Development of brain structural connectivity between ages 12 and 30: a 4-Tesla diffusion imaging study in 439 adolescents and adults

Gibbs distribution for statistical analysis of graphical data with a sample application to fcMRI brain images

This paper develops object-oriented data analysis (OODA) statistical methods that are novel and complementary to existing methods of analysis of human brain scan connectomes, defined as graphs representing brain anatomical or functional connectivity. OODA is an emerging field where classical statistical approaches (e.g., hypothesis testing, regression, estimation, and confidence intervals) are applied to data objects such as graphs or functions. By analyzing data objects directly we avoid loss of information that occurs when data objects are transformed into numerical summary statistics. By providing statistical tools that analyze sets of connectomes without loss of information, new insights into neurology and medicine may be achieved. In this paper we derive the formula for statistical model fitting, regression, and mixture models; test their performance in simulation experiments; and apply them to connectomes from fMRI brain scans collected during a serial reaction time task study. Software for fitting graphical object-oriented data analysis is provided.

The development of brain network architecture

Brain connectivity shows protracted development throughout childhood and adolescence, and, as such, the topology of brain networks changes during this period. The complexity of these changes with development is reflected by regional differences in maturation. This study explored age-related changes in network topology and regional developmental patterns during childhood and adolescence. We acquired two sets of Diffusion Weighted Imaging-scans and anatomical T1-weighted scans. The first dataset included 85 typically developing individuals (53 males; 32 females), aged between 7 and 23 years and was acquired on a Philips Achieva 1.5 Tesla scanner. A second dataset (N = 38) was acquired on a different (but identical) 1.5 T scanner and was used for independent replication of our results. We reconstructed whole brain networks using tractography. We operationalized fiber tract development as changes in mean diffusivity and radial diffusivity with age. Most fibers showed maturational changes in mean and radial diffusivity values throughout childhood and adolescence, likely reflecting increasing white matter integrity. The largest age-related changes were observed in association fibers within and between the frontal and parietal lobes. Furthermore, there was a simultaneous age-related decrease in average path length (P < 0.0001), increase in node strength (P < 0.0001) as well as network clustering (P = 0.001), which may reflect fine-tuning of topological organization. These results suggest a sequential maturational model where connections between unimodal regions strengthen in childhood, followed by connections from these unimodal regions to association regions, while adolescence is characterized by the strengthening of connections between association regions within the frontal and parietal cortex. Hum Brain Mapp 37:717-729, 2016. © 2015 Wiley Periodicals, Inc.

Connectome and Maturation Profiles of the Developing Mouse Brain Using Diffusion Tensor Imaging

This paper presents a comprehensive effort to establish a structural mouse connectome using diffusion tensor magnetic resonance imaging coupled with connectivity analysis tools. This work lays the foundation for imaging-based structural connectomics of the mouse brain, potentially facilitating a whole-brain network analysis to quantify brain changes in connectivity during development, as well as deviations from it related to genetic effects. A connectomic trajectory of maturation during postnatal ages 2-80 days is presented in the C57BL/6J mouse strain, using a whole-brain connectivity analysis, followed by investigations based on local and global network features. The global network measures of density, global efficiency, and modularity demonstrated a nonlinear relationship with age. The regional network metrics, namely degree and local efficiency, displayed a differential change in the major subcortical structures such as the thalamus and hippocampus, and cortical regions such as visual and motor cortex. Finally, the connectomes were used to derive an index of "brain connectivity index," which demonstrated a high correlation (r = 0.95) with the chronological age, indicating that brain connectivity is a good marker of normal age progression, hence valuable in detecting subtle deviations from normality caused by genetic, environmental, or pharmacological manipulations.

Developmental Changes in Brain Network Hub Connectivity in Late Adolescence

The human brain undergoes substantial development throughout adolescence and into early adulthood. This maturational process is thought to include the refinement of connectivity between putative connectivity hub regions of the brain, which collectively form a dense core that enhances the functional integration of anatomically distributed, and functionally specialized, neural systems. Here, we used longitudinal diffusion magnetic resonance imaging to characterize changes in connectivity between 80 cortical and subcortical anatomical regions over a 2 year period in 31 adolescents between the ages of 15 and 19 years. Connectome-wide analysis indicated that only a small subset of connections showed evidence of statistically significant developmental change over the study period, with 8% and 6% of connections demonstrating decreased and increased structural connectivity, respectively. Nonetheless, these connections linked 93% and 90% of the 80 regions, respectively, pointing to a selective, yet anatomically distributed pattern of developmental changes that involves most of the brain. Hub regions showed a distinct tendency to be highly connected to each other, indicating robust "rich-club" organization. Moreover, connectivity between hubs was disproportionately influenced by development, such that connectivity between subcortical hubs decreased over time, whereas frontal-subcortical and frontal-parietal hub-hub connectivity increased over time. These findings suggest that late adolescence is characterized by selective, yet significant remodeling of hub-hub connectivity, with the topological organization of hubs shifting emphasis from subcortical hubs in favor of an increasingly prominent role for frontal hub regions.

Reduced Gyrification Is Related to Reduced Interhemispheric Connectivity in Autism Spectrum Disorders

OBJECTIVE

Autism spectrum disorders (ASD) have been associated with atypical cortical gray and subcortical white matter development. Neurodevelopmental theories postulate that a relation between cortical maturation and structural brain connectivity may exist. We therefore investigated the development of gyrification and white matter connectivity and their relationship in individuals with ASD and their typically developing peers.

