Developmental Connectomics from Infancy through Early Childhood

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).

Infants' top-down perceptual modulation is specific to own-race faces

Recent studies have revealed the influence of higher-level cognitive systems in modulating perceptual processing (top-down perceptual modulation) in infancy. However, more research is needed to understand how top-down processes in infant perception contribute to early perceptual development. To this end, this study examined infants' top-down perception of own- and other-race faces to reveal whether top-down modulation is linked to the emergence of perceptual specialization. Infants first learned an association between a sound and faces, with the race of the faces manipulated between groups (own race vs. other race). We then tested infants' face perception across various levels of perceptual difficulty (manipulated by presentation duration) and indexed top-down perception by the change in perception when infants heard the sound previously associated with the face (predictive sound) versus an irrelevant sound. Infants exhibited top-down face perception for own-race faces (Experiment 1). However, we present new evidence that infants did not show evidence of top-down modulation for other-race faces (Experiment 2), suggesting an experience-based specificity of this capacity with more effective top-down modulation in familiar perceptual contexts. In addition, we ruled out the possibility that this face race effect was due to differences in infants' associative learning of the sound and faces between the two groups. This work has important implications for understanding the mechanisms supporting perceptual development and how they relate to top-down perception in infancy.

Development of segregation and integration of functional connectomes during the first 1,000 days

The first 1,000 days of human life lay the foundation for brain development and later cognitive growth. However, the developmental rules of the functional connectome during this critical period remain unclear. Using high-resolution, longitudinal, task-free functional magnetic resonance imaging data from 930 scans of 665 infants aged 28 postmenstrual weeks to 3 years, we report the early maturational process of connectome segregation and integration. We show the dominant development of local connections alongside a few global connections, the shift of brain hubs from primary regions to high-order association cortices, the developmental divergence of network segregation and integration along the anterior-posterior axis, the prediction of neurocognitive outcomes, and their associations with gene expression signatures of microstructural development and neuronal metabolic pathways. These findings advance our understanding of the principles of connectome remodeling during early life and its neurobiological underpinnings and have implications for studying typical and atypical development.

Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2

The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2  years of age. Our findings highlight network-level neural substrates underlying early cognitive development.

The interrelatedness of cognitive abilities in very preterm and full-term born children at 5.5 years of age: a psychometric network analysis approach

BACKGROUND

Very preterm (VP) birth is associated with a considerable risk for cognitive impairment, putting children at a disadvantage in academic and everyday life. Despite lower cognitive ability on the group level, there are large individual differences among VP born children. Contemporary theories define intelligence as a network of reciprocally connected cognitive abilities. Therefore, intelligence was studied as a network of interrelated abilities to provide insight into interindividual differences. We described and compared the network of cognitive abilities, including strength of interrelations between and the relative importance of abilities, of VP and full-term (FT) born children and VP children with below-average and average-high intelligence at 5.5 years.

METHODS

A total of 2,253 VP children from the EPIPAGE-2 cohort and 578 FT controls who participated in the 5.5-year-follow-up were eligible for inclusion. The WPPSI-IV was used to measure verbal comprehension, visuospatial abilities, fluid reasoning, working memory, and processing speed. Psychometric network analysis was applied to analyse the data.

RESULTS

Cognitive abilities were densely and positively interconnected in all networks, but the strength of connections differed between networks. The cognitive network of VP children was more strongly interconnected than that of FT children. Furthermore, VP children with below average IQ had a more strongly connected network than VP children with average-high IQ. Contrary to our expectations, working memory had the least central role in all networks.

CONCLUSIONS

In line with the ability differentiation hypothesis, children with higher levels of cognitive ability had a less interconnected and more specialised cognitive structure. Composite intelligence scores may therefore mask domain-specific deficits, particularly in children at risk for cognitive impairments (e.g., VP born children), even when general intelligence is unimpaired. In children with strongly and densely connected networks, domain-specific deficits may have a larger overall impact, resulting in lower intelligence levels.

Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture

From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.

Relationship between 4-month functional brain network topology and 24-month neurodevelopmental outcome in children with congenital heart disease

Survivors of complex forms of congenital heart disease (CHD)∗ are at high risk of neurodevelopmental disabilities. Neuroimaging studies have pointed to brain anomalies and immature networks in infants with CHD, yet less is known about their functional network topology and associations with neurodevelopment. To characterize the functional network topology in 4-month-old infants with repaired CHD, we compared graph theory metrics measured using resting-state functional near-infrared spectroscopy (rs-fNIRS) between infants with CHD (n = 22) and healthy controls (n = 30). We also investigated the moderating effect of graph theory metrics on the relationship between group (CHD vs. Controls) and developmental outcomes at 24 months. At 4 months, both groups presented similar functional brain network topology. At 24 months, children with CHD had lower scores on the language scale and the expressive communication subscale of the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III), as well as lower scores on the Grammatical Form scale of the MacArthur-Bates Communicative Development Inventory (MBCDI). The relationship between group and expressive language was moderated by the normalized characteristic path length (λ) and the degree (k). Although infants with CHD have functional brain topology similar to that of healthy controls, our findings suggest that they do not benefit from an optimal functional brain organization in comparison with healthy infants.

