Children's intellectual ability is associated with structural network integrity

Distributed associations among white matter hyperintensities and structural brain networks with fluid cognition in healthy aging

White matter hyperintensities (WMHs) are associated with age-related cognitive impairment and increased risk of Alzheimer's disease. However, the manner by which WMHs contribute to cognitive impairment is unclear. Using a combination of predictive modeling and network neuroscience, we investigated the relationship between structural white matter connectivity and age, fluid cognition, and WMHs in 68 healthy adults (18-78 years). Consistent with previous work, WMHs were increased in older adults and exhibited a strong negative association with fluid cognition. Extending previous work, using predictive modeling, we demonstrated that age, WMHs, and fluid cognition were jointly associated with widespread alterations in structural connectivity. Subcortical-cortical connections between the thalamus/basal ganglia and frontal and parietal regions of the default mode and frontoparietal networks were most prominent. At the network level, both age and WMHs were negatively associated with network density and communicability, and positively associated with modularity. Spatially, WMHs were most prominent in arterial zones served by the middle cerebral artery and associated lenticulostriate branches that supply subcortical regions. Finally, WMHs overlapped with all major white matter tracts, most prominently in tracts that facilitate subcortical-cortical communication and are implicated in fluid cognition, including the anterior thalamic-radiations and forceps minor. Finally, results of mediation analyses suggest that whole-brain WMH load influences age-related decline in fluid cognition. Thus, across multiple levels of analysis, we showed that WMHs were increased in older adults and associated with altered structural white matter connectivity and network topology involving subcortical-cortical pathways critical for fluid cognition.

Sex differences in human brain networks in normal and psychiatric populations from the perspective of small-world properties

Females and males are known to be different in the prevalences of multiple psychiatric disorders, while the underlying neural mechanisms are unclear. Based on non-invasive neuroimaging techniques and graph theory, many researchers have tried to use a small-world network model to elucidate sex differences in the brain. This manuscript aims to compile the related research findings from the past few years and summarize the sex differences in human brain networks in both normal and psychiatric populations from the perspective of small-world properties. We reviewed published reports examining altered small-world properties in both the functional and structural brain networks between males and females. Based on four patterns of altered small-world properties proposed: randomization, regularization, stronger small-worldization, and weaker small-worldization, we found that current results point to a significant trend toward more regularization in normal females and more randomization in normal males in functional brain networks. On the other hand, there seems to be no consensus to date on the sex differences in small-world properties of the structural brain networks in normal populations. Nevertheless, we noticed that the sample sizes in many published studies are small, and future studies with larger samples are warranted to obtain more reliable results. Moreover, the number of related studies conducted in psychiatric populations is still limited and more investigations might be needed. We anticipate that these conclusions will contribute to a deeper understanding of the sex differences in the brain, which may be also valuable for developing new methods in the treatment of psychiatric disorders.

Effects of second language acquisition on brain functional networks at different developmental stages

Previous studies have shown that language acquisition influences both the structure and function of the brain. However, whether the acquisition of a second language at different periods of life alters functional network organization in different ways remains unclear. Here, functional magnetic resonance imaging data from 27 English-speaking monolingual controls and 52 Spanish-English bilingual individuals, including 22 early bilinguals who began learning a second language before the age of ten and 30 late bilinguals who started learning a second language at age fourteen or later, were collected from the OpenNeuro database. Topological metrics of resting-state functional networks, including small-world attributes, network efficiency, and rich- and diverse-club regions, that characterize functional integration and segregation of the networks were computed via a graph theoretical approach. The results showed obvious increases in network efficiency in early bilinguals and late bilinguals relative to the monolingual controls; for example, the global efficiency of late bilinguals and early bilinguals was improved relative to that of monolingual controls, and the local efficiency of early bilinguals occupied an intermediate position between that of late bilinguals and monolingual controls. Obvious increases in rich-club and diverse-club functional connectivity were observed in the bilinguals relative to the monolingual controls. Three network metrics were positively correlated with Spanish proficiency test scores. These findings demonstrated that early and late acquisition of a second language had different impacts on the functional networks of the brain.

The significance of structural rich club hubs for the processing of hierarchical stimuli

The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.

Structural brain correlates of non-verbal cognitive ability in 5-year-old children: Findings from the FinnBrain birth cohort study

Non-verbal cognitive ability predicts multiple important life outcomes, for example, school and job performance. It has been associated with parieto-frontal cortical anatomy in prior studies in adult and adolescent populations, while young children have received relatively little attention. We explored the associations between cortical anatomy and non-verbal cognitive ability in 165 5-year-old participants (mean scan age 5.40 years, SD 0.13; 90 males) from the FinnBrain Birth Cohort study. T1-weighted brain magnetic resonance images were processed using FreeSurfer. Non-verbal cognitive ability was measured using the Performance Intelligence Quotient (PIQ) estimated from the Block Design and Matrix Reasoning subtests from the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III). In vertex-wise general linear models, PIQ scores associated positively with volumes in the left caudal middle frontal and right pericalcarine regions, as well as surface area in left the caudal middle frontal, left inferior temporal, and right lingual regions. There were no associations between PIQ and cortical thickness. To the best of our knowledge, this is the first study to examine structural correlates of non-verbal cognitive ability in a large sample of typically developing 5-year-olds. The findings are generally in line with prior findings from older age groups, with the important addition of the positive association between volume / surface area in the right medial occipital region and non-verbal cognitive ability. This finding adds to the literature by discovering a new brain region that should be considered in future studies exploring the role of cortical structure for cognitive development in young children.

Structural networking of the developing brain: from maturation to neurosurgical implications

Modern neuroscience agrees that neurological processing emerges from the multimodal interaction among multiple cortical and subcortical neuronal hubs, connected at short and long distance by white matter, to form a largely integrated and dynamic network, called the brain "connectome." The final architecture of these circuits results from a complex, continuous, and highly protracted development process of several axonal pathways that constitute the anatomical substrate of neuronal interactions. Awareness of the network organization of the central nervous system is crucial not only to understand the basis of children's neurological development, but also it may be of special interest to improve the quality of neurosurgical treatments of many pediatric diseases. Although there are a flourishing number of neuroimaging studies of the connectome, a comprehensive vision linking this research to neurosurgical practice is still lacking in the current pediatric literature. The goal of this review is to contribute to bridging this gap. In the first part, we summarize the main current knowledge concerning brain network maturation and its involvement in different aspects of normal neurocognitive development as well as in the pathophysiology of specific diseases. The final section is devoted to identifying possible implications of this knowledge in the neurosurgical field, especially in epilepsy and tumor surgery, and to discuss promising perspectives for future investigations.