METHOD

T1- and diffusion-weighted images were acquired from a representative sample of 30 children and adolescents with ASD (aged 8-18 years), and 29 typically developing children matched for age, sex, hand preference, and socioeconomic status. The FreeSurfer suite was used to calculate cortical volume, surface area, and gyrification index. Measures of structural connectivity were estimated using probabilistic tractography and tract-based spatial statistics (TBSS).

RESULTS

Left prefrontal and parietal cortex showed a relative, age-dependent decrease in gyrification index in children and adolescents with ASD compared to typically developing controls. This result was replicated in an age-and IQ-matched sample provided by the Autism Brain Imaging Data Exchange (ABIDE) initiative. Furthermore, tractography and TBSS showed a complementary pattern in which left prefrontal gyrification was negatively related to radial diffusivity in the forceps minor in participants with ASD.

CONCLUSION

The present study builds on earlier findings of abnormal gyrification and structural connectivity in the prefrontal cortex in ASD. It provides a more comprehensive neurodevelopmental characterization of ASD, involving interdependent changes in microstructural white and cortical gray matter. The findings of related abnormal patterns of gyrification and white matter connectivity support the notion of the intertwined development of 2 major morphometric domains in ASD.

Structure and function of large-scale brain systems

This article introduces the functional neuroanatomy of large-scale brain systems. Both the structure and functions of these brain networks are presented. All human behavior is the result of interactions within and between these brain systems. This system of brain function completely changes our understanding of how cognition and behavior are organized within the brain, replacing the traditional lesion model. Understanding behavior within the context of brain network interactions has profound implications for modifying abstract constructs such as attention, learning, and memory. These constructs also must be understood within the framework of a paradigm shift, which emphasizes ongoing interactions within a dynamically changing environment.

An integrative model of the maturation of cognitive control

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.

Developmental changes in spontaneous electrocortical activity and network organization from early to late childhood

We investigated the development of spontaneous (resting state) cerebral electric fields and their network organization from early to late childhood in a large community sample of children. Critically, we examined electrocortical maturation across one-year windows rather than creating aggregate averages that can miss subtle maturational trends. We implemented several novel methodological approaches including a more fine grained examination of spectral features across multiple electrodes, the use of phase-lagged functional connectivity to control for the confounding effects of volume conduction and applying topological network analyses to weighted cortical adjacency matrices. Overall, there were major decreases in absolute EEG spectral density (particularly in the slow wave range) across cortical lobes as a function of age. Moreover, the peak of the alpha frequency increased with chronological age and there was a redistribution of relative spectral density toward the higher frequency ranges, consistent with much of the previous literature. There were age differences in long range functional brain connectivity, particularly in the alpha frequency band, culminating in the most dense and spatially variable networks in the oldest children. We discovered age-related reductions in characteristic path lengths, modularity and homogeneity of alpha-band cortical networks from early to late childhood. In summary, there is evidence of large scale reorganization in endogenous brain electric fields from early to late childhood, suggesting reduced signal amplitudes in the presence of more functionally integrated and band limited coordination of neuronal activity across the cerebral cortex.

Sex differences in brain plasticity: a new hypothesis for sex ratio bias in autism

Several observations support the hypothesis that differences in synaptic and regional cerebral plasticity between the sexes account for the high ratio of males to females in autism. First, males are more susceptible than females to perturbations in genes involved in synaptic plasticity. Second, sex-related differences in non-autistic brain structure and function are observed in highly variable regions, namely, the heteromodal associative cortices, and overlap with structural particularities and enhanced activity of perceptual associative regions in autistic individuals. Finally, functional cortical reallocations following brain lesions in non-autistic adults (for example, traumatic brain injury, multiple sclerosis) are sex-dependent. Interactions between genetic sex and hormones may therefore result in higher synaptic and consecutively regional plasticity in perceptual brain areas in males than in females. The onset of autism may largely involve mutations altering synaptic plasticity that create a plastic reaction affecting the most variable and sexually dimorphic brain regions. The sex ratio bias in autism may arise because males have a lower threshold than females for the development of this plastic reaction following a genetic or environmental event.

Longitudinal changes in diffusion properties in white matter pathways of children with tuberous sclerosis complex

BACKGROUND

Abnormal white matter development in patients with tuberous sclerosis complex, a multisystem hamartomatous disorder caused by aberrant neural proliferation and axonal maturation, may be associated with poorer neurocognitive outcomes. The purpose of this study is to identify predictors of longitudinal changes in diffusion properties of white matter tracts in patients with tuberous sclerosis complex.

METHODS

Diffusion magnetic resonance imaging was carried out in 17 subjects with tuberous sclerosis complex (mean age, 7.2 ± 4.4 years) with at least two magnetic resonance imaging scans (mean number of days between scans, 419.4 ± 105.4). There were 10 males; 5 of 17 had autism spectrum disorder and 10 of 17 had epilepsy. Regions of interest were placed to delineate the internal capsule/corona radiata, cingulum, and corpus callosum. The outcomes were mean change in apparent diffusion coefficient and fractional anisotropy. Data were analyzed using Pearson's correlation and multiple linear regression analyses.

RESULTS

Gender was a significant predictor of mean change in apparent diffusion coefficient in the left internal capsule, right and left cingulum bundles, and corpus callosum and a significant predictor of mean change in fractional anisotropy in the corpus callosum. Epilepsy was a significant predictor of mean change in apparent diffusion coefficient in the left internal capsule. Autism spectrum disorder was not predictive of diffusion changes in any of the studied pathways.

