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

Mapping ADHD Heterogeneity and Biotypes through Topological Deviations in Morphometric Similarity Networks

Attention-deficit/hyperactivity disorder (ADHD) is characterized by considerable clinical heterogeneity. This study investigates whether normative modelling of topological properties derived from brain morphometry similarity networks can provide robust stratification markers for ADHD children. Leveraging multisite neurodevelopmental datasets (discovery: 446 ADHD, 708 controls; validation: 554 ADHD, 123 controls), we constructed morphometric similarity networks and developed normative models for three topological metrics: degree centrality, nodal efficiency, and participation coefficient. Through semi-supervised clustering, we delineated putative biotypes and examined their clinical profiles. We further contextualized brain profiles of these biotypes in terms of their neurochemical and functional correlates using large-scale databases, and assessed model generalizability in an independent cohort. ADHD exhibited atypical hub organization across all three topological metrics, with significant case-control differences primarily localized to a covarying multi-metric component in the orbitofrontal cortex. Three biotypes emerged: one characterized by severe overall symptoms and longitudinally persistent emotional dysregulation, accompanied by pronounced topological alterations in the medial prefrontal cortex and pallidum; a second by predominant hyperactivity/impulsivity accompanied by changes in the anterior cingulate cortex and pallidum; and a third by marked inattention with alterations in the superior frontal gyrus. These neural profiles of each biotype showed distinct neurochemical and functional correlates. Critically, the core findings were replicated in an independent validation cohort. Our comprehensive approach reveals three distinct ADHD biotypes with unique clinical-neural patterns, advancing our understanding of ADHD's neurobiological heterogeneity and laying the groundwork for personalized treatment.

Forming Connections: Functional Brain Connectivity is Associated With Executive Functioning Abilities in Early Childhood

Functional magnetic resonance imaging (fMRI) studies with adults provide evidence that functional brain networks, including the default mode network and frontoparietal network, underlie executive functioning (EF). However, given the challenges of using fMRI with infants and young children, little work has assessed the developmental trajectories of these networks or their associations with EF at key developmental stages. More recently, functional near-infrared spectroscopy (fNIRS) has emerged as a promising neuroimaging tool which can provide information on cortical functional networks and can be more easily implemented with young children. Children (N = 207; n = 116 male; n = 167 White) had fNIRS data recorded at infancy, 3, 5, and 7 years of age while watching a 2-min nonsocial video. At 3, 5, and 7 years, children completed behavioral assessments and parents completed questionnaires to assess child EF abilities. Results showed that, although early functional brain network connectivity was not associated with later functional brain connectivity, EF was concurrently and longitudinally associated with functional connectivity levels in both networks. Overall, these results inform the understanding of early emerging neural underpinnings of regulatory abilities and point to considerable change in the composition of functional brain networks and a conservation of function across development.

Language networks in Autism Spectrum Disorder: A systematic review of connectivity-based fMRI studies

Autism Spectrum Disorder (ASD) is a heterogeneous condition associated with differences in functional neural connectivity relative to neurotypical (NT) peers. Language-based functional connectivity represents an ideal context in which to characterize connectivity because language is heterogeneous and linked to core features in ASD, and NT language networks are well-defined. We conducted a systematic review of language-related functional connectivity literature on individuals with ASD using PubMed, PsychInfo, Scopus, ProQuest, and Google Scholar, yielding 96 studies. Language-task studies indicated local over-connectivity within the language network and global under-connectivity of language with out-of-network regions in ASD. Resting-state studies showed mixed patterns, and connectivity was associated ASD symptomology and language skills. This evidence indicates language-task elicited local over-connectivity and global under-connectivity in ASD, but not a local versus global distinction of resting-state language-related connectivity. Associations with behavior suggest that local over-connectivity and global under-connectivity characterize ASD, and heightened language-related connectivity may support social function.

Motor Functional Hierarchical Organization of Cerebrum and Its Underlying Genetic Architecture in Parkinson's Disease

Hierarchy has been identified as a principle underlying the organization of human brain networks. However, it remains unclear how the network hierarchy is disrupted in Parkinson's disease (PD) motor symptoms and how it is modulated by the underlying genetic architecture. The aim of this study was to explore alterations in the motor functional hierarchical organization of the cerebrum and their underlying genetic mechanism. In this study, the brain network hierarchy of each group was described through a connectome gradient analysis among 68 healthy controls (HC), 70 postural instability and gait difficulty (PIGD) subtype, and 69 tremor-dominant (TD) subtype, including both male and female participants, according to its motor symptoms. Furthermore, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control gradient differences were performed to identify genes associated with gradient alterations. Different PD motor subtypes exhibited contracted principal and secondary functional gradients relative to HC. The identified genes in different PD motor subtypes enriched for shared biological processes like metal ion transport and inorganic ion transmembrane transport. In addition, these genes were overexpressed in Ntsr+ neurons cell, enriched in extensive cortical regions and wide developmental time windows. Aberrant cerebral functional gradients in PD-related motor symptoms have been detected, and the motor-disturbed genes have shared biological functions. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying hierarchical alterations in PD.

Developmental dynamics of brain network modularity and temporal co-occurrence diversity in childhood

OBJECTIVE

Brain development during childhood involves significant structural, functional, and connectivity changes, reflecting the interplay between modularity, information interaction, and functional segregation. This study aims to understand the dynamic properties of brain connectivity and their impact on cognitive development, focusing on temporal co-occurrence diversity patterns.

