Genetic control of functional brain network efficiency in children

Cerebellocerebral connectivity predicts body mass index: a new open-source Python-based framework for connectome-based predictive modeling

BACKGROUND

The cerebellum is one of the major central nervous structures consistently altered in obesity. Its role in higher cognitive function, parts of which are affected by obesity, is mediated through projections to and from the cerebral cortex. We therefore investigated the relationship between body mass index (BMI) and cerebellocerebral connectivity.

METHODS

We utilized the Human Connectome Project's Young Adults dataset, including functional magnetic resonance imaging (fMRI) and behavioral data, to perform connectome-based predictive modeling (CPM) restricted to cerebellocerebral connectivity of resting-state fMRI and task-based fMRI. We developed a Python-based open-source framework to perform CPM, a data-driven technique with built-in cross-validation to establish brain-behavior relationships. Significance was assessed with permutation analysis.

RESULTS

We found that (i) cerebellocerebral connectivity predicted BMI, (ii) task-general cerebellocerebral connectivity predicted BMI more reliably than resting-state fMRI and individual task-based fMRI separately, (iii) predictive networks derived this way overlapped with established functional brain networks (namely, frontoparietal networks, the somatomotor network, the salience network, and the default mode network), and (iv) we found there was an inverse overlap between networks predictive of BMI and networks predictive of cognitive measures adversely affected by overweight/obesity.

CONCLUSIONS

Our results suggest obesity-specific alterations in cerebellocerebral connectivity, specifically with regard to task execution. With brain areas and brain networks relevant to task performance implicated, these alterations seem to reflect a neurobiological substrate for task performance adversely affected by obesity.

Engineered cortical microcircuits for investigations of neuroplasticity

Recent advances in neural engineering have opened new ways to investigate the impact of topology on neural network function. Leveraging microfluidic technologies, it is possible to establish modular circuit motifs that promote both segregation and integration of information processing in the engineered neural networks, similar to those observed in vivo. However, the impact of the underlying topologies on network dynamics and response to pathological perturbation remains largely unresolved. In this work, we demonstrate the utilization of microfluidic platforms with 12 interconnected nodes to structure modular, cortical engineered neural networks. By implementing geometrical constraints inspired by a Tesla valve within the connecting microtunnels, we additionally exert control over the direction of axonal outgrowth between the nodes. Interfacing these platforms with nanoporous microelectrode arrays reveals that the resulting laminar cortical networks exhibit pronounced segregated and integrated functional dynamics across layers, mirroring key elements of the feedforward, hierarchical information processing observed in the neocortex. The multi-nodal configuration also facilitates selective perturbation of individual nodes within the networks. To illustrate this, we induced hypoxia, a key factor in the pathogenesis of various neurological disorders, in well-connected nodes within the networks. Our findings demonstrate that such perturbations induce ablation of information flow across the hypoxic node, while enabling the study of plasticity and information processing adaptations in neighboring nodes and neural communication pathways. In summary, our presented model system recapitulates fundamental attributes of the microcircuit organization of neocortical neural networks, rendering it highly pertinent for preclinical neuroscience research. This model system holds promise for yielding new insights into the development, topological organization, and neuroplasticity mechanisms of the neocortex across the micro- and mesoscale level, in both healthy and pathological conditions.

Provincial and connector qualities of somatosensory brain network hubs in bipolar disorder

Brain network hubs are highly connected brain regions serving as important relay stations for information integration. Recent studies have linked mental disorders to impaired hub function. Provincial hubs mainly integrate information within their own brain network, while connector hubs share information between different brain networks. This study used a novel time-varying analysis to investigate whether hubs aberrantly follow the trajectory of other brain networks than their own. The aim was to characterize brain hub functioning in clinically remitted bipolar patients. We analyzed resting-state functional magnetic resonance imaging data from 96 euthymic individuals with bipolar disorder and 61 healthy control individuals. We characterized different hub qualities within the somatomotor network. We found that the somatomotor network comprised mainly provincial hubs in healthy controls. Conversely, in bipolar disorder patients, hubs in the primary somatosensory cortex displayed weaker provincial and stronger connector hub function. Furthermore, hubs in bipolar disorder showed weaker allegiances with their own brain network and followed the trajectories of the limbic, salience, dorsal attention, and frontoparietal network. We suggest that these hub aberrancies contribute to previously shown functional connectivity alterations in bipolar disorder and may thus constitute the neural substrate to persistently impaired sensory integration despite clinical remission.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Exploring functional dysconnectivity in schizophrenia: alterations in eigenvector centrality mapping and insights into related genes from transcriptional profiles

Schizophrenia is a mental health disorder characterized by functional dysconnectivity. Eigenvector centrality mapping (ECM) has been employed to investigate alterations in functional connectivity in schizophrenia, yet the results lack consistency, and the genetic mechanisms underlying these changes remain unclear. In this study, whole-brain voxel-wise ECM analyses were conducted on resting-state functional magnetic resonance imaging data. A cohort of 91 patients with schizophrenia and 91 matched healthy controls were included during the discovery stage. Additionally, in the replication stage, 153 individuals with schizophrenia and 182 healthy individuals participated. Subsequently, a comprehensive analysis was performed using an independent transcriptional database derived from six postmortem healthy adult brains to explore potential genetic factors influencing the observed functional dysconnectivity, and to investigate the roles of identified genes in neural processes and pathways. The results revealed significant and reliable alterations in the ECM across multiple brain regions in schizophrenia. Specifically, there was a significant decrease in ECM in the bilateral superior and middle temporal gyrus, and an increase in the bilateral thalamus in both the discovery and replication stages. Furthermore, transcriptional analysis revealed 420 genes whose expression patterns were related to changes in ECM, and these genes were enriched mainly in biological processes associated with synaptic signaling and transmission. Together, this study enhances our knowledge of the neural processes and pathways involved in schizophrenia, shedding light on the genetic factors that may be linked to functional dysconnectivity in this disorder.