Single-subject cortical morphological brain networks across the adult lifespan

Age-related changes in focal cortical morphology have been well documented in previous literature; however, how interregional coordination patterns of the focal cortical morphology reorganize with advancing age is not well established. In this study, we performed a comprehensive analysis of the topological changes in single-subject morphological brain networks across the adult lifespan. Specifically, we constructed four types of single-subject morphological brain networks for 650 participants (aged from 18 to 88 years old), and characterized their topological organization using graph-based network measures. Age-related changes in the network measures were examined via linear, quadratic, and cubic models. We found profound age-related changes in global small-world attributes and efficiency, local nodal centralities, and interregional similarities of the single-subject morphological brain networks. The age-related changes were mainly embodied in cortical thickness networks, involved in frontal regions and highly connected hubs, concentrated on short-range connections, characterized by linear changes, and susceptible to connections between limbic, frontoparietal, and ventral attention networks. Intriguingly, nonlinear (i.e., quadratic or cubic) age-related changes were frequently found in the insula and limbic regions, and age-related cubic changes preferred long-range morphological connections. Finally, we demonstrated that the morphological similarity in cortical thickness between two frontal regions mediated the relationship between age and cognition measured by Cattell scores. Taken together, these findings deepen our understanding of adaptive changes of the human brain with advancing age, which may account for interindividual variations in behaviors and cognition.

Early Childhood Development of Node Centrality in the White Matter Connectome and Its Relationship to IQ at Age 6 Years

BACKGROUND

The white matter (WM) connectome is important for cognitive development and intelligence and is altered in neuropsychiatric illnesses. Little is known about how the WM connectome develops or its relationship to IQ in early childhood.

METHODS

The development of node centrality in the WM connectome was studied in a longitudinal cohort of 226 (123 female) children from the University of North Carolina Early Brain Development Study. Structural and diffusion-weighted images were acquired after birth and at 1, 2, 4, and 6 years, and IQ was assessed at 6 years. Eigenvector centrality, betweenness centrality, and the global graph metrics of global efficiency, small worldness, and modularity were determined at each age.

RESULTS

The greatest developmental change in eigenvector centrality and betweenness centrality occurred during the first year of life, with relative stability between ages 1 and 6 years. Most of the high-centrality hubs at age 6 were also high-centrality hubs at 1 year, and many were already high-centrality hubs at birth. There were generally small but significant changes in global efficiency and modularity from birth to 6 years, while small worldness increased between 2 and 4 years. Individual node centrality was not significantly correlated with IQ at 6 years.

CONCLUSIONS

Node centrality in the WM connectome is established very early in childhood and is relatively stable from age 1 to 6 years. Many high-centrality hubs are established before birth, and most are present by age 1.

A critical period plasticity framework for the sensorimotor-association axis of cortical neurodevelopment

To understand human brain development it is necessary to describe not only the spatiotemporal patterns of neurodevelopment but also the neurobiological mechanisms that underlie them. Human neuroimaging studies have provided evidence for a hierarchical sensorimotor-to-association (S-A) axis of cortical neurodevelopment. Understanding the biological mechanisms that underlie this program of development using traditional neuroimaging approaches has been challenging. Animal models have been used to identify periods of enhanced experience-dependent plasticity - 'critical periods' - that progress along cortical hierarchies and are governed by a conserved set of neurobiological mechanisms that promote and then restrict plasticity. In this review we hypothesize that the S-A axis of cortical development in humans is partly driven by the cascading maturation of critical period plasticity mechanisms. We then describe how recent advances in in vivo neuroimaging approaches provide a promising path toward testing this hypothesis by linking signals derived from non-invasive imaging to critical period mechanisms.

Mapping the evolution of regional brain network efficiency and its association with cognitive abilities during the first twenty-eight months of life

Human brain undergoes rapid growth during the first few years of life. While previous research has employed graph theory to study early brain development, it has mostly focused on the topological attributes of the whole brain. However, examining regional graph-theory features may provide unique insights into the development of cognitive abilities. Utilizing a large and longitudinal rsfMRI dataset from the UNC/UMN Baby Connectome Project, we investigated the developmental trajectories of regional efficiency and evaluated the relationships between these changes and cognitive abilities using Mullen Scales of Early Learning during the first twenty-eight months of life. Our results revealed a complex and spatiotemporally heterogeneous development pattern of regional global and local efficiency during this age period. Furthermore, we found that the trajectories of the regional global efficiency at the left temporal occipital fusiform and bilateral occipital fusiform gyri were positively associated with cognitive abilities, including visual reception, expressive language, receptive language, and early learning composite scores (P < 0.05, FDR corrected). However, these associations were weakened with age. These findings offered new insights into the regional developmental features of brain topologies and their associations with cognition and provided evidence of ongoing optimization of brain networks at both whole-brain and regional levels.

New Challenges for Anatomists in the Era of Omics

Anatomic studies have traditionally relied on macroscopic, microscopic, and histological techniques to investigate the structure of tissues and organs. Anatomic studies are essential in many fields, including medicine, biology, and veterinary science. Advances in technology, such as imaging techniques and molecular biology, continue to provide new insights into the anatomy of living organisms. Therefore, anatomy remains an active and important area in the scientific field. The consolidation in recent years of some omics technologies such as genomics, transcriptomics, proteomics, and metabolomics allows for a more complete and detailed understanding of the structure and function of cells, tissues, and organs. These have been joined more recently by "omics" such as radiomics, pathomics, and connectomics, supported by computer-assisted technologies such as neural networks, 3D bioprinting, and artificial intelligence. All these new tools, although some are still in the early stages of development, have the potential to strongly contribute to the macroscopic and microscopic characterization in medicine. For anatomists, it is time to hitch a ride and get on board omics technologies to sail to new frontiers and to explore novel scenarios in anatomy.

Early development of the functional brain network in newborns

During the prenatal period and the first postnatal years, the human brain undergoes rapid growth, which establishes a preliminary infrastructure for the subsequent development of cognition and behavior. To understand the underlying processes of brain functioning and identify potential sources of developmental disorders, it is essential to uncover the developmental rules that govern this critical period. In this study, graph theory modeling and network science analysis were employed to investigate the impact of age, gender, weight, and typical and atypical development on brain development. Local and global topologies of functional connectomes obtained from rs-fMRI data were collected from 421 neonates aged between 31 and 45 postmenstrual weeks who were in natural sleep without any sedation. The results showed that global efficiency, local efficiency, clustering coefficient, and small-worldness increased with age, while modularity and characteristic path length decreased with age. The normalized rich-club coefficient displayed a U-shaped pattern during development. The study also examined the global and local impacts of gender, weight, and group differences between typical and atypical cases. The findings presented some new insights into the maturation of functional brain networks and their relationship with cognitive development and neurodevelopmental disorders.

