Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature

Functional Connectivity of Attention, Visual, and Language Networks During Audio, Illustrated, and Animated Stories in Preschool-Age Children

The American Academy of Pediatrics recommends that parents read with their children early and often and limits on screen-based media. While book sharing may benefit attention in children, effects of animated content are controversial, and the influence of either on attention networks has not previously been studied. This study involved functional magnetic resonance imaging (fMRI) of three separate active-task scans composed of similar 5-min stories presented in the same order for each child (audio → illustrated → animated), followed by assessment of comprehension. Five functional brain networks were defined a priori through literature review: dorsal attention network (DAN), ventral attention network (VAN), language (L), visual imagery (VI), and visual perception (VP). Analyses involved comparison of functional connectivity (FC) within- and between networks across formats, applying false discovery rate correction. Twenty-seven of 33 children completed fMRI (82%; 15 boys, 12 girls; mean 58 ± 8 months old). Comprehension of audio and illustrated stories was equivalent and lower for animation (p < 0.05). For illustration relative to audio, FC within DAN and VAN and between each of these and all other networks was similar, lower within-L, and higher between VI-VP, suggesting reduced strain on the language network using illustrations and imagery. For animation relative to illustration, FC was lower between DAN-L, VAN-VP, VAN-VI, L-VI, and L-VP, suggesting less focus on narrative, reorienting to imagery, and visual-language integration. These findings suggest that illustrated storybooks may be optimal at this age to encourage integration of attention, visual, and language networks, while animation may bias attention toward VP.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Associations between children's family environment, spontaneous brain oscillations, and emotional and behavioral problems

The family environment in childhood has a strong effect on mental health outcomes throughout life. This effect is thought to depend at least in part on modifications of neurodevelopment trajectories. In this exploratory study, we sought to investigate whether a feasible resting-state fMRI metric of local spontaneous oscillatory neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), is associated with the levels of children's family coherence and conflict. Moreover, we sought to further explore whether spontaneous activity in the brain areas influenced by family environment would also be associated with a mental health outcome, namely the incidence of behavioral and emotional problems. Resting-state fMRI data from 655 children and adolescents (6-15 years old) were examined. The quality of the family environment was found to be positively correlated with fALFF in the left temporal pole and negatively correlated with fALFF in the right orbitofrontal cortex. Remarkably, increased fALFF in the temporal pole was associated with a lower incidence of behavioral and emotional problems, whereas increased fALFF in the orbitofrontal cortex was correlated with a higher incidence.

The Protective Effects of Supportive Parenting on the Relationship Between Adolescent Poverty and Resting-State Functional Brain Connectivity During Adulthood

Children growing up in poverty are vulnerable to negative changes in the developing brain; however, these outcomes vary widely. We tested the hypothesis that receipt of supportive parenting would offset the association between living in poverty during adolescence and the connectivity of neural networks that support cognition and emotion regulation during young adulthood. In a sample of African American youths (N = 119) living in the rural South, poverty status and receipt of supportive parenting were assessed when youths were 11 to 13 and 16 to 18 years old. At age 25, resting-state functional connectivity of the central-executive and emotion-regulation neural networks was assessed using functional MRI. The results revealed that more years spent living in poverty presaged less connectivity in both neural networks among young adults who received low levels of supportive parenting but not among those who received high levels of such parenting.

Future Directions for Early Childhood Prevention of Mental Disorders: A Road Map to Mental Health, Earlier

Mental disorders are the predominant chronic diseases of youth, with substantial life span morbidity and mortality. A wealth of evidence demonstrates that the neurodevelopmental roots of common mental health problems are present in early childhood. Unfortunately, this has not been translated to systematic strategies for improving population-level mental health at this most malleable neurodevelopmental period. We lay out a translational Mental Health, Earlier road map as a key future direction for prevention of mental disorder. This paradigm shift aims to reduce population attributable risk of mental disorder emanating from early life, by preventing, attenuating, or delaying onset/course of chronic psychopathology via the promotion of self-regulation in early childhood within large-scale health care delivery systems. The Earlier Pillar rests on a "science of when to worry" that (a) optimizes clinical assessment methods for characterizing probabilistic clinical risk beginning in infancy via deliberate incorporation of neurodevelopmental heterogeneity, and (b) universal primary-care-based screening targeting patterns of dysregulated irritability as a robust transdiagnostic marker of vulnerability to life span mental health problems. The core of the Healthier Pillar is provision of low-intensity selective intervention promoting self-regulation for young children with developmentally atypical patterns of irritability within an implementation science framework in pediatric primary care to ensure highest population impact and sustainability. These Mental Health, Earlier strategies hold much promise for transforming clinical outlooks and ensuring young children's mental health and well-being in a manner that reverberates throughout the life span.

The development of brain network hubs

Some brain regions have a central role in supporting integrated brain function, marking them as network hubs. Given the functional importance of hubs, it is natural to ask how they emerge during development and to consider how they shape the function of the maturing brain. Here, we review evidence examining how brain network hubs, both in structural and functional connectivity networks, develop over the prenatal, neonate, childhood, and adolescent periods. The available evidence suggests that structural hubs of the brain arise in the prenatal period and show a consistent spatial topography through development, but undergo a protracted period of consolidation that extends into late adolescence. In contrast, the hubs of brain functional networks show a more variable topography, being predominantly located in primary cortical areas in early development, before moving to association areas by late childhood. These findings suggest that while the basic anatomical infrastructure of hubs may be established early, the functional viability and integrative capacity of these areas undergoes extensive postnatal maturation. Not all findings are consistent with this view however. We consider methodological factors that might drive these inconsistencies, and which should be addressed to promote a more rigorous investigation of brain network development.

Movies in the magnet: Naturalistic paradigms in developmental functional neuroimaging

The use of movie-watching as an acquisition state for functional connectivity (FC) MRI has recently enabled multiple groups to obtain rich data sets in younger children with both substantial sample sizes and scan durations. Using naturalistic paradigms such as movies has also provided analytic flexibility for these developmental studies that extends beyond conventional resting state approaches. This review highlights the advantages and challenges of using movies for developmental neuroimaging and explores some of the methodological issues involved in designing pediatric studies with movies. Emerging themes from movie-watching studies are discussed, including an emphasis on intersubject correlations, developmental changes in network interactions under complex naturalistic conditions, and dynamic age-related changes in both sensory and higher-order network FC even in narrow age ranges. Converging evidence suggests an enhanced ability to identify brain-behavior correlations in children when using movie-watching data relative to both resting state and conventional tasks. Future directions and cautionary notes highlight the potential and the limitations of using movies to study FC in pediatric populations.