Environmental effects on brain functional networks in a juvenile twin population

The brain's intrinsic organization into large-scale functional networks, the resting state networks (RSN), shows complex inter-individual variability, consolidated during development. Nevertheless, the role of gene and environment on developmental brain functional connectivity (FC) remains largely unknown. Twin design represents an optimal platform to shed light on these effects acting on RSN characteristics. In this study, we applied statistical twin methods to resting-state functional magnetic resonance imaging (rs-fMRI) scans from 50 young twin pairs (aged 10-30 years) to preliminarily explore developmental determinants of brain FC. Multi-scale FC features were extracted and tested for applicability of classical ACE and ADE twin designs. Epistatic genetic effects were also assessed. In our sample, genetic and environmental effects on the brain functional connections largely varied between brain regions and FC features, showing good consistency at multiple spatial scales. Although we found selective contributions of common environment on temporo-occipital connections and of genetics on frontotemporal connections, the unique environment showed a predominant effect on FC link- and node-level features. Despite the lack of accurate genetic modeling, our preliminary results showed complex relationships between genes, environment, and functional brain connections during development. A predominant role of the unique environment on multi-scale RSN characteristics was suggested, which needs replications on independent samples. Future investigations should especially focus on nonadditive genetic effects, which remain largely unexplored.

Parental education, cognition and functional connectivity of the salience network

The aim was to investigate the association of parental education at birth with cognitive ability in childhood and young adulthood and determine, whether functional connectivity of the salience network underlies this association. We studied participants of the Czech arm of the European Longitudinal Study of Pregnancy and Childhood who underwent assessment of their cognitive ability at age 8 (Wechsler Intelligence Scale for Children) and 28/29 years (Wechsler Adult Intelligence Scale) and measurement with resting state functional MRI at age 23/24. We estimated the associations of parental education with cognitive ability and functional connectivity between the seeds in the salience network and other voxels in the brain. We found that lower education of both mothers and fathers was associated with lower verbal IQ, performance IQ and full-scale IQ of the offspring at age 8. Only mother´s education was associated with performance IQ at age 28/29. Lower mother´s education correlated with greater functional connectivity between the right rostral prefrontal cortex and a cluster of voxels in the occipital cortex, which, in turn, was associated with lower performance IQ at age 28/29. We conclude that the impact of parental education, particularly father´s, on offspring´s cognitive ability weakens during the lifecourse. Functional connectivity between the right rostral prefrontal cortex and occipital cortex may be a biomarker underlying the transmission of mother´s education on performance IQ of their offspring.

Functional cortical associations and their intraclass correlations and heritability as revealed by the fMRI Human Connectome Project

We report on the functional connectivity (FC), its intraclass correlation (ICC), and heritability among 70 areas of the human cerebral cortex. FC was estimated as the Pearson correlation between averaged prewhitened Blood Oxygenation Level-Dependent time series of cortical areas in 988 young adult participants in the Human Connectome Project. Pairs of areas were assigned to three groups, namely homotopic (same area in the two hemispheres), ipsilateral (both areas in the same hemisphere), and heterotopic (nonhomotopic areas in different hemispheres). ICC for each pair of areas was computed for six genetic groups, namely monozygotic (MZ) twins, dizygotic (DZ) twins, singleton siblings of MZ twins (MZsb), singleton siblings of DZ twins (DZsb), non-twin siblings (SB), and unrelated individuals (UNR). With respect to FC, we found the following. (a) Homotopic FC was stronger than ipsilateral and heterotopic FC; (b) average FCs of left and right cortical areas were highly and positively correlated; and (c) FC varied in a systematic fashion along the anterior-posterior and inferior-superior dimensions, such that it increased from anterior to posterior and from inferior to superior. With respect to ICC, we found the following. (a) Homotopic ICC was significantly higher than ipsilateral and heterotopic ICC, but the latter two did not differ significantly from each other; (b) ICC was highest for MZ twins; (c) ICC of DZ twins was significantly lower than that of the MZ twins and higher than that of the three sibling groups (MZsb, DZsb, SB); and (d) ICC was close to zero for UNR. Finally, with respect to heritability, it was highest for homotopic areas, followed by ipsilateral, and heterotopic; however, it did not differ statistically significantly from each other.

Integrative Functional, Molecular, and Transcriptomic Analyses of Altered Intrinsic Timescale Gradient in Depression

The pathophysiology and pharmacology of depression are hypothesized to be related to the imbalance of excitation–inhibition that gives rise to hierarchical dynamics (or intrinsic timescale gradient), further supporting a hierarchy of cortical functions. On this assumption, intrinsic timescale gradient is theoretically altered in depression. However, it remains unknown. We investigated altered intrinsic timescale gradient recently developed to measure hierarchical brain dynamics gradient and its underlying molecular architecture and brain-wide gene expression in depression. We first presented replicable intrinsic timescale gradient in two independent Chinese Han datasets and then investigated altered intrinsic timescale gradient and its possible underlying molecular and transcriptional bases in patients with depression. As a result, patients with depression showed stage-specifically shorter timescales compared with healthy controls according to illness duration. The shorter timescales were spatially correlated with monoamine receptor/transporter densities, suggesting the underlying molecular basis of timescale aberrance and providing clues to treatment. In addition, we identified that timescale aberrance-related genes ontologically enriched for synapse-related and neurotransmitter (receptor) terms, elaborating the underlying transcriptional basis of timescale aberrance. These findings revealed atypical timescale gradient in depression and built a link between neuroimaging, transcriptome, and neurotransmitter information, facilitating an integrative understanding of depression.

Where the genome meets the connectome: Understanding how genes shape human brain connectivity

The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.

Genetic and Environmental Influence on the Human Functional Connectome

Detailed mapping of genetic and environmental influences on the functional connectome is a crucial step toward developing intermediate phenotypes between genes and clinical diagnoses or cognitive abilities. We analyzed resting-state functional magnetic resonance imaging data from two adult twin samples (Nos = 446 and 371) to quantify genetic and environmental influence on all pairwise functional connections between 264 brain regions (~35 000 functional connections). Nonshared environmental influence was high across the whole connectome. Approximately 14-22% of connections had nominally significant genetic influence in each sample, 4.6% were significant in both samples, and 1-2% had heritability estimates greater than 30%. Evidence of shared environmental influence was weak. Genetic influences on connections were distinct from genetic influences on a global summary measure of the connectome, network-based estimates of connectivity, and movement during the resting-state scan, as revealed by a novel connectome-wide bivariate genetic modeling procedure. The brain's genetic organization is diverse and not as one would expect based solely on structure evident in nongenetically informative data or lower resolution data. As follow-up, we make novel classifications of functional connections and examine highly localized connections with particularly strong genetic influence. This high-resolution genetic taxonomy of brain connectivity will be useful in understanding genetic influences on brain disorders.

Disruptions in global network segregation and integration in adolescents and young adults with fetal alcohol spectrum disorder

BACKGROUND

Fetal alcohol spectrum disorder (FASD) is a significant public health problem that is associated with a broad range of physical, neurocognitive, and behavioral effects resulting from prenatal alcohol exposure (PAE). Magnetic resonance imaging (MRI) has been an important tool for advancing our knowledge of abnormal brain structure and function in individuals with FASD. However, whereas only a small number of studies have applied graph theory-based network analysis to resting-state functional MRI (fMRI) data in individuals with FASD additional research in this area is needed.