METHODS

We recruited 481 children aged 6 to 12 years from the Healthy Brain Network database. Functional MRI data were used to construct dynamic functional connectivity matrices with a sliding window approach. Modular structures were identified using multilayer network community detection, and the Dagum Gini coefficient decomposition technique, which uniquely allows for multi-faceted exploration of modular temporal co-occurrence diversities, quantified these diversities. Mediation analysis assessed the impact on small-world properties.

RESULTS

Temporal co-occurrence diversity in brain networks increased with age, especially in the default mode, frontoparietal, and salience networks. These changes were driven by disparities within and between communities. The small-world coefficient increased with age, indicating improved information processing efficiency. To validate the impact of changes in spatiotemporal interaction disparities during childhood on information transmission within brain networks, we used mediation analysis to verify its effect on alterations in small-world properties.

CONCLUSION

This study highlights the critical developmental changes in brain modularity and spatiotemporal interaction patterns during childhood, emphasizing their role in cognitive maturation. These insights into neural mechanisms can inform the diagnosis and intervention of developmental disorders.

Premature birth changes wiring constraints in neonatal structural brain networks

Structural brain organization in infancy is associated with later cognitive, behavioral, and educational outcomes. Due to practical limitations, such as technological advancements and data availability of fetal MRI, there is still much we do not know about the early emergence of topological organization. We combine the developing Human Connectome Project's large infant dataset with generative network modeling to simulate the emergence of network organization over early development. Preterm infants had reduced connectivity, shorter connection lengths, and lower network efficiency compared to term-born infants. The models were able to recapitulate the organizational differences between term and preterm networks and revealed that preterm infant networks are better simulated under tighter wiring constraints than term infants. Tighter constraints for preterm models resulted in shorter connection lengths while preserving vital, long-range rich club connections. These simulations suggest that preterm birth is associated with a renegotiation of the cost-value wiring trade-off that may drive the emergence of different network organization.

Individual structural covariance connectome reveals aberrant brain developmental trajectories associated with childhood maltreatment

BACKGROUND

The long-term impact of childhood maltreatment (CM) on an individual's physical and mental health is suggested to be mediated by altered neurodevelopment. However, the exact neurobiological consequences of CM remain unclear.

METHODS

The present study aimed to investigate the relationship between CM and brain age based on structural magnetic resonance imaging data from a sample of 214 adults. The participants were divided into CM and non_CM groups according to Childhood Trauma Questionnaire. For each participant, brain connectome age was estimated from a large-scale structural covariance network through relevance vector regression. Brain predicted age difference (brain_PAD) was then calculated for each participant.

RESULTS

The brain connectome age matched well with chronological age in young adults (age range: 18-25 years) and adults (age range: 26-44 years) without CM, but not in individuals with CM. Compared with non_CM group, CM group was characterized by higher brain_PAD scores in young adults, whereas lower brain_PAD scores in adults. The finding revealed that brain development in individuals with CM seems to be accelerated in younger adults but retardation with increasing age. Moreover, individuals who suffered child abuse (but not neglect) showed higher brain_PAD scores than non_CM group, suggesting the different influence of abuse and neglect on neurodevelopment. Finally, the brain_PAD was positively correlated with attentional impulsivity in CM group.

CONCLUSIONS

CM affects different stages of adult brain development differently, and abuse and neglect have different influenced patterns, which may provide new evidence for the impact of CM on structural brain development.

Mother-child closeness and adolescent structural neural networks: a prospective longitudinal study of low-income families

Mother-child closeness, a mutually trusting and affectionate bond, is an important factor in shaping positive youth development. However, little is known about the neural pathways through which mother-child closeness is related to brain organization. Utilizing a longitudinal sample primarily from low-income families (N = 181; 76% African American youth and 54% female), this study investigated the associations between mother-child closeness at ages 9 and 15 years and structural connectivity organization (network integration, robustness, and segregation) at age 15 years. The assessment of mother-child closeness included perspectives from both mother and child. The results revealed that greater mother-child closeness is linked with increased global efficiency and transitivity, but not with modularity. Specifically, both the mother's and child's reports of closeness at age 15 years predicted network metrics, but report at age 9 years did not. Our findings suggest that mother-child closeness is associated with neural white matter organization, as adolescents who experienced greater mother-child closeness displayed topological properties indicative of more integrated and robust structural networks.

Functional connectivity of the pediatric brain

PURPOSE

This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders.

METHODS

The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents.

RESULTS

Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders.

CONCLUSION

Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies.

Functional network modules overlap and are linked to interindividual connectome differences during human brain development

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

Unconscious and Conscious Gaze-Triggered Attentional Orienting: Distinguishing Innate and Acquired Components of Social Attention in Children and Adults with Autistic Traits and Autism Spectrum Disorders

Typically developing (TD) individuals can readily orient attention according to others' eye-gaze direction, an ability known as social attention, which involves both innate and acquired components. To distinguish between these two components, we used a critical flicker fusion technique to render gaze cues invisible to participants, thereby largely reducing influences from consciously acquired strategies. Results revealed that both visible and invisible gaze cues could trigger attentional orienting in TD adults (aged 20 to 30 years) and children (aged 6 to 12 years). Intriguingly, only the ability to involuntarily respond to invisible gaze cues was negatively correlated with autistic traits among all TD participants. This ability was substantially impaired in adults with autism spectrum disorder (ASD) and in children with high autistic traits. No such association or reduction was observed with visible gaze cues. These findings provide compelling evidence for the functional demarcation of conscious and unconscious gaze-triggered attentional orienting that emerges early in life and develops into adulthood, shedding new light on the differentiation of the innate and acquired aspects of social attention. Moreover, they contribute to a comprehensive understanding of social endophenotypes of ASD.

