, infant Negative affectivity decreased by 0.02 standard deviations (95% confidence interval [CI] - 0.06, - 0.004) in VIDI, and 0.03 standard deviations (95% CI - 0.03, - 0.01) in Generation R. Cord blood 25(OH)D was associated with Negative affectivity in Generation R (- 0.03, 95% CI - 0.05, - 0.01), but not VIDI (0.00, 95% CI - 0.02, 0.02). Lower maternal 25(OH)D concentrations were consistently associated with higher infant Negative affectivity, while associations between cord blood 25(OH)D concentrations and Negative affectivity were less clear. Maternal vitamin D status during early- and mid-pregnancy may be linked with early-emerging differences in offspring behavior.

Meta-analyses of maternal exposure to atmospheric particulate matter and risk of congenital anomalies in offspring

Congenital anomalies are the main causes of infant death and disability. Previous studies have suggested that maternal exposure to particulate matter is related to congenital malformation. However, the conclusions of this study remain controversial. Hence, meta-analyses were performed to assess the relationship between maternal exposure to particulate matter and the risk of congenital anomalies. The Medline, Embase, and Web of Science databases were systemically searched from inception until August 2020 to find articles related to birth defects and particulate matter. The pooled risk estimated for the combination of pollution outcomes was calculated for each study by performing fixed effects or random effects models. The existence of heterogeneity and publication bias in relevant studies was also examined. Thirty studies were included in the analysis. A statistically increased summary risk valuation was found. PM₁₀ exposure was associated with an increased risk of congenital heart disease, neural tube defects, and cleft lip with or without cleft palate (OR per 10 μg/m³ = 1.05, 95% CI, 1.03-1.07; OR per 10 μg/m³ = 1.04, 95% CI, 1.01-1.06; OR per 10 μg/m³ = 1.03, 95% CI, 1.01-1.06). Maternal exposure to particulate matter might be associated with an increased risk of congenital anomalies. Our results indicate the dangers of particulate matter exposure on fetal development and the importance of protection against exposure to such particles during pregnancy. The schematic representation of the association between maternal exposure to PM_2.5/PM₁₀ and congenital anomalies in offspring, and geographic distribution of the included reports in the meta-analyses.

Altered Resting-State Functional Connectivity in the Default Mode Network in Male Juvenile Violent Offenders

Young males are often associated with more violence, leading to some serious negative consequences. However, the physiology and the neuroimaging patterns underlying juvenile violence remain unclear. Of the limited knowledge on juvenile violence, the default mode network has been known to be associated with its pathophysiology. This study aimed to investigate functional connectivity alterations of the default mode network in male juvenile violent offenders. 31 juvenile violent offenders in a high-security facility, who were convicted of aggressive behaviors by court, and 28 normal controls from a middle school were recruited as participants. They underwent a resting-state functional magnetic resonance imaging scan. And independent component analysis approaches were used to analyze their data. Compared to the normal controls, the juvenile violent offenders showed a different default mode network pattern, with the functional connectivity increased in the posterior cingulate, and decreased in the right middle temporal, left angular, right precuneus and right middle frontal cortex. Our findings revealed that the male juvenile violent offenders were associated with abnormal default mode network functional connectivity, which might be a neuroimaging basis for their tendency to violence.

Prenatal exposure to ambient fine particulate matter and early childhood neurodevelopment: A population-based birth cohort study