METHODS

Resting-state fMRI data were collected from adolescent and young adult participants (ages 12-22) with fetal alcohol syndrome (FAS) or alcohol-related neurodevelopmental disorder (ARND) and neurotypically developing controls (CNTRL) from previous studies. Group independent components analysis (gICA) was applied to fMRI data to extract components representing functional brain networks. Functional network connectivity (FNC), measured by Pearson correlation of the average independent component (IC) time series, was analyzed under a graph theory framework to compare network modularity, the average clustering coefficient, characteristic path length, and global efficiency between groups. Cognitive intelligence, measured by the Wechsler Abbreviated Scale of Intelligence (WASI), was compared and correlated to global network measures.

RESULTS

Group comparisons revealed significant differences in the average clustering coefficient, characteristic path length, and global efficiency. Modularity was not significantly different between groups. The FAS and ARND groups scored significantly lower than the CNTRL group on Full Scale IQ (FS-IQ) and the Vocabulary subtest, but not the Matrix Reasoning subtest. No significant associations between intelligence and graph theory measures were detected.

CONCLUSION

Our results partially agree with previous studies examining global graph theory metrics in children and adolescents with FASD and suggest that the exposure to alcohol during prenatal development leads to disruptions in aspects of functional network segregation and integration.

Intergenerational transmission of the patterns of functional and structural brain networks

There is clear evidence of intergenerational transmission of life values, cognitive traits, psychiatric disorders, and even aspects of daily decision making. To investigate biological substrates of this phenomenon, the brain has received increasing attention as a measurable biomarker and potential target for intervention. However, no previous study has quantitatively and comprehensively investigated the effects of intergenerational transmission on functional and structural brain networks. Here, by employing an unusually large cohort dataset (N = 84 parent-child dyads; 45 sons, 39 daughters, 81 mothers, and 3 fathers), we show that patterns of functional and structural brain networks are preserved over a generation. We also demonstrate that several demographic factors and behavioral/physiological phenotypes have a relationship with brain similarity. Collectively, our results provide a comprehensive picture of neurobiological substrates of intergenerational transmission and demonstrate the usability of our dataset for investigating the neurobiological substrates of intergenerational transmission.

Heritability of brain resilience to perturbation in humans

Resilience is the capacity of complex systems to persist in the face of external perturbations and retain their functional properties and performance. In the present study, we investigated how individual variations in brain resilience, which might influence response to stress, aging and disease, are influenced by genetics and/or the environment, with potential implications for the implementation of resilience-boosting interventions. Resilience estimates were derived from in silico lesioning of either brain regions or functional connections constituting the connectome of healthy individuals belonging to two different large and unique datasets of twins, specifically: 463 individual twins from the Human Connectome Project and 453 individual twins from the Colorado Longitudinal Twin Study. As has been reported previously, moderate heritability was found for several topological indexes of brain efficiency and modularity. Importantly, evidence of heritability was found for resilience measures based on removal of brain connections rather than specific single regions, suggesting that genetic influences on resilience are preferentially directed toward region-to-region communication rather than local brain activity. Specifically, the strongest genetic influence was observed for moderately weak, long-range connections between a specific subset of functional brain networks: the Default Mode, Visual and Sensorimotor networks. These findings may help identify a link between brain resilience and network-level alterations observed in neurological and psychiatric diseases, as well as inform future studies investigating brain shielding interventions against physiological and pathological perturbations.

Associations between Neighborhood SES and Functional Brain Network Development

Higher socioeconomic status (SES) in childhood is associated with stronger cognitive abilities, higher academic achievement, and lower incidence of mental illness later in development. While prior work has mapped the associations between neighborhood SES and brain structure, little is known about the relationship between SES and intrinsic neural dynamics. Here, we capitalize upon a large cross-sectional community-based sample (Philadelphia Neurodevelopmental Cohort, ages 8-22 years, n = 1012) to examine associations between age, SES, and functional brain network topology. We characterize this topology using a local measure of network segregation known as the clustering coefficient and find that it accounts for a greater degree of SES-associated variance than mesoscale segregation captured by modularity. High-SES youth displayed stronger positive associations between age and clustering than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. The moderating effect of SES on positive associations between age and clustering was strongest for connections of intermediate length and was consistent with a stronger negative relationship between age and local connectivity in these regions in low-SES youth. Our findings suggest that, in late childhood and adolescence, neighborhood SES is associated with variation in the development of functional network structure in the human brain.

Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

Finding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

Correlation between cortical gene expression and resting-state functional network centrality in healthy young adults

Resting‐state functional connectivity in the human brain is heritable, and previous studies have investigated the genetic basis underlying functional connectivity. However, at present, the molecular mechanisms associated with functional network centrality are still largely unknown. In this study, functional networks were constructed, and the graph‐theory method was employed to calculate network centrality in 100 healthy young adults from the Human Connectome Project. Specifically, functional connectivity strength (FCS), also known as the “degree centrality” of weighted networks, is calculated to measure functional network centrality. A multivariate technique of partial least squares regression (PLSR) was then conducted to identify genes whose spatial expression profiles best predicted the FCS distribution. We found that FCS spatial distribution was significantly positively correlated with the expression of genes defined by the first PLSR component. The FCS‐related genes we identified were significantly enriched for ion channels, axon guidance, and synaptic transmission. Moreover, FCS‐related genes were preferentially expressed in cortical neurons and young adulthood and were enriched in numerous neurodegenerative and neuropsychiatric disorders. Furthermore, a series of validation and robustness analyses demonstrated the reliability of the results. Overall, our results suggest that the spatial distribution of FCS is modulated by the expression of a set of genes associated with ion channels, axon guidance, and synaptic transmission.