Screen time, brain network development and socio-emotional competence in childhood: moderation of associations by parent-child reading

BACKGROUND

Screen time in infancy is linked to changes in social-emotional development but the pathway underlying this association remains unknown. We aim to provide mechanistic insights into this association using brain network topology and to examine the potential role of parent-child reading in mitigating the effects of screen time.

METHODS

We examined the association of screen time on brain network topology using linear regression analysis and tested if the network topology mediated the association between screen time and later socio-emotional competence. Lastly, we tested if parent-child reading time was a moderator of the link between screen time and brain network topology.

RESULTS

Infant screen time was significantly associated with the emotion processing-cognitive control network integration (p = 0.005). This network integration also significantly mediated the association between screen time and both measures of socio-emotional competence (BRIEF-2 Emotion Regulation Index, p = 0.04; SEARS total score, p = 0.04). Parent-child reading time significantly moderated the association between screen time and emotion processing-cognitive control network integration (β = -0.640, p = 0.005).

CONCLUSION

Our study identified emotion processing-cognitive control network integration as a plausible biological pathway linking screen time in infancy and later socio-emotional competence. We also provided novel evidence for the role of parent-child reading in moderating the association between screen time and topological brain restructuring in early childhood.

Disrupted morphological brain network organization in subjective cognitive decline and mild cognitive impairment

We aim to investigate the alterations in gray matter for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) from the perspective of the human connectome. High-resolution T1-weighted images were acquired from 54 patients with SCD, 95 patients with MCI, and 65 healthy controls (HC). Morphological brain networks (MBN) were constructed using similarities in the distribution of gray matter volumes between regions. The strength of morphological connections and topographic metrics derived from the graph-theoretical analysis were compared. Furthermore, we assessed the relationship between the observed morphological abnormalities and disease severity. According to the results, we found a significantly decreased morphological connection between the somatomotor network and ventral attention network in SCD compared to HC and MCI compared to SCD. The graph-theoretic analysis illustrated disruptions in the whole network organization, where the normalized shortest path increased and the global efficiency (Eg) decreased in MCI compared to SCD. In addition, Montreal Cognitive Assessment scores of SCD patients had a significantly negative correlation with Eg. The primary limitations of the present study include the cross-sectional design, no enrolled AD patients, no assessment of amyloidosis, and the need for more comprehensive neuropsychological tests. Our findings indicate the abnormalities of morphological networks at early stages in the AD continuum, which could be interpreted as compensatory changes to retain a normal level of cognitive function. The present study could provide new insight into the mechanism of AD.

Combining multimodal connectivity information improves modelling of pathology spread in Alzheimer's disease

Cortical atrophy and aggregates of misfolded tau proteins are key hallmarks of Alzheimer's disease. Computational models that simulate the propagation of pathogens between connected brain regions have been used to elucidate mechanistic information about the spread of these disease biomarkers, such as disease epicentres and spreading rates. However, the connectomes that are used as substrates for these models are known to contain modality-specific false positive and false negative connections, influenced by the biases inherent to the different methods for estimating connections in the brain. In this work, we compare five types of connectomes for modelling both tau and atrophy patterns with the network diffusion model, which are validated against tau PET and structural MRI data from individuals with either mild cognitive impairment or dementia. We then test the hypothesis that a joint connectome, with combined information from different modalities, provides an improved substrate for the model. We find that a combination of multimodal information helps the model to capture observed patterns of tau deposition and atrophy better than any single modality. This is validated with data from independent datasets. Overall, our findings suggest that combining connectivity measures into a single connectome can mitigate some of the biases inherent to each modality and facilitate more accurate models of pathology spread, thus aiding our ability to understand disease mechanisms, and providing insight into the complementary information contained in different measures of brain connectivity.

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

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

The adaptive stochasticity hypothesis: Modeling equifinality, multifinality, and adaptation to adversity

Neural phenotypes are the result of probabilistic developmental processes. This means that stochasticity is an intrinsic aspect of the brain as it self-organizes over a protracted period. In other words, while both genomic and environmental factors shape the developing nervous system, another significant-though often neglected-contributor is the randomness introduced by probability distributions. Using generative modeling of brain networks, we provide a framework for probing the contribution of stochasticity to neurodevelopmental diversity. To mimic the prenatal scaffold of brain structure set by activity-independent mechanisms, we start our simulations from the medio-posterior neonatal rich club (Developing Human Connectome Project, n = 630). From this initial starting point, models implementing Hebbian-like wiring processes generate variable yet consistently plausible brain network topologies. By analyzing repeated runs of the generative process (>107 simulations), we identify critical determinants and effects of stochasticity. Namely, we find that stochastic variation has a greater impact on brain organization when networks develop under weaker constraints. This heightened stochasticity makes brain networks more robust to random and targeted attacks, but more often results in non-normative phenotypic outcomes. To test our framework empirically, we evaluated whether stochasticity varies according to the experience of early-life deprivation using a cohort of neurodiverse children (Centre for Attention, Learning and Memory; n = 357). We show that low-socioeconomic status predicts more stochastic brain wiring. We conclude that stochasticity may be an unappreciated contributor to relevant developmental outcomes and make specific predictions for future research.