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.

Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.

Structural architecture and brain network efficiency link polygenic scores to intelligence

Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

First Trimester Dexamethasone Treatment Is Not Associated With Alteration in Resting-state Connectivity at Adolescent or Adult Age

CONTEXT

Prenatal treatment with dexamethasone (DEX) has been used to prevent virilization in females at risk of congenital adrenal hyperplasia (CAH). Both affected and unaffected girls, as well boys, are treated until the genotype and sex of the fetus is known (gestational weeks 10-12). After that, only affected girls are treated until term. Exposure to a high synthetic glucocorticoid dosage may alter the developmental trajectory of the brain, with alterations in resting-state functional connectivity of the brain at adult age.

OBJECTIVE

To investigate resting-state functional connectivity in subjects at risk of having CAH, exposed to DEX treatment during the first trimester of fetal life, both in the whole brain and in 3 regions of interest (amygdala, hippocampus, and superior frontal gyrus).

DESIGN, SETTING, AND PARTICIPANTS

Eighteen participants (8 females) at risk of having CAH, exposed to DEX treatment, and 38 controls (24 females), age range 16 to 26 years, from a single research institute, underwent functional magnetic resonance imaging of the brain during rest. We used 2 different approaches: an exploratory whole-brain analysis and seed-based analysis. For seed-based analysis, we chose 3 different brain regions (amygdala, hippocampus, and superior frontal gyrus) based on our previous findings and literature evidence.

RESULTS

We did not observe any differences in functional connectivity during rest, either in the whole brain nor in seed-based connectivity analyses at this adolescent and young adult age.

CONCLUSIONS

Our results are reassuring; however, future studies on larger samples and with more sensitive methodologies are needed to confirm these findings.

Structural connectivity and intelligence in brain-injured children

In children, higher general intelligence corresponds with better processing speed ability. However, the relationship between structural brain connectivity and processing speed in the context of intelligence is unclear. Furthermore, the impact of brain injury on this relationship is also unknown. Structural networks were constructed for 36 brain tumor patients (mean age: 13.45 ± 2.73, 58% males) and 35 typically developing children (13.30 ± 2.86, 51% males). Processing speed and general intelligence scores were acquired using standard batteries. The relationship between network properties, processing speed, and intelligence was assessed using a partial least squares analysis. Results indicated that structural networks in brain-injured children were less integrated (β = -.38, p = 0.001) and more segregated (β = 0.4, p = 0.0005) compared to typically developing children. There was an indirect effect of network segregation on general intelligence via processing speed, where greater network segregation predicted slower processing speed which in turn predicted worse general intelligence (GoF = 0.37). These findings provide the first evidence of relations between structural connectivity, processing speed, and intelligence in children. Injury-related disruption to the structural network may result in worse intelligence through impacts on information processing. Our findings are discussed in the context of a network approach to understanding brain-behavior relationships.

Auditory experience modulates frontoparietal theta activity serving fluid intelligence

Children who are hard of hearing are at risk for developmental language and academic delays compared with children with normal hearing. Some work suggests that high-order cognitive function, including fluid intelligence, may relate to language and academic outcomes in children with hearing loss, but findings in these studies have been mixed and to date, there have been no studies of the whole-brain neural dynamics serving fluid intelligence in the context of hearing loss. To this end, this study sought to identify the impact of hearing loss and subsequent hearing aid use on the neural dynamics serving abstract reasoning in children who are hard of hearing relative to children with normal hearing using magnetoencephalography. We found significant elevations in occipital and parietal theta activity during early stimulus evaluation in children who are hard of hearing relative to normal-hearing peers. In addition, we found that greater hearing aid use was significantly related to reduced activity throughout the fronto-parietal network. Notably, there were no differences in alpha dynamics between groups during later-stage processing nor did alpha activity correlate with hearing aid use. These cross-sectional data suggest that differences in auditory experience lead to widespread alterations in the neural dynamics serving initial stimulus processing in fluid intelligence in children.

Inner Hemispheric and Interhemispheric Connectivity Balance in the Human Brain

The connectome of the brain has a great impact on the function of the brain as the structure of the connectome affects the speed and efficiency of information transfer. As a highly energy-consuming organ, an efficient network structure is essential. A previous study has shown consistent overall brain connectivity across a large variety of species. This connectivity conservation was explained by a balance between interhemispheric and intrahemispheric connections; that is, spices with highly connected hemispheres appear to have weaker interhemisphere connections. This study examines this connectivity trade-off in the human brain using diffusion-based tractography and network analysis in the Human Connectome Project (970 subjects, 527 female). We explore the biological origins of this phenomenon, heritability, and the effect on cognitive measures.The proportion of commissural fibers in the brain had a negative correlation to hemispheric efficiency, pointing to a trade-off between inner hemispheric and interhemispheric connectivity. Network hubs including anterior and middle cingulate cortex, superior frontal areas, medial occipital areas, the parahippocampal gyrus, post- and precentral gyri, and the precuneus had the strongest contribution to this phenomenon. Other results show a high heritability as well as a strong connection to crystalized intelligence. This work presents cohort-based network analysis research, spanning a large variety of samples and exploring the overall architecture of the human connectome. Our results show a connectivity conservation phenomenon at the base of the overall brain network architecture. This network structure may explain much of the functional, behavioral, and cognitive variability among different brains.SIGNIFICANCE STATEMENT The network structure of the brain is at the basis of every brain function as it dictates the characteristics of information transfer. Understanding the patterns and mechanisms that guide the connectome structure is crucial to understanding the brain itself. Here we unravel the mechanism at the base of the connectivity conservation phenomenon by exploring the interaction between hemispheric and commissural connectivity in a large-scale cohort-based connectivity study. We describe the trade-off between the two components and examine the origins of the trade-off and observe the effect on cognitive abilities and behavior.