CONCLUSION

Clinical variables, including gender and epilepsy, have an effect on the development of white matter pathways. These variables should be taken into consideration when counseling tuberous sclerosis complex patients and in future imaging studies in this population.

Abnormal structural connectivity in the brain networks of children with hydrocephalus

Increased intracranial pressure and ventriculomegaly in children with hydrocephalus are known to have adverse effects on white matter structure. This study seeks to investigate the impact of hydrocephalus on topological features of brain networks in children. The goal was to investigate structural network connectivity, at both global and regional levels, in the brains in children with hydrocephalus using graph theory analysis and diffusion tensor tractography. Three groups of children were included in the study (29 normally developing controls, 9 preoperative hydrocephalus patients, and 17 postoperative hydrocephalus patients). Graph theory analysis was applied to calculate the global network measures including small-worldness, normalized clustering coefficients, normalized characteristic path length, global efficiency, and modularity. Abnormalities in regional network parameters, including nodal degree, local efficiency, clustering coefficient, and betweenness centrality, were also compared between the two patients groups (separately) and the controls using two tailed t-test at significance level of p < 0.05 (corrected for multiple comparison). Children with hydrocephalus in both the preoperative and postoperative groups were found to have significantly lower small-worldness and lower normalized clustering coefficient than controls. Children with hydrocephalus in the postoperative group were also found to have significantly lower normalized characteristic path length and lower modularity. At regional level, significant group differences (or differences at trend level) in regional network measures were found between hydrocephalus patients and the controls in a series of brain regions including the medial occipital gyrus, medial frontal gyrus, thalamus, cingulate gyrus, lingual gyrus, rectal gyrus, caudate, cuneus, and insular. Our data showed that structural connectivity analysis using graph theory and diffusion tensor tractography is sensitive to detect abnormalities of brain network connectivity associated with hydrocephalus at both global and regional levels, thus providing a new avenue for potential diagnosis and prognosis tool for children with hydrocephalus.

Prosocial behavior and gender

This study revisits different experimental data sets that explore social behavior in economic games and uncovers that many treatment effects may be gender-specific. In general, men and women do not differ in “neutral” baselines. However, we find that social framing tends to reinforce prosocial behavior in women but not men, whereas encouraging reflection decreases the prosociality of males but not females. The treatment effects are sometimes statistically different across genders and sometimes not but never go in the opposite direction. These findings suggest that (i) the social behavior of both sexes is malleable but each gender responds to different aspects of the social context; and (ii) gender differences observed in some studies might be the result of particular features of the experimental design. Our results contribute to the literature on prosocial behavior and may improve our understanding of the origins of human prosociality. We discuss the possible link between the observed differential treatment effects across genders and the differing male and female brain network connectivity, documented in recent neural studies.

Progressive gender differences of structural brain networks in healthy adults: a longitudinal, diffusion tensor imaging study

Sexual dimorphism in the brain maturation during childhood and adolescence has been repeatedly documented, which may underlie the differences in behaviors and cognitive performance. However, our understanding of how gender modulates the development of structural connectome in healthy adults is still not entirely clear. Here we utilized graph theoretical analysis of longitudinal diffusion tensor imaging data over a five-year period to investigate the progressive gender differences of brain network topology. The brain networks of both genders showed prominent economical “small-world” architecture (high local clustering and short paths between nodes). Additional analysis revealed a more economical “small-world” architecture in females as well as a greater global efficiency in males regardless of scan time point. At the regional level, both increased and decreased efficiency were found across the cerebral cortex for both males and females, indicating a compensation mechanism of cortical network reorganization over time. Furthermore, we found that weighted clustering coefficient exhibited significant gender-time interactions, implying different development trends between males and females. Moreover, several specific brain regions (e.g., insula, superior temporal gyrus, cuneus, putamen, and parahippocampal gyrus) exhibited different development trajectories between males and females. Our findings further prove the presence of sexual dimorphism in brain structures that may underlie gender differences in behavioral and cognitive functioning. The sex-specific progress trajectories in brain connectome revealed in this work provide an important foundation to delineate the gender related pathophysiological mechanisms in various neuropsychiatric disorders, which may potentially guide the development of sex-specific treatments for these devastating brain disorders.

Annual research review: Growth connectomics--the organization and reorganization of brain networks during normal and abnormal development

BACKGROUND

We first give a brief introduction to graph theoretical analysis and its application to the study of brain network topology or connectomics. Within this framework, we review the existing empirical data on developmental changes in brain network organization across a range of experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans).

SYNTHESIS

We discuss preliminary evidence and current hypotheses for how the emergence of network properties correlates with concomitant cognitive and behavioural changes associated with development. We highlight some of the technical and conceptual challenges to be addressed by future developments in this rapidly moving field. Given the parallels previously discovered between neural systems across species and over a range of spatial scales, we also review some recent advances in developmental network studies at the cellular scale. We highlight the opportunities presented by such studies and how they may complement neuroimaging in advancing our understanding of brain development. Finally, we note that many brain and mind disorders are thought to be neurodevelopmental in origin and that charting the trajectory of brain network changes associated with healthy development also sets the stage for understanding abnormal network development.

CONCLUSIONS

We therefore briefly review the clinical relevance of network metrics as potential diagnostic markers and some recent efforts in computational modelling of brain networks which might contribute to a more mechanistic understanding of neurodevelopmental disorders in future.