Visual Perception Is Highly Flexible and Context Dependent in Young Infants: A Case of Top-Down-Modulated Motion Perception

Top-down modulation is an essential cognitive component in human perception. Despite mounting evidence of top-down perceptual modulation in adults, it is largely unknown whether infants can engage in this cognitive function. Here, we examined top-down modulation of motion perception in 6- to 8-month-old infants (recruited in North America) via their smooth-pursuit eye movements. In four experiments, we demonstrated that infants' perception of motion direction can be flexibly shaped by briefly learned predictive cues when no coherent motion is available. The current findings present a novel insight into infant perception and its development: Infant perceptual systems respond to predictive signals engendered from higher-level learning systems, leading to a flexible and context-dependent modulation of perception. This work also suggests that the infant brain is sophisticated, interconnected, and active when placed in a context in which it can learn and predict.

Topological reorganization of functional hubs in patients with Parkinson's disease with freezing of gait

BACKGROUND AND PURPOSE

Resting-state functional MRI (rs-fMRI) studies in Parkinson's disease (PD) patients with freezing of gait (FOG) have implicated dysfunctional connectivity over multiple resting-state networks (RSNs). While these findings provided network-specific insights and information related to the aberrant or altered regional functional connectivity (FC), whether these alterations have any effect on topological reorganization in PD-FOG patients is incompletely understood. Understanding the higher order functional organization, which could be derived from the "hub" and the "rich-club" organization of the functional networks, could be crucial to identifying the distinct and unique pattern of the network connectivity associated with PD-FOG.

METHODS

In this study, we use rs-fMRI data and graph theoretical approaches to explore the reorganization of RSN topology in PD-FOG when compared to those without FOG. We also compared the higher order functional organization derived using the hub and rich-club measures in the FC networks of these PD-FOG patients to understand whether there is a topological reorganization of these hubs in PD-FOG.

RESULTS

We found that the PD-FOG patients showed a noticeable reorganization of hub regions. Regions that are part of the prefrontal cortex, primary somatosensory, motor, and visuomotor coordination areas were some of the regions exhibiting altered hub measures in PD-FOG patients. We also found a significantly altered feeder and local connectivity in PD-FOG.

CONCLUSIONS

Overall, our findings demonstrate a widespread topological reorganization and disrupted higher order functional network topology in PD-FOG that may further assist in improving our understanding of functional network disturbances associated with PD-FOG.

The effects of exposure to road traffic noise at school on central auditory pathway functional connectivity

As the world becomes more urbanized, more people become exposed to traffic and the risks associated with a higher exposure to road traffic noise increase. Excessive exposure to environmental noise could potentially interfere with functional maturation of the auditory brain in developing individuals. The aim of the present study was to assess the association between exposure to annual average road traffic noise (LAeq) in schools and functional connectivity of key elements of the central auditory pathway in schoolchildren. A total of 229 children from 34 representative schools in the city of Barcelona with ages between 8 and 12 years (49.2% girls) were evaluated. LAeq was obtained as the mean of 2-consecutive day measurements inside classrooms before lessons started following standard procedures to obtain an indicator of long-term road traffic noise levels. A region-of-interest functional connectivity Magnetic Resonance Imaging (MRI) approach was adopted. Functional connectivity maps were generated for the inferior colliculus, medial geniculate body of the thalamus and primary auditory cortex as key levels of the central auditory pathway. Road traffic noise in schools was significantly associated with stronger connectivity between the inferior colliculus and a bilateral thalamic region adjacent to the medial geniculate body, and with stronger connectivity between the medial geniculate body and a bilateral brainstem region adjacent to the inferior colliculus. Such a functional connectivity strengthening effect did not extend to the cerebral cortex. The anatomy of the association implicating subcortical relays suggests that prolonged road traffic noise exposure in developing individuals may accelerate maturation in the basic elements of the auditory pathway. Future research is warranted to establish whether such a faster maturation in early pathway levels may ultimately reduce the developing potential in the whole auditory system.

Toward a more informative representation of the fetal-neonatal brain connectome using variational autoencoder

Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal-neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the life span. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal-neonatal brain development, including nonlinear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a large sample (>500) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called variational autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting-state data in healthy adults. Here, we demonstrated that nonlinear brain features, that is, latent variables, derived with the VAE pretrained on rsfMRI of human adults, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction in the neonate cohort compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, nonlinear fetal-neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life.

Examining resting-state network connectivity in children exposed to perinatal maternal adversity using anatomically weighted functional connectivity (awFC) analyses; A preliminary report

Introduction

Environmental perturbations during critical periods can have pervasive, organizational effects on neurodevelopment. To date, the literature examining the long-term impact of early life adversity has largely investigated structural and functional imaging data outcomes independently. However, emerging research points to a relationship between functional connectivity and the brain’s underlying structural architecture. For instance, functional connectivity can be mediated by the presence of direct or indirect anatomical pathways. Such evidence warrants the use of structural and functional imaging in tandem to study network maturation. Accordingly, this study examines the impact of poor maternal mental health and socioeconomic context during the perinatal period on network connectivity in middle childhood using an anatomically weighted functional connectivity (awFC) approach. awFC is a statistical model that identifies neural networks by incorporating information from both structural and functional imaging data.

Methods

Resting-state fMRI and DTI scans were acquired from children aged 7–9 years old.