The maturation and cognitive relevance of structural brain network organization from early infancy to childhood

The interactions of brain regions with other regions at the network level likely provide the infrastructure necessary for cognitive processes to develop. Specifically, it has been theorized that in infancy brain networks become more modular, or segregated, to support early cognitive specialization, before integration across networks increases to support the emergence of higher-order cognition. The present study examined the maturation of structural covariance networks (SCNs) derived from longitudinal cortical thickness data collected between infancy and childhood (0–6 years). We assessed modularity as a measure of network segregation and global efficiency as a measure of network integration. At the group level, we observed trajectories of increasing modularity and decreasing global efficiency between early infancy and six years. We further examined subject-based maturational coupling networks (sbMCNs) in a subset of this cohort with cognitive outcome data at 8–10 years, which allowed us to relate the network organization of longitudinal cortical thickness maturation to cognitive outcomes in middle childhood. We found that lower global efficiency of sbMCNs throughout early development (across the first year) related to greater motor learning at 8–10 years. Together, these results provide novel evidence characterizing the maturation of brain network segregation and integration across the first six years of life, and suggest that specific trajectories of brain network maturation contribute to later cognitive outcomes.

Impact of Brain Functional Network Properties on Intelligence in Children and Adolescents with Focal Epilepsy: A Resting-state MRI Study

RATIONALE AND OBJECTIVE

Epilepsy is a common pediatric disease that often leads to cognitive and intellectual impairments. Here, we explore the reorganized functional networks in children and adolescents with focal epilepsy (CAFE) and analyze the relationship between network reorganization and intellectual deficits to reveal the underlying link between them.

MATERIALS AND METHODS

Fifty-four CAFE (6-16 years old; right-handed) and 42 well-matched healthy controls were recruited. Subjects underwent resting-state functional magnetic resonance imaging, and functional networks were analyzed by graph analysis. Intelligence testing (Wechsler Intelligence Scale for Children-Chinese revision) included measures for verbal IQ (VIQ), performance IQ, and full-scale IQ.

RESULTS

(1) In the CAFE compared with the healthy controls, (a) the local efficiency, clustering coefficient and standardized clustering coefficient were significantly decreased (p < 0.05); (b) the degree centrality and nodal efficiency of the left precentral gyrus (LPG) were significantly increased (p < 0.05, Bonferroni correction), and the nodal shortest path length was significantly decreased (p < 0.05, Bonferroni correction); and (c) functional connectivity of the LPG with the bilateral inferior frontal ventral gyrus, right lateral superior occipital gyrus, left middle occipital gyrus, bilateral superior parietal lobule, right anterior prefrontal cortex, and bilateral cerebellum was enhanced (p < 0.05,GRF correction), while functional connectivity with the bilateral superior temporal gyrus was decreased (p < 0.05, GRF correction). (2) The nodal shortest path length of the LPG in CAFE was associated with full-scale IQ, performance IQ, and VIQ, and local efficiency was associated with VIQ.

CONCLUSION

Our results showed that the middle LPG in CAFE undergoes network reorganization that positively influences intelligence. Differences in local efficiency of functional networks in children and early adolescents have a significant effect on intelligence.

White matter microarchitecture and structural network integrity correlate with children intelligence quotient

The neural substrate of high intelligence performances remains not well understood. Based on diffusion tensor imaging (DTI) which provides microstructural information of white matter fibers, we proposed in this work to investigate the relationship between structural brain connectivity and intelligence quotient (IQ) scores. Fifty-seven children (8–12 y.o.) underwent a MRI examination, including conventional T1-weighted and DTI sequences, and neuropsychological testing using the fourth edition of Wechsler Intelligence Scale for Children (WISC-IV), providing an estimation of the Full-Scale Intelligence Quotient (FSIQ) based on four subscales: verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), and processing speed index (PSI). Correlations between the IQ scores and both graphs and diffusivity metrics were explored. First, we found significant correlations between the increased integrity of WM fiber-bundles and high intelligence scores. Second, the graph theory analysis showed that integration and segregation graph metrics were positively and negatively correlated with WISC-IV scores, respectively. These results were mainly driven by significant correlations between FSIQ, VCI, and PRI and graph metrics in the temporal and parietal lobes. In conclusion, these findings demonstrated that intelligence performances are related to the integrity of WM fiber-bundles as well as the density and homogeneity of WM brain networks.

Connectivity of the Cerebello-Thalamo-Cortical Pathway in Survivors of Childhood Leukemia Treated With Chemotherapy Only

IMPORTANCE

Treatment with contemporary chemotherapy-only protocols is associated with risk for neurocognitive impairment among survivors of childhood acute lymphoblastic leukemia (ALL).

OBJECTIVE

To determine whether concurrent use of methotrexate and glucocorticoids is associated with interference with the antioxidant system of the brain and damage and disruption of glucocorticoid-sensitive regions of the cerebello-thalamo-cortical network.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study was conducted from December 2016 to July 2019 in a single pediatric cancer tertiary care center. Participants included survivors of childhood ALL who were more than 5 years from cancer diagnosis, age 8 years or older, and treated on an institutional chemotherapy-only protocol. Age-matched community members were recruited as a control group. Data were analyzed from August 2017 to August 2020.

EXPOSURE

ALL treatment using chemotherapy-only protocols.

MAIN OUTCOMES AND MEASURES

This study compared brain volumes between survivors and individuals in a community control group and examined associations among survivors of methotrexate and dexamethasone exposure with neurocognitive outcomes. Functional and effective connectivity measures were compared between survivors with and without cognitive impairment. The Rey-Osterrieth complex figure test, a neurocognitive evaluation in which individuals are asked to copy a figure and then draw the figure from memory, was scored according to published guidelines and transformed into age-adjusted z scores based on nationally representative reference data and used to measure organization and planning deficits. β values for neurocognitive tests represented the amount of change in cerebellar volume or chemotherapy exposure associated with 1 SD change in neurocognitive outcome by z score (mm3/1 SD in z score for cerebellum, mm3/[g×hr/L] for dexamethasone and methotrexate AUC, and mm3/intrathecal count for total intrathecal count).

