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Abstract
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.
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... This approach has demonstrated extraordinary utility in delineating abnormal topological patterns in the cortical morphology of individuals with various brain disorders, such as Alzheimer's disease, mild cognitive impairment, and autism spectrum disorder (He et al., 2008;Yao et al., 2010;Zheng et al., 2021c). Notably, this method has also been applied to study the development of fetal and infant brains (Alexander-Bloch et al., 2013;Fan et al., 2011;Geng et al., 2017;Nie et al., 2014;Woodburn et al., 2021;Xu et al., 2021). For instance, the infant GM network exhibited nonrandomized small-world organization and modular architecture, which were associated with later behavioral and cognitive abilities (Fan et al., 2011;Woodburn et al., 2021). ...
... Notably, this method has also been applied to study the development of fetal and infant brains (Alexander-Bloch et al., 2013;Fan et al., 2011;Geng et al., 2017;Nie et al., 2014;Woodburn et al., 2021;Xu et al., 2021). For instance, the infant GM network exhibited nonrandomized small-world organization and modular architecture, which were associated with later behavioral and cognitive abilities (Fan et al., 2011;Woodburn et al., 2021). However, this method has been constrained to group-level analyses, limiting its utility in quantifying individual cortical changes (Alexander-Bloch et al., 2013;Batalle et al., 2013;Li et al., 2017). ...
... Furthermore, the nonlinear changes of transitivity and clustering coefficient illustrated the developmental process of the MSN in more detail, which might initially develop towards a more locally connected organization before full-term age (around 40 PMW) and then optimize the connectivity among local clusters (decreased segregation after 40 PMW and linearly increased integration). Such a developmental pattern was consistent with findings of previous literature based on MSNs derived from correlations of regional volumes and SCN of term-born infants (Fan et al., 2011;Woodburn et al., 2021;Zhao et al., 2022), potentially laying the foundation for later complex cognitive functions. However, there were also studies reported different results of fiber-bundle connectivity networks in normal fetuses (Song et al., 2017) and preterm infants (Liu et al., 2021b;Sa de Almeida et al., 2021;Zheng et al., 2023a). ...
The topological organization of the macroscopic cortical networks are important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex differences and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65% accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.
... The latter are known to be associated with general brain and cognitive development, while their effect on SCN evolution has remained unknown. Previously, the SCN corresponding to a specific age bin was estimated by pooling data recorded within that age bin 9,10 , where potential bias may be introduced since actual ages of measurements were not utilized. Additionally, previous longitudinal studies suffered from small sample sizes that may lead to considerable random fluctuations. ...
... Modularity and global efficiency are widely used to characterize brain networks. These summary measures are especially useful to quantify time-varying changes in SCNs and thus can be used to assess the network evolution of brain volumes 9 . ...
... The acquisition matrix and field of view were varied according to child head size in order to maintain a constant voxel volume and spatial resolution across all ages 41 . Using a multistep registration procedure 42 , a series of age-specific anatomical T1-weighted templates were created corresponding to 3,6,9,12,15,18,21,24,30,36,42,48,60,72, 84, 96 and 108-month ages. At least 10 females and 10 males were included in each template. ...
The maturation of regional brain volumes from birth to preadolescence is a critical developmental process that underlies emerging brain structural connectivity and function. Regulated by genes and environment, the coordinated growth of different brain regions plays an important role in cognitive development. Current knowledge about structural network evolution is limited, partly due to the sparse and irregular nature of most longitudinal neuroimaging data. In particular, it is unknown how factors such as mother’s education or sex of the child impact the structural network evolution. To address this issue, we propose a method to construct evolving structural networks and study how the evolving connections among brain regions as reflected at the network level are related to maternal education and biological sex of the child and also how they are associated with cognitive development. Our methodology is based on applying local Fréchet regression to longitudinal neuroimaging data acquired from the RESONANCE cohort, a cohort of healthy children (245 females and 309 males) ranging in age from 9 weeks to 10 years. Our findings reveal that sustained highly coordinated volume growth across brain regions is associated with lower maternal education and lower cognitive development. This suggests that higher neurocognitive performance levels in children are associated with increased variability of regional growth patterns as children age.
... Recent studies described the longitudinal changes in brain structure across the lifespan (16,17), and similar approaches have only recently started to populate the scientific literature for what concerns functional connectivity and its association with age or development. More importantly, longitudinal approaches in neuroimaging studies are mostly focused on early infancy in order to characterize neurodevelopment (18)(19)(20), or on senescence in order to examine neurodegeneration (21,22). However, novel evidence has highlighted the important modulation in the functional organization of the brain during late childhood, adolescence and early adulthood (16,20,23,24). ...
... More importantly, longitudinal approaches in neuroimaging studies are mostly focused on early infancy in order to characterize neurodevelopment (18)(19)(20), or on senescence in order to examine neurodegeneration (21,22). However, novel evidence has highlighted the important modulation in the functional organization of the brain during late childhood, adolescence and early adulthood (16,20,23,24). This evidence seems of primary importance considering how a high proportion of psychiatric disorders arise during this time period (25), and how an earlier time of onset can be associated with a worse presentation or outcome (26). ...
Homotopic connectivity during resting state has been proposed as a risk marker for neurologic and psychiatric conditions, but a precise characterization of its trajectory through development is currently lacking. Voxel-Mirrored Homotopic Connectivity (VMHC) was evaluated in a sample of 85 neurotypical individuals aged 7–18 years. VMHC associations with age, handedness, sex, and motion were explored at the voxel-wise level. VMHC correlates were also explored within 14 functional networks. Primary and secondary outcomes were repeated in a sample of 107 adults aged 21–50 years. In adults, VMHC was negatively correlated with age only in the posterior insula (false discovery rate p < .05, >30-voxel clusters), while a distributed effect among the medial axis was observed in minors. Four out of 14 considered networks showed significant negative correlations between VMHC and age in minors (basal ganglia r = –.280, p = .010; anterior salience r = –.245, p = .024; language r = –.222, p = .041; primary visual r = –.257, p = .017), but not adults. In minors, a positive effect of motion on VMHC was observed only in the putamen. Sex did not significantly influence age effects on VMHC. The current study showed a specific decrease in VMHC for minors as a function of age, but not adults, supporting the notion that interhemispheric interactions can shape late neurodevelopment.
... Children's cognition is especially more vulnerable to adverse experiences as they are still in a developmental phase (Brown et al., 2012;Woodburn et al., 2021). Although several action plans addressing financial, health and safety problems of refugee populations have been made (e.g., European Commission, 2016; UNICEF, 2019), there is no comprehensive action plan addressing the enhancement of cognitive development of refugee children (Brown et al., 2012;Mehnert et al., 2013;Woodburn et al., 2021). ...
... Children's cognition is especially more vulnerable to adverse experiences as they are still in a developmental phase (Brown et al., 2012;Woodburn et al., 2021). Although several action plans addressing financial, health and safety problems of refugee populations have been made (e.g., European Commission, 2016; UNICEF, 2019), there is no comprehensive action plan addressing the enhancement of cognitive development of refugee children (Brown et al., 2012;Mehnert et al., 2013;Woodburn et al., 2021). It is highly crucial that the cognitive needs of refugee children are addressed because early cognitive skills predict later life achievements (Blair & Razza, 2007;Sasser, Bierman, & Heinrichs, 2015), physical health (Batty, Deary, & Gottfredson, 2007;Miller, Barnes, & Beaver, 2011) and social adaptability (Fong & Iarocci, 2020;Gligorović & Buha Ðurović, 2014). ...