Structural brain network connectivity in trichotillomania (hair-pulling disorder)

Neuroimaging studies suggest involvement of frontal, striatal, limbic and cerebellar regions in trichotillomania, an obsessive-compulsive related disorder. However, findings regarding the underlying neural circuitry remains limited and inconsistent. Graph theoretical analysis offers a way to identify structural brain networks in trichotillomania. T1-weighted MRI scans were acquired in adult females with trichotillomania (n = 23) and healthy controls (n = 16). Graph theoretical analysis was used to investigate structural networks as derived from cortical thickness and volumetric FreeSurfer output. Hubs, brain regions with highest connectivity in the global network, were identified, and group differences were determined. Regions with highest connectivity on a regional level were also determined. There were no differences in small-worldness or other network measures between groups. Hubs in the global network of trichotillomania patients included temporal, parietal, and occipital regions (at 2SD above mean network connectivity), as well as frontal and striatal regions (at 1SD above mean network connectivity). In contrast, in healthy controls hubs at 2SD represented different frontal, parietal and temporal regions, while at 1SD hubs were widespread. The inferior temporal gyrus, involved in object recognition as part of the ventral visual pathway, had significantly higher connectivity on a global and regional level in trichotillomania. The study included women only and sample size was limited. This study adds to the trichotillomania literature on structural brain network connectivity. Our study findings are consistent with previous studies that have implicated somatosensory, sensorimotor and frontal-striatal circuitry in trichotillomania, and partially overlap with structural connectivity findings in obsessive-compulsive disorder.

Multiscale entropy of ADHD children during resting state condition

This present study aims to investigate neural mechanisms underlying ADHD compared to healthy children through the analysis of the complexity and the variability of the EEG brain signal using multiscale entropy (MSE), EEG signal standard deviation (SDs), as well as the mean, standard deviation (SDp) and coefficient of variation (CV) of absolute spectral power (PSD). For this purpose, a sample of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) between 6 and 17 years old were selected based on the number of trials and diagnostic agreement, 32 for the open-eyes (OE) experimental condition and 25 children for the close-eyes (CE) experimental condition. Healthy control subjects were age- and gender-matched with the ADHD group. The MSE and SDs of resting-state EEG activity were calculated on 34 time scales using a coarse-grained procedure. In addition, the PSD was averaged in delta, theta, alpha, and beta frequency bands, and its mean, SDp, and CV were calculated. The results show that the MSE changes with age during development, increases as the number of scales increases and has a higher amplitude in controls than in ADHD. The absolute PSD results show CV differences between subjects in low and beta frequency bands, with higher variability values in the ADHD group. All these results suggest an increased EEG variability and reduced complexity in ADHD compared to controls.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09869-0.

Functional and Structural Longitudinal Change Patterns in Adolescent Brain

Functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) are two widely used techniques to analyze longitudinal brain functional and structural change in adolescents. Although longitudinal changes in intrinsic functional and structural changes have been studied separately, most studies focus on univariate change rather than estimating multivariate patterns of functional network connectivity (FNC) and gray matter (GM) changes with increased age. To analyze whole-brain structural and functional changes with increased age, we suggest two complementary techniques (1: linking of functional change pattern (FCP) to voxel-wise ∆GM and 2: the connection between FCP and structural change pattern (SCP)). In this study, we apply our approaches to the functional and GM data from the large-scale Adolescent Brain and Cognitive Development (ABCD) data. We find a significant correlation between FCP and voxel-wise ∆GM for two components. We also investigate the links between FCP and SCP and hypothesize that functional connectivity and GM continue to exhibit linked changes during adolescence.Clinical Relevance- This work captures the whole-brain functional and structural change patterns link by introducing two complementary techniques.

Early childhood household instability, adolescent structural neural network architecture, and young adulthood depression: A 21-year longitudinal study

Unstable and unpredictable environments are linked to risk for psychopathology, but the underlying neural mechanisms that explain how instability relate to subsequent mental health concerns remain unclear. In particular, few studies have focused on the association between instability and white matter structures despite white matter playing a crucial role for neural development. In a longitudinal sample recruited from a population-based study (N = 237), household instability (residential moves, changes in household composition, caregiver transitions in the first 5 years) was examined in association with adolescent structural network organization (network integration, segregation, and robustness of white matter connectomes; Mage = 15.87) and young adulthood anxiety and depression (six years later). Results indicate that greater instability related to greater global network efficiency, and this association remained after accounting for other types of adversity (e.g., harsh parenting, neglect, food insecurity). Moreover, instability predicted increased depressive symptoms via increased network efficiency even after controlling for previous levels of symptoms. Exploratory analyses showed that structural connectivity involving the left fronto-lateral and temporal regions were most strongly related to instability. Findings suggest that structural network efficiency relating to household instability may be a neural mechanism of risk for later depression and highlight the ways in which instability modulates neural development.

Altered longitudinal structural connectome in paediatric mild traumatic brain injury: an Advancing Concussion Assessment in Paediatrics study

Advanced diffusion-weighted imaging techniques have increased understanding of the neuropathology of paediatric mild traumatic brain injury (i.e. concussion). Most studies have examined discrete white-matter pathways, which may not capture the characteristically subtle, diffuse and heterogenous effects of paediatric concussion on brain microstructure. This study compared the structural connectome of children with concussion to those with mild orthopaedic injury to determine whether network metrics and their trajectories across time post-injury differentiate paediatric concussion from mild traumatic injury more generally. Data were drawn from of a large study of outcomes in paediatric concussion. Children aged 8-16.99 years were recruited from five paediatric emergency departments within 48 h of sustaining a concussion (n = 360; 56% male) or mild orthopaedic injury (n = 196; 62% male). A reliable change score was used to classify children with concussion into two groups: concussion with or without persistent symptoms. Children completed 3 T MRI at post-acute (2-33 days) and/or chronic (3 or 6 months, via random assignment) post-injury follow-ups. Diffusion-weighted images were used to calculate the diffusion tensor, conduct deterministic whole-brain fibre tractography and compute connectivity matrices in native (diffusion) space for 90 supratentorial regions. Weighted adjacency matrices were constructed using average fractional anisotropy and used to calculate global and local (regional) graph theory metrics. Linear mixed effects modelling was performed to compare groups, correcting for multiple comparisons. Groups did not differ in global network metrics. However, the clustering coefficient, betweenness centrality and efficiency of the insula, cingulate, parietal, occipital and subcortical regions differed among groups, with differences moderated by time (days) post-injury, biological sex and age at time of injury. Post-acute differences were minimal, whereas more robust alterations emerged at 3 and especially 6 months in children with concussion with persistent symptoms, albeit differently by sex and age. In the largest neuroimaging study to date, post-acute regional network metrics distinguished concussion from mild orthopaedic injury and predicted symptom recovery 1-month post-injury. Regional network parameters alterations were more robust and widespread at chronic timepoints than post-acutely after concussion. Results suggest that increased regional and local subnetwork segregation (modularity) and inefficiency occurs across time after concussion, emerging after post-concussive symptom resolve in most children. These differences persist up to 6 months after concussion, especially in children who showed persistent symptoms. While prognostic, the small to modest effect size of group differences and the moderating effects of sex likely would preclude effective clinical application in individual patients.