Results

Our results indicate that maternal adversity during the perinatal period can affect offspring’s resting-state network connectivity during middle childhood. Specifically, in comparison to controls, children of mothers who had poor perinatal maternal mental health and/or low socioeconomic status exhibited greater awFC in the ventral attention network.

Discussion

These group differences were discussed in terms of the role this network plays in attention processing and maturational changes that may accompany the consolidation of a more adult-like functional cortical organization. Furthermore, our results suggest that there is value in using an awFC approach as it may be more sensitive in highlighting connectivity differences in developmental networks associated with higher-order cognitive and emotional processing, as compared to stand-alone FC or SC analyses.

Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data

IMPORTANCE

Neurodevelopmental conditions, such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), have highly heterogeneous and overlapping phenotypes and neurobiology. Data-driven approaches are beginning to identify homogeneous transdiagnostic subgroups of children; however, findings have yet to be replicated in independently collected data sets, a necessity for translation into clinical settings.

OBJECTIVE

To identify subgroups of children with and without neurodevelopmental conditions with shared functional brain characteristics using data from 2 large, independent data sets.

DESIGN, SETTING, AND PARTICIPANTS

This case-control study used data from the Province of Ontario Neurodevelopmental (POND) network (study recruitment began June 2012 and is ongoing; data were extracted April 2021) and the Healthy Brain Network (HBN; study recruitment began May 2015 and is ongoing; data were extracted November 2020). POND and HBN data are collected from institutions across Ontario and New York, respectively. Participants who had diagnoses of ASD, ADHD, and OCD or were typically developing (TD); were aged between 5 and 19 years; and successfully completed the resting-state and anatomical neuroimaging protocol were included in the current study.

MAIN OUTCOMES AND MEASURES

The analyses consisted of a data-driven clustering procedure on measures derived from each participant's resting-state functional connectome, performed independently on each data set. Differences between each pair of leaves in the resulting clustering decision trees in the demographic and clinical characteristics were tested.

RESULTS

Overall, 551 children and adolescents were included from each data set. POND included 164 participants with ADHD; 217 with ASD; 60 with OCD; and 110 with TD (median [IQR] age, 11.87 [9.51-14.76] years; 393 [71.2%] male participants; 20 [3.6%] Black, 28 [5.1%] Latino, and 299 [54.2%] White participants) and HBN included 374 participants with ADHD; 66 with ASD; 11 with OCD; and 100 with TD (median [IQR] age, 11.50 [9.22-14.20] years; 390 [70.8%] male participants; 82 [14.9%] Black, 57 [10.3%] Hispanic, and 257 [46.6%] White participants). In both data sets, subgroups with similar biology that differed significantly in intelligence as well as hyperactivity and impulsivity problems were identified, yet these groups showed no consistent alignment with current diagnostic categories. For example, there was a significant difference in Strengths and Weaknesses ADHD Symptoms and Normal Behavior Hyperactivity/Impulsivity subscale (SWAN-HI) between 2 subgroups in the POND data (C and D), with subgroup D having increased hyperactivity and impulsivity traits compared with subgroup C (median [IQR], 2.50 [0.00-7.00] vs 1.00 [0.00-5.00]; U = 1.19 × 104; P = .01; η2 = 0.02). A significant difference in SWAN-HI scores between subgroups g and d in the HBN data was also observed (median [IQR], 1.00 [0.00-4.00] vs 0.00 [0.00-2.00]; corrected P = .02). There were no differences in the proportion of each diagnosis between the subgroups in either data set.

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that homogeneity in the neurobiology of neurodevelopmental conditions transcends diagnostic boundaries and is instead associated with behavioral characteristics. This work takes an important step toward translating neurobiological subgroups into clinical settings by being the first to replicate our findings in independently collected data sets.

3D-MASNet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old infant brain MR images

Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infants. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.

Polygenic risk score for attention-deficit/hyperactivity disorder and brain functional networks segregation in a community-based sample

Neuroimaging studies suggest that brain development mechanisms might explain at least some behavioural and cognitive attention-deficit/hyperactivity disorder (ADHD) symptoms. However, the putative mechanisms by which genetic susceptibility factors influence clinical features via alterations of brain development remain largely unknown. Here, we set out to integrate genomics and connectomics tools by investigating the associations between an ADHD polygenic risk score (ADHD-PRS) and functional segregation of large-scale brain networks. With this aim, ADHD symptoms score, genetic and rs-fMRI (resting-state functional magnetic resonance image) data obtained in a longitudinal community-based cohort of 227 children and adolescents were analysed. A follow-up was conducted approximately 3 years after the baseline, with rs-fMRI scanning and ADHD likelihood assessment in both stages. We hypothesised a negative correlation between probable ADHD and the segregation of networks involved in executive functions, and a positive correlation with the default-mode network (DMN). Our findings suggest that ADHD-PRS is correlated with ADHD at baseline, but not at follow-up. Despite not surviving for multiple comparison correction, we found significant correlations between ADHD-PRS and segregation of cingulo-opercular networks and DMN at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation. These directions of associations corroborate the proposed counter-balanced role of attentional networks and DMN in attentional processes. However, the association between ADHD-PRS and brain networks functional segregation was not found at follow-up. Our results provide evidence for specific influences of genetic factors on development of attentional networks and DMN. We found significant correlations between polygenic risk score for ADHD (ADHD-PRS) and segregation of cingulo-opercular networks and default-mode network (DMN) at baseline. ADHD-PRS was negatively correlated with the segregation level of cingulo-opercular networks but positively correlated with the DMN segregation.

Emerging functional connectivity patterns during sevoflurane anaesthesia in the developing human brain

BACKGROUND

Spectral-based EEG is used to monitor anaesthetic state during surgical procedures in adults. Spectral EEG features that can resemble the patterns seen in adults emerge in children after the age of 10 months and cannot distinguish wakefulness and anaesthesia in the youngest children. There is a need to explore alternative EEG measures. We hypothesise that functional connectivity is one of the measures that can help distinguish between consciousness states in children.