Effects of the dopamine transporter gene on neuroimaging findings in different attention deficit hyperactivity disorder presentations

Attention-Deficit/Hyperactivity Disorder (ADHD) is a phenotipically and neurobiologically heterogeneous disorder. Deficiencies at different levels in response inhibition, differences in dopamine transporter genotype (DAT1) and various symptomatic presentations contribute to ADHD heterogeneity. Integrating these three aspects into a functional neuroimaging research could help unreval specific neurobiological components of more phenotipically homogeneous groups of patients with ADHD. During the Go-NoGo trial, we investigated the effect of the DAT1 gene using 3 T MRI in 72 ADHD cases and 24 (TD) controls that typically developed between the ages 8 and 15 years. In the total ADHD group, DAT1 predicted homozygosity for the 10R allele and hypoactivation in the anterior cingulate cortex and paracingulate cortex. There were no significant activation differences between DAT1 10R/10R homozygotes and 9R carriers in TD controls. Subjects with predominantly inattentive ADHD (ADHD-I) presentation with DAT1 10R/10R homozygous reduced neuronal activation during Go trial particularly in the frontal regions and insular cortex, and in the parietal regions during NoGo trial (brain regions reported as part of Default Mode Network- DMN). Additionally, DAT1 10R/10R homozygousness was associated with increased occipital zone activation during only the Go trial in the ADHD combined presentation (ADHD-C) group. Our results point the three main findings: 1) The DAT1 gene is 10R homozygous for differentiated brain activation in ADHD cases but not in the TD controls, supporting the DAT1 gene as a potential marker for ADHD, 2) The relationship between the DAT1 gene and the occipital regions in ADHD-C group which may reflect compensatory mechanisms, 3) The relationship between DAT1 gene and the reduced DMN suppression for 9R carriers probabaly stems from the ADHD-I group.

Heritability of Functional Connectivity in Resting State: Assessment of the Dynamic Mean, Dynamic Variance, and Static Connectivity across Networks

Recent efforts to evaluate the heritability of the brain's functional connectome have predominantly focused on static connectivity. However, evaluating connectivity changes across time can provide valuable insight about the inherent dynamic nature of brain function. Here, the heritability of Human Connectome Project resting-state fMRI data was examined to determine whether there is a genetic basis for dynamic fluctuations in functional connectivity. The dynamic connectivity variance, in addition to the dynamic mean and standard static connectivity, was evaluated. Heritability was estimated using Accelerated Permutation Inference for the ACE (APACE), which models the additive genetic (h2), common environmental (c2), and unique environmental (e2) variance. Heritability was moderate (mean h2: dynamic mean = 0.35, dynamic variance = 0.45, and static = 0.37) and tended to be greater for dynamic variance compared to either dynamic mean or static connectivity. Further, heritability of dynamic variance was reliable across both sessions for several network connections, particularly between higher-order cognitive and visual networks. For both dynamic mean and static connectivity, similar patterns of heritability were found across networks. The findings support the notion that dynamic connectivity is genetically influenced. The flexibility of network connections, not just their strength, is a heritable endophenotype that may predispose trait behavior.

High-Expanding Regions in Primate Cortical Brain Evolution Support Supramodal Cognitive Flexibility

Primate cortical evolution has been characterized by massive and disproportionate expansion of a set of specific regions in the neocortex. The associated increase in neocortical neurons comes with a high metabolic cost, thus the functions served by these regions must have conferred significant evolutionary advantage. In the present series of analyses, we show that evolutionary high-expanding cortex - as estimated from patterns of surface growth from several primate species - shares functional connections with different brain networks in a context-dependent manner. Specifically, we demonstrate that high-expanding cortex is characterized by high internetwork functional connectivity; is recruited flexibly over many different cognitive tasks; and changes its functional coupling pattern between rest and a multimodal task-state. The capacity of high-expanding cortex to connect flexibly with various specialized brain networks depending on particular cognitive requirements suggests that its selective growth and sustainment in evolution may have been linked to an involvement in supramodal cognition. In accordance with an evolutionary-developmental view, we find that this observed ability of high-expanding regions - to flexibly modulate functional connections as a function of cognitive state - emerges gradually through childhood, with a prolonged developmental trajectory plateauing in young adulthood.

Age-related structural and functional variations in 5,967 individuals across the adult lifespan

Exploring brain changes across the human lifespan is becoming an important topic in neuroscience. Though there are multiple studies which investigated the relationship between age and brain imaging, the results are heterogeneous due to small sample sizes and relatively narrow age ranges. Here, based on year-wise estimation of 5,967 subjects from 13 to 72 years old, we aimed to provide a more precise description of adult lifespan variation trajectories of gray matter volume (GMV), structural network correlation (SNC), and functional network connectivity (FNC) using independent component analysis and multivariate linear regression model. Our results revealed the following relationships: (a) GMV linearly declined with age in most regions, while parahippocampus showed an inverted U-shape quadratic relationship with age; SNC presented a U-shape quadratic relationship with age within cerebellum, and inverted U-shape relationship primarily in the default mode network (DMN) and frontoparietal (FP) related correlation. (b) FNC tended to linearly decrease within resting-state networks (RSNs), especially in the visual network and DMN. Early increase was revealed between RSNs, primarily in FP and DMN, which experienced a decrease at older ages. U-shape relationship was also revealed to compensate for the cognition deficit in attention and subcortical related connectivity at late years. (c) The link between middle occipital gyrus and insula, as well as precuneus and cerebellum, exhibited similar changing trends between SNC and FNC across the adult lifespan. Collectively, these results highlight the benefit of lifespan study and provide a precise description of age-related regional variation and SNC/FNC changes based on a large dataset.

Partitioning heritability analyses unveil the genetic architecture of human brain multidimensional functional connectivity patterns

Resting-state functional connectivity profiles have been increasingly shown to be important endophenotypes that are tightly linked to human cognitive functions and psychiatric diseases, yet the genetic architecture of this multidimensional trait is barely understood. Using a unique sample of 1,704 unrelated, young and healthy Chinese Han individuals, we revealed a significant heritability of functional connectivity patterns in the whole brain and several subnetworks. We further proposed a partitioned heritability analysis for multidimensional functional connectivity patterns, which revealed the common and unique enrichment patterns of the genetic contributions to brain connectivity patterns for several gene sets linked to brain functions, including the genes expressed preferentially in the central nervous system and those associated with intelligence, educational attainment, attention-deficit/hyperactivity disorder, and schizophrenia. These results for the first time reveal the genetic architecture of multidimensional brain connectivity patterns across different networks and advance our understanding of the complex relationship between gene sets, neural networks, and behaviors.

Twin MRI studies on genetic and environmental determinants of brain morphology and function in the early lifespan

Neurodevelopment represents a period of increased opportunity and vulnerability, during which a complex confluence of genetic and environmental factors influences brain growth trajectories, cognitive and mental health outcomes. Recently, magnetic resonance imaging (MRI) studies on twins have increased our knowledge of the extent to which genes, the environment and their interactions shape inter-individual brain variability. The present review draws from highly salient MRI studies in young twin samples to provide a robust assessment of the heritability of structural and functional brain changes during development. The available studies suggest that (as with many other traits), global brain morphology and network organization are highly heritable from early childhood to young adulthood. Conversely, genetic correlations among brain regions exhibit heterogeneous trajectories, and this heterogeneity reflects the progressive, experience-related increase in brain network complexity. Studies also support the key role of environment in mediating brain network differentiation via changes of genetic expression and hormonal levels. Thus, rest- and task-related functional brain circuits seem to result from a contextual and dynamic expression of heritability.