Although previous studies have reported the adverse effect of air pollution exposure during pregnancy on neurodevelopment in children, epidemiological evidence is limited, and the results are inconsistent. This study aimed to explore the association between prenatal ambient fine particulate matter (PM_2.5) exposure and early childhood neurodevelopment in a large birth cohort study of 4009 maternal-child pairs. Prenatal daily PM_2.5 exposure concentrations at 1 km spatial revolution were estimated using high-performance machine-learning models. Neurodevelopmental outcomes of children at ages 2, 6, 12, and 24 months were assessed using the Ages and Stages Questionnaire (ASQ). Distributed lag nonlinear models were used to identify critical windows of prenatal PM_2.5 exposure. General linear mixed models with binomially distributed errors were used to estimate the effect of prenatal PM_2.5 exposure on suspected developmental delay (SDD) in five developmental domains based on the longitudinal design. Prenatal PM_2.5 exposure was significantly associated with decreased scores for all neurodevelopmental domains of children at ages 2, 6, and 24 months. Each 10-μg/m³ increase in PM_2.5 exposure was significantly associated with increased risk of SDD for all subjects (RR: 1.52 95% CI: 1.19, 2.03), specifically, in problem-solving domain for girls (RR: 2.23, 95% CI: 1.22, 4.35). Prenatal PM_2.5 exposure in weeks 18 to 34 was significantly associated with both ASQ scores and SDDs. Our study proposed that prenatal PM_2.5 exposure affected early childhood neurodevelopment evaluated with the ASQ scale. PM_2.5 exposure might increase the risk of SDD for boys and girls, specifically in the problem-solving domain for girls.

The prenatal brain readiness for speech processing: A review on foetal development of auditory and primordial language networks

Despite consolidated evidence for the prenatal ability to elaborate and respond to sounds and speech stimuli, the ontogenetic functional brain maturation of language responsiveness in the foetus is still poorly understood. Recent advances in in-vivo foetal neuroimaging have contributed to a finely detailed picture of the anatomo-functional hallmarks that define the prenatal neurodevelopment of auditory and language-related networks. Here, we first outline available evidence for the prenatal development of auditory and language-related brain structures and of their anatomical connections. Second, we focus on functional connectivity data showing the emergence of auditory and primordial language networks in the foetal brain. Third, we recapitulate functional neuroimaging studies assessing the prenatal readiness for sound processing, as a crucial prerequisite for the foetus to experientially respond to spoken language. In conclusion, we suggest that the state of the art has reached sufficient maturity to directly assess the neural mechanisms underlying the prenatal readiness for speech processing and to evaluate whether foetal neuromarkers can predict the postnatal development of language acquisition abilities and disabilities.

Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity

The architecture of the human connectome changes with brain maturation. Pivotal to understanding these changes is defining developmental periods when transitions in network topology occur. Here, using 110 resting-state functional connectivity data sets from healthy fetuses between 19 and 40 gestational weeks, we estimated optimal gestational-age (GA) cut points for dichotomizing fetuses into 'young' and 'old' groups based on global network features. We computed the small-world index, normalized clustering and path length, global and local efficiency, and modularity from connectivity matrices comprised 200 regions and their corresponding pairwise connectivity. We modeled the effect of GA at scan on each metric using separate repeated-measures generalized estimating equations. Our modeling strategy involved stratifying fetuses into 'young' and 'old' based on the scan occurring before or after a selected GA (i.e., 28 to 33). We then used the quasi-likelihood independence criterion statistic to compare model fit between 'old' and 'young' cohorts and determine optimal cut points for each graph metric. Trends for all metrics, except for global efficiency, decreased with increasing gestational age. Optimal cut points fell within 30-31 weeks for all metrics coinciding with developmental events that include a shift from endogenous neuronal activity to sensory-driven cortical patterns.

The emergence of a theta social brain network during infancy

Infants' socio-cognitive ability develops dramatically during the first year of life. From the perspective of ontogeny, the early development of social behavior allows for parent-child attachment, which in turn enhances survival. Thus, it is theorized that the development of social behavior, driven by social brain networks, forms the core of developmental acquisitions during this period. Further, understanding the maturation within the neural networks during social development is crucial to obtain a better grasp of the development of social developmental disorders. Therefore, we performed a longitudinal study in 854 infants measured at around 5 and 10 months to map the development of functional networks in the brain when infants were processing social and non-social videos. Using EEG, we focused on the frequency bands most commonly connected to social behavior: theta and alpha. We found that alpha networks remained relatively stable over the first year of life and showed no selectivity for social versus non-social stimuli, theta networks, showed strong global reconfigurations. The development of the theta networks progressed from a parietal occipital network in early infancy to a frontoparietal network towards the end of the first year of life. This reconfiguration coincided with selectivity for social versus non-social stimuli, with infants approaching the end of their first year of life showing increased synchronicity of theta communication when watching social videos versus non-social videos. Our findings provide strong evidence for the involvement of a frontoparietal theta network in the development of the social brain.