War trauma is often accompanied by poor living conditions in the new environment in a manner preserving or even deteriorating the negative influences of war. Several researchers have investigated the refugee experiences of displaced children. Often they have focused on the detrimental effects of war on psychological well-being, mental health, educational settings, social adaptation, quality of nutrition, financial difficulties, safety and language learning experiences. Each of these effects has been proven to negatively affect cognitive abilities; however, the current study reviews the key studies to reveal the cognitive and linguistic outcomes of holding refugee status in the early childhood period. Doing this, we aim to reveal the adverse conditions that affect refugee children’s three core abilities of executive functions, namely working memory, inhibitory control and shifting. In addition to cognitive outcomes, we present the factors that may affect these children’s mother tongue development and their experiences with the language spoken in the host country in the context of schooling. This study suggests that refugee children should be assessed for their cognitive and language abilities after arriving in the country of resettlement so that their needs can be identified and addressed effectively. Caretakers should also be given both psychological and financial support to enrich their children’s language and cognitive input. Also, the outcomes of the research in this field should be effectively shared with different stakeholders from the caregivers and teachers of the refugee children to the NGOs and policymakers responsible to take solid actions to counter the adverse effects of displacement.
... Structural covariance network analyses have been widely used in diseases, such as depression (Mak et al., 2016), multiple sclerosis (Fleischer et al., 2019a) and AD (Phillips et al., 2015), and physiological processes, such as maturation (Woodburn et al., 2021) and aging (Aboud et al., 2019). SCN provides a whole new approach to exploring the disruption and reorganization of complex brain networks. ...
... SCN provides a whole new approach to exploring the disruption and reorganization of complex brain networks. Moreover, several SCN studies found that the global efficiency increases as children grow and mature (Woodburn et al., 2021), and the degree of change/reorganization of the SCN is correlated with the severity of schizophrenia (Kim et al., 2020) and cognitive impairment in multiple sclerosis patients (Hawkins et al., 2020). Therefore, the SCN indices could be promising biomarkers in further studies. ...
Objective:
Ischemic moyamoya (MMD) disease could alter the cerebral structure, but little is known about the topological organization of the structural covariance network (SCN). This study employed structural magnetic resonance imaging and graph theory to evaluate SCN reorganization in ischemic MMD patients.
Method:
Forty-nine stroke-free ischemic MMD patients and 49 well-matched healthy controls (HCs) were examined by T1-MPRAGE imaging. Structural images were pre-processed using the Computational Anatomy Toolbox 12 (CAT 12) based on the diffeomorphic anatomical registration through exponentiated lie (DARTEL) algorithm and both the global and regional SCN parameters were calculated and compared using the Graph Analysis Toolbox (GAT).
Results:
Most of the important metrics of global network organization, including characteristic path length (Lp), clustering coefficient (Cp), assortativity, local efficiency, and transitivity, were significantly reduced in MMD patients compared with HCs. In addition, the regional betweenness centrality (BC) values of the bilateral medial orbitofrontal cortices were significantly lower in MMD patients than in HCs after false discovery rate (FDR) correction for multiple comparisons. The BC was also reduced in the left medial superior frontal gyrus and hippocampus, and increased in the bilateral middle cingulate gyri of patients, but these differences were not significant after FDR correlation. No differences in network resilience were detected by targeted attack analysis or random failure analysis.
Conclusions:
Both global and regional properties of the SCN are altered in MMD, even in the absence of major stroke or hemorrhagic damage. Patients exhibit a less optimal and more randomized SCN than HCs, and the nodal BC of the bilateral medial orbitofrontal cortices is severely reduced. These changes may account for the cognitive impairments in MMD patients.
... The interaction of brain regions at the network level may provide the necessary infrastructure for the development of cognitive processes. The work of Woodburn et al. (2021) describes the maturation of network separation and integration in the child's brain, suggesting that a specific trajectory of maturation of brain networks contributes to cognitive outcomes after growth. The research work described above has focused on studying the structural and functional connectivity of neurons or brain regions and has not examined the information processing mechanisms of memory at the scale of neuronal networks. ...
The brain, an exceedingly intricate information processing system, poses a constant challenge to memory research, particularly in comprehending how it encodes, stores, and retrieves information. Cognitive psychology studies memory mechanism from behavioral experiment level and fMRI level, and neurobiology studies memory mechanism from anatomy and electrophysiology level. Current research findings are insufficient to provide a comprehensive, detailed explanation of memory processes within the brain. Numerous unknown details must be addressed to establish a complete information processing mechanism connecting micro molecular cellular levels with macro cognitive behavioral levels. Key issues include characterizing and distributing content within biological neural networks, coexisting information with varying content, and sharing limited resources and storage capacity. Compared with the hard disk of computer mass storage, it is very clear from the polarity of magnetic particles in the bottom layer, the division of tracks and sectors in the middle layer, to the directory tree and file management system in the high layer, but the understanding of memory is not sufficient. Biological neural networks are abstracted as directed graphs, and the encoding, storage, and retrieval of information within directed graphs at the cellular level are explored. A memory computational model based on active directed graphs and node-adaptive learning is proposed. First, based on neuronal local perspectives, autonomous initiative, limited resource competition, and other neurobiological characteristics, a resource-based adaptive learning algorithm for directed graph nodes is designed. To minimize resource consumption of memory content in directed graphs, two resource-occupancy optimization strategies—lateral inhibition and path pruning—are proposed. Second, this paper introduces a novel memory mechanism grounded in graph theory, which considers connected subgraphs as the physical manifestation of memory content in directed graphs. The encoding, storage, consolidation, and retrieval of the brain's memory system correspond to specific operations such as forming subgraphs, accommodating multiple subgraphs, strengthening connections and connectivity of subgraphs, and activating subgraphs. Lastly, a series of experiments were designed to simulate cognitive processes and evaluate the performance of the directed graph model. Experimental results reveal that the proposed adaptive connectivity learning algorithm for directed graphs in this paper possesses the following four features: (1) Demonstrating distributed, self-organizing, and self-adaptive properties, the algorithm achieves global-level functions through local node interactions; (2) Enabling incremental storage and supporting continuous learning capabilities; (3) Displaying stable memory performance, it surpasses the Hopfield network in memory accuracy, capacity, and diversity, as demonstrated in experimental comparisons. Moreover, it maintains high memory performance with large-scale datasets; (4) Exhibiting a degree of generalization ability, the algorithm's macroscopic performance remains unaffected by the topological structure of the directed graph. Large-scale, decentralized, and node-autonomous directed graphs are suitable simulation methods. Examining storage problems within directed graphs can reveal the essence of phenomena and uncover fundamental storage rules hidden within complex neuronal mechanisms, such as synaptic plasticity, ion channels, neurotransmitters, and electrochemical activities.
... 3 . For more information, please refer to [36]. ...
Studies have shown that there is a tight connection between cognition skills and brain morphology during infancy. Nonetheless, it is still a great challenge to predict individual cognitive scores using their brain morphological features, considering issues like the excessive feature dimension, small sample size and missing data. Due to the limited data, a compact but expressive feature set is desirable as it can reduce the dimension and avoid the potential overfitting issue. Therefore, we pioneer the path signature method to further explore the essential hidden dynamic patterns of longitudinal cortical features. To form a hierarchical and more informative temporal representation, in this work, a novel cortical feature based path signature neural network (CF-PSNet) is proposed with stacked differentiable temporal path signature layers for prediction of individual cognitive scores. By introducing the existence embedding in path generation, we can improve the robustness against the missing data. Benefiting from the global temporal receptive field of CF-PSNet, characteristics consisted in the existing data can be fully leveraged. Further, as there is no need for the whole brain to work for a certain cognitive ability, a top
selection module is used to select the most influential brain regions, decreasing the model size and the risk of overfitting. Extensive experiments are conducted on an in-house longitudinal infant dataset within 9 time points. By comparing with several recent algorithms, we illustrate the state-of-the-art performance of our CF-PSNet (i.e., root mean square error of 0.027 with the time latency of 518 milliseconds for each sample).
... Based on the high spatial similarity between the VBM patterns found in the five patient groups with the GM covariation patterns and SCA matrices, the authors (13) concluded that the five neurodegenerative syndromes did not evolve randomly and affect brain areas independently from each other, but that they targeted syndromespecific networks closely resembling the system-level ICNs also found in healthy participants. Since the report of Seeley et al. (13), GM covariance analysis has not just been used to better understand the spreading of neurodegenerative disorders but also individual brain development over the lifespan (14)(15)(16)(17), neurological conditions such as autism (18,19), ADHD (20), traumatic brain injury (21), epilepsy (22), psychiatric disorders such as schizophrenia (23) and major depression (24). Furthermore, several meta-analyses including thousands of healthy control data sets have confirmed that the structural GM organization is recapitulating on a system-level the intrinsic functional organization of the brain with a high concordance of 64% (25) to 68% (26). ...