Maternal immune activation during pregnancy impacts on brain structure and function in the adult offspring

Gestational infection constitutes a risk factor for the occurrence of psychiatric disorders in the offspring. Activation of the maternal immune system (MIA) with subsequent impact on the development of the fetal brain is considered to form the neurobiological basis for aberrant neural wiring and the psychiatric manifestations later in offspring life. The examination of validated animal models constitutes a premier resource for the investigation of the neural underpinnings. Here we used a mouse model of MIA based upon systemic treatment of pregnant mice with Poly(I:C) (polyriboinosinic-polyribocytidilic acid), for the unbiased and comprehensive analysis of the impact of MIA on adult offspring brain activity, morphometry, connectivity and function by a magnetic resonance imaging (MRI) approach. Overall lower neural activity, smaller brain regions and less effective fiber structure were observed for Poly(I:C) offspring compared to the control group. The corpus callosum was significantly smaller and presented with a disruption in myelin/ fiber structure in the MIA progeny. Subsequent resting-state functional MRI experiments demonstrated a paralleling dysfunctional interhemispheric connectivity. Additionally, while the overall flow of information was intact, cortico-limbic connectivity was hampered and limbic circuits revealed hyperconnectivity in Poly(I:C) offspring. Our study sheds new light on the impact of maternal infection during pregnancy on the offspring brain and identifies aberrant resting-state functional connectivity patterns as possible correlates of the behavioral phenotype with relevance for psychiatric disorders.

Genetic and environmental influences on functional connectivity within and between canonical cortical resting-state networks throughout adolescent development in boys and girls

The human brain is active during rest and hierarchically organized into intrinsic functional networks. These functional networks are largely established early in development, with reports of a shift from a local to more distributed organization during childhood and adolescence. It remains unknown to what extent genetic and environmental influences on functional connectivity change throughout adolescent development. We measured functional connectivity within and between eight cortical networks in a longitudinal resting-state fMRI study of adolescent twins and their older siblings on two occasions (mean ages 13 and 18 years). We modelled the reliability for these inherently noisy and head-motion sensitive measurements by analyzing data from split-half sessions. Functional connectivity between resting-state networks decreased with age whereas functional connectivity within resting-state networks generally increased with age, independent of general cognitive functioning. Sex effects were sparse, with stronger functional connectivity in the default mode network for girls compared to boys, and stronger functional connectivity in the salience network for boys compared to girls. Heritability explained up to 53% of the variation in functional connectivity within and between resting-state networks, and common environment explained up to 33%. Genetic influences on functional connectivity remained stable during adolescent development. In conclusion, longitudinal age-related changes in functional connectivity within and between cortical resting-state networks are subtle but wide-spread throughout adolescence. Genes play a considerable role in explaining individual variation in functional connectivity with mostly stable influences throughout adolescence.

Uncovering the Transcriptional Correlates of Hub Connectivity in Neural Networks

Connections in nervous systems are disproportionately concentrated on a small subset of neural elements that act as network hubs. Hubs have been found across different species and scales ranging from C. elegans to mouse, rat, cat, macaque, and human, suggesting a role for genetic influences. The recent availability of brain-wide gene expression atlases provides new opportunities for mapping the transcriptional correlates of large-scale network-level phenotypes. Here we review studies that use these atlases to investigate gene expression patterns associated with hub connectivity in neural networks and present evidence that some of these patterns are conserved across species and scales.

A method for building a genome-connectome bipartite graph model

It has been widely shown that genomic factors influence both risk for schizophrenia and variation in functional brain connectivity. Moreover, schizophrenia is characterized by disrupted brain connectivity. In this work, we proposed a genome-connectome bipartite graph model to perform imaging genomic analysis. Functional network connectivity (FNC) was estimated after decomposing resting state functional magnetic resonance imaging data from both healthy controls (HC) and patients with schizophrenia (SZ) into spatial brain components using group independent component analysis (G-ICA). Then 83 FNC connections showing a group difference (HC vs SZ) were selected as fMRI nodes, and eighty-one schizophrenia-related single nucleotide polymorphisms (SNPs) were selected as genetic nodes respectively in the bipartite graph. Edges connecting pairs of genetic and fMRI nodes were defined based on the SNP-FNC associations across subjects evaluated by a general linear model. Results show that some SNP nodes in the bipartite graph have a high degree implying they are influential in modulating brain connectivity and may be more strongly associated with the risk of schizophrenia than other SNPs. A bi-clustering analysis detected a cluster with 15 SNPs interacting with 38 FNC connections, most of which were within or between somato-motor and visual brain areas. This suggests that the activity of these brain regions may be related to common SNPs and provides insights into the pathology of schizophrenia. The findings suggest that the SNP-FNC bipartite graph approach is a novel model to investigate genetic influences on functional brain connectivity in mental illness.

Neurometabolic and functional connectivity basis of prosocial behavior in early adolescence

Human prosocial behavior (PB) emerges in childhood and matures during adolescence. Previous task-related functional magnetic resonance imaging (fMRI) studies have reported involvement of the medial prefrontal cortex including the anterior cingulate cortex (ACC) in social cognition in adolescence. However, neurometabolic and functional connectivity (FC) basis of PB in early adolescence remains unclear. Here, we measured GABA levels in the ACC and FC in a subsample (aged 10.5–13.4 years) of a large-scale population-based cohort with MR spectroscopy (MEGA-PRESS) and resting-state fMRI. PB was negatively correlated with GABA levels in the ACC (N = 221), and positively correlated with right ACC-seeded FC with the right precentral gyrus and the bilateral middle and posterior cingulate gyrus (N = 187). Furthermore, GABA concentrations and this FC were negatively correlated, and the FC mediated the association between GABA levels and PB (N = 171). Our results from a minimally biased, large-scale sample provide new insights into the neurometabolic and neurofunctional correlates of prosocial development during early adolescence.