The intrahemispheric functional properties of the developing sensorimotor cortex are influenced by maturation

The investigation of the functional changes in the sensorimotor cortex has important clinical implications as deviations from normal development can anticipate developmental disorders. The functional properties of the sensorimotor cortex can be characterized through the rolandic mu rhythm, already present during infancy. However, how the sensorimotor network develops from early infancy to adulthood, and how sensorimotor processing contributes to the generation of perceptual-motor coupling remains largely unknown. Here, we analyzed magnetoencephalographic (MEG) data recorded in two groups of infants (11-24 and 26-47 weeks), two groups of children (24-34 and 36-60 months), and a control group of adults (20-39 years), during intermixed conditions of rest and prehension. The MEG sensor array was positioned over the sensorimotor cortex of the contralateral hemisphere. We characterized functional connectivity and topological properties of the sensorimotor network across ages and conditions through synchronization likelihood and segregation/integration measures in an individual mu rhythm frequency range. All functional measures remained almost unchanged during the first year of life, whereas they varied afterwards through childhood to reach adult values, demonstrating an increase of both segregation and integration properties. With age, the sensorimotor network evolved from a more random (infants) to a "small-world" organization (children and adults), more efficient both locally and globally. These findings are in line with prior studies on structural and functional brain development in infants, children and adults. We could not demonstrate any significant change in the functional properties of the sensorimotor cortex in the prehension condition with respect to rest. Our results support the view that, since early infancy, the functional properties of the developing sensorimotor cortex are modulated by maturation.

Unraveling the miswired connectome: a developmental perspective

The vast majority of mental illnesses can be conceptualized as developmental disorders of neural interactions within the connectome, or developmental miswiring. The recent maturation of pediatric in vivo brain imaging is bringing the identification of clinically meaningful brain-based biomarkers of developmental disorders within reach. Even more auspicious is the ability to study the evolving connectome throughout life, beginning in utero, which promises to move the field from topological phenomenology to etiological nosology. Here, we scope advances in pediatric imaging of the brain connectome as the field faces the challenge of unraveling developmental miswiring. We highlight promises while also providing a pragmatic review of the many obstacles ahead that must be overcome to significantly impact public health.

Aging alterations in whole-brain networks during adulthood mapped with the minimum spanning tree indices: the interplay of density, connectivity cost and life-time trajectory

The organizational network changes in the human brain across the lifespan have been mapped using functional and structural connectivity data. Brain network changes provide valuable insights into the processes underlying senescence. Nonetheless, the altered network density in the elderly severely compromises the usefulness of network analysis to study the aging brain. We successfully circumvented this problem by focusing on the critical structural network backbone, using a robust tree representation. Whole-brain networks' minimum spanning trees were determined in a dataset of diffusion-weighted images from 382 healthy subjects, ranging in age from 20.2 to 86.2 years. Tree-based metrics were compared with classical network metrics. In contrast to the tree-based metrics, classical metrics were highly influenced by age-related changes in network density. Tree-based metrics showed linear and non-linear correlation across adulthood and are in close accordance with results from previous histopathological characterizations of the changes in white matter integrity in the aging brain.

Seemingly unrelated regression empowers detection of network failure in dementia

Brain connectivity is progressively disrupted in Alzheimer's disease (AD). Here, we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain's fiber networks from diffusion-weighted magnetic resonance imaging scans of 200 subjects from the Alzheimer's Disease Neuroimaging Initiative. We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of diffusion tensor imaging scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of mild cognitive impairment or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model-combining genotype, educational level, and clinical diagnosis.

Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain's traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations.

Structural connectivity abnormality in children with acute mild traumatic brain injury using graph theoretical analysis

The traumatic biomechanical forces associated with mild traumatic brain injury (mTBI) typically impart diffuse, as opposed to focal, brain injury potentially disrupting the structural connectivity between neural networks. Graph theoretical analysis using diffusion tensor imaging was used to assess injury-related differences in structural connectivity between 23 children (age 11-16 years) with mTBI and 20 age-matched children with isolated orthopedic injuries (OI) scanned within 96 h postinjury. The distribution of hub regions and the associations between alterations in regional network measures and symptom burden, as assessed by the postconcussion symptom scale score (PCSS), were also examined. In comparison to the OI group, the mTBI group was found to have significantly higher small-worldness (P < 0.0001), higher normalized clustering coefficients (P < 0.0001), higher normalized characteristic path length (P = 0.007), higher modularity (P = 0.0005), and lower global efficiency (P < 0.0001). A series of hub regions in the mTBI group were found to have significant alterations in regional network measures including nodal degree, nodal clustering coefficient, and nodal between-ness centrality. Correlation analysis showed that PCSS total score acquired at the time of imaging was significantly associated with the nodal degree of two hubs, the superior frontal gyrus at orbital section and the middle frontal gyrus. These findings provide new evidence of acute white matter alteration at both global and regional network level following mTBI in children furthering our understanding of underlying mechanisms of acute neurological insult associated with mTBI.

Fetal functional imaging portrays heterogeneous development of emerging human brain networks

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction, and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st to 38th gestational weeks (GWs) with a network-based statistical inference approach. The overall connectivity network, short range, and interhemispheric connections showed sigmoid expansion curve peaking at the 26-29 GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW), temporal (peak: 26 GW), frontal (peak: 26.4 GW), and parietal expansion (peak: 27.5 GW). We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macro connectivity.