Sex differences in functional connectivity during fetal brain development

Sex-related differences in brain and behavior are apparent across the life course, but the exact set of processes that guide their emergence in utero remains a topic of vigorous scientific inquiry. Here, we evaluate sex and gestational age (GA)-related change in functional connectivity (FC) within and between brain wide networks. Using resting-state functional magnetic resonance imaging we examined FC in 118 human fetuses between 25.9 and 39.6 weeks GA (70 male; 48 female). Infomap was applied to the functional connectome to identify discrete prenatal brain networks in utero. A consensus procedure produced an optimal model comprised of 16 distinct fetal neural networks distributed throughout the cortex and subcortical regions. We used enrichment analysis to assess network-level clustering of strong FC-GA correlations separately in each sex group, and to identify network pairs exhibiting distinct patterns of GA-related change in FC between males and females. We discovered both within and between network FC-GA associations that varied with sex. Specifically, associations between GA and posterior cingulate-temporal pole and fronto-cerebellar FC were observed in females only, whereas the association between GA and increased intracerebellar FC was stronger in males. These observations confirm that sexual dimorphism in functional brain systems emerges during human gestation.

Increased Functional Connectivity Within and Between Cognitive-Control Networks from Early Infancy to Nine Years During Story Listening

The cingulo-opercular (CO) and frontoparietal (FP) networks are part of the cognitive-control system of the brain. Evidence suggests that over the course of development, brain regions supporting cognitive-control functions become more integrated within their networks (i.e., have increased within-network connectivity), more separated from other networks, and, due to increased maturation along development, are more functionally connected between the networks. The focus of this study was to characterize the developmental trajectory of the CO and FP networks from early infancy (17 months) to 9 years of age in typically developing children while listening to stories, using functional connectivity analyses. Seventy-four children underwent a functional magnetic resonance imaging session while listening to stories inside the scanner. Within- and between-network functional connectivity and graph theory measures were compared during development. Developmental increase in functional connectivity within the CO network and between the CO and FP networks, as well as global efficiency of the CO network from 17 months to 9 years of age, was observed. These findings highlight the involvement of the CO and FP networks in story listening from early infancy, which increases along development. Future studies examining failures in language acquisition to further explore the role of these networks in story listening are warranted.

Differentiation in prefrontal cortex recruitment during childhood: Evidence from cognitive control demands and social contexts

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Using fNIRS, we examined how children recruit PFC while engaging cognitive control.

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Activation increased with cognitive control demands more in left than right PFC.

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It was higher in left PFC in competitive than cooperative contexts, and in right PFC in cooperative and neutral compared to competitive contexts.

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Older children showed greater variations in PFC activation than younger children.

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Cognitive control is supported by more differentiated PFC recruitment with age.

Emerging cognitive control during childhood is largely supported by the development of distributed neural networks in which the prefrontal cortex (PFC) is central. The present study used fNIRS to examine how PFC is recruited to support cognitive control in 5–6 and 8-9-year-old children, by (a) progressively increasing cognitive control demands within the same task, and (b) manipulating the social context in which the task was performed (neutral, cooperative, or competitive), a factor that has been shown to influence cognitive control. Activation increased more in left than right PFC with cognitive control demands, a pattern which was more pronounced in older than younger children. In addition, activation was higher in left PFC in competitive than cooperative contexts, and higher in right PFC in cooperative and neutral than competitive contexts. These findings suggest that increasingly efficient cognitive control during childhood is supported by more differentiated recruitment of PFC as a function of cognitive control demands with age.

Normalization enhances brain network features that predict individual intelligence in children with epilepsy

BACKGROUND AND PURPOSE

Architecture of the cerebral network has been shown to associate with IQ in children with epilepsy. However, subject-level prediction on this basis, a crucial step toward harnessing network analyses for the benefit of children with epilepsy, has yet to be achieved. We compared two network normalization strategies in terms of their ability to optimize subject-level inferences on the relationship between brain network architecture and brain function.

MATERIALS AND METHODS

Patients with epilepsy and resting state fMRI were retrospectively identified. Brain network nodes were defined by anatomic parcellation, first in patient space (nodes defined for each patient) and again in template space (same nodes for all patients). Whole-brain weighted graphs were constructed according to pair-wise correlation of BOLD-signal time courses between nodes. The following metrics were then calculated: clustering coefficient, transitivity, modularity, path length, and global efficiency. Metrics computed on graphs in patient space were normalized to the same metric computed on a random network of identical size. A machine learning algorithm was used to predict patient IQ given access to only the network metrics.

RESULTS

Twenty-seven patients (8-18 years) comprised the final study group. All brain networks demonstrated expected small world properties. Accounting for intrinsic population heterogeneity had a significant effect on prediction accuracy. Specifically, transformation of all patients into a common standard space as well as normalization of metrics to those computed on a random network both substantially outperformed the use of non-normalized metrics.

CONCLUSION

Normalization contributed significantly to accurate subject-level prediction of cognitive function in children with epilepsy. These findings support the potential for quantitative network approaches to contribute clinically meaningful information in children with neurological disorders.

Understanding the Role of Puberty in Structural and Functional Development of the Adolescent Brain

Over the past two decades, there has been a tremendous increase in our understanding of structural and functional brain development in adolescence. However, understanding the role of puberty in this process has received much less attention. This review examines this relationship by summarizing recent research studies where the role of puberty was investigated in relation to brain structure, connectivity, and task-related functional magnetic resonance imaging (fMRI). The studies together suggest that puberty may contribute to adolescent neural reorganization and maturational advancement, and sex differences also emerge in puberty. The current body of work shows some mixed results regarding impact and exact direction of pubertal influence. We discuss several limitations of current studies and propose future directions on how to move the field forward.

Spatial and spectral trajectories in typical neurodevelopment from childhood to middle age

Detailed characterization of typical human neurodevelopment is key if we are to understand the nature of mental and neurological pathology. While research on the cellular processes of neurodevelopment has made great advances, in vivo human imaging is crucial to understand our uniquely human capabilities, as well as the pathologies that affect them. Using magnetoencephalography data in the largest normative sample currently available (324 participants aged 6-45 years), we assess the developmental trajectory of resting-state oscillatory power and functional connectivity from childhood to middle age. The maturational course of power, indicative of local processing, was found to both increase and decrease in a spectrally dependent fashion. Using the strength of phase-synchrony between parcellated regions, we found significant linear and nonlinear (quadratic and logarithmic) trajectories to be characterized in a spatially heterogeneous frequency-specific manner, such as a superior frontal region with linear and nonlinear trajectories in theta and gamma band respectively. Assessment of global efficiency revealed similar significant nonlinear trajectories across all frequency bands. Our results link with the development of human cognitive abilities; they also highlight the complexity of neurodevelopment and provide quantitative parameters for replication and a robust footing from which clinical research may map pathological deviations from these typical trajectories.

Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks

The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age.