Default Mode Network Maturation and Environmental Adversities During Childhood

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

Heritability of neural reactions to social exclusion and prosocial compensation in middle childhood

Experiencing and observing social exclusion and inclusion, as well as prosocial behavior, are important aspects of social relationships in childhood. However, it is currently unknown to what extent these processes and their neural correlates differ in heritability. We investigated influences of genetics and environment on experiencing social exclusion and compensating for social exclusion of others with the Prosocial Cyberball Game using fMRI in a twin sample (aged 7-9; N = 500). Neuroimaging analyses (N = 283) revealed that experiencing possible self-exclusion resulted in activity in inferior frontal gyrus and medial prefrontal cortex, which was influenced by genetics and unique environment. Experiencing self-inclusion was associated with activity in anterior cingulate cortex, insula and striatum, but this was not significantly explained by genetics or shared environment. We found that children show prosocial compensating behavior when observing social exclusion. Prosocial compensating behavior was associated with activity in posterior cingulate cortex/precuneus, and showed unique environmental effects or measurement error at both behavioral and neural level. Together, these findings show that in children neural activation for experiencing possible self-exclusion and self-inclusion, and for displaying prosocial compensating behavior, is accounted for by unique environmental factors and measurement error, with a small genetic effect on possible self-exclusion.

Heritability of the human connectome: A connectotyping study

Recent progress in resting-state neuroimaging demonstrates that the brain exhibits highly individualized patterns of functional connectivity-a "connectotype." How these individualized patterns may be constrained by environment and genetics is unknown. Here we ask whether the connectotype is familial and heritable. Using a novel approach to estimate familiality via a machine-learning framework, we analyzed resting-state fMRI scans from two well-characterized samples of child and adult siblings. First we show that individual connectotypes were reliably identified even several years after the initial scanning timepoint. Familial relationships between participants, such as siblings versus those who are unrelated, were also accurately characterized. The connectotype demonstrated substantial heritability driven by high-order systems including the fronto-parietal, dorsal attention, ventral attention, cingulo-opercular, and default systems. This work suggests that shared genetics and environment contribute toward producing complex, individualized patterns of distributed brain activity, rather than constraining local aspects of function. These insights offer new strategies for characterizing individual aberrations in brain function and evaluating heritability of brain networks.

Multimodal Connectomics in Psychiatry: Bridging Scales From Micro to Macro

The human brain is a highly complex system, with a large variety of microscale cellular morphologies and macroscale global properties. Working at multiple scales, it forms an efficient system for processing and integration of multimodal information. Studies have repeatedly demonstrated strong associations between modalities of both microscales and macroscales of brain organization. These consistent observations point toward potential common organization principles where regions with a microscale architecture supportive of a larger computational load have more and stronger connections in the brain network on the macroscale. Conversely, disruptions observed on one organizational scale could modulate the other. First neuropsychiatric micro-macro comparisons in, among other conditions, Alzheimer's disease and schizophrenia, have, for example, shown overlapping alterations across both scales. We give an overview of recent findings on associations between microscale and macroscale organization observed in the healthy brain, followed by a summary of microscale and macroscale findings reported in the context of brain disorders. We conclude with suggestions for future multiscale connectome comparisons linking multiple scales and modalities of organization and suggest how such comparisons could contribute to a more complete fundamental understanding of brain organization and associated disease-related alterations.

Distinctive heritability patterns of subcortical-prefrontal cortex resting state connectivity in childhood: A twin study

Connectivity between limbic/subcortical and prefrontal-cortical brain regions develops considerably across childhood, but less is known about the heritability of these networks at this age. We tested the heritability of limbic/subcortical-cortical and limbic/subcortical-subcortical functional brain connectivity in 7- to 9-year-old twins (N = 220), focusing on two key limbic/subcortical structures: the ventral striatum and the amygdala, given their combined influence on changing incentivised behavior during childhood and adolescence. Whole brain analyses with ventral striatum (VS) and amygdala as seeds in genetically independent groups showed replicable functional connectivity patterns. The behavioral genetic analyses revealed that in general VS and amygdala connectivity showed distinct influences of genetics and environment. VS-prefrontal cortex connections were best described by genetic and unique environmental factors (the latter including measurement error), whereas amygdala-prefrontal cortex connectivity was mainly explained by environmental influences. Similarities were also found: connectivity between both the VS and amygdala and ventral anterior cingulate cortex (vACC) showed influences of shared environment, while connectivity with the orbitofrontal cortex (OFC) showed heritability. These findings may inform future interventions that target behavioral control and emotion regulation, by taking into account genetic dispositions as well as shared and unique environmental factors such as child rearing.

Predicting personality from network-based resting-state functional connectivity

Personality is associated with variation in all kinds of mental faculties, including affective, social, executive, and memory functioning. The intrinsic dynamics of neural networks underlying these mental functions are reflected in their functional connectivity at rest (RSFC). We, therefore, aimed to probe whether connectivity in functional networks allows predicting individual scores of the five-factor personality model and potential gender differences thereof. We assessed nine meta-analytically derived functional networks, representing social, affective, executive, and mnemonic systems. RSFC of all networks was computed in a sample of 210 males and 210 well-matched females and in a replication sample of 155 males and 155 females. Personality scores were predicted using relevance vector machine in both samples. Cross-validation prediction accuracy was defined as the correlation between true and predicted scores. RSFC within networks representing social, affective, mnemonic, and executive systems significantly predicted self-reported levels of Extraversion, Neuroticism, Agreeableness, and Openness. RSFC patterns of most networks, however, predicted personality traits only either in males or in females. Personality traits can be predicted by patterns of RSFC in specific functional brain networks, providing new insights into the neurobiology of personality. However, as most associations were gender-specific, RSFC-personality relations should not be considered independently of gender.

Heritability of aggression following social evaluation in middle childhood: An fMRI study

Middle childhood marks an important phase for developing and maintaining social relations. At the same time, this phase is marked by a gap in our knowledge of the genetic and environmental influences on brain responses to social feedback and their relation to behavioral aggression. In a large developmental twin sample (509 7‐ to 9‐year‐olds), the heritability and neural underpinnings of behavioral aggression following social evaluation were investigated, using the Social Network Aggression Task (SNAT). Participants viewed pictures of peers that gave positive, neutral, or negative feedback to the participant's profile. Next, participants could blast a loud noise toward the peer as an index of aggression. Genetic modeling revealed that aggression following negative feedback was influenced by both genetics and environmental (shared as well as unique environment). On a neural level (n = 385), the anterior insula and anterior cingulate cortex gyrus (ACCg) responded to both positive and negative feedback, suggesting they signal for social salience cues. The medial prefrontal cortex (mPFC) and inferior frontal gyrus (IFG) were specifically activated during negative feedback, whereas positive feedback resulted in increased activation in caudate, supplementary motor cortex (SMA), and dorsolateral prefrontal cortex (DLPFC). Decreased SMA and DLPFC activation during negative feedback was associated with more aggressive behavior after negative feedback. Moreover, genetic modeling showed that 13%–14% of the variance in dorsolateral PFC activity was explained by genetics. Our results suggest that the processing of social feedback is partly explained by genetic factors, whereas shared environmental influences play a role in behavioral aggression following feedback.