Classification of major depressive disorder using an attention-guided unified deep convolutional neural network and individual structural covariance network

Major depressive disorder (MDD) is the second leading cause of disability worldwide. Currently, the structural magnetic resonance imaging-based MDD diagnosis models mainly utilize local grayscale information or morphological characteristics in a single site with small samples. Emerging evidence has demonstrated that different brain structures in different circuits have distinct developmental timing, but mature coordinately within the same functional circuit. Thus, establishing an attention-guided unified classification framework with deep learning and individual structural covariance networks in a large multisite dataset could facilitate developing an accurate diagnosis strategy. Our results showed that attention-guided classification could improve the classification accuracy from primary 75.1% to ultimate 76.54%. Furthermore, the discriminative features of regional covariance connectivities and local structural characteristics were found to be mainly located in prefrontal cortex, insula, superior temporal cortex, and cingulate cortex, which have been widely reported to be closely associated with depression. Our study demonstrated that our attention-guided unified deep learning framework may be an effective tool for MDD diagnosis. The identified covariance connectivities and structural features may serve as biomarkers for MDD.

Computational Models of Typical and Atypical Brain Network Development

Over the last decade, the organization of brain networks at both micro- and macroscales has become a key focus of neuroscientific inquiry. This has revealed fundamental features of brain network organization-small-worldness, modularity, heavy-tailed degree distributions-and has highlighted how these structural features support brain function. However, the driving forces that shape brain networks over the course of development have begun to be explored only recently. Here, we review recent efforts to gain insights into the mechanisms of brain development through generative modeling of both macroscale human brain networks and microscale cellular connectomes in Caenorhabditis elegans and other organisms. We show how these mathematical models can begin to shed light on the biological processes that drive and constrain the development of brain networks. Finally, we show how generative network models can translate genetic and environmental differences into variability in developmental trajectories, leading to diverse cognitive and mental health outcomes in children and young people.

Cognitive control training enhances the integration of intrinsic functional networks in adolescents

Introduction

We have demonstrated that intensive cognitive training can produce sustained improvements in cognitive performance in adolescents. Few studies, however, have investigated the neural basis of these training effects, leaving the underlying mechanism of cognitive plasticity during this period unexplained.

Methods

In this study, we trained 51 typically developing adolescents on cognitive control tasks and examined how their intrinsic brain networks changed by applying graph theoretical analysis. We hypothesized that the training would accelerate the process of network integration, which is a key feature of network development throughout adolescence.

Results

We found that the cognitive control training enhanced the integration of functional networks, particularly the cross-network integration of the cingulo-opercular network. Moreover, the analysis of additional data from older adolescents revealed that the cingulo-opercular network was more integrated with other networks in older adolescents than in young adolescents.

Discussion

These findings are consistent with the hypothesis that cognitive control training may speed up network development, such that brain networks exhibit more mature patterns after training.

Reduced basal ganglia tissue-iron concentration in school-age children with attention-deficit/hyperactivity disorder is localized to limbic circuitry

Dopamine-related abnormalities in the basal ganglia have been implicated in attention-deficit/hyperactivity disorder (ADHD). Iron plays a critical role in supporting dopaminergic function, and reduced brain iron and serum ferritin levels have been linked to ADHD symptom severity in children. Furthermore, the basal ganglia is a central brain region implicated in ADHD psychopathology and involved in motor and reward functions as well as emotional responding. The present study repurposed diffusion tensor imaging (DTI) to examine effects of an ADHD diagnosis and sex on iron deposition within the basal ganglia in children ages 8-12 years. We further explored associations between brain iron levels and ADHD symptom severity and affective symptoms. We observed reduced iron levels in children with ADHD in the bilateral limbic region of the striatum, as well as reduced levels of iron-deposition in males in the sensorimotor striatal subregion, regardless of diagnosis. Across the whole sample, iron-deposition increased with age in all regions. Brain-behavior analyses revealed that, across diagnostic groups, lower tissue-iron levels in bilateral limbic striatum correlated with greater ADHD symptom severity, whereas lower tissue-iron levels in the left limbic striatum only correlated with anxious, depressive and affective symptom severity. This study sheds light on the neurobiological underpinnings of ADHD, specifically highlighting the localization of tissue-iron deficiency in limbic regions, and providing support for repurposing DTI for brain iron analyses. Our findings highlight the need for further investigation of iron as a biomarker in the diagnosis and treatment of ADHD and sex differences.