Basic Units of Inter-Individual Variation in Resting State Connectomes

Resting state functional connectomes are massive and complex. It is an open question, however, whether connectomes differ across individuals in a correspondingly massive number of ways, or whether most differences take a small number of characteristic forms. We systematically investigated this question and found clear evidence of low-rank structure in which a modest number of connectomic components, around 50-150, account for a sizable portion of inter-individual connectomic variation. This number was convergently arrived at with multiple methods including estimation of intrinsic dimensionality and assessment of reconstruction of out-of-sample data. In addition, we show that these connectomic components enable prediction of a broad array of neurocognitive and clinical symptom variables at levels comparable to a leading method that is trained on the whole connectome. Qualitative observation reveals that these connectomic components exhibit extensive community structure reflecting interrelationships between intrinsic connectivity networks. We provide quantitative validation of this observation using novel stochastic block model-based methods. We propose that these connectivity components form an effective basis set for quantifying and interpreting inter-individual connectomic differences, and for predicting behavioral/clinical phenotypes.

Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts

Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects. Recently, infant rs-fMRI studies have become an area of active research. After a decade of gap filling studies, many facets of the brain functional development from early infancy to toddler has been uncovered. However, infant rs-fMRI is still in its infancy. The image analysis tools for neonates and young infants can be quite different from those for adults. From data analysis to result interpretation, more questions and issues have been raised, and new hypotheses have been formed. With the anticipated availability of unprecedented high-resolution rs-fMRI and dedicated analysis pipelines from the Baby Connectome Project (BCP), it is important now to revisit previous findings and hypotheses, discuss and comment existing issues and problems, and make a "to-do-list" for the future studies. This review article aims to comprehensively review a decade of the findings, unveiling hidden jewels of the fields of developmental neuroscience and neuroimage computing. Emphases will be given to early infancy, particularly the first few years of life. In this review, an end-to-end summary, from infant rs-fMRI experimental design to data processing, and from the development of individual functional systems to large-scale brain functional networks, is provided. A comprehensive summary of the rs-fMRI findings in developmental patterns is highlighted. Furthermore, an extensive summary of the neurodevelopmental disorders and the effects of other hazardous factors is provided. Finally, future research trends focusing on emerging dynamic functional connectivity and state-of-the-art functional connectome analysis are summarized. In next decade, early infant rs-fMRI and developmental connectome study could be one of the shining research topics.

Neonatal brain injury and aberrant connectivity

Brain injury sustained during the neonatal period may disrupt development of critical structural and functional connectivity networks leading to subsequent neurodevelopmental impairment in affected children. These networks can be characterized using structural (via diffusion MRI) and functional (via resting state-functional MRI) neuroimaging techniques. Advances in neuroimaging have led to expanded application of these approaches to study term- and prematurely-born infants, providing improved understanding of cerebral development and the deleterious effects of early brain injury. Across both modalities, neuroimaging data are conducive to analyses ranging from characterization of individual white matter tracts and/or resting state networks through advanced 'connectome-style' approaches capable of identifying highly connected network hubs and investigating metrics of network topology such as modularity and small-worldness. We begin this review by summarizing the literature detailing structural and functional connectivity findings in healthy term and preterm infants without brain injury during the postnatal period, including discussion of early connectome development. We then detail common forms of brain injury in term- and prematurely-born infants. In this context, we next review the emerging body of literature detailing studies employing diffusion MRI, resting state-functional MRI and other complementary neuroimaging modalities to characterize structural and functional connectivity development in infants with brain injury. We conclude by reviewing technical challenges associated with neonatal neuroimaging, highlighting those most relevant to studying infants with brain injury and emphasizing the need for further targeted study in this high-risk population.

Maternal Interleukin-6 concentration during pregnancy is associated with variation in frontolimbic white matter and cognitive development in early life

Maternal inflammation during pregnancy can alter the trajectory of fetal brain development and increase risk for offspring psychiatric disorders. However, the majority of relevant research to date has been conducted in animal models. Here, in humans, we focus on the structural connectivity of frontolimbic circuitry as it is both critical for socioemotional and cognitive development, and commonly altered in a range of psychiatric disorders associated with intrauterine inflammation. Specifically, we test the hypothesis that elevated maternal concentration of the proinflammatory cytokine interleukin-6 (IL-6) during pregnancy will be associated with variation in microstructural properties of this circuitry in the neonatal period and across the first year of life. Pregnant mothers were recruited in early pregnancy and maternal blood samples were obtained for assessment of maternal IL-6 concentrations in early (12.6 ± 2.8 weeks [S.D.]), mid (20.4 ± 1.5 weeks [S.D.]) and late (30.3 ± 1.3 weeks [S.D.]) gestation. Offspring brain MRI scans were acquired shortly after birth (N = 86, scan age = 3.7 ± 1.7 weeks [S.D.]) and again at 12-mo age (N = 32, scan age = 54.0 ± 3.1 weeks [S.D.]). Diffusion Tensor Imaging (DTI) was used to characterize fractional anisotropy (FA) along the left and right uncinate fasciculus (UF), representing the main frontolimbic fiber tract. In N = 30 of the infants with serial MRI data at birth and 12-mo age, cognitive and socioemotional developmental status was characterized using the Bayley Scales of Infant Development. All analyses tested for potentially confounding influences of household income, prepregnancy Body-Mass-Index, obstetric risk, smoking during pregnancy, and infant sex, and outcomes at 12-mo age were additionally adjusted for the quality of the postnatal caregiving environment. Maternal IL-6 concentration (averaged across pregnancy) was prospectively and inversely associated with FA (suggestive of reduced integrity under high inflammatory conditions) in the newborn offspring (bi-lateral, p < 0.01) in the central portion of the UF proximal to the amygdala. Furthermore, maternal IL-6 concentration was positively associated with rate of FA increase across the first year of life (bi-lateral, p < 0.05), resulting in a null association between maternal IL-6 and UF FA at 12-mo age. Maternal IL-6 was also inversely associated with offspring cognition at 12-mo age, and this association was mediated by FA growth across the first year of postnatal life. Findings from the current study support the premise that susceptibility for cognitive impairment and potentially psychiatric disorders may be affected in utero, and that maternal inflammation may constitute an intrauterine condition of particular importance in this context.

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review

Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

Control networks of the frontal lobes

The human brain is organized into specialized functional brain networks. Some networks are dedicated to early sensory processing, and others to generating motor outputs. Yet, the bulk of the human brain's functional networks is actually dedicated to control processes. The two control networks most important for the impressive repertoire of control-related behaviors that humans are able to instantiate and maintain are the frontoparietal and cinguloopercular networks. We provide evidence that these two control networks largely contribute to nonoverlapping domains of control. These networks largely have been studied using fMRI, which is sensitive only to infraslow activity. Complementary electrophysiological techniques have provided evidence that these networks manifest at substantially faster frequencies (delta-alpha band), supporting their role in coordination of whole-brain functional network activity. Both the frontoparietal and cinguloopercular networks demonstrate protracted development, supporting increases in control-related performance. Recent studies from our lab indicate these control networks exhibit measurable individual specificity, highlighting the importance of individualized paradigms in neuroimaging studies to advance our understanding of typical and atypical control network function throughout the life span.