Longer gestation is associated with more efficient brain networks in preadolescent children

Neurodevelopmental benefits of increased gestation have not been fully characterized in terms of network organization. Since brain function can be understood as an integrated network of neural information from distributed brain regions, investigation of the effects of gestational length on network properties is a critical goal of human developmental neuroscience. Using diffusion tensor imaging and fiber tractography, we investigated the effects of gestational length on the small-world attributes and rich club organization of 147 preadolescent children, whose gestational length ranged from 29 to 42 weeks. Higher network efficiency was positively associated with longer gestation. The longer gestation was correlated with increased local efficiency in the posterior medial cortex, including the precuneus, cuneus, and superior parietal regions. Rich club organization was also observed indicating the existence of highly interconnected structural hubs formed in preadolescent children. Connectivity among rich club members and from rich club regions was positively associated with the length of gestation, indicating the higher level of topological benefits of structural connectivity from longer gestation in the predominant regions of brain networks. The findings provide evidence that longer gestation is associated with improved topological organization of the preadolescent brain, characterized by the increased communication capacity of the brain network and enhanced directional strength of brain connectivity with central hub regions.

Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance

Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R² = 0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4 ± 35.4 ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p < 0.001) than for either SPM8 (R² = 0.577 CI (0.500, 0.644)) or FreeSurfer (R² = 0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV.

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288 T1 MRI from multiple scanners were manually segmented for intracranial volume.

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We compare SPM12 with the current methods of estimating intracranial volume.

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SPM12 shows a very high correlation with manual measures and little bias.

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Newer automated volume measures are more accurate controls for head size variation.

The small-world organization of large-scale brain systems and relationships with subcortical structures

Brain structure and function is characterized by large-scale brain systems. However, each system has its own "small-world" organization, with sub-regions, or "hubs," that have varying degrees of specialization for certain cognitive and behavioral processes. This article describes this small-world organization, and the concepts of functional specialization and functional integration are defined and explained through practical examples. We also describe the development of large-scale brain systems and this small-world organization as a sensitive, protracted process, vulnerable to a variety of influences that generate neurodevelopmental disorders.

Structural Connectivity Networks of Transgender People

Although previous investigations of transsexual people have focused on regional brain alterations, evaluations on a network level, especially those structural in nature, are largely missing. Therefore, we investigated the structural connectome of 23 female-to-male (FtM) and 21 male-to-female (MtF) transgender patients before hormone therapy as compared with 25 female and 25 male healthy controls. Graph theoretical analysis of whole-brain probabilistic tractography networks (adjusted for differences in intracranial volume) showed decreased hemispheric connectivity ratios of subcortical/limbic areas for both transgender groups. Subsequent analysis revealed that this finding was driven by increased interhemispheric lobar connectivity weights (LCWs) in MtF transsexuals and decreased intrahemispheric LCWs in FtM patients. This was further reflected on a regional level, where the MtF group showed mostly increased local efficiencies and FtM patients decreased values. Importantly, these parameters separated each patient group from the remaining subjects for the majority of significant findings. This work complements previously established regional alterations with important findings of structural connectivity. Specifically, our data suggest that network parameters may reflect unique characteristics of transgender patients, whereas local physiological aspects have been shown to represent the transition from the biological sex to the actual gender identity.

Hemispheric lateralization of topological organization in structural brain networks

The study on structural brain asymmetries in healthy individuals plays an important role in our understanding of the factors that modulate cognitive specialization in the brain. Here, we used fiber tractography to reconstruct the left and right hemispheric networks of a large cohort of 346 healthy participants (20-86 years) and performed a graph theoretical analysis to investigate this brain laterality from a network perspective. Findings revealed that the left hemisphere is significantly more "efficient" than the right hemisphere, whereas the right hemisphere showed higher values of "betweenness centrality" and "small-worldness." In particular, left-hemispheric networks displayed increased nodal efficiency in brain regions related to language and motor actions, whereas the right hemisphere showed an increase in nodal efficiency in brain regions involved in memory and visuospatial attention. In addition, we found that hemispheric networks decrease in efficiency with age. Finally, we observed significant gender differences in measures of global connectivity. By analyzing the structural hemispheric brain networks, we have provided new insights into understanding the neuroanatomical basis of lateralized brain functions.

Typical and atypical brain development: a review of neuroimaging studies

In the course of development, the brain undergoes a remarkable process of restructuring as it adapts to the environment and becomes more efficient in processing information. A variety of brain imaging methods can be used to probe how anatomy, connectivity, and function change in the developing brain. Here we review recent discoveries regarding these brain changes in both typically developing individuals and individuals with neurodevelopmental disorders. We begin with typical development, summarizing research on changes in regional brain volume and tissue density, cortical thickness, white matter integrity, and functional connectivity. Space limits preclude the coverage of all neurodevelopmental disorders; instead, we cover a representative selection of studies examining neural correlates of autism, attention deficit/hyperactivity disorder, Fragile X, 22q11.2 deletion syndrome, Williams syndrome, Down syndrome, and Turner syndrome. Where possible, we focus on studies that identify an age by diagnosis interaction, suggesting an altered developmental trajectory. The studies we review generally cover the developmental period from infancy to early adulthood. Great progress has been made over the last 20 years in mapping how the brain matures with MR technology. With ever-improving technology, we expect this progress to accelerate, offering a deeper understanding of brain development, and more effective interventions for neurodevelopmental disorders.

Is "efficiency" a useful concept in cognitive neuroscience?