Maternal warmth is associated with network segregation across late childhood: A longitudinal neuroimaging study

The negative impact of adverse experiences in childhood on neurodevelopment is well documented. Less attention however has been given to the impact of variations in “normative” parenting behaviors. The influence of these parenting behaviors is likely to be marked during periods of rapid brain reorganization, such as late childhood. The aim of the current study was to investigate associations between normative parenting behaviors and the development of structural brain networks across late childhood. Data were collected from a longitudinal sample of 114 mother-child dyads (54% female children, M age 8.41 years, SD = 0.32 years), recruited from low socioeconomic areas of Melbourne, Australia. At the first assessment parenting behaviors were coded from two lab-based interaction tasks and structural magnetic resonance imaging (MRI) scans of the children were performed. At the second assessment, approximately 18 months later (M age 9.97 years, SD = 0.37 years) MRI scans were repeated. Cortical thickness (CT) was extracted from T1-weighted images using FreeSurfer. Structural covariance (SC) networks were constructed from partial correlations of CT estimates between brain regions and estimates of network efficiency and modularity were obtained for each time point. The change in these network measures, from Time 1 to Time 2, was also calculated. At Time 2, less positive maternal affective behavior was associated with higher modularity (more segregated networks), while negative maternal affective behavior was not related. No support was found for an association between local or global efficacy and maternal affective behaviors at Time 2. Similarly, no support was demonstrated for associations between maternal affective behaviors and change in network efficiency and modularity, from Time 1 to Time 2. These results indicate that normative variations in parenting may influence the development of structural brain networks in late childhood and extend current knowledge about environmental influences on structural connectivity in a developmental context.

Correction of global physiology in resting-state functional near-infrared spectroscopy

Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

Functional individual variability development of the neonatal brain

Individual variability in cognition and behavior results from the differences in brain structure and function that have already emerged before birth. However, little is known about individual variability in brain functional architecture at local level in neonates which is of great significance to explore owing to largely undeveloped long-range functional connectivity and segregated functions in early brain development. To address this, resting-state fMRI data of 163 neonates ranged from 32 to 45 postconceptional weeks (PCW) were used in this study, and various functional features including functional parcellation similarity, local brain activity and local functional connectivity were used to characterize individual functional variability. We observed significantly higher local functional individual variability in superior parietal, sensorimotor, and visual cortex, and lower variability in the frontal, insula and cingulate cortex relative to other regions within each hemisphere. The mean local functional individual variability significantly increased with age, and the age effect was found larger in brain regions such as the occipital, temporal, prefrontal and parietal cortex. Our findings promote the understanding of brain plasticity and regional differential maturation in the early stage.

The “Primitive Brain Dysfunction” Theory of Autism: The Superior Colliculus Role

A better understanding of the pathogenesis of autism will help clarify our conception of the complexity of normal brain development. The crucial deficit may lie in the postnatal changes that vision produces in the brainstem nuclei during early life. The superior colliculus is the primary brainstem visual center. Although difficult to examine in humans with present techniques, it is known to support behaviors essential for every vertebrate to survive, such as the ability to pay attention to relevant stimuli and to produce automatic motor responses based on sensory input. From birth to death, it acts as a brain sentinel that influences basic aspects of our behavior. It is the main brainstem hub that lies between the environment and the rest of the higher neural system, making continuous, implicit decisions about where to direct our attention. The conserved cortex-like organization of the superior colliculus in all vertebrates allows the early appearance of primitive emotionally-related behaviors essential for survival. It contains first-line specialized neurons enabling the detection and tracking of faces and movements from birth. During development, it also sends the appropriate impulses to help shape brain areas necessary for social-communicative abilities. These abilities require the analysis of numerous variables, such as the simultaneous evaluation of incoming information sustained by separate brain networks (visual, auditory and sensory-motor, social, emotional, etc.), and predictive capabilities which compare present events to previous experiences and possible responses. These critical aspects of decision-making allow us to evaluate the impact that our response or behavior may provoke in others. The purpose of this review is to show that several enigmas about the complexity of autism might be explained by disruptions of collicular and brainstem functions. The results of two separate lines of investigation: 1. the cognitive, etiologic, and pathogenic aspects of autism on one hand, and two. the functional anatomy of the colliculus on the other, are considered in order to bridge the gap between basic brain science and clinical studies and to promote future research in this unexplored area.

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

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

Enhanced immune activation following acute social stress among adolescents with early life adversity

Background

Early-life adversity (ELA) has been linked to higher depression risk across the life span and chronic inflammatory conditions that contribute to earlier mortality. In this study, we characterized innate immune responses to acute social stress in a community sample of adolescents (mean age = 13.9 ± 1.6 years; 46.4% female) as a potential pathway linking ELA and depression pathogenesis.

Methods

Parents reported their child's exposure to 9 ELAs, and adolescents participated in the Trier Social Stress Test for Children, with blood collected immediately before and then at 60 and 90 minutes thereafter. Overall, 65 adolescents had complete data for analysis of stress-induced changes in gene expression and 84 adolescents had complete data for circulating inflammatory markers.

Results

Relative to adolescents exposed to no ELA (11.9%) or low ELA (ELA = 1-3; 67.9%), those exposed to high ELA (ELA = 4+; 20.2%) showed larger stress-associated increases in expression of both proinflammatory and innate antiviral gene transcripts in circulating blood. Consistent with a potential mediating role of sympathetic nervous system activity, promoter-based bioinformatics analyses implicated CREB transcription factor activity in structuring observed gene expression differences. These effects were accompanied by a smaller initial but protracted increase in circulating interleukin 6 in adolescents with high ELA.