The frontoparietal network: function, electrophysiology, and importance of individual precision mapping

The frontoparietal network is critical for our ability to coordinate behavior in a rapid, accurate, and flexible goal-driven manner. In this review, we outline support for the framing of the frontoparietal network as a distinct control network, in part functioning to flexibly interact with and alter other functional brain networks. This network coordination likely occurs in a 4 Hz to 73 Hz θ/α rhythm, both during resting state and task state. Precision mapping of individual human brains has revealed that the functional topography of the frontoparietal network is variable between individuals, underscoring the notion that group-average studies of the frontoparietal network may be obscuring important typical and atypical features. Many forms of psychopathology implicate the frontoparietal network, such as schizophrenia and attention-deficit/hyperactivity disorder. Given the interindividual variability in frontoparietal network organization, clinical studies will likely benefit greatly from acquiring more individual subject data to accurately characterize resting-state networks compromised in psychopathology.

Age-related differences in task-induced brain activation is not task specific: Multivariate pattern generalization between metacognition, cognition and perception

Adolescence is associated with widespread maturation of brain structures and functional connectivity profiles that shift from local to more distributed and better integrated networks, which are active during a variety of cognitive tasks. Nevertheless, the approach to examine task-induced developmental brain changes is function-specific, leaving the question open whether functional maturation is specific to the particular cognitive demands of the task used, or generalizes across different tasks. In the present study we examine the hypothesis that functional brain maturation is driven by global changes in how the brain handles cognitive demands. Multivariate pattern classification analysis (MVPA) was used to examine whether age discriminative task-induced activation patterns generalize across a wide range of information processing levels. 25 young (13-years old) and 22 old (17-years old) adolescents performed three conceptually different tasks of metacognition, cognition and visual processing. MVPA applied within each task indicated that task-induced brain activation is consistent and reliably different between ages 13 and 17. These age-discriminative activation patterns proved to be common across the different tasks used, despite the differences in cognitive demands and brain structures engaged by each of the three tasks. MVP classifiers trained to detect age-discriminative patterns in brain activation during one task were significantly able to decode age from brain activation maps during execution of other tasks with accuracies between 63 and 75%. The results emphasize that age-specific characteristics of task-induced brain activation have to be understood at the level of brain-wide networks that show maturational changes in their organization and processing efficacy during adolescence.

Adolescent development of cortical oscillations: Power, phase, and support of cognitive maturation

During adolescence, the integration of specialized functional brain networks related to cognitive control continues to increase. Slow frequency oscillations (4-10 Hz) have been shown to support cognitive control processes, especially within prefrontal regions. However, it is unclear how neural oscillations contribute to functional brain network development and improvements in cognitive control during adolescence. To bridge this gap, we employed magnetoencephalography (MEG) to explore changes in oscillatory power and phase coupling across cortical networks in a sample of 68 adolescents and young adults. We found a redistribution of power from lower to higher frequencies throughout adolescence, such that delta band (1-3 Hz) power decreased, whereas beta band power (14-16 and 22-26 Hz) increased. Delta band power decreased with age most strongly in association networks within the frontal lobe and operculum. Conversely, beta band power increased throughout development, most strongly in processing networks and the posterior cingulate cortex, a hub of the default mode (DM) network. In terms of phase, theta band (5-9 Hz) phase-locking robustly decreased with development, following an anterior-to-posterior gradient, with the greatest decoupling occurring between association networks. Additionally, decreased slow frequency phase-locking between frontolimbic regions was related to decreased impulsivity with age. Thus, greater decoupling of slow frequency oscillations may afford functional networks greater flexibility during the resting state to instantiate control when required.

Network abnormalities in generalized anxiety pervade beyond the amygdala-pre-frontal cortex circuit: Insights from graph theory

Generalized anxiety disorder (GAD) has excessive anxiety and uncontrollable worry as core symptoms. Abnormal cerebral functioning underpins the expression and perhaps pathogenesis of GAD:. Studies implicate impaired communication between the amygdala and the pre-frontal cortex (PFC). Our aim was to longitudinally investigate whether such network abnormalities are spatially restricted to this circuit or if the integrity of functional brain networks is globally disrupted in GAD. We acquired resting-state functional magnetic resonance imaging data from 16 GAD patients and 16 matched controls at baseline and after 1 year. Using network modeling and graph-theory, whole-brain connectivity was characterized from local and global perspectives. Overall lower global efficiency, indicating sub-optimal brain-wide organization and integration, was present in patients with GAD compared to controls. The amygdala and midline cortices showed higher betweenness centrality, reflecting functional dominance of these brain structures. Third, lower betweenness centrality and lower degree emerged for PFC, suggesting weakened inhibitory control. Overall, network organization showed impairments consistent with neurobiological models of GAD (involving amygdala, PFC, and cingulate cortex) and further pointed to an involvement of temporal regions. Such impairments tended to progress over time and predict anxiety symptoms. A graph-analytic approach represents a powerful approach to deepen our understanding of GAD.

Default Mode Network Maturation and Environmental Adversities During Childhood

Default mode network (DMN) plays a central role in cognition and brain disorders. It has been shown that adverse environmental conditions impact neurodevelopment, but how these conditions impact in DMN maturation is still poorly understood. This article reviews representative neuroimaging functional studies addressing the interactions between DMN development and environmental factors, focusing on early life adversities, a critical period for brain changes. Studies focused on this period of life offer a special challenge: to disentangle the neurodevelopmental connectivity changes from those related to environmental conditions. We first summarized the literature on DMN maturation, providing an overview of both typical and atypical development patterns in childhood and early adolescence. Afterward, we focused on DMN changes associated with chronic exposure to environmental adversities during childhood. This summary suggests that changes in DMN development could be a potential allostatic neural feature associated with an embodiment of environmental circumstances. Finally, we discuss about some key methodological issues that should be considered in paradigms addressing environmental adversities and open questions for future investigations.

Functional connectivity in central executive network protects youth against cardiometabolic risks linked with neighborhood violence

Although violent crime has declined in recent decades, it remains a recurring feature of daily life in some neighborhoods. Mounting evidence indicates that such violence has a long reach, which goes beyond family and friends of the victim and undermines the health of people in the surrounding community. However, like all forms of adversity, community violence elicits a heterogeneous response: Some remain healthy, but others deteriorate. Despite much scientific attention, the neural circuitries that contribute to differential adaptation remain poorly understood. Drawing on knowledge of the brain's intrinsic functional architecture, we predicted that individual differences in resting-state connectivity would explain variability in the strength of the association between neighborhood violence and cardiometabolic health. We enrolled 218 urban youth (age 12-14 years, 66% female; 65% black or Latino) and used geocoding to characterize their exposure to neighborhood murder over the past five years. Multiple aspects of cardiometabolic health were assessed, including obesity, insulin resistance, and metabolic syndrome. Functional MRI was used to quantify the connectivity of major intrinsic networks. Consistent with predictions, resting-state connectivity within the central executive network (CEN) emerged as a moderator of adaptation. Across six distinct outcomes, a higher neighborhood murder rate was associated with greater cardiometabolic risk, but this relationship was apparent only among youth who displayed lower CEN resting-state connectivity. By contrast, there was little evidence of moderation by the anterior salience and default mode networks. These findings advance basic and applied knowledge about adaptation by highlighting intrinsic CEN connectivity as a potential neurobiological contributor to resilience.