Developmental heatmaps of brain functional connectivity from newborns to 6-year-olds

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Heatmaps quantify degrees of developmental changes in functional connectivity.

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More changes are observed in the first than second postnatal year, driven by girls.

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Most change is observed from ages 2–4 compared with any other age span.

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Limbic and subcortical areas show more changes than primary sensory regions.

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Consistent trajectories of functional connectivity are found across validations.

Different functional networks exhibit distinct longitudinal trajectories throughout development, but the timeline of the dynamics of functional connectivity across the whole brain remains to be elucidated. Here we used resting-state fMRI to investigate the development of voxel-level changes in functional connectivity across the first six years of life. Globally, we found that developmental changes in functional connectivity are nonlinear with more changes during the first postnatal year than the second, followed by most significant changes from ages 2–4 and from ages 4–6. However, the overall global difference observed between the first and second year appears to have been driven by girls. Limbic and subcortical areas consistently demonstrated the most substantial changes, whereas primary sensory areas were the most stable. These patterns were consistent in full-term and preterm subgroups. Validation on randomly divided subsamples as well as in an independent cross-sectional sample revealed global patterns consistent with the main results. Overall, the derived developmental heatmaps reveal novel dynamics underlying functional circuit development during the first 6 years of life.

Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses

Premature birth affects the developmental trajectory of the brain during a period of intense maturation with possible lifelong consequences. To better understand the effect of prematurity on brain structure and function, we performed blood‐oxygen‐level dependent (BOLD) and anatomical magnetic resonance imaging (MRI) at 40 weeks of postmenstrual age on 88 newborns with variable gestational age (GA) at birth and no evident radiological alterations. We extracted measures of resting‐state functional connectivity and activity in a set of 90 cortical and subcortical brain regions through the evaluation of BOLD correlations between regions and of fractional amplitude of low‐frequency fluctuation (fALFF) within regions, respectively. Anatomical information was acquired through the assessment of regional volumes. We performed univariate analyses on each metric to examine the association with GA at birth, the spatial distribution of the effects, and the consistency across metrics. Moreover, a data‐driven multivariate analysis (i.e., Machine Learning) framework exploited the high dimensionality of the data to assess the sensitivity of each metric to the effect of premature birth. Prematurity was associated with bidirectional alterations of functional connectivity and regional volume and, to a lesser extent, of fALFF. Notably, the effects of prematurity on functional connectivity were spatially diffuse, mainly within cortical regions, whereas effects on regional volume and fALFF were more focal, involving subcortical structures. While the two analytical approaches delivered consistent results, the multivariate analysis was more sensitive in capturing the complex pattern of prematurity effects. Future studies might apply multivariate frameworks to identify premature infants at risk of a negative neurodevelopmental outcome.

MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances

In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.

Structural and functional brain network alterations in prenatal alcohol exposed neonates

Prenatal alcohol exposure leads to alterations in cognition, behavior and underlying brain architecture. However, prior studies have not integrated structural and functional imaging data in children with prenatal alcohol exposure. The aim of this study was to characterize disruptions in both structural and functional brain network organization after prenatal alcohol exposure in very early life. A group of 11 neonates with prenatal alcohol exposure and 14 unexposed controls were investigated using diffusion weighted structural and resting state functional magnetic resonance imaging. Covariance networks were created using graph theoretical analyses for each data set, controlling for age and sex. Group differences in global hub arrangement and regional connectivity were determined using nonparametric permutation tests. Neonates with prenatal alcohol exposure and controls exhibited similar global structural network organization. However, global functional networks of neonates with prenatal alcohol exposure comprised of temporal and limbic hubs, while hubs were more distributed in controls representing an early default mode network. On a regional level, controls showed prominent structural and functional connectivity in parietal and occipital regions. Neonates with prenatal alcohol exposure showed regionally, predominant structural and functional connectivity in several subcortical regions and occipital regions. The findings suggest early functional disruption on a global and regional level after prenatal alcohol exposure and indicate suboptimal organization of functional networks. These differences likely underlie sensory dysregulation and behavioral difficulties in prenatal alcohol exposure.