Conclusions

Results are consistent with the hypothesis that ELA may enhance cellular and gene regulatory reactivity to stress, which may, in turn, increase vulnerability to depression and other inflammation-related disease processes.

Dissect Relationships Between Gene Co-expression and Functional Connectivity in Human Brain

Although recent evidence indicates an association between gene co-expression and functional connectivity in human brain, specific association patterns remain largely unknown. Here, using neuroimaging-based functional connectivity data of living brains and brain-wide gene expression data of postmortem brains, we performed comprehensive analyses to dissect relationships between gene co-expression and functional connectivity. We identified 125 connectivity-related genes (20 novel genes) enriched for dendrite extension, signaling pathway and schizophrenia, and 179 gene-related functional connections mainly connecting intra-network regions, especially homologous cortical regions. In addition, 51 genes were associated with connectivity in all brain functional networks and enriched for action potential and schizophrenia; in contrast, 51 genes showed network-specific modulatory effects and enriched for ion transportation. These results indicate that functional connectivity is unequally affected by gene expression, and connectivity-related genes with different biological functions are involved in connectivity modulation of different networks.

Resting-state EEG Connectivity in Young Children with ADHD

Objective: Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and impairing neurodevelopmental disorder. While early childhood is a crucial time for early intervention, it is characterized by instability of ADHD diagnosis. Neural correlates of ADHD have potential to improve diagnostic accuracy; however, minimal research has focused on early childhood. Research indicates that disrupted neural connectivity is associated with ADHD in older children. Here, we explore network connectivity as a potential neural correlate of ADHD diagnosis in early childhood.Method: We collected EEG data in 52 medication-naïve children with ADHD and in 77 typically developing controls (3-7 years). Data was collected with the EGI 128 HydroCel Sensor Net System, but to optimize the ICA, the data was down sampled to the 10-10 system. Connectivity was measured as the synchronization of the time series of each pair of electrodes. Subsequent analyses utilized graph theoretical methods to further characterize network connectivity.Results: Increased global efficiency, which measures the efficiency of information transfer across the entire brain, was associated with increased inattentive symptom severity. Further, this association was robust to controls for age, IQ, SES, and internalizing psychopathology.Conclusions: Overall, our findings indicate that increased global efficiency, which suggests a hyper-connected neural network, is associated with elevated ADHD symptom severity. These findings extend previous work reporting disruption of neural network connectivity in older children with ADHD into early childhood.

Altered topographical organization of grey matter structural network in early-onset schizophrenia

Schizophrenia is characterized by both disrupted neurodevelopmental processes and abnormal brain connectivity. However, few studies have examined the atypical features of brain network topography associated with schizophrenia during childhood and adolescence. We used graph theory to compare the grey matter structural networks of individuals (aged 10-15 years) with early-onset schizophrenia (EOS) (n = 25) and a typically-developing (TD) comparison group (n = 31). Compared with the TD group, EOS patients showed significantly increased clustering and local efficiency across a range of network densities (0.3 - 0.4). The network of EOS patients also had more modules (6 modules in EOS vs. 3 modules in controls), indicating a more segregated network at the cost of functional integration. Although our results were preliminary and failed to survive corrections for multiple comparisons, EOS patients might be characterized by altered nodal centrality in several higher-order associative regions including the prefrontal cortex, the hippocampus and the cerebellum. The EOS structural network also lacked the typical left-hemispheric-dominant hub distribution compared with the TD group. These findings suggest that brain structural network was not only globally but also regionally altered in EOS patients.

Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence

Functional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.

Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

A generative network model of neurodevelopmental diversity in structural brain organization

The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition.

Individual Uniqueness in the Neonatal Functional Connectome

The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0-30 mm) and middle-range (30-60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

Organizing principles of the C. elegans contactome

Two recent studies published in Nature generate and analyze, for the first time, the network of ∼100,000 membrane contacts between neurons in the C. elegans nerve ring. These novel data, extracted from legacy electron microscographs, represent a shift in our understanding of C. elegans neural networks and their similarity to mammalian brains.

The development of structural covariance networks during the transition from childhood to adolescence

Structural covariance conceptualizes how morphologic properties of brain regions are related to one another (across individuals). It can provide unique information to cortical structure (e.g., thickness) about the development of functionally meaningful networks. The current study investigated how structural covariance networks develop during the transition from childhood to adolescence, a period characterized by marked structural re-organization. Participants (N = 192; scans = 366) completed MRI assessments between 8.5 and 14.5 years of age. A sliding window approach was used to create “age-bins”, and structural covariance networks (based on cortical thickness) were created for each bin. Next, generalized additive models were used to characterize trajectories of age-related changes in network properties. Results revealed nonlinear trajectories with “peaks” in mean correlation and global density that are suggestive of a period of convergence in anatomical properties across the cortex during early adolescence, prior to regional specialization. “Hub” regions in sensorimotor cortices were present by late childhood, but the extent and strength of association cortices as “hubs” increased into mid-adolescence. Moreover, these regional changes were found to be related to rates of thinning across the cortex. In the context of neurocognitive networks, the frontoparietal, default mode, and attention systems exhibited age-related increases in within-network and between-network covariance. These regional and modular developmental patterns are consistent with continued refinement of socioemotional and other complex executive functions that are supported by higher-order cognitive networks during early adolescence.