Substance use disorders (SUD) have been shown to be associated with gray matter (GM) loss, particularly in the frontal cortex. However, unclear is to what degree these regional GM alterations are substance-specific or shared across different substances, and if these regional GM alterations are independent of each other or the result of system-level processes at the intrinsic connectivity network level. The T1 weighted MRI data of 65 treated patients with alcohol use disorder (AUD), 27 patients with opioid use disorder (OUD) on maintenance therapy, 21 treated patients with stimulant use disorder comorbid with alcohol use disorder (polysubstance use disorder patients, PSU), and 21 healthy controls were examined via data-driven vertex-wise and voxel-wise GM analyses. Then, structural covariance analyses and open-access fMRI database analyses were used to map the cortical thinning patterns found in the three SUD groups onto intrinsic functional systems. Among AUD and OUD, we identified both common cortical thinning in right anterior brain regions as well as SUD-specific regional GM alterations that were not present in the PSU group. Furthermore, AUD patients had not only the most extended regional thinning but also significantly smaller subcortical structures and cerebellum relative to controls, OUD and PSU individuals. The system-level analyses revealed that AUD and OUD showed cortical thinning in several functional systems. In the AUD group the default mode network was clearly most affected, followed by the salience and executive control networks, whereas the salience and somatomotor network were highlighted as critical for understanding OUD. Structural brain alterations in groups with different SUDs are largely unique in their spatial extent and functional network correlates.
Background
Sudden sensorineural hearing loss (SSNHL) is associated with abnormal changes in the brain's central nervous system. Previous studies on the brain networks of SSNHL have primarily focused on functional connectivity within the brain. However, in addition to functional connectivity, structural connectivity also plays a crucial role in brain networks. Moreover, traditional functional connectivity analyses often overlook the spatial and temporal characteristics of connectivity changes and fail to provide directional information and causal relationships.
Aims
This study utilized Structural Covariance Network (SCN), multilayer network analysis, and Dynamic Causal Modeling (DCM) to investigate the cross‐scale changes in neural network structure and function in SSNHL patients with accompanying cognitive and emotional disorders.
Materials & Methods
We collected 3D‐T1 structural magnetic resonance image data and functional magnetic resonance image data from 70 SSNHL patients and 81 healthy controls (HCs). SCN analysis was performed based on gray matter volume, and multilayer network analysis was used to calculate node switching rates. Based on the results of multilayer network analysis, six nodes exhibiting significant inter‐group differences in node switching rates were selected as regions of interest (ROIs). DCM was then conducted to explore the causal relationships of functional connectivity between these nodes.
Results
Based on SCN, there were no significant inter‐group differences in global network properties between SSNHL and HCs. At the node level, the left precentral gyrus in SSNHL showed a significant decrease in node efficiency. In the multilayer network analysis, SSNHL showed a significantly increased node switching rate at the level of the Left Superior Frontal Gyrus (L.SFG), Left Supplementary Motor Area (L.SMA), Left Superior Parietal Gyrus (L.SPG), Right Superior Parietal Gyrus (R.SPG), Right Inferior Parietal Lobe(R.IPL), and Left Thalamus (L.THA). Furthermore, the node switching rate of L.SFG showed a significant negative correlation with the Self‐Rating Anxiety Scale (SAS) scores. DCM analysis of these six nodes revealed differences in the functional effective connectivity between the left superior parietal gyrus (L.SPG) and the left supplementary motor area (L.SMA), which were positively correlated with the AVLT‐delay scores.
Discussion
These findings suggest that SSNHL patients experience structural and functional remodeling of the cerebral cortex, with hearing loss leading to the reallocation of cognitive resources.
Conclusion
This provides new insights into understanding the potential mechanisms between cross‐scale networks and cognitive‐emotional disorders in SSNHL.
The development of cerebral cortex during the fetal period is complex yet well-coordinated. MRI-based morphological brain network provides a powerful tool for describing this process at a network level. Due to the challenges of in-utero MRI acquisition and image processing, the fetal brain morphological network has not been established. In this study, utilizing high-resolution in-utero MRI image, we constructed individual morphometric similarity network for each fetus based on multiple cortical features and characterized the spatiotemporal changes of morphological connections and the network topology. Edge analysis demonstrated a decline of morphological symmetry between hemispheres, especially for the parietal cortex. The limbic and parieto-occipital regions exhibited the most drastic changes of network connections. Graph theoretical analysis indicated that the small-world structure of the network appeared as early as 22 weeks and that the network topology exhibited an enhanced integration and reduced segregation during the prenatal period. In summary, this study provides an important evidence for understanding the normal development of fetal brain connectome during second-third trimester.
Human milk contains all of the essential nutrients required by the infant within a complex matrix that enhances the bioavailability of many of those nutrients. In addition, human milk is a source of bioactive components, living cells and microbes that facilitate the transition to life outside the womb. Our ability to fully appreciate the importance of this matrix relies on the recognition of short- and long-term health benefits and, as highlighted in previous sections of this supplement, its ecology (i.e., interactions among the lactating parent and breastfed infant as well as within the context of the human milk matrix itself). Designing and interpreting studies to address this complexity depends on the availability of new tools and technologies that account for such complexity. Past efforts have often compared human milk to infant formula, which has provided some insight into the bioactivity of human milk, as a whole, or of individual milk components supplemented with formula. However, this experimental approach cannot capture the contributions of the individual components to the human milk ecology, the interaction between these components within the human milk matrix, or the significance of the matrix itself to enhance human milk bioactivity on outcomes of interest. This paper presents approaches to explore human milk as a biological system and the functional implications of that system and its components. Specifically, we discuss study design and data collection considerations and how emerging analytical technologies, bioinformatics, and systems biology approaches could be applied to advance our understanding of this critical aspect of human biology.
Hippocampal-cortical networks play an important role in neurocognitive development. Applying the method of Connectivity-Based Parcellation (CBP) on hippocampal-cortical structural covariance (SC) networks computed from T1-weighted magnetic resonance images, we examined how the hippocampus differentiates into subregions during childhood and adolescence (N=1105, 6-18 years). In late childhood, the hippocampus mainly differentiated along the anterior-posterior axis similar to previous reported functional differentiation patterns of the hippocampus. In contrast, in adolescence a differentiation along the medial-lateral axis was evident, reminiscent of the cytoarchitectonic division into cornu ammonis and subiculum. Further meta-analytical characterization of hippocampal subregions in terms of related structural co-maturation networks, behavioural and gene profiling suggested that the hippocampal head is related to higher order functions (e.g. language, theory of mind, autobiographical memory) in late childhood morphologically co-varying with almost the whole brain. In early adolescence but not in childhood, posterior subicular SC networks were associated with action-oriented and reward systems. The findings point to late childhood as an important developmental period for hippocampal head morphology and to early adolescence as a crucial period for hippocampal integration into action- and reward-oriented cognition. The latter may constitute a developmental feature that conveys increased propensity for addictive disorders.
Background
Gestational diabetes (GD) and maternal excess weight are common pregnancy conditions which increase the risk of future complications for both the mother and her offspring. Their consequences on neurodevelopment are widely described in the literature, but less is known concerning the potential transgenerational influence on the brain structure.
Methods
We used a combination of support vectors machine and hierarchical clustering to investigate the potential presence of anatomical brain differences in a sample of 109 children aged 6 years, born to mothers with overweight or obesity, or to mothers diagnosed of GD during pregnancy.
Results
Significant effects are visible in the brain of children born to mothers with GD associating pre-gestational excess weight, and especially in case of overweight instead of obesity. No differences in children’s brain were observed when considering those born to normal-weight mothers.