Spatial and Temporal Organization of the Individual Human Cerebellum

The cerebellum contains the majority of neurons in the human brain and is unique for its uniform cytoarchitecture, absence of aerobic glycolysis, and role in adaptive plasticity. Despite anatomical and physiological differences between the cerebellum and cerebral cortex, group-average functional connectivity studies have identified networks related to specific functions in both structures. Recently, precision functional mapping of individuals revealed that functional networks in the cerebral cortex exhibit measurable individual specificity. Using the highly sampled Midnight Scan Club (MSC) dataset, we found the cerebellum contains reliable, individual-specific network organization that is significantly more variable than the cerebral cortex. The frontoparietal network, thought to support adaptive control, was the only network overrepresented in the cerebellum compared to the cerebral cortex (2.3-fold). Temporally, all cerebellar resting state signals lagged behind the cerebral cortex (125-380 ms), supporting the hypothesis that the cerebellum engages in a domain-general function in the adaptive control of all cortical processes.

Functional connectivity of intrinsic cognitive networks during resting state and task performance in preadolescent children

Earlier studies on adults have shown that functional connectivity (FC) of brain networks can vary depending on the brain state and cognitive challenge. Network connectivity has been investigated quite extensively in children in resting state, much less during tasks and is largely unexplored between these brain states. Here we used functional magnetic resonance imaging and independent component analysis to investigate the functional architecture of large-scale brain networks in 16 children (aged 7–11 years, 11 males) and 16 young adults (aged 22–29 years, 10 males) during resting state and visual working memory tasks. We identified the major neurocognitive intrinsic connectivity networks (ICNs) in both groups. Children had stronger FC than adults within the cingulo-opercular network in resting state, during task performance, and after controlling for performance differences. During tasks, children had stronger FC than adults also within the default mode (DMN) and right frontoparietal (rFPN) networks, and between the anterior DMN and the frontopolar network, whereas adults had stronger coupling between the anterior DMN and rFPN. Furthermore, children compared to adults modulated the FC strength regarding the rFPN differently between the brain states. The FC within the anterior DMN correlated with age and performance in children so that the younger they were, the stronger was the FC, and the stronger the FC within this network, the slower they performed the tasks. The group differences in the network connectivity reported here, and the observed correlations with task performance, provide insight into the normative development of the preadolescent brain and link maturation of functional connectivity with improving cognitive performance.

Miniature pig magnetic resonance spectroscopy model of normal adolescent brain development

BACKGROUND

We are developing the miniature pig (Sus scrofa domestica), an in-vivo translational, gyrencephalic model for brain development, as an alternative to laboratory rodents/non-human primates. We analyzed longitudinal changes in adolescent pigs using proton magnetic resonance spectroscopy (¹H-MRS) and examined the relationship with white matter (WM) integrity derived from diffusion weighted imaging (DWI).

NEW METHOD

Twelve female Sinclair^™ pigs underwent three imaging/spectroscopy sessions every 23.95 ± 3.73 days beginning at three months of age using a clinical 3 T scanner. ¹H-MRS data were collected using 1.2 × 1.0 × 3.0 cm voxels placed in left and right hemisphere WM using a Point Resolved Spectroscopy sequence (TR = 2000 ms, TE = 30 ms). Concentrations of N-acetylaspartate, myo-inositol (MI), glutamate + glutamine, choline, creatine, and macromolecules (MM) 09 and 14 were averaged from both hemispheres. DWI data were collected using 15 shells of b-values (b = 0-3500 s/mm2) with 32 directions/shell and fit using the WM Tract Integrity model to calculate fractional anisotropy (FA), kurtosis anisotropy (KA) and permeability-diffusivity index.

RESULTS

MI and MM09 significantly declined with age. Increased FA and KA significantly correlated with decline in MI and MM09. Correlations lost significance once corrected for age.

COMPARISON WITH EXISTING METHODS

MRI scanners/protocols can be used to collect 1H-MRS and DWI data in pigs. Pigs have a larger, more complex, gyrencephalic brain than laboratory rodents but are less complex than non-human primates, thus satisfying the "replacement" principle of animal research.

CONCLUSIONS

Longitudinal effects in MRS measurements were similar to those reported in adolescent humans. MRS changes correlated with diffusion measurements indicating ongoing WM myelination/maturation.

Brain Network Connectivity and Executive Function in Long-Term Survivors of Childhood Acute Lymphoblastic Leukemia

Chemotherapeutic agents used to treat acute lymphoblastic leukemia (ALL), the most common cancer affecting young children, have been associated with long-term cognitive impairments that reduce quality of life. Executive dysfunction is one of the most consistently observed deficits and can have substantial and pervasive effects on academic success, occupational achievement, psychosocial function, and psychiatric status. We examined the neural mechanisms of executive dysfunction by measuring structural and functional connectomes in 161 long-term survivors of pediatric ALL, age 8-21 years, who were treated on a single contemporary chemotherapy-only protocol for standard/high- or low-risk disease. Lower global efficiency, a measure of information exchange and network integration, of both structural and functional connectomes was found in survivors with executive dysfunction compared with those without dysfunction (p < 0.046). Patients with standard/high- versus low-risk disease and those who received greater number of intrathecal treatments containing methotrexate had the lowest network efficiencies. Patients with executive dysfunction also showed hyperconnectivity in sensorimotor, visual, and auditory-processing regions (p = 0.037) and poor separation between sensorimotor, executive/attention, salience, and default mode networks (p < 0.0001). Connectome disruption was consistent with a pattern of delayed neurodevelopment that may be associated with reduced resilience, adaptability, and flexibility of the brain network. These findings highlight the need for interventions that will prevent or manage cognitive impairment in survivors of pediatric acute lymphoblastic leukemia.