Understanding Vulnerability and Adaptation in Early Brain Development using Network Neuroscience

Early adversity influences brain development and emerging behavioral phenotypes relevant for psychiatric disorders. Understanding the effects of adversity before and after conception on brain development has implications for contextualizing current public health crises and pervasive health inequities. The use of functional magnetic resonance imaging (fMRI) to study the brain at rest has shifted understanding of brain functioning and organization in the earliest periods of life. Here we review applications of this technique to examine effects of early life stress (ELS) on neurodevelopment in infancy, and highlight targets for future research. Building on the foundation of existing work in this area will require tackling significant challenges, including greater inclusion of often marginalized segments of society, and conducting larger, properly powered studies.

Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants

Significance: Early monolingual versus bilingual experience induces adaptations in the development of linguistic and cognitive processes, and it modulates functional activation patterns during the first months of life. Resting-state functional connectivity (RSFC) is a convenient approach to study the functional organization of the infant brain. RSFC can be measured in infants during natural sleep, and it allows to simultaneously investigate various functional systems. Adaptations have been observed in RSFC due to a lifelong bilingual experience. Investigating whether bilingualism-induced adaptations in RSFC begin to emerge early in development has important implications for our understanding of how the infant brain's organization can be shaped by early environmental factors. Aims: We attempt to describe RSFC using functional near-infrared spectroscopy (fNIRS) and to examine whether it adapts to early monolingual versus bilingual environments. We also present an fNIRS data preprocessing and analysis pipeline that can be used to reliably characterize RSFC in development and to reduce false positives and flawed results interpretations. Methods: We measured spontaneous hemodynamic brain activity in a large cohort ( N = 99 ) of 4-month-old monolingual and bilingual infants using fNIRS. We implemented group-level approaches based on independent component analysis to examine RSFC, while providing proper control for physiological confounds and multiple comparisons. Results: At the group level, we describe the functional organization of the 4-month-old infant brain in large-scale cortical networks. Unbiased group-level comparisons revealed no differences in RSFC between monolingual and bilingual infants at this age. Conclusions: High-quality fNIRS data provide a means to reliably describe RSFC patterns in the infant brain. The proposed group-level RSFC analyses allow to assess differences in RSFC across experimental conditions. An effect of early bilingual experience in RSFC was not observed, suggesting that adaptations might only emerge during explicit linguistic tasks, or at a later point in development.

The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity

The Developing Human Connectome Project is an Open Science project that provides the first large sample of neonatal functional MRI data with high temporal and spatial resolution. These data enable mapping of intrinsic functional connectivity between spatially distributed brain regions under normal and adverse perinatal circumstances, offering a framework to study the ontogeny of large-scale brain organization in humans. Here, we characterize in unprecedented detail the maturation and integrity of resting state networks (RSNs) at term-equivalent age in 337 infants (including 65 born preterm). First, we applied group independent component analysis to define 11 RSNs in term-born infants scanned at 43.5–44.5 weeks postmenstrual age (PMA). Adult-like topography was observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among six higher-order, association RSNs, analogues of the adult networks for language and ocular control were identified, but a complete default mode network precursor was not. Next, we regressed the subject-level datasets from an independent cohort of infants scanned at 37–43.5 weeks PMA against the group-level RSNs to test for the effects of age, sex and preterm birth. Brain mapping in term-born infants revealed areas of positive association with age across four of six association RSNs, indicating active maturation in functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased connectivity in inferotemporal regions of the visual association network. Preterm birth was associated with striking impairments of functional connectivity across all RSNs in a dose-dependent manner; conversely, connectivity of the superior parietal lobules within the lateral motor network was abnormally increased in preterm infants, suggesting a possible mechanism for specific difficulties such as developmental coordination disorder, which occur frequently in preterm children. Overall, we found a robust, modular, symmetrical functional brain organization at normal term age. A complete set of adult-equivalent primary RSNs is already instated, alongside emerging connectivity in immature association RSNs, consistent with a primary-to-higher order ontogenetic sequence of brain development. The early developmental disruption imposed by preterm birth is associated with extensive alterations in functional connectivity.