Conclusions
Our study highlights the need for clinical attention of pregnant women at risk to develop GD, and especially those with pre-gestational excess weight, since this status was found to be associated with detectable transgenerational brain changes. These effects may be due to the absence of specific and individualized intervention in these mothers during pregnancy.
Complex human cognition arises from the integrated processing of multiple brain systems. However, little is known about how brain systems and their interactions might relate to, or perhaps even explain, human cognitive capacities. Here, we address this gap in knowledge by proposing a mechanistic framework linking frontoparietal system activity, default mode system activity, and the interactions between them, with individual differences in working memory capacity. We show that working memory performance depends on the strength of functional interactions between the frontoparietal and default mode systems. We find that this strength is modulated by the activation of two newly described brain regions, and demonstrate that the functional role of these systems is underpinned by structural white matter. Broadly, our study presents a holistic account of how regional activity, functional connections, and structural linkages together support integrative processing across brain systems in order for the brain to execute a complex cognitive process.
Large-scale functional connectome formation and reorganization is apparent in the second trimester of pregnancy, making it a crucial and vulnerable time window in connectome development. Here we identified which architectural principles of functional connectome organization are initiated before birth, and contrast those with topological characteristics observed in the mature adult brain. A sample of 105 pregnant women participated in human fetal resting-state fMRI studies (fetal gestational age between 20 and 40 weeks). Connectome analysis was used to analyze weighted network characteristics of fetal macroscale brain wiring. We identified efficient network attributes, common functional modules, and high overlap between the fetal and adult brain network. Our results indicate that key features of the functional connectome are present in the second and third trimesters of pregnancy. Understanding the organizational principles of fetal connectome organization may bring opportunities to develop markers for early detection of alterations of brain function.
SIGNIFICANCE STATEMENT The fetal to neonatal period is well known as a critical stage in brain development. Rapid neurodevelopmental processes establish key functional neural circuits of the human brain. Prenatal risk factors may interfere with early trajectories of connectome formation and thereby shape future health outcomes. Recent advances in MRI have made it possible to examine fetal brain functional connectivity. In this study, we evaluate the network topography of normative functional network development during connectome genesis in utero . Understanding the developmental trajectory of brain connectivity provides a basis for understanding how the prenatal period shapes future brain function and disease dysfunction.
Gifted children learn more rapidly and effectively than others, presumably due to neurophysiological differences that affect efficiency in neuronal communication. Identifying the topological features that support its capabilities is relevant to understanding how the brain structure is related to intelligence. We proposed the analysis of the structural covariance network to assess which organizational patterns are characteristic of gifted children. The graph theory was used to analyse topological properties of structural covariance across a group of gifted children. The analysis was focused on measures of brain network integration, such as, participation coefficient and versatility, which quantifies the strength of specific modular affiliation of each regional node. We found that the gifted group network was more integrated (and less segregated) than the control group network. Brain regional nodes in the gifted group network had higher versatility and participation coefficient, indicating greater inter-modular communication mediated by connector hubs with links to many modules. Connector hubs of the networks of both groups were located mainly in association with neocortical areas (which had thicker cortex), with fewer hubs in primary or secondary neocortical areas (which had thinner cortex), as well as a few connector hubs in limbic cortex and insula. In the group of gifted children, a larger proportion of connector hubs were located in association cortex. In conclusion, gifted children have a more integrated and versatile brain network topology. This is compatible with the global workspace theory and other data linking integrative network topology to cognitive performance.
4D (spatial + temporal) infant cortical surface atlases covering dense time points are highly needed for understanding dynamic early brain development. In this article, we construct a set of 4D infant cortical surface atlases with longitudinally consistent and sharp cortical attribute patterns at 11 time points in the first six postnatal years, that is, at 1, 3, 6, 9, 12, 18, 24, 36, 48, 60, and 72 months of age, which is targeted for better normalization of the dynamic changing early brain cortical surfaces. To ensure longitudinal consistency and unbiasedness, we adopt a two‐stage group‐wise surface registration. To preserve sharp cortical attribute patterns on the atlas, instead of simply averaging over the coregistered cortical surfaces, we leverage a spherical patch‐based sparse representation using the augmented dictionary to overcome the potential registration errors. Our atlases provide not only geometric attributes of the cortical folding, but also cortical thickness and myelin content. Therefore, to address the consistency across different cortical attributes on the atlas, instead of sparsely representing each attribute independently, we jointly represent all cortical attributes with a group‐wise sparsity constraint. In addition, to further facilitate region‐based analysis using our atlases, we have also provided two widely used parcellations, that is, FreeSurfer parcellation and multimodal parcellation, on our 4D infant cortical surface atlases. Compared to cortical surface atlases constructed with other methods, our cortical surface atlases preserve sharper cortical folding attribute patterns, thus leading to better accuracy in registration of individual infant cortical surfaces to the atlas.
In the past decade, resting-state functional magnetic resonance imaging (rs-fMRI) and graph-based measures have been widely used to quantitatively characterize the architectures of brain functional networks in healthy individuals and in patients with abnormalities related to psychopathic and neurological disorders. To accurately evaluate the topological organization of brain functional networks, the definition of the nodes and edges for the construction of functional networks is critical. Furthermore, both types of brain functional networks (binarized networks and weighted networks) are widely used to analyze topological organization. However, how to best select the network type is still debated. Consequently, we investigated the test-retest reliability of brain functional networks with binarized and weighted edges using two independent datasets and four strategies for defining nodes. We revealed fair to good reliability for a majority of network topological attributes and overall higher reliabilities for weighted networks than for binarized networks. For regional nodal efficiency, weighted networks also showed higher reliability across nodes. Thus, our findings imply that weighted networks contain more information, leading to more stable results. In addition, we found that the reliability of brain functional networks was influenced by the node definition strategy and that more precise of nodal definition were associated with higher reliability. The time effect of reliability was restricted, as no differences between long-term and short-term reliability were observed. In conclusion, our results suggest that weighted networks have better reliability because they reflect more topological information, implying broader applications of weighted networks related to normal and disordered function of the human brain.
Cortical morphology is known to differ with age, as measured by cortical thickness, fractal dimensionality, and gyrification. However, head motion during MRI scanning has been shown to influence estimates of cortical thickness as well as increase with age. Studies have also found task-related differences in head motion and relationships between body–mass index (BMI) and head motion. Here I replicated these prior findings, as well as several others, within a large, open-access dataset (Centre for Ageing and Neuroscience, CamCAN). This is a larger dataset than these results have been demonstrated previously, within a sample size of more than 600 adults across the adult lifespan. While replicating prior findings is important, demonstrating these key findings concurrently also provides an opportunity for additional related analyses: critically, I test for the influence of head motion on cortical fractal dimensionality and gyrification; effects were statistically significant in some cases, but small in magnitude.
In humans, the period from term birth to ∼2 years of age is characterized by rapid and dynamic brain development and plays an important role in cognitive development and risk of disorders such as autism and schizophrenia. Recent imaging studies have begun to delineate the growth trajectories of brain structure and function in the first years after birth and their relationship to cognition and risk of neuropsychiatric disorders. This Review discusses the development of grey and white matter and structural and functional networks, as well as genetic and environmental influences on early-childhood brain development. We also discuss initial evidence regarding the usefulness of early imaging biomarkers for predicting cognitive outcomes and risk of neuropsychiatric disorders.
Infant gross motor development is vital to adaptive function and predictive of both cognitive outcomes and neurodevelopmental disorders. However, little is known about neural systems underlying the emergence of walking and general gross motor abilities. Using resting state fcMRI, we identified functional brain networks associated with walking and gross motor scores in a mixed cross-sectional and longitudinal cohort of infants at high and low risk for autism spectrum disorder, who represent a dimensionally distributed range of motor function. At age 12 months, functional connectivity of motor and default mode networks was correlated with walking, whereas dorsal attention and posterior cingulo-opercular networks were implicated at age 24 months. Analyses of general gross motor function also revealed involvement of motor and default mode networks at 12 and 24 months, with dorsal attention, cingulo-opercular, frontoparietal, and subcortical networks additionally implicated at 24 months. These findings suggest that changes in network-level brain-behavior relationships underlie the emergence and consolidation of walking and gross motor abilities in the toddler period. This initial description of network substrates of early gross motor development may inform hypotheses regarding neural systems contributing to typical and atypical motor outcomes, as well as neurodevelopmental disorders associated with motor dysfunction.