Prenatal neural origins of infant motor development: Associations between fetal brain and infant motor development

Functional circuits of the human brain emerge and change dramatically over the second half of gestation. It is possible that variation in neural functional system connectivity in utero predicts individual differences in infant behavioral development, but this possibility has yet to be examined. The current study examines the association between fetal sensorimotor brain system functional connectivity and infant postnatal motor ability. Resting-state functional connectivity data was obtained in 96 healthy human fetuses during the second and third trimesters of pregnancy. Infant motor ability was measured 7 months after birth using the Bayley Scales of Infant Development. Increased connectivity between the emerging motor network and regions of the prefrontal cortex, temporal lobes, posterior cingulate, and supplementary motor regions was observed in infants that showed more mature motor functions. In addition, females demonstrated stronger fetal-brain to infant-behavior associations. These observations extend prior longitudinal research back into prenatal brain development and raise exciting new ideas about the advent of risk and the ontogeny of early sex differences.

Neurocognitive Development of the Resolution of Selective Visuo-Spatial Attention: Functional MRI Evidence From Object Tracking

Our ability to select relevant information from the environment is limited by the resolution of attention – i.e., the minimum size of the region that can be selected. Neural mechanisms that underlie this limit and its development are not yet understood. Functional magnetic resonance imaging (fMRI) was performed during an object tracking task in 7- and 11-year-old children, and in young adults. Object tracking activated canonical fronto-parietal attention systems and motion-sensitive area MT in children as young as 7 years. Object tracking performance improved with age, together with stronger recruitment of parietal attention areas and a shift from low-level to higher-level visual areas. Increasing the required resolution of spatial attention – which was implemented by varying the distance between target and distractors in the object tracking task – led to activation increases in fronto-insular cortex, medial frontal cortex including anterior cingulate cortex (ACC) and supplementary motor area, superior colliculi, and thalamus. This core circuitry for attentional precision was recruited by all age groups, but ACC showed an age-related activation reduction. Our results suggest that age-related improvements in selective visual attention and in the resolution of attention are characterized by an increased use of more functionally specialized brain regions during the course of development.

Individual differences in functional brain connectivity predict temporal discounting preference in the transition to adolescence

The transition from childhood to adolescence is marked by distinct changes in behavior, including how one values waiting for a large reward compared to receiving an immediate, yet smaller, reward. While previous research has emphasized the relationship between this preference and age, it is also proposed that this behavior is related to circuitry between valuation and cognitive control systems. In this study, we examined how age and intrinsic functional connectivity strength within and between these neural systems relate to changes in discounting behavior across the transition into adolescence. We used mixed-effects modeling and linear regression to assess the contributions of age and connectivity strength in predicting discounting behavior. First, we identified relevant connections in a longitudinal sample of 64 individuals who completed MRI scans and behavioral assessments 2-3 times across ages 7-15 years (137 scans). We then repeated the analysis in a separate, cross-sectional, sample of 84 individuals (7-13 years). Both samples showed an age-related increase in preference for waiting for larger rewards. Connectivity strength within and between valuation and cognitive control systems accounted for further variance not explained by age. These results suggest that individual differences in functionalbrain organization can account for behavioral changes typically associated with age.

Altered temporal stability in dynamic neural networks underlies connectivity changes in neurodevelopment

Network connectivity is an integral feature of human brain function, and characterising its maturational trajectory is a critical step towards understanding healthy and atypical neurodevelopment. Here, we used magnetoencephalography (MEG) to investigate both stationary (i.e. time averaged) and rapidly modulating (dynamic) electrophysiological connectivity, in participants aged from mid-childhood to early adulthood (youngest participant 9 years old; oldest participant 25 years old). Stationary functional connectivity (measured via inter-regional coordination of neural oscillations) increased with age in the alpha and beta frequency bands, particularly in bilateral parietal and temporo-parietal connections. Our dynamic analysis (also applied to alpha/beta oscillations) revealed the spatiotemporal signatures of 8 dynamic networks; these modulate on a ∼100 ms time scale, and temporal stability in attentional networks was found to increase with age. Significant overlap was found between age-modulated dynamic networks and inter-regional oscillatory coordination, implying that altered network dynamics underlie age related changes in functional connectivity. Our results provide novel insights into brain network electrophysiology, and lay a foundation for future work in childhood disorders.

Correlated Gene Expression and Anatomical Communication Support Synchronized Brain Activity in the Mouse Functional Connectome

Cognition and behavior depend on synchronized intrinsic brain activity that is organized into functional networks across the brain. Research has investigated how anatomical connectivity both shapes and is shaped by these networks, but not how anatomical connectivity interacts with intra-areal molecular properties to drive functional connectivity. Here, we present a novel linear model to explain functional connectivity by integrating systematically obtained measurements of axonal connectivity, gene expression, and resting-state functional connectivity MRI in the mouse brain. The model suggests that functional connectivity arises from both anatomical links and inter-areal similarities in gene expression. By estimating these effects, we identify anatomical modules in which correlated gene expression and anatomical connectivity support functional connectivity. Along with providing evidence that not all genes equally contribute to functional connectivity, this research establishes new insights regarding the biological underpinnings of coordinated brain activity measured by BOLD fMRI.SIGNIFICANCE STATEMENT Efforts at characterizing the functional connectome with fMRI have risen exponentially over the last decade. Yet despite this rise, the biological underpinnings of these functional measurements are still primarily unknown. The current report begins to fill this void by investigating the molecular underpinnings of the functional connectome through an integration of systematically obtained structural information and gene expression data throughout the rodent brain. We find that both white matter connectivity and similarity in regional gene expression relate to resting-state functional connectivity. The current report furthers our understanding of the biological underpinnings of the functional connectome and provides a linear model that can be used to streamline preclinical animal studies of disease.

A systematic literature review of sex differences in childhood language and brain development

The extent of sex differences in childhood language development is unclear. We conducted a systematic literature review synthesizing results from studies examining sex differences in brain structure and function relevant to language development during childhood. We searched PubMed and Scopus databases, and this returned a total of 46 published studies meeting criteria for inclusion that directly examined sex differences in brain development relevant to language function in children. The results indicate that: (a) sex differences in brain structure or function do not necessarily lead to differences in language task performance; (b) evidence for sex differences in brain and language development are limited; (c) when present, sex differences often interact with a variety of factors such as age and task. Overall, the magnitude of sexual dimorphism of brain developmental trajectories associated with language is not as significant as previously thought. Sex differences were found, however, in studies employing tighter age ranges. This suggests that sex differences may be more prominent during certain developmental stages but are negligible in other stages, likely due to different rates of maturation between the sexes. More research is needed to improve our understanding of how sex differences may arise due to the influence of sex hormones and developmental stages, and how these differences may lead to differences in various language task performance. These studies are expected to provide normative information that may be used in studies examining neurodevelopmental disorders that frequently affect more males than females, and also often affect language development.