RESULTS

Among 302 eligible individuals, 218 (72%) participated in the study and 176 (58%) had usable magnetic resonance imaging (MRI) results. Among these, 89 (51%) were female participants and the mean (range) age was 6.8 (1-18) years at diagnosis and 14.5 (8-27) years at evaluation. Of 100 community individuals recruited as the control group, 82 had usable MRI results; among these, 35 (43%) were female individuals and the mean (range) age was 13.8 (8-26) years at evaluation. There was no significant difference in total brain volume between survivors and individuals in the control group. Survivors of both sexes showed decreased mean (SD) cerebellar volumes compared with the control population (female: 70 568 [6465] mm3 vs 75 134 [6780] mm3; P < .001; male: 77 335 [6210] mm3 vs 79 020 [7420] mm3; P < .001). In female survivors, decreased cerebellar volume was associated with worse performance in Rey-Osterrieth complex figure test (left cerebellum: β = 55.54; SE = 25.55; P = .03; right cerebellum: β = 52.57; SE = 25.50; P = .04) and poorer dominant-hand motor processing speed (ie, grooved pegboard performance) (left cerebellum: β = 82.71; SE = 31.04; P = .009; right cerebellum: β = 91.06; SE = 30.72; P = .004). In female survivors, increased number of intrathecal treatments (ie, number of separate injections) was also associated with Worse Rey-Osterrieth test performance (β = -0.154; SE = 0.063; P = .02), as was increased dexamethasone exposure (β = -0.0014; SE = 0.0005; P = .01). Executive dysfunction was correlated with increased global efficiency between smaller brain regions (Pearson r = -0.24; P = .01) compared with individuals without dysfunction. Anatomical connectivity showed differences between impaired and nonimpaired survivors. Analysis of variance of effective-connectivity weights identified a significant interaction association (F = 3.99; P = .02) among the direction and strength of connectivity between the cerebellum and DLPFC, female sex, and executive dysfunction. Finally, no effective connectivity was found between the precuneus and DLPFC in female survivors with executive dysfunction.

CONCLUSIONS AND RELEVANCE

These findings suggest that dexamethasone exposure was associated with smaller cerebello-thalamo-cortical regions in survivors of ALL and that disruption of effective connectivity was associated with impairment of executive function in female survivors.

Localization of epileptic seizure focus by computerized analysis of fMRI recordings

By computerized analysis of cortical activity recorded via fMRI for pediatric epilepsy patients, we implement algorithmic localization of epileptic seizure focus within one of eight cortical lobes. Our innovative machine learning techniques involve intensive analysis of large matrices of mutual information coefficients between pairs of anatomically identified cortical regions. Drastic selection of pairs of regions with biologically significant inter-connectivity provides efficient inputs for our multi-layer perceptron (MLP) classifier. By imposing rigorous parameter parsimony to avoid overfitting, we construct a small-size MLP with very good percentages of successful classification.

Neuropsychological development in adolescents: Longitudinal associations with white matter microstructure

Important neuropsychological changes during adolescence coincide with the maturation of white matter microstructure. Few studies have investigated the association between neuropsychological development and white matter maturation longitudinally. We aimed to characterize developmental trajectories of inhibition, planning, emotion recognition and risk-taking and examine whether white matter microstructural characteristics were associated with neuropsychological development above and beyond age. In an accelerated longitudinal cohort design, n = 112 healthy adolescents between ages 9 and 16 underwent cognitive assessment and diffusion MRI over three years. Fractional anisotropy (FA) and mean diffusivity (MD) were extracted for major white matter pathways using an automatic probabilistic reconstruction technique and mixed models were used for statistical analyses. Inhibition, planning and emotion recognition performance improved linearly across adolescence. Risk-taking developed in a quadratic fashion, with stable performance between 9 and 12 and an increase between ages 12 and 16. Including cingulum and superior longitudinal fasciculus FA slightly improved model fit for emotion recognition across age. We found no evidence that FA or MD were related to inhibition, planning or risk-taking across age. Our results challenge the additional value of white matter microstructure to explain neuropsychological development in healthy adolescents, but more longitudinal research with large datasets is needed to identify the potential role of white matter microstructure in cognitive development.

Rich-club in the brain's macrostructure: Insights from graph theoretical analysis

The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis

Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

Tracking regional brain growth up to age 13 in children born term and very preterm

Serial regional brain growth from the newborn period to adolescence has not been described. Here, we measured regional brain growth in 216 very preterm (VP) and 45 full-term (FT) children. Brain MRI was performed at term-equivalent age, 7 and 13 years in 82 regions. Brain volumes increased between term-equivalent and 7 years, with faster growth in the FT than VP group. Perinatal brain abnormality was associated with less increase in brain volume between term-equivalent and 7 years in the VP group. Between 7 and 13 years, volumes were relatively stable, with some subcortical and cortical regions increasing while others reduced. Notably, VP infants continued to lag, with overall brain size generally less than that of FT peers at 13 years. Parieto–frontal growth, mainly between 7 and 13 years in FT children, was associated with higher intelligence at 13 years. This study improves understanding of typical and atypical regional brain growth.

In this longitudinal study, the authors tracked the course of brain development from birth to adolescence (age 13 years) and examined the effects of very preterm birth. Very preterm children showed slower brain growth from age 0 (term equivalent) to age 7.

Neuroanatomical Dysconnectivity Underlying Cognitive Deficits in Bipolar Disorder

BACKGROUND

Graph theory applied to brain networks is an emerging approach to understanding the brain's topological associations with human cognitive ability. Despite well-documented cognitive impairments in bipolar disorder (BD) and recent reports of altered anatomical network organization, the association between connectivity and cognitive impairments in BD remains unclear.

METHODS

We examined the role of anatomical network connectivity derived from T1- and diffusion-weighted magnetic resonance imaging in impaired cognitive performance in individuals with BD (n = 32) compared with healthy control individuals (n = 38). Fractional anisotropy- and number of streamlines-weighted anatomical brain networks were generated by mapping constrained spherical deconvolution-reconstructed white matter among 86 cortical/subcortical bilateral brain regions delineated in the individual's own coordinate space. Intelligence and executive function were investigated as distributed functions using measures of global, rich-club, and interhemispheric connectivity, while memory and social cognition were examined in relation to subnetwork connectivity.

RESULTS

Lower executive functioning related to higher global clustering coefficient in participants with BD, and lower IQ performance may present with a differential relationship between global and interhemispheric efficiency in individuals with BD relative to control individuals. Spatial recognition memory accuracy and response times were similar between diagnostic groups and associated with basal ganglia and thalamus interconnectivity and connectivity within extended anatomical subnetworks in all participants. No anatomical subnetworks related to episodic memory, short-term memory, or social cognition generally or differently in BD.