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The concept of “efficiency” is often used to describe differences in brain activation between groups or individuals.

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I argue that this concept is empty and simply redescribes the data.

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I review different explanations for differences in activation and highlight the challenges in understanding these differences.

It is common in the cognitive neuroscience literature to explain differences in activation in terms of differences in the “efficiency” of neural function. I argue here that this usage of the concept of efficiency is empty and simply redescribes activation differences rather than providing a useful explanation of them. I examine a number of possible explanations for differential activation in terms of task performance, neuronal computation, neuronal energetics, and network organization. While the concept of “efficiency” is vacuous as it is commonly employed in the neuroimaging literature, an examination of brain development in the context of neural coding, neuroenergetics, and network structure provides a roadmap for future investigation, which is fundamental to an improved understanding of developmental effects and group differences in neuroimaging signals.

Structural MRI connectome in development: challenges of the changing brain

MRI connectomics is an emerging approach to study the brain as a network of interconnected brain regions. Understanding and mapping the development of the MRI connectome may offer new insights into the development of brain connectivity and plasticity, ultimately leading to improved understanding of normal development and to more effective diagnosis and treatment of developmental disorders. In this review, we describe the attempts made to date to map the whole-brain structural MRI connectome in the developing brain and pay a special attention to the challenges associated with the rapid changes that the brain is undergoing during maturation. The two main steps in constructing a structural brain network are (i) choosing connectivity measures that will serve as the network "edges" and (ii) finding an appropriate way to divide the brain into regions that will serve as the network "nodes". We will discuss how these two steps are usually performed in developmental studies and the rationale behind different strategies. Changes in local and global network properties that have been described during maturation in neonates and children will be reviewed, along with differences in network topology between typically and atypically developing subjects, for example, owing to pre-mature birth or hypoxic ischaemic encephalopathy. Finally, future directions of connectomics will be discussed, addressing important steps necessary to advance the study of the structural MRI connectome in development.

Brain without anatomy: construction and comparison of fully network-driven structural MRI connectomes

MRI connectomics methods treat the brain as a network and provide new information about its organization, efficiency, and mechanisms of disruption. The most commonly used method of defining network nodes is to register the brain to a standardized anatomical atlas based on the Brodmann areas. This approach is limited by inter-subject variability and can be especially problematic in the context of brain maturation or neuroplasticity (cerebral reorganization after brain damage). In this study, we combined different image processing and network theory methods and created a novel approach that enables atlas-free construction and connection-wise comparison of diffusion MRI-based brain networks. We illustrated the proposed approach in three age groups: neonates, 6-month-old infants, and adults. First, we explored a data-driven method of determining the optimal number of equal-area nodes based on the assumption that all cortical areas of the brain are connected and, thus, no part of the brain is structurally isolated. Second, to enable a connection-wise comparison, alignment to a “reference brain” was performed in the network domain within each group using a matrix alignment algorithm with simulated annealing. The correlation coefficients after pair-wise network alignment ranged from 0.6102 to 0.6673. To test the method’s reproducibility, one subject from the 6-month-old group and one from the adult group were scanned twice, resulting in correlation coefficients of 0.7443 and 0.7037, respectively. While being less than 1 due to parcellation and noise, statistically, these values were significantly higher than inter-subject values. Rotation of the parcellation largely explained the variability. Through the abstraction from anatomy, the developed framework allows for a fully network-driven analysis of structural MRI connectomes and can be applied to subjects at any stage of development and with substantial differences in cortical anatomy.

Automatic clustering and population analysis of white matter tracts using maximum density paths

We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.

Development of insula connectivity between ages 12 and 30 revealed by high angular resolution diffusion imaging

The insula, hidden deep within the Sylvian fissures, has proven difficult to study from a connectivity perspective. Most of our current information on the anatomical connectivity of the insula comes from studies of nonhuman primates and post mortem human dissections. To date, only two neuroimaging studies have successfully examined the connectivity of the insula. Here we examine how the connectivity of the insula develops between ages 12 and 30, in 307 young adolescent and adult subjects scanned with 4-Tesla high angular resolution diffusion imaging (HARDI). The density of fiber connections between the insula and the frontal and parietal cortex decreased with age, but the connection density between the insula and the temporal cortex generally increased with age. This trajectory is in line with well-known patterns of cortical development in these regions. In addition, males and females showed different developmental trajectories for the connection between the left insula and the left precentral gyrus. The insula plays many different roles, some of them affected in neuropsychiatric disorders; this information on the insula's connectivity may help efforts to elucidate mechanisms of brain disorders in which it is implicated.

Reprint of: Mapping connectivity in the developing brain

Recently, there has been a wealth of research into structural and functional brain connectivity, and how they change over development. While we are far from a complete understanding, these studies have yielded important insights into human brain development. There is an ever growing variety of methods for assessing connectivity, each with its own advantages. Here we review research on the development of structural and/or functional brain connectivity in both typically developing subjects and subjects with neurodevelopmental disorders. Space limitations preclude an exhaustive review of brain connectivity across all developmental disorders, so we review a representative selection of recent findings on brain connectivity in autism, Fragile X, 22q11.2 deletion syndrome, Williams syndrome, Turner syndrome, and ADHD. Major strides have been made in understanding the developmental trajectory of the human connectome, offering insight into characteristic features of brain development and biological processes involved in developmental brain disorders. We also discuss some common themes, including hemispheric specialization - or asymmetry - and sex differences. We conclude by discussing some promising future directions in connectomics, including the merger of imaging and genetics, and a deeper investigation of the relationships between structural and functional connectivity.