METHODS

An EEG data set of children undergoing sevoflurane general anaesthesia (age 0-3 yr) was reanalysed using debiased weighted phase lag index as a measure of functional connectivity in wakefulness (n=38) and anaesthesia (n=73). Network topology measures were compared between states in 0- to 6-, 6- to 10-, and >10-month-old children.

RESULTS

Functional connectivity was reduced in anaesthesia vs wakefulness in delta band (n=cluster of 17 significant connections; P=0.013; 58% connections surviving thresholding in wakefulness and 49% in anaesthesia). Network density and node degree were lower in anaesthesia even in the youngest children (0.57 in wakefulness; 0.48 in anaesthesia; t [9]=3.39; P=0.029; G=0.98; confidence interval [CI] [0.25-1.77]). Modularity was higher in anaesthesia (0-6 months: 0.16 in wakefulness and 0.19 in anaesthesia, t [9]=-2.95, P=0.04, G=-0.85, CI [-1.60 to -0.16]; >10 months: 0.16 vs 0.21, t [13]=-6.45, P<0.001, G=-1.62, CI [-2.49 to -0.85]) and decreased with age (ρ [73]=-0.456; P<0.001).

CONCLUSIONS

Anaesthesia modulates functional connectivity. Increased segregation into a more modular structure in anaesthesia decreases with age as adult-like features develop. These findings advance our understanding of the network architecture underlying the effects of anaesthesia on the developing brain.

Infant brain regional cerebral blood flow increases supporting emergence of the default-mode network

Human infancy is characterized by most rapid regional cerebral blood flow (rCBF) increases across lifespan and emergence of a fundamental brain system default-mode network (DMN). However, how infant rCBF changes spatiotemporally across the brain and how the rCBF increase supports emergence of functional networks such as DMN remains unknown. Here, by acquiring cutting-edge multi-modal MRI including pseudo-continuous arterial-spin-labeled perfusion MRI and resting-state functional MRI of 48 infants cross-sectionally, we elucidated unprecedented 4D spatiotemporal infant rCBF framework and region-specific physiology-function coupling across infancy. We found that faster rCBF increases in the DMN than visual and sensorimotor networks. We also found strongly coupled increases of rCBF and network strength specifically in the DMN, suggesting faster local blood flow increase to meet extraneuronal metabolic demands in the DMN maturation. These results offer insights into the physiological mechanism of brain functional network emergence and have important implications in altered network maturation in brain disorders.

Different functional alteration in attention-deficit/hyperactivity disorder across developmental age groups: A meta-analysis and an independent validation of resting-state functional connectivity studies

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a highly complex and heterogeneous disorder. Abnormal brain connectivity in ADHD might be influenced by developmental ages which might lead to the lacking of significant spatial convergence across studies. However, the developmental patterns and mechanisms of ADHD brain connectivity remain to be fully uncovered.

METHODS

In the present study, we searched PubMed, Scopus, Web of Science, and Embase for seed-based whole-brain resting-state functional connectivity studies of ADHD published through October 12th, 2020. The seeds meeting inclusion criteria were categorized into the cortex group and subcortex group, as previous studies suggested that the cortex and subcortex have different temporal patterns of development. Activation likelihood estimation meta-analysis was performed to investigate the abnormal connectivity in different age groups (all-age group, younger: <12 years, older: ≥12 years). Moreover, significant convergence of reported foci was used as seeds for validation with our independent dataset.

RESULTS

As with previous studies, scarce results were found in the all-age group. However, we found that the younger group consistently exhibited hyper-connectivity between different parts of the cortex and left middle frontal gyrus, and hypo-connectivity between different parts of the cortex and left putamen/pallidus/amygdala. Whereas, the older group (mainly for adults) showed hyper-connectivity between the cortex and right precuneus/sub-gyral/cingulate gyrus. Besides, the abnormal cortico-cortical and cortico-subcortical functional connectivity in children, and the abnormal cortico-cortical functional connectivity in adults were verified in our independent dataset.

CONCLUSION

Our study emphasizes the importance of developmental age effects on the study of brain networks in ADHD. Further, we proposed that cortico-cortical and cortico-subcortical connectivity might play an important role in the pathophysiology of children with ADHD, while abnormal cortico-cortical connections were more important for adults with ADHD. This work provided a potential new insight to understand the neurodevelopmental mechanisms and possible clinical application of ADHD.

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

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

Brainnetome atlas of preadolescent children based on anatomical connectivity profiles

During the preadolescent period, when the cerebral thickness, curvature, and myelin are constantly changing, the brain's regionalization patterns underwent persistent development, contributing to the continuous improvements of various higher cognitive functions. Using a brain atlas to study the development of these functions has attracted much attention. However, the brains of children do not always have the same topological patterns as those of adults. Therefore, age-specific brain mapping is particularly important, serving as a basic and indispensable tool to study the normal development of children. In this study, we took advantage of longitudinal data to create the brain atlas specifically for preadolescent children. The resulting human Child Brainnetome Atlas, with 188 cortical and 36 subcortical subregions, provides a precise period-specific and cross-validated version of the brain atlas that is more appropriate for adoption in the preadolescent period. In addition, we compared and illustrated for regions with different topological patterns in the child and adult atlases, providing a topologically consistent reference for subsequent research studying child and adolescent development.

Variations of Resting-State EEG-Based Functional Networks in Brain Maturation From Early Childhood to Adolescence

Background and Purpose

Alterations in human brain functional networks with maturation have been explored extensively in numerous electroencephalography (EEG) and functional magnetic resonance imaging studies. It is known that the age-related changes in the functional networks occurring prior to adulthood deviate from ordinary trajectories of network-based brain maturation across the adult lifespan.