CONCLUSIONS

Results demonstrate selective influence of subnetwork patterns of connectivity in underlying cognitive performance generally and abnormal global topology underlying discrete cognitive impairments in BD.

Topological Modification of Brain Networks Organization in Children With High Intelligence Quotient: A Resting-State fMRI Study

The idea that intelligence is embedded not only in a single brain network, but instead in a complex, well-optimized system of complementary networks, has led to the development of whole brain network analysis. Using graph theory to analyze resting-state functional MRI data, we investigated the brain graph networks (or brain networks) of high intelligence quotient (HIQ) children. To this end, we computed the "hub disruption index κ," an index sensitive to graph network modifications. We found significant topological differences in the integration and segregation properties of brain networks in HIQ compared to standard IQ children, not only for the whole brain graph, but also for each hemispheric graph, and for the homotopic connectivity. Moreover, two profiles of HIQ children, homogenous and heterogeneous, based on the differences between the two main IQ subscales [verbal comprehension index (VCI) and perceptual reasoning index (PRI)], were compared. Brain network changes were more pronounced in the heterogeneous than in the homogeneous HIQ subgroups. Finally, we found significant correlations between the graph networks' changes and the full-scale IQ (FSIQ), as well as the subscales VCI and PRI. Specifically, the higher the FSIQ the greater was the brain organization modification in the whole brain, the left hemisphere, and the homotopic connectivity. These results shed new light on the relation between functional connectivity topology and high intelligence, as well as on different intelligence profiles.

Whole-brain white matter organization, intelligence, and educational attainment

General cognitive ability, sometimes referred to as intelligence, is associated with educational attainment throughout childhood. Most studies that have explored the neural correlates of intelligence in childhood focus on individual brain regions. This analytical approach is designed to identify restricted sets of voxels that overlap across participants. By contrast, we explored the relationship between white matter connectome organization, intelligence, and education. In both a sample of typically-developing children (N = 63) and a sample of struggling learners (N = 139), the white matter connectome efficiency was strongly associated with intelligence and educational attainment. Further, intelligence partially mediated the relationship between connectome efficiency and educational attainment. In contrast, a canonical voxel-wise analysis failed to identify any significant relationships. The results emphasize the importance of distributed brain network properties for cognitive or educational ability in childhood. Our findings are interpreted in the context of a developmental theory, which emphasizes the interaction between different subsystems over developmental time.

Does Anhedonia Presage Increased Risk of Posttraumatic Stress Disorder? : Adolescent Anhedonia and Posttraumatic Disorders

Anhedonia, the reduced ability to experience pleasure, is a dimensional entity linked to multiple neuropsychiatric disorders, where it is associated with diminished treatment response, reduced global function, and increased suicidality. It has been suggested that anhedonia and the related disruption in reward processing may be critical precursors to development of psychiatric symptoms later in life. Here, we examine cross-species evidence supporting the hypothesis that early life experiences modulate development of reward processing, which if disrupted, result in anhedonia. Importantly, we find that anhedonia may confer risk for later neuropsychiatric disorders, especially posttraumatic stress disorder (PTSD). Whereas childhood trauma has long been associated with increased anhedonia and increased subsequent risk for trauma-related disorders in adulthood, here we focus on an additional novel, emerging direct contributor to anhedonia in rodents and humans: fragmented, chaotic environmental signals ("FRAG") during critical periods of development. In rodents, recent data suggest that adolescent anhedonia may derive from aberrant pleasure/reward circuit maturation. In humans, recent longitudinal studies support that FRAG is associated with increased anhedonia in adolescence. Both human and rodent FRAG exposure also leads to aberrant hippocampal function. Prospective studies are underway to examine if anhedonia is also a marker of PTSD risk. These preliminary cross-species studies provide a critical construct for future examination of the etiology of trauma-related symptoms in adults and for and development of prophylactic and therapeutic interventions. In addition, longitudinal studies of reward circuit development with and without FRAG will be critical to test the mechanistic hypothesis that early life FRAG modifies reward circuitry with subsequent consequences for adolescent-emergent anhedonia and contributes to risk and resilience to trauma and stress in adulthood.

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.

Mapping alterations of gray matter volume and white matter integrity in children with autism spectrum disorder: evidence from fMRI findings

This study aimed to identify the neuroanatomical substrates and white matter connectivity in children with autism spectrum disorder (ASD) and the association between gray matter and structural connectivity. A total of 36 children including patients with ASD and healthy controls between 6 and 15 years of age were enrolled in this study. High-resolution structural MRI and functional MRI were performed and analyzed using voxel-based morphometry and tract-based spatial statistics. The relationship between gray matter volume and structural connectivity was generated using Pearson correlation analysis. Voxel-based morphometry analysis showed significantly reduced areas of gray matter in the left cerebellum. Tract-based spatial statistics analysis showed white matter abnormalities in several distinct clusters within the right inferior frontal gyrus (opercular part), the left inferior parietal lobule, and the right mentary motor area. Neither ASD nor healthy controls showed a significant correlation between gray matter volume and white matter integrity. Our study confirmed the presence of several structural and regional abnormalities in ASD children. These findings suggest that there are significant differences in some brain regions in children with autism relative to healthy children, but no association between them.

Early human brain development: insights into macroscale connectome wiring

BACKGROUND

Early brain development is closely dictated by distinct neurobiological principles. Here, we aimed to map early trajectories of structural brain wiring in the neonatal brain.

METHODS

We investigated structural connectome development in 44 newborns, including 23 preterm infants and 21 full-term neonates scanned between 29 and 45 postmenstrual weeks. Diffusion-weighted imaging data were combined with cortical segmentations derived from T2 data to construct neonatal connectome maps.

RESULTS

Projection fibers interconnecting primary cortices and deep gray matter structures were noted to mature faster than connections between higher-order association cortices (fractional anisotropy (FA) F = 58.9, p < 0.001, radial diffusivity (RD) F = 28.8, p < 0.001). Neonatal FA-values resembled adult FA-values more than RD, while RD approximated the adult brain faster (F = 358.4, p < 0.001). Maturational trajectories of RD in neonatal white matter pathways revealed substantial overlap with what is known about the sequence of subcortical white matter myelination from histopathological mappings as recorded by early neuroanatomists (mean RD 68 regions r = 0.45, p = 0.008).