Structural and functional brain network alterations in prenatal alcohol exposed neonates

Prenatal alcohol exposure leads to alterations in cognition, behavior and underlying brain architecture. However, prior studies have not integrated structural and functional imaging data in children with prenatal alcohol exposure. The aim of this study was to characterize disruptions in both structural and functional brain network organization after prenatal alcohol exposure in very early life. A group of 11 neonates with prenatal alcohol exposure and 14 unexposed controls were investigated using diffusion weighted structural and resting state functional magnetic resonance imaging. Covariance networks were created using graph theoretical analyses for each data set, controlling for age and sex. Group differences in global hub arrangement and regional connectivity were determined using nonparametric permutation tests. Neonates with prenatal alcohol exposure and controls exhibited similar global structural network organization. However, global functional networks of neonates with prenatal alcohol exposure comprised of temporal and limbic hubs, while hubs were more distributed in controls representing an early default mode network. On a regional level, controls showed prominent structural and functional connectivity in parietal and occipital regions. Neonates with prenatal alcohol exposure showed regionally, predominant structural and functional connectivity in several subcortical regions and occipital regions. The findings suggest early functional disruption on a global and regional level after prenatal alcohol exposure and indicate suboptimal organization of functional networks. These differences likely underlie sensory dysregulation and behavioral difficulties in prenatal alcohol exposure.

Towards understanding neurocognitive mechanisms of parenting: Maternal behaviors and structural brain network organization in late childhood

A substantial body of knowledge suggests that exposure to adverse family environments - including violence and neglect - influences many aspects of brain development. Relatively less attention has been directed toward the influence of "normative" differences in parenting behaviors. Given the rapid brain reorganization during late childhood, parenting behaviors are particularly likely to impact the structure of the brain during this time. This study investigated associations between maternal parenting behaviors and the organization of structural brain networks in late childhood, as measured by structural covariance. One hundred and forty-five typically developing 8-year-olds and their mothers completed questionnaire measures and two observed interaction tasks; magnetic resonance imaging (MRI) scans were obtained from the children. Measures of maternal negative, positive, and communicative behavior were derived from the interaction tasks. Structural covariance networks based on partial correlations between cortical thickness estimates were constructed and estimates of modularity were obtained using graph theoretical analysis. High levels of negative maternal behavior were associated with low modularity. Minimal support was found for an association between positive maternal behaviors and modularity and between maternal communicative behaviors and modularity. Our findings suggest that variation in negative maternal behavior is associated with the structural organization of brain networks in children.

Far and wide: Associations between childhood socio-economic status and brain connectomics

Previous studies have identified localized associations between childhood environment - namely their socio-economic status (SES) - and particular neural structures. The primary aim of the current study was to test whether associations between SES and brain structure are widespread or limited to specific neural pathways. We employed advances in whole-brain structural connectomics to address this. Diffusion tensor imaging was used to construct whole-brain connectomes in 113 6-12 year olds. We then applied an adapted multi-block partial-least squares (PLS) regression to explore how connectome organisation is associated with childhood SES (parental income, education levels, and neighbourhood deprivation). The Fractional Anisotropy (FA) connectome was significantly associated with childhood SES and this effect was widespread. We then pursued a secondary aim, and demonstrated that the connectome mediated the relationship between SES and cognitive ability (matrix reasoning and vocabulary). However, the connectome did not significantly mediate SES relationships with academic ability (maths and reading) or internalising and externalising behavior. This multivariate approach is important for advancing our theoretical understanding of how brain development may be shaped by childhood environment, and the role that it plays in predicting key outcomes. We also discuss the limitations with this new methodological approach.

Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients' families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.

Altered structural connectome and motor problems of very preterm born children at school-age

Infants born very preterm (<32 weeks of gestation) show distinct cognitive and motor problems throughout childhood. This study aims 1) to investigate differences in the structural connectome between very preterm born children and term born controls at school-age, and 2) to examine the relationship of the structural connectome with cognitive and motor problems. This study included 29 very preterm (12 males, mean age 8.6 years) and 52 term born peers (25 males, mean age 8.7 years). Wechsler Intelligence Scale for Children and Movement Assessment Battery for Children were used. Brain network measures of smallworldness, clustering coefficient and shortest path length based on fiber density of white matter tracts were determined from Diffusion Tensor Imaging data using probabilistic tractography. Smallworldness (F(1,79) = -2.09, p = .04, d = 0.52) and clustering coefficient (F(1,79) = -2.63, p = .01, d = 0.64) were significantly higher for very preterm children as compared to term peers. For Total Motor Impairment score and Manual Dexterity, there was a significant interaction between group and smallworldness (Beta = -10.81, p = .03 and Beta = -2.99, p = .004, respectively). Greater Total Motor Impairment and poorer Manual Dexterity were only significantly related to higher smallworldness in term controls (r = 0.35, p = .01 and r = 0.27, p = .04, respectively). Poorer Ball Skills were significantly related to higher smallworldness in both groups (Beta = -0.30, p = .03). This study clearly shows a more segregated network organization in very preterm children as compared to term peers. Importantly, motor problems go together with altered organization of the structural connectome in term born children, whereas this potential compensational process is only found for Ball Skills for very preterm children.

Generative network models of altered structural brain connectivity in schizophrenia

Alterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of altered brain network formation. We evaluated different generative network models in healthy controls (n=152), schizophrenia patients (n=66), and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated how these factors relate to cognition and to polygenic risk for schizophrenia. Our data show that among the four tested classes of generative network models, structural brain networks were optimally accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation across study groups. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. Further exploratory analyses point to potential associations of the model parameter reflecting spatial constraints with the polygenic risk for schizophrenia and cognitive performance. Our results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to regular brain network formation as well as altered connectomes in schizophrenia and healthy individuals at familial risk for schizophrenia. On an exploratory level, our data further point to the potential relevance of spatial constraints for the genetic risk for schizophrenia and general cognitive functioning, thereby encouraging future studies in following up on these observations to gain further insights into the biological basis and behavioral relevance of model parameters.