Methods

This study investigated the longitudinal evolution of resting-state EEG-based functional networks from early childhood to adolescence among 212 pediatric patients (age 12.2±3.5 years, range 4.4–17.9) in 6 frequency bands using 8 types of functional connectivity measures in the amplitude, frequency, and phase domains.

Results

Electrophysiological aspects of network-based pediatric brain maturation were characterized by increases in both functional segregation and integration up to middle adolescence. EEG oscillations in the upper alpha band reflected the age-related increases in mean node strengths and mean clustering coefficients and a decrease in the characteristic path lengths better than did those in the other frequency bands, especially for the phase-domain functional connectivity. The frequency-band-specific age-related changes in the global network metrics were influenced more by volume-conduction effects than by the domain specificity of the functional connectivity measures.

Conclusions

We believe that this is the first study to reveal EEG-based functional network properties during preadult brain maturation based on various functional connectivity measures. The findings potentially have clinical applications in the diagnosis and treatment of age-related brain disorders.

The expanding horizons of network neuroscience: From description to prediction and control

The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.

Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds

Resting-state functional connectivity (rsFC) measured with fMRI has been used to characterize functional brain maturation in typically and atypically developing children and adults. However, its reliability and utility for predicting development in infants and toddlers is less well understood. Here, we use fMRI data from the Baby Connectome Project study to measure the reliability and uniqueness of rsFC in infants and toddlers and predict age in this sample (8-to-26 months old; n = 170). We observed medium reliability for within-session infant rsFC in our sample, and found that individual infant and toddler's connectomes were sufficiently distinct for successful functional connectome fingerprinting. Next, we trained and tested support vector regression models to predict age-at-scan with rsFC. Models successfully predicted novel infants' age within ± 3.6 months error and a prediction R² = .51. To characterize the anatomy of predictive networks, we grouped connections into 11 infant-specific resting-state functional networks defined in a data-driven manner. We found that connections between regions of the same network-i.e. within-network connections-predicted age significantly better than between-network connections. Looking ahead, these findings can help characterize changes in functional brain organization in infancy and toddlerhood and inform work predicting developmental outcome measures in this age range.

Influence of Gestational Diabetes and Pregestational Maternal BMI on the Brain of Six-Year-Old Offspring

BACKGROUND

Gestational diabetes (GD) and maternal excess weight are common pregnancy conditions that increase the risk of future complications for both the mother and her offspring. Their consequences on neurodevelopment are widely described in the literature, but less is known concerning the potential transgenerational influence on the brain structure.

METHODS

We used a combination of support vectors machine and hierarchical clustering to investigate the potential presence of anatomical brain differences in a sample of 109 children aged six years, born to mothers with overweight or obesity, or to mothers diagnosed with GD during pregnancy.

RESULTS

Significant effects are visible in the brain of children born to mothers with GD associated with pregestational excess weight, especially overweight instead of obesity. No differences in children's brain were observed when considering those born to normal-weight mothers.

CONCLUSIONS

Our study highlights the need for clinical attention of pregnant women at risk to develop GD, and especially those with pregestational excess weight, since this status was found to be associated with detectable transgenerational brain changes. These effects may be due to the absence of specific and individualized intervention in these mothers during pregnancy.

Early Development and the Functional Correlation of Brain Structural Connectivity in Preterm-Born Infants

Exuberant axon growth and competitive pruning lead to dramatic and comprehensive changes in white matter pathways of the infant brain during the first few postnatal months, yet the development of structural configuration in early infancy has not been fully characterized. This study aimed to investigate the developmental trajectory of structural connectivity reflecting relative fiber density in 43 preterm-born infants aged 0–3 months of corrected age without any complications utilizing probabilistic tractography based on fiber orientation distribution and to explore the potential function correlation associated with the network properties based on the Chinese Communication Development of Infant at 10 months of corrected age. The findings revealed significant increases in global efficiency, local efficiency, normalized clustering coefficient, and small-worldness (p_adj < 0.001 for each), while the normalized characteristic path length showed a non-significant decrease with age (p_adj = 0.118). Furthermore, those findings were validated by another parcelation strategy. In addition, the early local efficiency was found to be significantly correlated with words understood at 10 months of corrected age. A unique developmental pattern of structural networks with enhancing efficiency and the small-world property was found in early infancy, which was different from those of neonates or toddlers. In addition, this study revealed a significant correlation between local efficiency and late language comprehension, which indicated that enhanced structural connectivity may lay the structural foundation for language specialization.

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.

Structural insight into the individual variability architecture of the functional brain connectome

Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

Neural connectome features of procrastination: Current progress and future direction

Procrastination refers to an irrationally delay for intended courses of action despite of anticipating a negative consequence due to this delay. Previous studies tried to reveal the neural substrates of procrastination in terms of connectome-based biomarkers. Based on this, we proposed a unified triple brain network model for procrastination and pinpointed out what challenges we are facing in understanding neural mechanism of procrastination. Specifically, based on neuroanatomical features, the unified triple brain network model proposed that connectome-based underpinning of procrastination could be ascribed to the abnormalities of self-control network (i.e., dorsolateral prefrontal cortex, DLPFC), emotion-regulation network (i.e., orbital frontal cortex, OFC), and episodic prospection network (i.e., para-hippocampus cortex, PHC). Moreover, based on the brain functional features, procrastination had been attributed to disruptive neural circuits on FPN (frontoparietal network)-SCN (subcortical network) and FPN-SAN (salience network), which led us to hypothesize the crucial roles of interplay between these networks on procrastination in unified triple brain network model. Despite of these findings, poor interpretability and computational model limited further understanding for procrastination from theoretical and neural perspectives. On balance, the current study provided an overview to show current progress on the connectome-based biomarkers for procrastination, and proposed the integrative neurocognitive model of procrastination.