CONCLUSION

Employing postnatal neuroimaging we reveal that early maturational trajectories of white matter pathways display discriminative developmental features of the neonatal brain network. These findings provide valuable insight into the early stages of structural connectome development.

Mutual Information Better Quantifies Brain Network Architecture in Children with Epilepsy

Purpose

Metrics of the brain network architecture derived from resting-state fMRI have been shown to provide physiologically meaningful markers of IQ in children with epilepsy. However, traditional measures of functional connectivity (FC), specifically the Pearson correlation, assume a dominant linear relationship between BOLD time courses; this assumption may not be valid. Mutual information is an alternative measure of FC which has shown promise in the study of complex networks due to its ability to flexibly capture association of diverse forms. We aimed to compare network metrics derived from mutual information-defined FC to those derived from traditional correlation in terms of their capacity to predict patient-level IQ.

Materials and Methods

Patients were retrospectively identified with the following: (1) focal epilepsy; (2) resting-state fMRI; and (3) full-scale IQ by a neuropsychologist. Brain network nodes were defined by anatomic parcellation. Parcellation was performed at the size threshold of 350 mm², resulting in networks containing 780 nodes. Whole-brain, weighted graphs were then constructed according to the pairwise connectivity between nodes. In the traditional condition, edges (connections) between each pair of nodes were defined as the absolute value of the Pearson correlation coefficient between their BOLD time courses. In the mutual information condition, edges were defined as the mutual information between time courses. The following metrics were then calculated for each weighted graph: clustering coefficient, modularity, characteristic path length, and global efficiency. A machine learning algorithm was used to predict the IQ of each individual based on their network metrics. Prediction accuracy was assessed as the fractional variation explained for each condition.

Results

Twenty-four patients met the inclusion criteria (age: 8-18 years). All brain networks demonstrated expected small-world properties. Network metrics derived from mutual information-defined FC significantly outperformed the use of the Pearson correlation. Specifically, fractional variation explained was 49% (95% CI: 46%, 51%) for the mutual information method; the Pearson correlation demonstrated a variation of 17% (95% CI: 13%, 19%).

Conclusion

Mutual information-defined functional connectivity captures physiologically relevant features of the brain network better than correlation.

Clinical Relevance

Optimizing the capacity to predict cognitive phenotypes at the patient level is a necessary step toward the clinical utility of network-based biomarkers.

Prenatal Maternal Cortisol Has Sex-Specific Associations with Child Brain Network Properties

Elevated maternal cortisol concentrations have the potential to alter fetal development in a sex-specific manner. Female brains are known to show adaptive behavioral and anatomical flexibility in response to early-life exposure to cortisol, but it is not known how these sex-specific effects manifest at the whole-brain structural networks. A prospective longitudinal study of 49 mother child dyads was conducted with serial assessments of maternal cortisol levels from 15 to 37 gestational weeks. We modeled the structural network of typically developing children (aged 6-9 years) and examined its global connectome properties, rich-club organization, and modular architecture. Network segregation was susceptible only for girls to variations in exposure to maternal cortisol during pregnancy. Girls generated more connections than boys to maintain topologically capable and efficient neural circuits, and this increase in neural cost was associated with higher levels of internalizing problems. Maternal cortisol concentrations at 31 gestational weeks gestation were most strongly associated with altered neural connectivity in girls, suggesting a sensitive period for the maternal cortisol-offspring brain associations. Our data suggest that girls exhibit an adaptive response by increasing the neural network connectivity necessary for maintaining homeostasis and efficient brain function across the lifespan.

Association between structural brain network efficiency and intelligence increases during adolescence

Adolescence represents an important period during which considerable changes in the brain take place, including increases in integrity of white matter bundles, and increasing efficiency of the structural brain network. A more efficient structural brain network has been associated with higher intelligence. Whether development of structural network efficiency is related to intelligence, and if so to which extent genetic and environmental influences are implicated in their association, is not known. In a longitudinal study, we mapped FA-weighted efficiency of the structural brain network in 310 twins and their older siblings at an average age of 10, 13, and 18 years. Age-trajectories of global and local FA-weighted efficiency were related to intelligence. Contributions of genes and environment were estimated using structural equation modeling. Efficiency of brain networks changed in a non-linear fashion from childhood to early adulthood, increasing between 10 and 13 years, and leveling off between 13 and 18 years. Adolescents with higher intelligence had higher global and local network efficiency. The dependency of FA-weighted global efficiency on IQ increased during adolescence (rph =0.007 at age 10; 0.23 at age 18). Global efficiency was significantly heritable during adolescence (47% at age 18). The genetic correlation between intelligence and global and local efficiency increased with age; genes explained up to 87% of the observed correlation at age 18. In conclusion, the brain's structural network differentiates depending on IQ during adolescence, and is under increasing influence of genes that are also associated with intelligence as it develops from late childhood to adulthood.

A multisample study of longitudinal changes in brain network architecture in 4-13-year-old children

Recent advances in human neuroimaging research have revealed that white-matter connectivity can be described in terms of an integrated network, which is the basis of the human connectome. However, the developmental changes of this connectome in childhood are not well understood. This study made use of two independent longitudinal diffusion-weighted imaging data sets to characterize developmental changes in the connectome by estimating age-related changes in fractional anisotropy (FA) for reconstructed fibers (edges) between 68 cortical regions. The first sample included 237 diffusion-weighted scans of 146 typically developing children (4-13 years old, 74 females) derived from the Pediatric Longitudinal Imaging, Neurocognition, and Genetics (PLING) study. The second sample included 141 scans of 97 individuals (8-13 years old, 62 females) derived from the BrainTime project. In both data sets, we compared edges that had the most substantial age-related change in FA to edges that showed little change in FA. This allowed us to investigate if developmental changes in white matter reorganize network topology. We observed substantial increases in edges connecting peripheral and a set of highly connected hub regions, referred to as the rich club. Together with the observed topological differences between regions connecting to edges showing the smallest and largest changes in FA, this indicates that changes in white matter affect network organization, such that highly connected regions become even more strongly imbedded in the network. These findings suggest that an important process in brain development involves organizing patterns of inter-regional interactions. Hum Brain Mapp 39:157-170, 2018. © 2017 Wiley Periodicals, Inc.