The frontoparietal network: function, electrophysiology, and importance of individual precision mapping

The frontoparietal network is critical for our ability to coordinate behavior in a rapid, accurate, and flexible goal-driven manner. In this review, we outline support for the framing of the frontoparietal network as a distinct control network, in part functioning to flexibly interact with and alter other functional brain networks. This network coordination likely occurs in a 4 Hz to 73 Hz θ/α rhythm, both during resting state and task state. Precision mapping of individual human brains has revealed that the functional topography of the frontoparietal network is variable between individuals, underscoring the notion that group-average studies of the frontoparietal network may be obscuring important typical and atypical features. Many forms of psychopathology implicate the frontoparietal network, such as schizophrenia and attention-deficit/hyperactivity disorder. Given the interindividual variability in frontoparietal network organization, clinical studies will likely benefit greatly from acquiring more individual subject data to accurately characterize resting-state networks compromised in psychopathology.

Future Directions for Examination of Brain Networks in Neurodevelopmental Disorders

Neurodevelopmental disorders are associated with atypical development and maturation of brain networks. A recent focus on human connectomics research and the growing popularity of open science initiatives has created the ideal climate in which to make real progress toward understanding the neurobiology of disorders affecting youth. Here we outline future directions for neuroscience researchers examining brain networks in neurodevelopmental disorders, highlighting gaps in the current literature. We emphasize the importance of leveraging large neuroimaging and phenotypic data sets recently made available to the research community, and we suggest specific novel methodological approaches, including analysis of brain dynamics and structural connectivity, that have the potential to produce the greatest clinical insight. Transdiagnostic approaches will also become increasingly necessary as the Research Domain Criteria framework put forth by the National Institute of Mental Health permeates scientific discourse. During this exciting era of big data and increased computational sophistication of analytic tools, the possibilities for significant advancement in understanding neurodevelopmental disorders are limitless.

Maternal IL-6 during pregnancy can be estimated from newborn brain connectivity and predicts future working memory in offspring

Several lines of evidence support the link between maternal inflammation during pregnancy and increased likelihood of neurodevelopmental and psychiatric disorders in offspring. This longitudinal study seeks to advance understanding regarding implications of systemic maternal inflammation during pregnancy, indexed by plasma interleukin-6 (IL-6) concentrations, for large-scale brain system development and emerging executive function skills in offspring. We assessed maternal IL-6 during pregnancy, functional magnetic resonance imaging acquired in neonates, and working memory (an important component of executive function) at 2 years of age. Functional connectivity within and between multiple neonatal brain networks can be modeled to estimate maternal IL-6 concentrations during pregnancy. Brain regions heavily weighted in these models overlap substantially with those supporting working memory in a large meta-analysis. Maternal IL-6 also directly accounts for a portion of the variance of working memory at 2 years of age. Findings highlight the association of maternal inflammation during pregnancy with the developing functional architecture of the brain and emerging executive function.

Different brain networks underlying intelligence in autism spectrum disorders

There has been sustained clinical and cognitive neuroscience research interest in how network correlates of brain-behavior relationships might be altered in Autism Spectrum Disorders (ASD) and other neurodevelopmental disorders. As previous work has mostly focused on adults, the nature of whole-brain connectivity networks underlying intelligence in pediatric cohorts with abnormal neurodevelopment requires further investigation. We used network-based statistics (NBS) to examine the association between resting-state functional Magnetic Resonance Imaging (fMRI) connectivity and fluid intelligence ability in male children (n = 50) with Autism Spectrum Disorders (ASD; M = 10.45, SD = 1.58 years and in controls (M = 10.38, SD = 0.96 years) matched on fluid intelligence performance, age and sex. Repeat analyses were performed in independent sites for validation and replication. Despite being equivalent on fluid intelligence ability to strictly matched neurotypical controls, boys with ASD displayed a subnetwork of significantly increased associations between functional connectivity and fluid intelligence. Between-group differences remained significant at higher edge thresholding, and results were validated in independent-site replication analyses in an equivalent age and sex-matched cohort with ASD. Regions consistently implicated in atypical connectivity correlates of fluid intelligence in ASD were the angular gyrus, posterior middle temporal gyrus, occipital and temporo-occipital regions. Development of fluid intelligence neural correlates in young ASD males is aberrant, with an increased strength in intrinsic connectivity association during childhood. Alterations in whole-brain network correlates of fluid intelligence in ASD may be a compensatory mechanism that allows equal task performance to neurotypical peers.

Functional network integration and attention skills in young children

Children acquire attention skills rapidly during early childhood as their brains undergo vast neural development. Attention is well studied in the adult brain, yet due to the challenges associated with scanning young children, investigations in early childhood are sparse. Here, we examined the relationship between age, attention and functional connectivity (FC) during passive viewing in multiple intrinsic connectivity networks (ICNs) in 60 typically developing girls between 4 and 7 years whose sustained, selective and executive attention skills were assessed. Visual, auditory, sensorimotor, default mode (DMN), dorsal attention (DAN), ventral attention (VAN), salience, and frontoparietal ICNs were identified via Independent Component Analysis and subjected to a dual regression. Individual spatial maps were regressed against age and attention skills, controlling for age. All ICNs except the VAN showed regions of increasing FC with age. Attention skills were associated with FC in distinct networks after controlling for age: selective attention positively related to FC in the DAN; sustained attention positively related to FC in visual and auditory ICNs; and executive attention positively related to FC in the DMN and visual ICN. These findings suggest distributed network integration across this age range and highlight how multiple ICNs contribute to attention skills in early childhood.

The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development

The human brain undergoes extensive and dynamic growth during the first years of life. The UNC/UMN Baby Connectome Project (BCP), one of the Lifespan Connectome Projects funded by NIH, is an ongoing study jointly conducted by investigators at the University of North Carolina at Chapel Hill and the University of Minnesota. The primary objective of the BCP is to characterize brain and behavioral development in typically developing infants across the first 5 years of life. The ultimate goals are to chart emerging patterns of structural and functional connectivity during this period, map brain-behavior associations, and establish a foundation from which to further explore trajectories of health and disease. To accomplish these goals, we are combining state of the art MRI acquisition and analysis techniques, including high-resolution structural MRI (T1-and T2-weighted images), diffusion imaging (dMRI), and resting state functional connectivity MRI (rfMRI). While the overall design of the BCP largely is built on the protocol developed by the Lifespan Human Connectome Project (HCP), given the unique age range of the BCP cohort, additional optimization of imaging parameters and consideration of an age appropriate battery of behavioral assessments were needed. Here we provide the overall study protocol, including approaches for subject recruitment, strategies for imaging typically developing children 0-5 years of age without sedation, imaging protocol and optimization, a description of the battery of behavioral assessments, and QA/QC procedures. Combining HCP inspired neuroimaging data with well-established behavioral assessments during this time period will yield an invaluable resource for the scientific community.