Miswiring the brain: Δ9-tetrahydrocannabinol disrupts cortical development by inducing an SCG10/stathmin-2 degradation pathway

Children exposed in utero to cannabis present permanent neurobehavioral and cognitive impairments. Psychoactive constituents from Cannabis spp., particularly Δ(9)-tetrahydrocannabinol (THC), bind to cannabinoid receptors in the fetal brain. However, it is unknown whether THC can trigger a cannabinoid receptor-driven molecular cascade to disrupt neuronal specification. Here, we show that repeated THC exposure disrupts endocannabinoid signaling, particularly the temporal dynamics of CB1 cannabinoid receptor, to rewire the fetal cortical circuitry. By interrogating the THC-sensitive neuronal proteome we identify Superior Cervical Ganglion 10 (SCG10)/stathmin-2, a microtubule-binding protein in axons, as a substrate of altered neuronal connectivity. We find SCG10 mRNA and protein reduced in the hippocampus of midgestational human cannabis-exposed fetuses, defining SCG10 as the first cannabis-driven molecular effector in the developing cerebrum. CB1 cannabinoid receptor activation recruits c-Jun N-terminal kinases to phosphorylate SCG10, promoting its rapid degradation in situ in motile axons and microtubule stabilization. Thus, THC enables ectopic formation of filopodia and alters axon morphology. These data highlight the maintenance of cytoskeletal dynamics as a molecular target for cannabis, whose imbalance can limit the computational power of neuronal circuitries in affected offspring.

Preferential detachment during human brain development: age- and sex-specific structural connectivity in diffusion tensor imaging (DTI) data

Human brain maturation is characterized by the prolonged development of structural and functional properties of large-scale networks that extends into adulthood. However, it is not clearly understood which features change and which remain stable over time. Here, we examined structural connectivity based on diffusion tensor imaging (DTI) in 121 participants between 4 and 40 years of age. DTI data were analyzed for small-world parameters, modularity, and the number of fiber tracts at the level of streamlines. First, our findings showed that the number of fiber tracts, small-world topology, and modular organization remained largely stable despite a substantial overall decrease in the number of streamlines with age. Second, this decrease mainly affected fiber tracts that had a large number of streamlines, were short, within modules and within hemispheres; such connections were affected significantly more often than would be expected given their number of occurrences in the network. Third, streamline loss occurred earlier in females than in males. In summary, our findings suggest that core properties of structural brain connectivity, such as the small-world and modular organization, remain stable during brain maturation by focusing streamline loss to specific types of fiber tracts.

Sex differences in the structural connectome of the human brain

Sex differences in human behavior show adaptive complementarity: Males have better motor and spatial abilities, whereas females have superior memory and social cognition skills. Studies also show sex differences in human brains but do not explain this complementarity. In this work, we modeled the structural connectome using diffusion tensor imaging in a sample of 949 youths (aged 8-22 y, 428 males and 521 females) and discovered unique sex differences in brain connectivity during the course of development. Connection-wise statistical analysis, as well as analysis of regional and global network measures, presented a comprehensive description of network characteristics. In all supratentorial regions, males had greater within-hemispheric connectivity, as well as enhanced modularity and transitivity, whereas between-hemispheric connectivity and cross-module participation predominated in females. However, this effect was reversed in the cerebellar connections. Analysis of these changes developmentally demonstrated differences in trajectory between males and females mainly in adolescence and in adulthood. Overall, the results suggest that male brains are structured to facilitate connectivity between perception and coordinated action, whereas female brains are designed to facilitate communication between analytical and intuitive processing modes.

Graph theoretical analysis of developmental patterns of the white matter network

Understanding the development of human brain organization is critical for gaining insight into how the enhancement of cognitive processes is related to the fine-tuning of the brain network. However, the developmental trajectory of the large-scale white matter (WM) network is not fully understood. Here, using graph theory, we examine developmental changes in the organization of WM networks in 180 typically-developing participants. WM networks were constructed using whole brain tractography and 78 cortical regions of interest were extracted from each participant. The subjects were first divided into 5 equal sample size (n = 36) groups (early childhood: 6.0–9.7 years; late childhood: 9.8–12.7 years; adolescence: 12.9–17.5 years; young adult: 17.6–21.8 years; adult: 21.9–29.6 years). Most prominent changes in the topological properties of developing brain networks occur at late childhood and adolescence. During late childhood period, the structural brain network showed significant increase in the global efficiency but decrease in modularity, suggesting a shift of topological organization toward a more randomized configuration. However, while preserving most topological features, there was a significant increase in the local efficiency at adolescence, suggesting the dynamic process of rewiring and rebalancing brain connections at different growth stages. In addition, several pivotal hubs were identified that are vital for the global coordination of information flow over the whole brain network across all age groups. Significant increases of nodal efficiency were present in several regions such as precuneus at late childhood. Finally, a stable and functionally/anatomically related modular organization was identified throughout the development of the WM network. This study used network analysis to elucidate the topological changes in brain maturation, paving the way for developing novel methods for analyzing disrupted brain connectivity in neurodevelopmental disorders.