Structural covariance has recently emerged as a tool to study brain connectivity in health and disease. The main assumption behind the phenomenon of structural covariance is that changes in brain structure during development occur in a coordinated fashion. However, no study has yet explored the correlation of structural brain changes within individuals across development. Here, we used longitudinal magnetic resonance imaging scans from 141 normally developing children and adolescents (scanned 3 times) to introduce a novel subject-based maturational coupling approach. For each subject, maturational coupling was defined as similarity in the trajectory of cortical thickness (across the time points) between any two cortical regions. Our approach largely captured features seen in population-based structural covariance, and confirmed strong maturational coupling between homologous and near-neighbour cortical regions. Stronger maturational coupling among several homologous regions was observed for females compared to males, possibly indicating greater interhemispheric connectivity in females. Developmental changes in maturational coupling within the default-mode network (DMN) aligned with developmental changes in structural and functional DMN connectivity. Our findings indicate that patterns of maturational coupling within individuals may provide mechanistic explanation for the phenomenon of structural covariance, and allow investigation of individual brain variability with respect to cognition and disease vulnerability.
Significance statement:
The dynamic nature of the human brain gives rise to the wide range of behaviors and cognition of which humans are capable. We collected fMRI data from healthy young adults and measured large-scale functional connectivity patterns between regions distributed across the entire brain. We implemented graph theoretical analyses to quantify network organization during two tasks hypothesized to require different combinations of brain networks. During motor execution, segregation of distinct networks increased. Conversely, during working memory, integration across networks increased. These changes in network organization were related to better behavioral performance. These results underscore the human brain's ability to reconfigure network organization selectively and adaptively when confronted with changing cognitive demands to achieve an optimal balance between segregation and integration.
Verbal and non-verbal intelligence in children is highly correlated, and thus, it has been difficult to differentiate their neural substrates. Nevertheless, recent studies have shown that verbal and non-verbal intelligence can be dissociated and focal cortical regions corresponding to each have been demonstrated. However, the pattern of structural covariance corresponding to verbal and non-verbal intelligence remains unexplored. In this study, we used 586 longitudinal anatomical MRI scans of subjects aged 6-18 years, who had concurrent intelligence quotient (IQ) testing on the Wechsler Abbreviated Scale of Intelligence. Structural covariance networks (SCNs) were constructed using interregional correlations in cortical thickness for low-IQ (Performance IQ=100±8, Verbal IQ=100±7) and high-IQ (PIQ=121±8, VIQ=120±9) groups. From low- to high-VIQ group, we observed constrained patterns of anatomical coupling among cortical regions, complemented by observations of higher global efficiency and modularity, and lower local efficiency in high-VIQ group, suggesting a shift towards a more optimal topological organization. Analysis of nodal topological properties (regional efficiency and participation coefficient) revealed greater involvement of left-hemispheric language related regions including inferior frontal and superior temporal gyri for high-VIQ group. From low- to high-PIQ group, we did not observe significant differences in anatomical coupling patterns, global and nodal topological properties. Our findings indicate that people with higher verbal intelligence have structural brain differences from people with lower verbal intelligence - not only in localized cortical regions, but also in the patterns of anatomical coupling among widely distributed cortical regions, possibly resulting to a system-level reorganization that might lead to a more efficient organization in high-VIQ group.
Brain structural covariance networks (SCNs) composed of regions with correlated variation are altered in neuropsychiatric disease and change with age. Little is known about the development of SCNs in early childhood, a period of rapid cortical growth. We investigated the development of structural and maturational covariance networks, including default, dorsal attention, primary visual and sensorimotor networks in a longitudinal population of 118 children after birth to 2 years old and compared them with intrinsic functional connectivity networks. We found that structural covariance of all networks exhibit strong correlations mostly limited to their seed regions. By Age 2, default and dorsal attention structural networks are much less distributed compared with their functional maps. The maturational covariance maps, however, revealed significant couplings in rates of change between distributed regions, which partially recapitulate their functional networks. The structural and maturational covariance of the primary visual and sensorimotor networks shows similar patterns to the corresponding functional networks. Results indicate that functional networks are in place prior to structural networks, that correlated structural patterns in adult may arise in part from coordinated cortical maturation, and that regional co-activation in functional networks may guide and refine the maturation of SCNs over childhood development.
Objective, early, and non-invasive assessment of brain function in high-risk newborns is critical to initiate timely interventions and to minimize long-term neurodevelopmental disabilities. A prerequisite to identifying deviations from normal, however, is the availability of baseline measures of brain function derived from healthy, full-term newborns. Recent advances in functional MRI combined with graph theoretic techniques may provide important, currently unavailable, quantitative markers of normal neurodevelopment. In the current study, we describe important properties of resting state networks in 60 healthy, full-term, unsedated newborns. The neonate brain exhibited an efficient and economical small world topology: densely connected nearby regions, sparse, but well integrated, distant connections, a small world index greater than 1, and global/local efficiency greater than network cost. These networks showed a heavy-tailed degree distribution, suggesting the presence of regions that are more richly connected to others ('hubs'). These hubs, identified using degree and betweenness centrality measures, show a more mature hub organization than previously reported. Targeted attacks on hubs show that neonate networks are more resilient than simulated scale-free networks. Networks fragmented faster and global efficiency decreased faster when betweenness, as opposed to degree, hubs were attacked suggesting a more influential role of betweenness hub in the neonate network.
The mature brain is organized into distinct neural networks defined by regions demonstrating correlated activity during task performance as well as rest. While research has begun to examine differences in these networks between children and adults, little is known about developmental changes during early adolescence. Using functional magnetic resonance imaging (fMRI), we examined the Default Mode Network (DMN) and the Central Executive Network (CEN) at ages 10 and 13 in a longitudinal sample of 45 participants. In the DMN, participants showed increasing integration (i.e., stronger within-network correlations) between the posterior cingulate cortex (PCC) and the medial prefrontal cortex. During this time frame participants also showed increased segregation (i.e., weaker between-network correlations) between the PCC and the CEN. Similarly, from age 10 to 13, participants showed increased connectivity between the dorsolateral prefrontal cortex and other CEN nodes, as well as increasing DMN segregation. IQ was significantly positively related to CEN integration at age 10, and between-network segregation at both ages. These findings highlight early adolescence as a period of significant maturation for the brain's functional architecture and demonstrate the utility of longitudinal designs to investigate neural network development.
Although commonly viewed as a sensory information relay center, the thalamus has been increasingly recognized as an essential node in various higher-order cognitive circuits, and the underlying thalamocortical interaction mechanism has attracted increasing scientific interest. However, the development of thalamocortical connections and how such development relates to cognitive processes during the earliest stages of life remain largely unknown. Leveraging a large human pediatric sample (N = 143) with longitudinal resting-state fMRI scans and cognitive data collected during the first 2 years of life, we aimed to characterize the age-dependent development of thalamocortical connectivity patterns by examining the functional relationship between the thalamus and nine cortical functional networks and determine the correlation between thalamocortical connectivity and cognitive performance at ages 1 and 2 years. Our results revealed that the thalamus-sensorimotor and thalamus-salience connectivity networks were already present in neonates, whereas the thalamus-medial visual and thalamus-default mode network connectivity emerged later, at 1 year of age. More importantly, brain-behavior analyses based on the Mullen Early Learning Composite Score and visual-spatial working memory performance measured at 1 and 2 years of age highlighted significant correlations with the thalamus-salience network connectivity. These results provide new insights into the understudied early functional brain development process and shed light on the behavioral importance of the emerging thalamocortical connectivity during infancy.