Risk and resilience brain networks in treatment-resistant schizophrenia

BACKGROUND

Genes, molecules and neural circuits that are associated with, or confer risk to developing schizophrenia have been studied and mapped. It is hypothesized that certain neural systems may counterbalance familial risk of schizophrenia, and thus confer resilience to developing the disorder. This study sought to identify resting-state functional brain connectivity (rs-FC) representing putative risk or resilience endophenotypes in schizophrenia.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) was performed in 42 individuals with treatment resistant schizophrenia (TRS), 16 unaffected first-degree family members (UFM) and 42 healthy controls. Whole-brain rs-FC networks were mapped for each individual and analysed graph theoretically to identify network markers associated with schizophrenia risk or resilience.

RESULTS

The ~900 functional connections showing between-group differences were operationalized as conferring: i) resilience, ii) risk, or iii) precipitating risk and/or illness effects. Approximately 95% of connections belonged to the latter two categories, with substantially fewer connections associated with resilience. Schizophrenia risk primarily involved reduced frontal and occipital rs-FC, with patients showing additional reduced frontal and temporal rs-FC. Functional brain networks were characterized by greater local efficiency in UFM, compared to TRS and controls.

CONCLUSIONS

TRS and UFM share frontal and occipital rs-FC deficits, representing a 'risk' endophenotype. Additional reductions in frontal and temporal rs-FC appear to be associated with risk that precipitates psychosis in vulnerable individuals, or may be due to other illness-related effects, such as medication. Functional brain networks are more topologically resilient in UFM compared to TRS, which may protect UFM from psychosis onset despite familial liability.

Imaging structural and functional brain development in early childhood

In humans, the period from term birth to ∼2 years of age is characterized by rapid and dynamic brain development and plays an important role in cognitive development and risk of disorders such as autism and schizophrenia. Recent imaging studies have begun to delineate the growth trajectories of brain structure and function in the first years after birth and their relationship to cognition and risk of neuropsychiatric disorders. This Review discusses the development of grey and white matter and structural and functional networks, as well as genetic and environmental influences on early-childhood brain development. We also discuss initial evidence regarding the usefulness of early imaging biomarkers for predicting cognitive outcomes and risk of neuropsychiatric disorders.

Heritability of the Effective Connectivity in the Resting-State Default Mode Network

The default mode network (DMN) is thought to reflect endogenous neural activity, which is considered as one of the most intriguing phenomena in cognitive neuroscience. Previous studies have found that key regions within the DMN are highly interconnected. Here, we characterized the genetic influences on causal or directed information flow within the DMN during the resting state. In this study, we recruited 46 pairs of twins and collected fMRI imaging data using a 3.0 T scanner. Dynamic causal modeling was conducted for each participant, and a structural equation model was used to calculate the heritability of DMN in terms of its effective connectivity. Model comparison favored a full-connected model. Structural equal modeling was used to estimate the additive genetics (A), common environment (C) and unique environment (E) contributions to variance for the DMN effective connectivity. The ACE model was preferred in the comparison of structural equation models. Heritability of DMN effective connectivity was 0.54, suggesting that the genetic made a greater contribution to the effective connectivity within DMN. Establishing the heritability of default-mode effective connectivity endorses the use of resting-state networks as endophenotypes or intermediate phenotypes in the search for the genetic basis of psychiatric or neurological illnesses.

The utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design

The ABCD twin study will elucidate the genetic and environmental contributions to a wide range of mental and physical health outcomes in children, including substance use, brain and behavioral development, and their interrelationship. Comparisons within and between monozygotic and dizygotic twin pairs, further powered by multiple assessments, provide information about genetic and environmental contributions to developmental associations, and enable stronger tests of causal hypotheses, than do comparisons involving unrelated children. Thus a sub-study of 800 pairs of same-sex twins was embedded within the overall Adolescent Brain and Cognitive Development (ABCD) design. The ABCD Twin Hub comprises four leading centers for twin research in Minnesota, Colorado, Virginia, and Missouri. Each site is enrolling 200 twin pairs, as well as singletons. The twins are recruited from registries of all twin births in each State during 2006-2008. Singletons at each site are recruited following the same school-based procedures as the rest of the ABCD study. This paper describes the background and rationale for the ABCD twin study, the ascertainment of twin pairs and implementation strategy at each site, and the details of the proposed analytic strategies to quantify genetic and environmental influences and test hypotheses critical to the aims of the ABCD study.

Functional and effective whole brain connectivity using magnetoencephalography to identify monozygotic twin pairs

Resting-state functional connectivity patterns are highly stable over time within subjects. This suggests that such 'functional fingerprints' may have strong genetic component. We investigated whether the functional (FC) or effective (EC) connectivity patterns of one monozygotic twin could be used to identify the co-twin among a larger sample and determined the overlap in functional fingerprints within monozygotic (MZ) twin pairs using resting state magnetoencephalography (MEG). We included 32 cognitively normal MZ twin pairs from the Netherlands Twin Register who participate in the EMIF-AD preclinAD study (average age 68 years). Combining EC information across multiple frequency bands we obtained an identification rate over 75%. Since MZ twin pairs are genetically identical these results suggest a high genetic contribution to MEG-based EC patterns, leading to large similarities in brain connectivity patterns between two individuals even after 60 years of life or more.

The heritability of multi-modal connectivity in human brain activity

Patterns of intrinsic human brain activity exhibit a profile of functional connectivity that is associated with behaviour and cognitive performance, and deteriorates with disease. This paper investigates the relative importance of genetic factors and the common environment between twins in determining this functional connectivity profile. Using functional magnetic resonance imaging (fMRI) on 820 subjects from the Human Connectome Project, and magnetoencephalographic (MEG) recordings from a subset, the heritability of connectivity among 39 cortical regions was estimated. On average over all connections, genes account for about 15% of the observed variance in fMRI connectivity (and about 10% in alpha-band and 20% in beta-band oscillatory power synchronisation), which substantially exceeds the contribution from the environment shared between twins. Therefore, insofar as twins share a common upbringing, it appears that genes, rather than the developmental environment, have the dominant role in determining the coupling of neuronal activity.