Network attributes underlying intellectual giftedness in the developing brain

Brain network is organized to maximize the efficiency of both segregated and integrated information processing that may be related to human intelligence. However, there have been surprisingly few studies that focus on the topological characteristics of brain network underlying extremely high intelligence that is intellectual giftedness, particularly in adolescents. Here, we examined the network topology in 25 adolescents with superior intelligence (SI-Adol), 25 adolescents with average intelligence (AI-Adol), and 27 young adults with AI (AI-Adult). We found that SI-Adol had network topological properties of high global efficiency as well as high clustering with a low wiring cost, relative to AI-Adol. However, contrary to the suggested role that brain hub regions play in general intelligence, the network efficiency of rich club connection matrix, which represents connections among brain hubs, was low in SI-Adol in comparison to AI-Adol. Rather, a higher level of local connection density was observed in SI-Adol than in AI-Adol. The highly intelligent brain may not follow this efficient yet somewhat stereotypical process of information integration entirely. Taken together, our results suggest that a highly intelligent brain may communicate more extensively, while being less dependent on rich club communications during adolescence.

White matter maturation in the neonatal brain is predictive of school age cognitive capacities in children born very preterm

AIM

To investigate the association between white matter organization in the neonatal brain and cognitive capacities at early school age in children born very preterm.

METHOD

Thirty children born very preterm (gestational age median 27.5wks, interquartile range [IQR] 25.5-29.5; 18 males, 12 females) were included in this retrospective observational cohort study. Diffusion-weighted imaging (DWI) had been performed on a 3T system in the neonatal period (median 41.3 [IQR 40.0-42.6]wks) and cognitive functioning was formally assessed at age 5 years and 7 months (IQR 5.4-5.9y) using the Wechsler Preschool and Primary Scale of Intelligence. Structural connectivity maps were reconstructed from the DWI data using deterministic streamline tractography. Network metrics of global and local communication and mean fractional anisotropy of white matter pathways were related to IQ and processing speed at age 5 years using linear regression analyses.

RESULTS

Mean fractional anisotropy was significantly related to Performance IQ at age 5 years (F=8.48, p=0.007). Findings persisted after adjustment for maternal education level.

INTERPRETATION

Our findings provide evidence that the blueprint of later cognitive achievement is already present at term-equivalent age and suggest that white matter connectivity strength may be a valuable predictor for long-term cognitive functioning.

Repeatability of graph theoretical metrics derived from resting-state functional networks in paediatric epilepsy patients

OBJECTIVE

To measure the repeatability of metrics that quantify brain network architecture derived from resting-state functional MRI in a cohort of paediatric patients with epilepsy.

METHODS

We identified patients with: (1) epilepsy; (2) brain MRI at 3 T; (3) two identical resting-state functional MRI acquisitions performed on the same day. Undirected, weighted networks were constructed based on the resting-state time series using a range of processing parameters including parcellation size and graph threshold. The following topological properties were calculated: degree, strength, characteristic path length, global efficiency, clustering coefficient, modularity and small worldness. Based on repeated measures, we then calculated: (1) Pearson correlation coefficient; (2) intraclass correlation coefficient; (3) root-mean-square coefficient of variation; (4) repeatability coefficient; and (5) 95% confidence limits for change.

RESULTS

26 patients were included (age range: 4-21 years). Correlation coefficients demonstrated a highly consistent relationship between repeated observations for all metrics, and the intraclass correlation coefficients were generally in the excellent range. Repeatability in the data set was not significantly influenced by parcellation size. However, trends towards decreased repeatability were observed at higher graph thresholds.

CONCLUSION

These findings demonstrate the reliability of network metrics in a cohort of paediatric patients with epilepsy. Advances in knowledge: Our results point to the potential for graph theoretical analyses of resting-state data to provide reliable markers of network architecture in children with epilepsy. At the level of an individual patient, change over time greater than the repeatability coefficient or 95% confidence limits for change is unlikely to be related to intrinsic variability of the method.

Intelligence as a Developing Function: A Neuroconstructivist Approach

The concept of intelligence encompasses the mental abilities necessary to survival and advancement in any environmental context. Attempts to grasp this multifaceted concept through a relatively simple operationalization have fostered the notion that individual differences in intelligence can often be expressed by a single score. This predominant position has contributed to expect intelligence profiles to remain substantially stable over the course of ontogenetic development and, more generally, across the life-span. These tendencies, however, are biased by the still limited number of empirical reports taking a developmental perspective on intelligence. Viewing intelligence as a dynamic concept, indeed, implies the need to identify full developmental trajectories, to assess how genes, brain, cognition, and environment interact with each other. In the present paper, we describe how a neuroconstructivist approach better explains why intelligence can rise or fall over development, as a result of a fluctuating interaction between the developing system itself and the environmental factors involved at different times across ontogenesis.

Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to [corrected] IQ and Gender

During the last years, it has been established that the prefrontal and posterior parietal brain lobes, which are mostly related to intelligence, have many connections to cerebellum. However, there is a limited research investigating cerebellum's relationship with cognitive processes. In this study, the network of cerebellum was analyzed in order to investigate its overall organization in individuals with low and high fluid Intelligence Quotient (IQ). Functional magnetic resonance imaging (fMRI) data were selected from 136 subjects in resting-state from the Human Connectome Project (HCP) database and were further separated into two IQ groups composed of 69 low-IQ and 67 high-IQ subjects. Cerebellum was parcellated into 28 lobules/ROIs (per subject) using a standard cerebellum anatomical atlas. Thereafter, correlation matrices were constructed by computing Pearson's correlation coefficients between the average BOLD time-series for each pair of ROIs inside the cerebellum. By computing conventional graph metrics, small-world network properties were verified using the weighted clustering coefficient and the characteristic path length for estimating the trade-off between segregation and integration. In addition, a connectivity metric was computed for extracting the average cost per network. The concept of the Minimum Spanning Tree (MST) was adopted and implemented in order to avoid methodological biases in graph comparisons and retain only the strongest connections per network. Subsequently, six global and three local metrics were calculated in order to retrieve useful features concerning the characteristics of each MST. Moreover, the local metrics of degree and betweenness centrality were used to detect hubs, i.e., nodes with high importance. The computed set of metrics gave rise to extensive statistical analysis in order to examine differences between low and high-IQ groups, as well as between all possible gender-based group combinations. Our results reveal that both male and female networks have small-world properties with differences in females (especially in higher IQ females) indicative of higher neural efficiency in cerebellum. There is a trend toward the same direction in men, but without significant differences. Finally, three lobules showed maximum correlation with the median response time in low-IQ individuals, implying that there is an increased effort dedicated locally by this population in cognitive tasks.