The human connectome is the result of an elaborate developmental trajectory. Acquiring diffusion-weighted imaging and resting-state
fMRI, we studied connectome formation during the preterm phase of macroscopic connectome genesis. In total, 27 neonates were
scanned at week 30 and/or week 40 gestational age (GA). Examining the architecture of the neonatal anatomical brain network
revealed a clear presence of a small-world modular organization before term birth. Analysis of neonatal functional connectivity
(FC) showed the early formation of resting-state networks, suggesting that functional networks are present in the preterm
brain, albeit being in an immature state. Moreover, structural and FC patterns of the neonatal brain network showed strong
overlap with connectome architecture of the adult brain (85 and 81%, respectively). Analysis of brain development between
week 30 and week 40 GA revealed clear developmental effects in neonatal connectome architecture, including a significant increase
in white matter microstructure (P < 0.01), small-world topology (P < 0.01) and interhemispheric FC (P < 0.01). Computational analysis further showed that developmental changes involved an increase in integration capacity of
the connectivity network as a whole. Taken together, we conclude that hallmark organizational structures of the human connectome
are present before term birth and subject to early development.
The first postnatal year is characterized by the most dramatic functional network development of the human lifespan. Yet,
the relative sequence of the maturation of different networks and the impact of socioeconomic status (SES) on their development
during this critical period remains poorly characterized. Leveraging a large, normally developing infant sample with multiple
longitudinal resting-state functional magnetic resonance imaging scans during the first year (N = 65, scanned every 3 months), we aimed to delineate the relative maturation sequence of 9 key brain functional networks
and examine their SES correlations. Our results revealed a maturation sequence from primary sensorimotor/auditory to visual
to attention/default-mode, and finally to executive control networks. Network-specific critical growth periods were also identified.
Finally, marginally significant positive SES–brain correlations were observed at 6 months of age for both the sensorimotor
and default-mode networks, indicating interesting SES effects on functional brain maturation. To the best of our knowledge,
this is the first study delineating detailed longitudinal growth trajectories of all major functional networks during the
first year of life and their SES correlations. Insights from this study not only improve our understanding of early brain
development, but may also inform the critical periods for SES expression during infancy.
Distributed networks of brain areas interact with one another in a
time-varying fashion to enable complex cognitive and sensorimotor functions.
Here we use novel network analysis algorithms to test the recruitment and
integration of large-scale functional neural circuitry during learning. Using
functional magnetic resonance imaging (fMRI) data acquired from healthy human
participants, from initial training through mastery of a simple motor skill, we
investigate changes in the architecture of functional connectivity patterns
that promote learning. Our results reveal that learning induces an autonomy of
sensorimotor systems and that the release of cognitive control hubs in frontal
and cingulate cortices predicts individual differences in the rate of learning
on other days of practice. Our general statistical approach is applicable
across other cognitive domains and provides a key to understanding
time-resolved interactions between distributed neural circuits that enable task
performance.
Although there is now substantial evidence that developmental change occurs in implicit learning abilities over the lifespan, disparate results exist regarding the specific developmental trajectory of implicit learning skills. One possible reason for discrepancies across implicit learning studies may be that younger children show an increased sensitivity to variations in implicit learning task procedures and demands relative to adults. Studies using serial-reaction time (SRT) tasks have suggested that in adults, measurements of implicit learning are robust across variations in task procedures. Most classic SRT tasks have used response-contingent pacing in which the participant's own reaction time determines the duration of each trial. However, recent paradigms with adults and children have used fixed trial pacing, which leads to alterations in both response and attention demands, accuracy feedback, perceived agency, and task motivation for participants. In the current study, we compared learning on fixed-paced and self-paced versions of a spatial sequence learning paradigm in 4-year-old children and adults. Results indicated that preschool-aged children showed reduced evidence of implicit sequence learning in comparison to adults, regardless of the SRT paradigm used. In addition, we found the preschoolers showed significantly greater learning when stimulus presentation was self-paced. These data provide evidence for developmental differences in implicit sequence learning that are dependent on specific task demands such as stimulus pacing, which may be related to developmental changes in the impact of broader constructs such as attention and task motivation on implicit learning.
During human brain development through infancy and childhood, microstructural and macrostructural changes take place to reshape the brain's structural networks and better adapt them to sophisticated functional and cognitive requirements. However, structural topological configuration of the human brain during this specific development period is not well understood. In this study, diffusion magnetic resonance image (dMRI) of 25 neonates, 13 toddlers, and 25 preadolescents were acquired to characterize network dynamics at these 3 landmark cross-sectional ages during early childhood. dMRI tractography was used to construct human brain structural networks, and the underlying topological properties were quantified by graph-theory approaches. Modular organization and small-world attributes are evident at birth with several important topological metrics increasing monotonically during development. Most significant increases of regional nodes occur in the posterior cingulate cortex, which plays a pivotal role in the functional default mode network. Positive correlations exist between nodal efficiencies and fractional anisotropy of the white matter traced from these nodes, while correlation slopes vary among the brain regions. These results reveal substantial topological reorganization of human brain structural networks through infancy and childhood, which is likely to be the outcome of both heterogeneous strengthening of the major white matter tracts and pruning of other axonal fibers.
Etiological studies of many neurological and psychiatric disorders are increasingly turning toward longitudinal investigations of infant brain development in order to discern predisposing structural and/or functional differences prior to the onset of overt clinical symptoms. While MRI provides a noninvasive window into the developing brain, MRI of infants and toddlers is challenging due to the modality's extreme motion sensitivity and children's difficulty in remaining still during image acquisition.
Here, we outline a broad research protocol for successful MRI of children under 4 years of age during natural, non-sedated sleep.
All children were imaged during natural, non-sedated sleep. Active and passive measures to reduce acoustic noise were implemented to reduce the likelihood of the children waking up during acquisition. Foam cushions and vacuum immobilizers were used to limit intra-scan motion artifacts.
More than 380 MRI datasets have been successfully acquired from 220 children younger than 4 years of age within the past 39 months. Implemented measures permitted children to remain asleep for the duration of the scan and allowed the data to be acquired with an overall 97% success rate.
The proposed method greatly advances current pediatric imaging techniques and may be readily implemented in other research and clinical settings to facilitate and further improve pediatric neuroimaging.
Brain structure varies between people in a markedly organized fashion. Communities of brain regions co-vary in their morphological properties. For example, cortical thickness in one region influences the thickness of structurally and functionally connected regions. Such networks of structural co-variance partially recapitulate the functional networks of healthy individuals and the foci of grey matter loss in neurodegenerative disease. This architecture is genetically heritable, is associated with behavioural and cognitive abilities and is changed systematically across the lifespan. The biological meaning of this structural co-variance remains controversial, but it appears to reflect developmental coordination or synchronized maturation between areas of the brain. This Review discusses the state of current research into brain structural co-variance, its underlying mechanisms and its potential value in the understanding of various neurological and psychiatric conditions.
Large-scale covariance of cortical thickness or volume in distributed brain regions has been consistently reported by human neuroimaging studies. The mechanism of this population covariance of regional cortical anatomy has been hypothetically related to synchronized maturational changes in anatomically connected neuronal populations. Brain regions that grow together, i.e., increase or decrease in volume at the same rate over the course of years in the same individual, are thus expected to demonstrate strong structural covariance or anatomical connectivity across individuals. To test this prediction, we used a structural MRI dataset on healthy young people (N = 108; aged 9-22 years at enrollment), comprising 3-6 longitudinal scans on each participant over 6-12 years of follow-up. At each of 360 regional nodes, and for each participant, we estimated the following: (1) the cortical thickness in the median scan and (2) the linear rate of change in cortical thickness over years of serial scanning. We constructed structural and maturational association matrices and networks from these measurements. Both structural and maturational networks shared similar global and nodal topological properties, as well as mesoscopic features including a modular community structure, a relatively small number of highly connected hub regions, and a bias toward short distance connections. Using resting-state functional magnetic resonance imaging data on a subset of the sample (N = 32), we also demonstrated that functional connectivity and network organization was somewhat predictable by structural/maturational networks but demonstrated a stronger bias toward short distance connections and greater topological segregation. Brain structural covariance networks are likely to reflect synchronized developmental change in distributed cortical regions.