The developing brain structural and functional connectome fingerprint

In the mature brain, structural and functional 'fingerprints' of brain connectivity can be used to identify the uniqueness of an individual. However, whether the characteristics that make a given brain distinguishable from others already exist at birth remains unknown. Here, we used neuroimaging data from the developing Human Connectome Project (dHCP) of preterm born neonates who were scanned twice during the perinatal period to assess the developing brain fingerprint. We found that 62% of the participants could be identified based on the congruence of the later structural connectome to the initial connectivity matrix derived from the earlier timepoint. In contrast, similarity between functional connectomes of the same subject at different time points was low. Only 10% of the participants showed greater self-similarity in comparison to self-to-other-similarity for the functional connectome. These results suggest that structural connectivity is more stable in early life and can represent a potential connectome fingerprint of the individual: a relatively stable structural connectome appears to support a changing functional connectome at a time when neonates must rapidly acquire new skills to adapt to their new environment.

Development of functional connectome gradients during childhood and adolescence

Connectome mapping studies have documented a principal primary-to-transmodal gradient in the adult brain network, capturing a functional spectrum that ranges from perception and action to abstract cognition. However, how this gradient pattern develops and whether its development is linked to cognitive growth, topological reorganization, and gene expression profiles remain largely unknown. Using longitudinal resting-state functional magnetic resonance imaging data from 305 children (aged 6-14 years), we describe substantial changes in the primary-to-transmodal gradient between childhood and adolescence, including emergence as the principal gradient, expansion of global topography, and focal tuning in primary and default-mode regions. These gradient changes are mediated by developmental changes in network integration and segregation, and are associated with abstract processing functions such as working memory and expression levels of calcium ion regulated exocytosis and synaptic transmission-related genes. Our findings have implications for understanding connectome maturation principles in normal development and developmental disorders.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Neonatal quantitative electroencephalography and long-term outcomes: a systematic review

AIM

To evaluate quantitative electroencephalogram (EEG) measures as predictors of long-term neurodevelopmental outcome in infants with a postconceptional age below 46 weeks, including typically developing infants born at term, infants with heterogeneous underlying pathologies, and infants born preterm.

METHOD

A comprehensive search was performed using PubMed, Embase, and Web of Science from study inception up to 8th January 2021. Studies that examined associations between neonatal quantitative EEG measures, based on conventional and amplitude-integrated EEG, and standardized neurodevelopmental outcomes at 2 years of age or older were reviewed. Significant associations between neonatal quantitative EEG and long-term outcome measures were grouped into one or more of the following categories: cognitive outcome; motor outcome; composite scores; and other standardized outcome assessments.

RESULTS

Twenty-four out of 1740 studies were included. Multiple studies showed that conventional EEG-based absolute power in the delta, theta, alpha, and beta frequency bands and conventional and amplitude-integrated EEG-related amplitudes were positively associated with favourable long-term outcome across several domains, including cognition and motor performance. Furthermore, a lower presence of discontinuous background pattern was also associated with favourable outcomes. However, interpretation of the results is limited by heterogeneity in study design and populations.

INTERPRETATION

Neonatal quantitative EEG measures may be used as prognostic biomarkers to identify those infants who will develop long-term difficulties and who might benefit from early interventions.

Effect of Anesthetics on Functional Connectivity of Developing Brain

The potential anesthetic neurotoxicity on the neonate is an important focus of research investigation in the field of pediatric anesthesiology. It is essential to understand how these anesthetics may affect the development and growth of neonatal immature and vulnerable brains. Functional magnetic resonance imaging (fMRI) has suggested that using anesthetics result in reduced functional connectivity may consider as core sequence for the neurotoxicity and neurodegenerative changes in the developed brain. Anesthetics either directly impact the primary structures and functions of the brain or indirectly alter the hemodynamic parameters that contribute to cerebral blood flow (CBF) in neonatal patients. We hypothesis that anesthetic agents may either decrease the brain functional connectivity in neonatal patients or animals, which was observed by fMRI. This review will summarize the effect and mechanism of anesthesia on the rapid growth and development infant and neonate brain with fMRI through functional connectivity. It is possible to provide the new mechanism of neuronal injury induced by anesthetics and objective imaging evidence in animal developing brain.

Decoding functional brain networks through graph measures in infancy: The case of emotional faces

Graph measures represent an optimal way to investigate neural networks' organization, yet their application is still limited in developmental samples. To uncover the organization of 7-month-old infants' functional brain networks during an emotional perception task, we combined a decoding technique (i.e., Principal Component Regression) to graph metrics computation. Nodes' Within Module Degree Z Score (WMDZ) was computed as a measure of modular organization, and we decoded networks' functional organizations across EEG alpha and theta bands in response to static and dynamic facial expressions of emotions. We found that infants' brain topological activity differentiates between static and dynamic emotional faces due to the involvement of visual streams and sensorimotor areas, as often observed in adults. Moreover, network invariances point toward an already present rudimental network structure tuned to face processing already at 7-months of age. Overall, our results affirm the fruitfulness of the application of graph measures in developmental samples, due to their flexibility and the wealth of information they provide over infants' networks functional organization.