Adolescent Brain Development: Implications for Understanding Risk and Resilience Processes Through Neuroimaging Research

This special section focuses on research that utilizes neuroimaging methods to examine the impact of social relationships and socioemotional development on adolescent brain function. Studies include novel neuroimaging methods that further our understanding of adolescent brain development. This special section has a particular focus on how study findings add to our understanding of risk and resilience. In this introduction to the special section, we discuss the role of neuroimaging in developmental science and provide a brief review of neuroimaging methods. We present key themes that are covered in the special section articles including: (1) emerging methods in developmental neuroscience, (2) emotion-cognition interaction, and (3) the role of social relationships in brain function. We conclude our introduction with future directions for integrating developmental neuroscience into the study of adolescence, and highlight key points from the special section's commentaries which include information on the landmark Adolescent Brain Cognitive Development (ABCD) study.

PAGANI Toolkit: Parallel graph-theoretical analysis package for brain network big data

The recent collection of unprecedented quantities of neuroimaging data with high spatial resolution has led to brain network big data. However, a toolkit for fast and scalable computational solutions is still lacking. Here, we developed the PArallel Graph-theoretical ANalysIs (PAGANI) Toolkit based on a hybrid central processing unit-graphics processing unit (CPU-GPU) framework with a graphical user interface to facilitate the mapping and characterization of high-resolution brain networks. Specifically, the toolkit provides flexible parameters for users to customize computations of graph metrics in brain network analyses. As an empirical example, the PAGANI Toolkit was applied to individual voxel-based brain networks with ∼200,000 nodes that were derived from a resting-state fMRI dataset of 624 healthy young adults from the Human Connectome Project. Using a personal computer, this toolbox completed all computations in ∼27 h for one subject, which is markedly less than the 118 h required with a single-thread implementation. The voxel-based functional brain networks exhibited prominent small-world characteristics and densely connected hubs, which were mainly located in the medial and lateral fronto-parietal cortices. Moreover, the female group had significantly higher modularity and nodal betweenness centrality mainly in the medial/lateral fronto-parietal and occipital cortices than the male group. Significant correlations between the intelligence quotient and nodal metrics were also observed in several frontal regions. Collectively, the PAGANI Toolkit shows high computational performance and good scalability for analyzing connectome big data and provides a friendly interface without the complicated configuration of computing environments, thereby facilitating high-resolution connectomics research in health and disease.

Inter-subject synchrony as an index of functional specialization in early childhood

Early childhood is a time of significant change within multiple cognitive domains including social cognition, memory, executive function, and language; however, the corresponding neural changes remain poorly understood. This is likely due to the difficulty in acquiring artifact-free functional MRI data during complex task-based or unconstrained resting-state experiments in young children. In addition, task-based and resting state experiments may not capture dynamic real-world processing. Here we overcome both of these challenges through use of naturalistic viewing (i.e., passively watching a movie in the scanner) combined with inter-subject neural synchrony to examine functional specialization within 4- and 6-year old children. Using a novel and stringent crossed random effect statistical analysis, we find that children show more variable patterns of activation compared to adults, particularly within regions of the default mode network (DMN). In addition, we found partial evidence that child-to-adult synchrony increased as a function of age within a DMN region: the temporoparietal junction. Our results suggest age-related differences in functional brain organization within a cross-sectional sample during an ecologically valid context and demonstrate that neural synchrony during naturalistic viewing fMRI can be used to examine functional specialization during early childhood - a time when neural and cognitive systems are in flux.

Network-based approaches to examining stress in the adolescent brain

Exposure to stress, particularly in periods of rapid brain maturation such as adolescence, can profoundly influence developmental processes that undergird the organization of structural and functional brain networks and that may mediate the association between stressful experiences and maladaptive outcomes. While studies in translational developmental neuroscience often focus on how specific brain regions or targeted connections are altered by stress and psychiatric disease, the emerging field of network science may be especially valuable for elucidating the impact of stress on the intricate connectomics of the adolescent brain. Here we review recent studies that use graph theory and other network science approaches to understand normative adolescent brain development, effects of childhood maltreatment on the brain, and disorders characterized by pathological responses to stress in adolescents. Overall, these studies demonstrate that graph theory can be useful in identifying and quantifying developmental processes related to segregation, integration, and localized hub influence that are affected by stress exposure and that may lead to psychopathology. Finally, we discuss limitations in the current application of graph theory in this area and suggest what we believe are important directions for future work.

Hypersynchrony in MEG spectral amplitude in prospectively-identified 6-month-old infants prenatally exposed to alcohol

Early identification of children who experience developmental delays due to prenatal alcohol exposure (PAE) remains a challenge for individuals who do not exhibit facial dysmorphia. It is well-established that children with PAE may still exhibit the cognitive and behavioral difficulties, and individuals without facial dysmorphia make up the majority of individuals affected by PAE. This study employed a prospective cohort design to capture alcohol consumption patterns during pregnancy and then followed the infants to 6 months of age. Infants were assessed using magnetoencephalography to capture neurophysiological indicators of brain development and the Bayley Scales of Infant Development-III to measure behavioral development. To account for socioeconomic and family environmental factors, we employed a two-by-two design with pregnant women who were or were not using opioid maintenance therapy (OMT) and did or did not consume alcohol during pregnancy. Based on prior studies, we hypothesized that infants with PAE would exhibit broad increased spectral amplitude relative to non-PAE infants. We also hypothesized that the developmental shift from low to high frequency spectral amplitude would be delayed in infants with PAE relative to controls. Our results demonstrated broadband increased spectral amplitude, interpreted as hypersynchrony, in PAE infants with no significant interaction with OMT. Unlike prior EEG studies in neonates, our results indicate that this hypersynchrony was highly lateralized to left hemisphere and primarily focused in temporal/lateral frontal regions. Furthermore, there was a significant positive correlation between estimated number of drinks consumed during pregnancy and spectral amplitude revealing a dose-response effect of increased hypersynchrony corresponding to greater alcohol consumption. Contrary to our second hypothesis, we did not see a significant group difference in the contribution of low frequency to high frequency amplitude at 6 months of age. These results provide new evidence that hypersynchrony, previously observed in neonates prenatally exposed to high levels of alcohol, persists until 6 months of age and this measure is detectable with low to moderate exposure of alcohol with a dose-response effect. These results indicate that hypersynchrony may provide a sensitive early marker of prenatal alcohol exposure in infants up to 6 months of age.

Shyness and Trajectories of Functional Network Connectivity Over Early Adolescence

High shyness during early adolescence is associated with impaired peer relationships and risk for psychiatric disorders. Little is known, however, about the relation between shyness and trajectories of brain development over early adolescence. The current study longitudinally examined trajectories of resting-state functional connectivity (rs-fc) within four brain networks in 147 adolescents. Subjects underwent functional magnetic resonance imaging at three different time points, at average ages 10.5 (range = 7.8-13.0), 11.7 (range = 9.3-14.1), and 12.9 years (range = 10.1-15.2). Multilevel linear modeling indicated that high shyness was associated with a less steep negative slope of default mode network (DMN) rs-fc over early adolescence relative to low shyness. Less steep decreases in DMN rs-fc may relate to increased self-focus in adolescents with high shyness.