Recent studies have demonstrated developmental changes of functional brain networks derived from functional connectivity using graph theoretical analysis, which has been rapidly translated to studies of brain network organization. However, little is known about sex- and IQ-related differences in the topological organization of functional brain networks during development. In this study, resting-state fMRI (rs-fMRI) was used to map the functional brain networks in 51 healthy children. We then investigated the effects of age, sex, and IQ on economic small-world properties and regional nodal properties of the functional brain networks. At a global level of whole networks, we found significant age-related increases in the small-worldness and local efficiency, significant higher values of the global efficiency in boys compared with girls, and no significant IQ-related difference. Age-related increases in the regional nodal properties were found predominately in the frontal brain regions, whereas the parietal, temporal, and occipital brain regions showed age-related decreases. Significant sex-related differences in the regional nodal properties were found in various brain regions, primarily related to the default mode, language, and vision systems. Positive correlations between IQ and the regional nodal properties were found in several brain regions related to the attention system, whereas negative correlations were found in various brain regions primarily involved in the default mode, emotion, and language systems. Together, our findings of the network topology of the functional brain networks in healthy children and its relationship with age, sex, and IQ bring new insights into the understanding of brain maturation and cognitive development during childhood and adolescence.
Recent findings from developmental neuroimaging studies suggest that the enhancement of cognitive processes during development may be the result of a fine-tuning of the structural and functional organization of brain with maturation. However, the details regarding the developmental trajectory of large-scale structural brain networks are not yet understood. Here, we used graph theory to examine developmental changes in the organization of structural brain networks in 203 normally growing children and adolescents. Structural brain networks were constructed using interregional correlations in cortical thickness for 4 age groups (early childhood: 4.8-8.4 year; late childhood: 8.5-11.3 year; early adolescence: 11.4-14.7 year; late adolescence: 14.8-18.3 year). Late childhood showed prominent changes in topological properties, specifically a significant reduction in local efficiency, modularity, and increased global efficiency, suggesting a shift of topological organization toward a more random configuration. An increase in number and span of distribution of connector hubs was found in this age group. Finally, inter-regional connectivity analysis and graph-theoretic measures indicated early maturation of primary sensorimotor regions and protracted development of higher order association and paralimbic regions. Our finding reveals a time window of plasticity occurring during late childhood which may accommodate crucial changes during puberty and the new developmental tasks that an adolescent faces.
Cortical structure has been consistently related to cognitive abilities in children and adults, yet we know little about how the cortex develops to support emergent cognition in infancy and toddlerhood when cortical thickness (CT) and surface area (SA) are maturing rapidly. In this report, we assessed how regional and global measures of CT and SA in a sample (N = 487) of healthy neonates, 1-year-olds, and 2-year-olds related to motor, language, visual reception, and general cognitive ability. We report novel findings that thicker cortices at ages 1 and 2 and larger SA at birth, age 1, and age 2 confer a cognitive advantage in infancy and toddlerhood. While several expected brain-cognition relationships were observed, overlapping cortical regions were also implicated across cognitive domains, suggesting that infancy marks a period of plasticity and refinement in cortical structure to support burgeoning motor, language, and cognitive abilities. CT may be a particularly important morphological indicator of ability, but its impact on cognition is relatively weak when compared with gestational age and maternal education. Findings suggest that prenatal and early postnatal cortical developments are important for cognition in infants and toddlers but should be considered in relation to other child and demographic factors.
Human brain structure topography is thought to be related in part to functional specialization. However, the extent of such relationships is unclear. Here, using a data-driven, multimodal approach for studying brain structure across the lifespan (N = 484, n = 260 females), we demonstrate that numerous structural networks, covering the entire brain, follow a functionally meaningful architecture. These gray matter networks (GMNs) emerge from the covariation of gray matter volume and cortical area at the population level. We further reveal fine-grained anatomical signatures of functional connectivity. For example, within the cerebellum, a structural separation emerges between lobules that are functionally connected to distinct, mainly sensorimotor, cognitive and limbic regions of the cerebral cortex and subcortex. Structural modes of variation also replicate the fine-grained functional architecture seen in eight well defined visual areas in both task and resting-state fMRI. Furthermore, our study shows a structural distinction corresponding to the established segregation between anterior and posterior default-mode networks (DMNs). These fine-grained GMNs further cluster together to form functionally meaningful larger-scale organization. In particular, we identify a structural architecture bringing together the functional posterior DMN and its anticorrelated counterpart. In summary, our results demonstrate that the relationship between structural and functional connectivity is fine-grained, widespread across the entire brain, and driven by covariation in cortical area, i.e. likely differences in shape, depth, or number of foldings. These results suggest that neurotrophic events occur during development to dictate that the size and folding pattern of distant, functionally connected brain regions should vary together across subjects.SIGNIFICANCE STATEMENT Questions about the relationship between structure and function in the human brain have engaged neuroscientists for centuries in a debate that continues to this day. Here, by investigating intersubject variation in brain structure across a large number of individuals, we reveal modes of structural variation that map onto fine-grained functional organization across the entire brain, and specifically in the cerebellum, visual areas, and default-mode network. This functionally meaningful structural architecture emerges from the covariation of gray matter volume and cortical folding. These results suggest that the neurotrophic events at play during development, and possibly evolution, which dictate that the size and folding pattern of distant brain regions should vary together across subjects, might also play a role in functional cortical specialization.
The human brain undergoes explosive growth during the prenatal period and the first few postnatal years, establishing an early infrastructure for the later development of behaviors and cognitions. Revealing the developmental rules during the early phrase is essential in understanding the emergence of brain function and the origin of developmental disorders. The graph-theoretical network modeling in combination with multiple neuroimaging probes provides an important research framework to explore early development of the topological wiring and organizational paradigms of the brain. Here, we reviewed studies which employed neuroimaging and graph-theoretical modeling to investigate brain network development from approximately 20 gestational weeks to 2 years of age. Specifically, the structural and functional brain networks have evolved to highly efficient topological architectures in the early stage; where the structural network remains ahead and paves the way for the development of functional network. The brain network develops in a heterogeneous order, from primary to higher-order systems and from a tendency of network segregation to network integration in the prenatal and postnatal periods. The early brain network topologies show abilities in predicting certain cognitive and behavior performance in later life, and their impairments are likely to continue into childhood and even adulthood. These macroscopic topological changes are found to be associated with possible microstructural maturations, such as axonal growth and myelinations. Collectively, this review provides a detailed delineation of the early changes of the baby brains in the graph-theoretical modeling framework, which opens up a new avenue to understand the developmental principles of the connectome.
Human brain networks based on neuroimaging data have already proven useful in characterising both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders which first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder and we conclude the review with some suggestions for future directions.
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.
Human brain networks based on neuroimaging data have already proven useful in characterising both normal and abnormal brain structure and function. However, many brain disorders are neurodevelopmental in origin, highlighting the need to go beyond characterizing brain organization in terms of static networks. Here we review the fast-growing literature shedding light on developmental changes in network phenotypes. We begin with an overview of recent large-scale efforts to map healthy brain development, and we describe the key role played by longitudinal data including repeated measurements over a long period of follow-up. We also discuss the subtle ways in which healthy brain network development can inform our understanding of disorders, including work bridging the gap between macroscopic neuroimaging results and the microscopic level. Finally, we turn to studies of three specific neurodevelopmental disorders which first manifest primarily in childhood and adolescence/early adulthood, namely psychotic disorders, attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In each case we discuss recent progress in understanding the atypical features of brain network development associated with the disorder and we conclude the review with some suggestions for future directions.