Small-world human brain networks: Perspectives and challenges

Modelling the human brain as a complex network has provided a powerful mathematical framework to characterize the structural and functional architectures of the brain. In the past decade, the combination of non-invasive neuroimaging techniques and graph theoretical approaches enable us to map human structural and functional connectivity patterns (i.e., connectome) at the macroscopic level. One of the most influential findings is that human brain networks exhibit prominent small-world organization. Such a network architecture in the human brain facilitates efficient information segregation and integration at low wiring and energy costs, which presumably results from natural selection under the pressure of a cost-efficiency balance. Moreover, the small-world organization undergoes continuous changes during normal development and ageing and exhibits dramatic alterations in neurological and psychiatric disorders. In this review, we survey recent advances regarding the small-world architecture in human brain networks and highlight the potential implications and applications in multidisciplinary fields, including cognitive neuroscience, medicine and engineering. Finally, we highlight several challenging issues and areas for future research in this rapidly growing field.

Commentary: The Development of Creativity--Ability, Motivation, and Potential

A major question for research on the development of creativity is whether it is interested in creative potential (a prospective approach that uses measures early in life to predict adult creativity) or in children's creativity for its own sake. We suggest that a focus on potential for future creativity diminishes the fascinating creative world of childhood. The contributions to this issue can be organized in light of an ability × motivation framework, which offers a fruitful way for thinking about the many factors that foster and impede creativity. The contributions reflect a renewed interest in the development of creativity and highlight how this area can illuminate broader problems in creativity studies.

Brain Network Architecture and Global Intelligence in Children with Focal Epilepsy

BACKGROUND AND PURPOSE

The biologic basis for intelligence rests to a large degree on the capacity for efficient integration of information across the cerebral network. We aimed to measure the relationship between network architecture and intelligence in the pediatric, epileptic brain.

MATERIALS AND METHODS

Patients were retrospectively identified with the following: 1) focal epilepsy; 2) brain MR imaging at 3T, including resting-state functional MR imaging; and 3) full-scale intelligence quotient measured by a pediatric neuropsychologist. The cerebral cortex was parcellated into approximately 700 gray matter network "nodes." The strength of a connection between 2 nodes was defined by the correlation between their blood oxygen level-dependent time-series. We calculated the following topologic properties: clustering coefficient, transitivity, modularity, path length, and global efficiency. A machine learning algorithm was used to measure the independent contribution of each metric to the intelligence quotient after adjusting for all other metrics.

RESULTS

Thirty patients met the criteria (4-18 years of age); 20 patients required anesthesia during MR imaging. After we accounted for age and sex, clustering coefficient and path length were independently associated with full-scale intelligence quotient. Neither motion parameters nor general anesthesia was an important variable with regard to accurate intelligence quotient prediction by the machine learning algorithm. A longer history of epilepsy was associated with shorter path lengths (P = .008), consistent with reorganization of the network on the basis of seizures. Considering only patients receiving anesthesia during machine learning did not alter the patterns of network architecture contributing to global intelligence.

CONCLUSIONS

These findings support the physiologic relevance of imaging-based metrics of network architecture in the pathologic, developing brain.

Metrics of brain network architecture capture the impact of disease in children with epilepsy

BACKGROUND AND OBJECTIVE

Epilepsy is associated with alterations in the structural framework of the cerebral network. The aim of this study was to measure the potential of global metrics of network architecture derived from resting state functional MRI to capture the impact of epilepsy on the developing brain.

METHODS

Pediatric patients were retrospectively identified with: 1. Focal epilepsy; 2. Brain MRI at 3 Tesla, including resting state functional MRI; 3. Full scale IQ measured by a pediatric neuropsychologist. The cerebral cortex was parcellated into approximately 700 gray matter network nodes. The strength of a connection between two nodes was defined as the correlation between their resting BOLD signal time series. The following global network metrics were then calculated: clustering coefficient, transitivity, modularity, path length, and global efficiency. Epilepsy duration was used as an index for the cumulative impact of epilepsy on the brain.

RESULTS

45 patients met criteria (age: 4-19 years). After accounting for age of epilepsy onset, epilepsy duration was inversely related to IQ (p: 0.01). Epilepsy duration predicted by a machine learning algorithm on the basis of the five global network metrics was highly correlated with actual epilepsy duration (r: 0.95; p: 0.0001). Specifically, modularity and to a lesser extent path length and global efficiency were independently associated with epilepsy duration.

CONCLUSIONS

We observed that a machine learning algorithm accurately predicted epilepsy duration based on global metrics of network architecture derived from resting state fMRI. These findings suggest that network metrics have the potential to form the basis for statistical models that translate quantitative imaging data into patient-level markers of cognitive deterioration.

Altered gray matter organization in children and adolescents with ADHD: a structural covariance connectome study

Although multiple studies have reported structural deficits in multiple brain regions in attention-deficit hyperactivity disorder (ADHD), we do not yet know if these deficits reflect a more systematic disruption to the anatomical organization of large-scale brain networks. Here we used a graph theoretical approach to quantify anatomical organization in children and adolescents with ADHD. We generated anatomical networks based on covariance of gray matter volumes from 92 regions across the brain in children and adolescents with ADHD (n=34) and age- and sex-matched healthy controls (n=28). Using graph theory, we computed metrics that characterize both the global organization of anatomical networks (interconnectivity (clustering), integration (path length) and balance of global integration and localized segregation (small-worldness)) and their local nodal measures (participation (degree) and interaction (betweenness) within a network). Relative to Controls, ADHD participants exhibited altered global organization reflected in more clustering or network segregation. Locally, nodal degree and betweenness were increased in the subcortical amygdalae in ADHD, but reduced in cortical nodes in the anterior cingulate, posterior cingulate, mid temporal pole and rolandic operculum. In ADHD, anatomical networks were disrupted and reflected an emphasis on subcortical local connections centered around the amygdala, at the expense of cortical organization. Brains of children and adolescents with ADHD may be anatomically configured to respond impulsively to the automatic significance of stimulus input without having the neural organization to regulate and inhibit these responses. These findings provide a novel addition to our current understanding of the ADHD connectome.