Neonatal brain injury influences structural connectivity and childhood functional outcomes

Neonatal brain injury may impact brain development and lead to lifelong functional impairments. Hypoxic-ischemic encephalopathy (HIE) and congenital heart disease (CHD) are two common causes of neonatal brain injury differing in timing and mechanism. Maturation of whole-brain neural networks can be quantified during development using diffusion magnetic resonance imaging (dMRI) in combination with graph theory metrics. DMRI of 35 subjects with CHD and 62 subjects with HIE were compared to understand differences in the effects of HIE and CHD on the development of network topological parameters and functional outcomes. CHD newborns had worse 12–18 month language (P<0.01) and 30 month cognitive (P<0.01), language (P = 0.05), motor outcomes (P = 0.01). Global efficiency, a metric of brain integration, was lower in CHD (P = 0.03) than in HIE, but transitivity, modularity and small-worldness were similar. After controlling for clinical factors known to affect neurodevelopmental outcomes, we observed that global efficiency was highly associated with 30 month motor outcomes (P = 0.02) in both groups. To explore neural correlates of adverse language outcomes in CHD, we used hypothesis-based and data-driven approaches to identify pathways with altered structural connectivity. We found that connectivity strength in the superior longitudinal fasciculus (SLF) tract 2 was inversely associated with expressive language. After false discovery rate correction, a whole connectome edge analysis identified 18 pathways that were hypoconnected in the CHD cohort as compared to HIE. In sum, our study shows that neonatal structural connectivity predicts early motor development after HIE or in subjects with CHD, and regional SLF connectivity is associated with language outcomes. Further research is needed to determine if and how brain networks change over time and whether those changes represent recovery or ongoing dysfunction. This knowledge will directly inform strategies to optimize neurologic functional outcomes after neonatal brain injury.

Testing predictions of a neural process model of visual attention in infancy across competitive and non-competitive contexts

A key question in early development is how changes in neural systems give rise to changes in infants' behavior. We examine this question by testing predictions of a dynamic field (DF) model of infant spatial attention. We tested 5‐, 7‐, and 10‐month‐old infants in the Infant Orienting With Attention (IOWA) task containing the original non‐competitive cue conditions (when a central stimulus disappeared before a cue onset) and new competitive cue conditions (when a central stimulus remained visible throughout the trial). This allowed testing of five model predictions: (1) that orienting accuracy would be higher and (2) reaction times would be slower for all competitive conditions; (3) that all infants would be slower to orient in the competitive conditions, though (4) older infants would show the strongest competition costs; and (5) that reaction times would be particularly slow for un‐cued competitive conditions. Four of these five predictions were supported, and the remaining prediction was supported in part. We next examined fits of the model to the expanded task. New simulation results reveal close fits to the present findings after parameter modification. Critically, developmental parameters of the model were not altered, providing support for the DF model's account of neuro‐developmental change.

Flattened Structural Network Changes and Association of Hyperconnectivity With Symptom Severity in 2-7-Year-Old Children With Autism

Understanding the brain differences present at the earliest possible diagnostic age for autism spectrum disorder (ASD) is crucial for delineating the underlying neuropathology of the disorder. However, knowledge of brain structural network changes in the early important developmental period between 2 and 7 years of age is limited in children with ASD. In this study, we aimed to fill the knowledge gap by characterizing age-related brain structural network changes in ASD from 2 to 7 years of age, and identify sensitive network-based imaging biomarkers that are significantly correlated with the symptom severity. Diffusion MRI was acquired in 30 children with ASD and 21 typically developmental (TD) children. With diffusion MRI and quantified clinical assessment, we conducted network-based analysis and correlation between graph-theory-based measurements and symptom severity. Significant age-by-group interaction was found in global network measures and nodal efficiencies during the developmental period of 2-7 years old. Compared with significant age-related growth of the structural network in TD, relatively flattened maturational trends were observed in ASD. Hyper-connectivity in the structural network with higher global efficiency, global network strength, and nodal efficiency were observed in children with ASD. Network edge strength in ASD also demonstrated hyper-connectivity in widespread anatomical connections, including those in default-mode, frontoparietal, and sensorimotor networks. Importantly, identified higher nodal efficiencies and higher network edge strengths were significantly correlated with symptom severity in ASD. Collectively, structural networks in ASD during this early developmental period of 2-7 years of age are characterized by hyper-connectivity and slower maturation, with aberrant hyper-connectivity significantly correlated with symptom severity. These aberrant network measures may serve as imaging biomarkers for ASD from 2 to 7 years of age.

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.

Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months

The functional connectome fingerprint is a cluster of individualized brain functional connectivity patterns that are capable of distinguishing one individual from others. Although its existence has been demonstrated in adolescents and adults, whether such individualized patterns exist during infancy is barely investigated despite its importance in identifying the origin of the intrinsic connectome patterns that potentially mirror distinct behavioral phenotypes. To fill this knowledge gap, capitalizing on a longitudinal high-resolution structural and resting-state functional MRI dataset with 104 human infants (53 females) with 806 longitudinal scans (age, 16-876 d) and infant-specific functional parcellation maps, we observe that the brain functional connectome fingerprint may exist since infancy and keeps stable over months during early brain development. Specifically, we achieve an ∼78% individual identification rate by using ∼5% selected functional connections, compared with the best identification rate of 60% without connection selection. The frontoparietal networks recognized as the most contributive networks in adult functional connectome fingerprinting retain their superiority in infants despite being widely acknowledged as rapidly developing systems during childhood. The existence and stability of the functional connectome fingerprint are further validated on adjacent age groups. Moreover, we show that the infant frontoparietal networks can reach similar accuracy in predicting individual early learning composite scores as the whole-brain connectome, again resembling the observations in adults and highlighting the relevance of functional connectome fingerprint to cognitive performance. For the first time, these results suggest that each individual may retain a unique and stable marker of functional connectome during early brain development.SIGNIFICANCE STATEMENT Functional connectome fingerprinting during infancy featuring rapid brain development remains almost uninvestigated even though it is essential for understanding the early individual-level intrinsic pattern of functional organization and its relationship with distinct behavioral phenotypes. With an infant-tailored functional connection selection and validation strategy, we strive to provide the delineation of the infant functional connectome fingerprint by examining its existence, stability, and relationship with early cognitive performance. We observe that the brain functional connectome fingerprint may exist since early infancy and remains stable over months during the first 2 years. The identified key contributive functional connections and networks for fingerprinting are also verified to be highly predictive for cognitive score prediction, which reveals the association between infant connectome fingerprint and cognitive performance.

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.