Abnormal Rich-Club Organization Associated with Compromised Cognitive Function in Patients with Schizophrenia and Their Unaffected Parents

Schizophrenia is considered to be a disorder of brain connectivity, which might result from a disproportionally impaired rich-club organization. The rich-club is composed of highly interconnected hub regions that play crucial roles in integrating information between different brain regions. Few studies have yet investigated whether the structural rich-club organization is impaired in patients and their first-degree relatives. In this study, we established a weighted network model of white matter connections using diffusion tensor imaging of 19 patients and 39 unaffected parents, 22 young healthy controls for the patients, and 25 old healthy controls for the parents. Feeder edges between rich-club nodes and non-rich-club nodes were significantly decreased in both schizophrenic patients and their unaffected parents compared with controls. Furthermore, the feeder edges showed significant positive correlations with the scores in Category Fluency Test-animal naming in the unaffected parents. Specific feeder edges exhibited discriminative power with accuracy of 84.4% in distinguishing unaffected parents from old healthy controls. Our findings suggest that impaired rich-club organization, especially impaired feeder edges, may be related to familial vulnerability to schizophrenia, possibly reflecting a genetic predisposition for schizophrenia.

Heritability analysis with repeat measurements and its application to resting-state functional connectivity

Heritability, defined as the proportion of phenotypic variation attributable to genetic variation, provides important information about the genetic basis of a trait. Existing heritability analysis methods do not discriminate between stable effects (e.g., due to the subject's unique environment) and transient effects, such as measurement error. This can lead to misleading assessments, particularly when comparing the heritability of traits that exhibit different levels of reliability. Here, we present a linear mixed effects model to conduct heritability analyses that explicitly accounts for intrasubject fluctuations (e.g., due to measurement noise or biological transients) using repeat measurements. We apply the proposed strategy to the analysis of resting-state fMRI measurements-a prototypic data modality that exhibits variable levels of test-retest reliability across space. Our results reveal that the stable components of functional connectivity within and across well-established large-scale brain networks can be considerably heritable. Furthermore, we demonstrate that dissociating intra- and intersubject variation can reveal genetic influence on a phenotype that is not fully captured by conventional heritability analyses.

Estimating the Heritability of Structural and Functional Brain Connectivity in Families Affected by Attention-Deficit/Hyperactivity Disorder

IMPORTANCE

Despite its high heritability, few risk genes have been identified for attention-deficit/hyperactivity disorder (ADHD). Brain-based phenotypes could aid gene discovery. There is a myriad of structural and functional connections that support cognition. Disruption of such connectivity is a key pathophysiologic mechanism for ADHD, and identifying heritable phenotypes within these connections could provide candidates for genomic studies.

OBJECTIVE

To identify the structural and functional connections that are heritable and pertinent to ADHD.

DESIGN, SETTING, AND PARTICIPANTS

Members of extended multigenerational families enriched for ADHD were evaluated. Structural connectivity was defined by diffusion tensor imaging (DTI) of white matter tract microstructure and functional connectivity through resting-state functional magnetic resonance imaging (rsfMRI). Heritability and association with ADHD symptoms were estimated in 24 extended multigenerational families enriched for ADHD (305 members with clinical phenotyping, 213 with DTI, and 193 with rsfMRI data). Findings were confirmed in 52 nuclear families (132 members with clinical phenotypes, 119 with DTI, and 84 with rsfMRI). The study and data analysis were conducted from April 1, 2010, to September 1, 2016.

RESULTS

In the 52 nuclear families, 86 individuals (65.2%) were male and the mean (SD) age at imaging was 20.9 (15.0) years; in the 24 multigenerational extended families, 145 individuals (47.5%) were male and mean age at imaging was 30.4 (19.7) years. Microstructural properties of white matter tracts connecting ipsilateral cortical regions and the corpus callosum were significantly heritable, ranging from total additive genetic heritability (h2) = 0.69 (SE, 0.13; P = .0000002) for radial diffusivity of the right superior longitudinal fasciculus to h2 = 0.46 (SE, 0.15; P = .0009) for fractional anisotropy of the right inferior fronto-occipital fasciculus. Association with ADHD symptoms was found in several tracts, most strongly for the right superior longitudinal fasciculus (t = -3.05; P = .003). Heritable patterns of functional connectivity were detected within the default mode (h2 = 0.36; SE, 0.16; cluster level significance, P < .002), cognitive control (h2 = 0.32; SE, 0.15; P < .002), and ventral attention networks (h2 = 0.36; SE, 0.16; P < .002). In all cases, subregions within each network showed heritable functional connectivity with the rest of that network. More symptoms of hyperactivity/impulsivity (t = -2.63; P = .008) and inattention (t = -2.34; P = .02) were associated with decreased functional connectivity within the default mode network. Some cross-modal correlations were purely phenotypic, such as that between axial diffusivity of the right superior longitudinal fasciculus and heritable aspects of the default mode network (phenotypic correlation, ρp = -0.12; P = .03). A genetic cross-modal correlation was seen between the ventral attention network and radial diffusivity of the right inferior fronto-occipital fasciculus (genetic correlation, ρg = -0.45, P = .02).

CONCLUSIONS

Analysis of data on multigenerational extended and nuclear families identified the features of structural and functional connectivity that are both significantly heritable and associated with ADHD. In addition, shared genetic factors account for some phenotypic correlations between functional and structural connections. Such work helps to prioritize the facets of the brain's connectivity for future genomic studies.

Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability

Resting state fMRI (R-fMRI) is a powerful in-vivo tool for examining the functional architecture of the human brain. Recent studies have demonstrated the ability to characterize transitions between functionally distinct cortical areas through the mapping of gradients in intrinsic functional connectivity (iFC) profiles. To date, this novel approach has primarily been applied to iFC profiles averaged across groups of individuals, or in one case, a single individual scanned multiple times. Here, we used a publically available R-fMRI dataset, in which 30 healthy participants were scanned 10 times (10 min per session), to investigate differences in full-brain transition profiles (i.e., gradient maps, edge maps) across individuals, and their reliability. 10-min R-fMRI scans were sufficient to achieve high accuracies in efforts to "fingerprint" individuals based upon full-brain transition profiles. Regarding test-retest reliability, the image-wise intraclass correlation coefficient (ICC) was moderate, and vertex-level ICC varied depending on region; larger durations of data yielded higher reliability scores universally. Initial application of gradient-based methodologies to a recently published dataset obtained from twins suggested inter-individual variation in areal profiles might have genetic and familial origins. Overall, these results illustrate the utility of gradient-based iFC approaches for studying inter-individual variation in brain function.