Throughout infancy, childhood, and adolescence, our brains undergo remarkable changes. Processes including myelination and synaptogenesis occur rapidly across the first 2-3 years of life, and ongoing brain remodeling continues into young adulthood. Studies have sought to characterize the patterns of structural brain development, and early studies predominately relied upon gross anatomical measures of brain structure, morphology, and organization. MRI offers the ability to characterize and quantify a range of microstructural aspects of brain tissue that may be more closely related to fundamental neurodevelopmental processes. Techniques such as diffusion, magnetization transfer, relaxometry, and myelin water imaging provide insight into changing cyto- and myeloarchitecture, neuronal density, and structural connectivity. In this review, we focus on the growing body of literature exploiting these MRI techniques to better understand the microstructural changes that occur in brain white matter during maturation. Our review focuses on studies of normative brain development from birth to early adulthood (∼25 years), and places particular emphasis on longitudinal studies and newer techniques that are being used to study microstructural white matter development. All imaging methods demonstrate consistent, rapid microstructural white matter development over the first 3 years of life, suggesting increased myelination and axonal packing. Diffusion studies clearly demonstrate continued white matter maturation during later childhood and adolescence, though the lack of consistent findings in other modalities suggests changes may be mainly due to axonal packing. An emerging literature details differential microstructural development in boys and girls, and connects developmental trajectories to cognitive abilities, behaviour, and/or environmental factors, though the nature of these relationships remains unclear. Future research will need to focus on newer imaging techniques and longitudinal studies to provide more detailed information about microstructural white matter development, particularly in the childhood years.
Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how such measures of quality relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.
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.
The human brain undergoes rapid growth in both structure and function from infancy through early childhood, and this significantly influences cognitive and behavioral development in later life. A newly emerging research framework, developmental connectomics, provides unprecedented opportunities for exploring the developing brain through non-invasive mapping of structural and functional connectivity patterns. Within this framework, we review recent neuroimaging and neurophysiological studies investigating connectome development from 20 postmenstrual weeks to 5 years of age. Specifically, we highlight five fundamental principles of brain network development during the critical first years of life, emphasizing strengthened segregation/integration balance, a remarkable hierarchical order from primary to higher-order regions, unparalleled structural and functional maturations, substantial individual variability, and high vulnerability to risk factors and developmental disorders.
The development of human cognition results from the emergence of coordinated brain activity betweeen distant brain areas. Network science, combined with non-invasive functional imaging, has generated unprecedented insights regarding the adult brain's functional organization, and promises to help elucidate the development of functional architectures supporting complex behavior. Here we review what is known about functional network development from birth until adulthood, particularly as understood through the use of resting-state functional connectivity MRI (rs-fcMRI). We attempt to synthesize rs-fcMRI findings with other functional imaging techniques, with macro-scale structural connectivity, and with knowledge regarding the development of micro-scale structure. We highlight a number of outstanding conceptual and technical barriers that need to be addressed, as well as previous developmental findings that may need to be revisited. Finally, we discuss key areas ripe for future research in order to 1) better characterize normative developmental trajectories, 2) link these trajectories to biologic mechanistic events, as well as component behaviors and 3) better understand the clinical implications and pathophysiological basis of aberrant network development.
In executing purposeful actions, adults select sufficient and necessary limbs. But infants often move goal-irrelevant limbs, suggesting a developmental process of motor specialization. Two experiments with 9- and 12-month-olds revealed gradual decreases in extraneous movements in non-acting limbs during unimanual actions. In Experiment 1, 9-month-olds produced more extraneous movements in the non-acting hand/arm and feet/legs than 12-month-olds. In Experiment 2, analysis of the spatiotemporal dynamics of infants' movements revealed developmental declines in the spatiotemporal coupling of movements between acting and non-acting arms. We also showed that the degree of specialization in infants' unimanual actions is associated with individual differences in motor experience and visual attention, indicating the experience-dependent and broad functional nature of these developmental changes. Our study provides important new insights into motor development: as in cognitive domains, motor behaviours are initially broadly tuned to their goal, becoming progressively specialized during the first year of life.
A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data.
Brains systems undergo unique and specific dynamic changes at the cellular, circuit, and systems level that underlie the transition to adult-level cognitive control. We integrate literature from these different levels of analyses to propose a novel model of the brain basis of the development of cognitive control. The ability to consistently exert cognitive control improves into adulthood as the flexible integration of component processes, including inhibitory control, performance monitoring, and working memory, increases. Unique maturational changes in brain structure, supported by interactions between dopaminergic and GABAergic systems, contribute to enhanced network synchronization and an improved signal-to-noise ratio. In turn, these factors facilitate the specialization and strengthening of connectivity in networks supporting the transition to adult levels of cognitive control. This model provides a novel understanding of the adolescent period as an adaptive period of heightened experience-seeking necessary for the specialization of brain systems supporting cognitive control.
Quantitative measurement of dynamic cortex development during early postnatal stages is of great importance to understand early cortical structural and functional development. Conventional methods usually independently reconstruct cortical surfaces of longitudinal images from the same infant, which often generates longitudinally-inconsistent cortical surfaces and leads to inconsistence in cortex development measurement. This paper aims to address this problem by presenting a method to reconstruct consistent cortical surfaces from longitudinal brain MR images in the first-year infants for accurate and consistent measurement of dynamic cortex development. Specifically, longitudinal development of the inner cortical surface is first modeled by a deformable sheet with elasto-plasticity property to establish longitudinally smooth correspondences of inner cortical surfaces. Then, the modeled longitudinal inner cortical surfaces are jointly deformed to locate inner and outer cortical surfaces with a spatial-temporal deformable surface. The method has been applied on 10 infants, each with 5 or 6 scans acquired at every 3 months from birth. Experimental results show that our method can accurately and consistently reconstruct dynamic cortical surfaces from longitudinal infant images, with the average surface distance as low as 0.2mm. By using our method, we can quantitatively characterize longitudinally dynamic cortical thickness development in the first-year infants.
Human brain matures in temporal and regional heterogeneity, with some areas matured at early adulthood. In this study, the relationship of cortical structural developments between different cortical sheet regions is systematically analyzed using interregional correlation coefficient and network methods. Specifically, 951 longitudinal T1 brain MR images from 445 healthy subjects with ages from 3 to 20 years old are used. The result shows that the development of cortex reaches a turning point at around 7 years of age: a) the cortical thickness reaches its highest value and also the cortical folding becomes stable at this age; b) both global and local efficiency of anatomical correlation networks reach the lowest and highest values at this age, respectively; c) the change of anatomical correlation networks reach the highest level at this age, and the convergence of different anatomical correlation networks starts to decrease from this age. These results might inspire more studies on why there exists a turning point at this age from different viewpoints. For example, is there any change of synaptic pruning, or is it related to the starting of school life? And how can we benefit from this in the real life?
It's a great challenge to analyze infant brain MR images due to the small brain size and low contrast of the developing brain tissues. We have developed an Infant Brain Extraction and Analysis Toolbox (iBEAT) for various processing of magnetic resonance (MR) images of infant brains. Several major functions generally used in infant brain analysis are integrated in iBEAT, including image preprocessing, brain extraction, tissue segmentation, and brain labeling. The functions of brain extraction, tissue segmentation, and brain labeling are provided respectively by three state-of-the-art algorithms. First, a learning-based meta-algorithm which integrates a group of brain extraction results generated by the two existing brain extraction algorithms (BET and BSE) was implemented in iBEAT for extraction of infant brains from MR images. Second, a level-sets-based tissue segmentation algorithm that utilizes multimodality information, cortical thickness constraint, and longitudinal consistency constraint was also included in iBEAT for segmentation of infant brain tissues. Third, HAMMER (standing for Hierarchical Attribute Matching Mechanism for Elastic Registration) registration algorithm was further included in iBEAT to label regions of interest (ROIs) of infant brain images by warping the pre-labeled ROIs of a template to the infant brain image space. By integration of these state-of-the-art methods, iBEAT is able to segment and label infant brain MR images accurately. Moreover, it can process not only single-time-point images for cross-sectional studies, but also multiple-time-point images of the same infant for longitudinal studies. The performance of iBEAT has been comprehensively evaluated with hundreds of infant brain images. A Linux-based standalone package of iBEAT is freely available at http://www.nitrc.org/projects/ibeat .