Mapping connectivity in the developing brain

Recently, there has been a wealth of research into structural and functional brain connectivity, and how they change over development. While we are far from a complete understanding, these studies have yielded important insights into human brain development. There is an ever growing variety of methods for assessing connectivity, each with its own advantages. Here we review research on the development of structural and/or functional brain connectivity in both typically developing subjects and subjects with neurodevelopmental disorders. Space limitations preclude an exhaustive review of brain connectivity across all developmental disorders, so we review a representative selection of recent findings on brain connectivity in autism, Fragile X, 22q11.2 deletion syndrome, Williams syndrome, Turner syndrome, and ADHD. Major strides have been made in understanding the developmental trajectory of the human connectome, offering insight into characteristic features of brain development and biological processes involved in developmental brain disorders. We also discuss some common themes, including hemispheric specialization - or asymmetry - and sex differences. We conclude by discussing some promising future directions in connectomics, including the merger of imaging and genetics, and a deeper investigation of the relationships between structural and functional connectivity.

Connectomics and epilepsy

PURPOSE OF REVIEW

Tremendous advances have occurred in recent years in elucidating basic mechanisms of epilepsy at the level of ion channels and neurotransmitters. Epilepsy, however, is ultimately a disease of functionally and/or structurally aberrant connections between neurons and groups of neurons at the systems level. Recent advances in neuroimaging and electrophysiology now make it possible to investigate structural and functional connectivity of the entire brain, and these techniques are currently being used to investigate diseases that manifest as global disturbances of brain function. Epilepsy is such a disease, and our understanding of the mechanisms underlying the development of epilepsy and the generation of epileptic seizures will undoubtedly benefit from research utilizing these connectomic approaches.

RECENT FINDINGS

MRI using diffusion tensor imaging provides structural information, whereas functional MRI and electroencephalography provide functional information about connectivity at the whole brain level. Optogenetics, tracers, electrophysiological approaches, and calcium imaging provide connectivity information at the level of local circuits. These approaches are revealing important neuronal network disturbances underlying epileptic abnormalities.

SUMMARY

An understanding of the fundamental mechanisms underlying the development of epilepsy and the generation of epileptic seizures will require delineation of the aberrant functional and structural connections of the whole brain. The field of connectomics now provides approaches to accomplish this.

Autism as the Early Closure of a Neuroplastic Critical Period Normally Seen in Adolescence

The most severe cases of autism are diagnosed by extreme social dysfunction and other behavioral abnormalities. A number of genetic studies have been conducted to correlate behavioral phenotypes to genetic dysfunctions, but no "autism gene" has yet been discovered. In addition, environmental factors have been found to influence the development of autistic traits with high probability. This review will examine the role of a shortened period of neuroplasticity as a unifying feature of the autistic phenotype. The neuroplastic period of interest normally extends into adolescence, allowing for neural integration and the development of language and social skills. Early closure of this period may result in a shortened period of development, forcing the brain to rely on underdeveloped structures.

WITHDRAWN: Mapping Connectivity in the Developing Brain

The Publisher regrets that this article is an accidental duplication of an article that has already been published, http://dx.doi.org/10.1016/j.ijdevneu.2013.05.007. The duplicate article has therefore been withdrawn.

A DTI-based template-free cortical connectome study of brain maturation

Improved understanding of how the human brain is “wired” on a macroscale may now be possible due to the emerging field of MRI connectomics. However, mapping the rapidly developing infant brain networks poses challenges. In this study, we applied an automated template-free “baby connectome” framework using diffusion MRI to non-invasively map the structural brain networks in subjects of different ages, including premature neonates, term-born neonates, six-month-old infants, and adults. We observed increasing brain network integration and decreasing segregation with age in term-born subjects. We also explored how the equal area nodes can be grouped into modules without any prior anatomical information – an important step toward a fully network-driven registration and analysis of brain connectivity.

Structural connectivity asymmetry in the neonatal brain

Asymmetry of the neonatal brain is not yet understood at the level of structural connectivity. We utilized DTI deterministic tractography and structural network analysis based on graph theory to determine the pattern of structural connectivity asymmetry in 124 normal neonates. We tracted white matter axonal pathways characterizing interregional connections among brain regions and inferred asymmetry in left and right anatomical network properties. Our findings revealed that in neonates, small-world characteristics were exhibited, but did not differ between the two hemispheres, suggesting that neighboring brain regions connect tightly with each other, and that one region is only a few paths away from any other region within each hemisphere. Moreover, the neonatal brain showed greater structural efficiency in the left hemisphere than that in the right. In neonates, brain regions involved in motor, language, and memory functions play crucial roles in efficient communication in the left hemisphere, while brain regions involved in emotional processes play crucial roles in efficient communication in the right hemisphere. These findings suggest that even at birth, the topology of each cerebral hemisphere is organized in an efficient and compact manner that maps onto asymmetric functional specializations seen in adults, implying lateralized brain functions in infancy.

Locality preserving non-negative basis learning with graph embedding

The high dimensionality of connectivity networks necessitates the development of methods identifying the connectivity building blocks that not only characterize the patterns of brain pathology but also reveal representative population patterns. In this paper, we present a non-negative component analysis framework for learning localized and sparse sub-network patterns of connectivity matrices by decomposing them into two sets of discriminative and reconstructive bases. In order to obtain components that are designed towards extracting population differences, we exploit the geometry of the population by using a graphtheoretical scheme that imposes locality-preserving properties as well as maintaining the underlying distance between distant nodes in the original and the projected space. The effectiveness of the proposed framework is demonstrated by applying it to two clinical studies using connectivity matrices derived from DTI to study a population of subjects with ASD, as well as a developmental study of structural brain connectivity that extracts gender differences.