Genetic influences on resting-state functional networks: A twin study

Heritability of white matter in twins: A diffusion neuroimaging review

Diffusion neuroimaging has emerged as an essential non-invasive technique to explore in vivo microstructural characteristics of white matter (WM), whose integrity allows complex behaviors and cognitive abilities. Studying the factors contributing to inter-individual variability in WM microstructure can provide valuable insight into structural and functional differences of brain among individuals. Genetic influence on this variation has been largely investigated in twin studies employing different measures derived from diffusion neuroimaging. In this context, we performed a comprehensive literature search across PubMed, Scopus and Web of Science of original twin studies focused on the heritability of WM. Overall, our results highlighted a consistent heritability of diffusion indices (i.e., fractional anisotropy, mean, axial and radial diffusivity), and network topology among twins. The genetic influence resulted prominent in frontal and occipital regions, in the limbic system, and in commissural fibers. To enhance the understanding of genetic influence on WM microstructure further studies in less heterogeneous experimental settings, encompassing all diffusion indices, are warranted.

Comparative analysis of resting-state EEG functional connectivity in depression and obsessive-compulsive disorder

Major depressive disorder (MDD) and obsessive-compulsive disorder (OCD) are psychiatric disorders that often co-occur. We aimed to investigate whether their high comorbidity could be traced not only by clinical manifestations, but also at the level of functional brain activity. In this paper, we examined the differences in functional connectivity (FC) at the whole-brain level and within the default mode network (DMN). Resting-state EEG was obtained from 43 controls, 26 OCD patients, and 34 MDD patients. FC was analyzed between 68 cortical sources, and between-group differences in the 4-30 Hz range were assessed via the Network Based Statistic method. The strength of DMN intra-connectivity was compared between groups in the theta, alpha and beta frequency bands. A cluster of 67 connections distinguished the OCD, MDD and control groups. The majority of the connections, 8 of which correlated with depressive symptom severity, were found to be weaker in the clinical groups. Only 3 connections differed between the clinical groups, and one of them correlated with OCD severity. The DMN strength was reduced in the clinical groups in the alpha and beta bands. It can be concluded that the high comorbidity of OCD and MDD can be traced at the level of FC.

Two common and distinct forms of variation in human functional brain networks

The cortex has a characteristic layout with specialized functional areas forming distributed large-scale networks. However, substantial work shows striking variation in this organization across people, which relates to differences in behavior. While most previous work treats individual differences as linked to boundary shifts between the borders of regions, here we show that cortical 'variants' also occur at a distance from their typical position, forming ectopic intrusions. Both 'border' and 'ectopic' variants are common across individuals, but differ in their location, network associations, properties of subgroups of individuals, activations during tasks, and prediction of behavioral phenotypes. Border variants also track significantly more with shared genetics than ectopic variants, suggesting a closer link between ectopic variants and environmental influences. This work argues that these two dissociable forms of variation-border shifts and ectopic intrusions-must be separately accounted for in the analysis of individual differences in cortical systems across people.

Changes in Resting-State Networks in Children with Growth Hormone Deficiency

Purpose: Growth hormone deficiency (GHD) refers to the partial or complete lack of growth hormone. Short stature and slow growth are characteristic of patients with GHD. Previous neuroimaging studies have suggested that GHD may cause cognitive and behavioral impairments in patients. Resting-state networks (RSNs) are regions of the brain that exhibit synchronous activity and are closely related to our cognition and behavior. Therefore, the purpose of the current study was to explore cognitive and behavioral abnormalities in children with GHD by investigating changes in RSNs. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 26 children with GHD and 15 healthy controls (HCs) were obtained. Independent component analysis was used to identify seven RSNs from rs-fMRI data. Group differences in RSNs were estimated using two-sample t-tests. Correlation analysis was employed to investigate the associations among the areas of difference and clinical measures. Results: Compared with HCs, children with GHD had significant differences in the salience network (SN), default mode network (DMN), language network (LN), and sensorimotor network (SMN). Moreover, within the SN, the functional connectivity (FC) value of the right posterior supramarginal gyrus was negatively correlated with the adrenocorticotropic hormone and the FC value of the left anterior inferior parietal gyrus was positively correlated with insulin-like growth factor 1. Conclusions: These results suggest that alterations in RSNs may account for abnormal cognition and behavior in children with GHD, such as decreased motor function, language withdrawal, anxiety, and social anxiety. These findings provide neuroimaging support for uncovering the pathophysiological mechanisms of GHD in children. Impact statement Children with growth hormone deficiency (GHD) generally experience cognitive and behavioral abnormalities. However, there are few neuroimaging studies on children with GHD. Moreover, prior research has not investigated the aberrant brain function in patients with GHD from the perspective of brain functional networks. Therefore, this study employed the independent component analysis method to investigate alterations within seven commonly observed resting-state networks due to GHD. The results showed that children with GHD had significant differences in the salience network, default mode network, language network, and sensorimotor network. This provides neuroimaging support for revealing the pathophysiological mechanisms of GHD in children.

Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9-10 years; n = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.

Brain network integration underpins differential susceptibility of adolescent anxiety

BACKGROUND

Parenting is a common and potent environmental factor influencing adolescent anxiety. Yet, the underlying neurobiological susceptibility signatures remain elusive. Here, we used a longitudinal twin neuroimaging study to investigate the brain network integration and its heritable relation to underpin the neural differential susceptibility of adolescent anxiety to parenting environments.

METHODS

216 twins from the Beijing Twin Study completed the parenting and anxiety assessments and fMRI scanning. We first identified the brain network integration involved in the influences of parenting at age 12 on anxiety symptoms at age 15. We then estimated to what extent heritable sensitive factors are responsible for the susceptibility of brain network integration.

RESULTS

Consistent with the differential susceptibility theory, the results showed that hypo-connectivity within the central executive network amplified the impact of maternal hostility on anxiety symptoms. A high anti-correlation between the anterior salience and default mode networks played a similar modulatory role in the susceptibility of adolescent anxiety to paternal hostility. Genetic influences (21.18%) were observed for the connectivity pattern in the central executive network.

CONCLUSIONS

Brain network integration served as a promising neurobiological signature of the differential susceptibility to adolescent anxiety. Our findings deepen the understanding of the neural sensitivity in the developing brain and can inform early identification and personalized interventions for adolescents at risk of anxiety disorders.

Genetic and environmental influence on resting state networks in young male and female adults: a cartographer mapping study

We propose a unique, minimal assumption, approach based on variance analyses (compared with standard approaches) to investigate genetic influence on individual differences on the functional connectivity of the brain using 65 monozygotic and 65 dizygotic healthy young adult twin pairs' low-frequency oscillation resting state functional Magnetic Resonance Imaging (fMRI) data from the Human Connectome Project. Overall, we found high number of genetically-influenced functional (GIF) connections involving posterior to posterior brain regions (occipital/temporal/parietal) implicated in low-level processes such as vision, perception, motion, categorization, dorsal/ventral stream visuospatial, and long-term memory processes, as well as high number across midline brain regions (cingulate) implicated in attentional processes, and emotional responses to pain. We found low number of GIF connections involving anterior to anterior/posterior brain regions (frontofrontal > frontoparietal, frontotemporal, frontooccipital) implicated in high-level processes such as working memory, reasoning, emotional judgment, language, and action planning. We found very low number of GIF connections involving subcortical/noncortical networks such as basal ganglia, thalamus, brainstem, and cerebellum. In terms of sex-specific individual differences, individual differences in males were more genetically influenced while individual differences in females were more environmentally influenced in terms of the interplay of interactions of Task positive networks (brain regions involved in various task-oriented processes and attending to and interacting with environment), extended Default Mode Network (a central brain hub for various processes such as internal monitoring, rumination, and evaluation of self and others), primary sensorimotor systems (vision, audition, somatosensory, and motor systems), and subcortical/noncortical networks. There were >8.5-19.1 times more GIF connections in males than females. These preliminary (young adult cohort-specific) findings suggest that individual differences in the resting state brain may be more genetically influenced in males and more environmentally influenced in females; furthermore, standard approaches may suggest that it is more substantially nonadditive genetics, rather than additive genetics, which contribute to the differences in sex-specific individual differences based on this young adult (male and female) specific cohort. Finally, considering the preliminary cohort-specific results, based on standard approaches, environmental influences on individual differences may be substantially greater than that of genetics, for either sex, frontally and brain-wide. [Correction added on 10 May 2023, after first online publication: added: functional Magnetic Resonance Imaging. Added: individual differences in, twice. Added statement between furthermore … based on standard approaches.].

Hypergraph-regularized multimodal learning by graph diffusion for imaging genetics based Alzheimer's Disease diagnosis

Recent studies show that multi-modal data fusion techniques combining information from diverse sources are helpful to diagnose and predict complex brain disorders. However, most existing diagnosis methods have only simply employed a feature combination strategy for multiple imaging and genetic data, ignoring the imaging phenotypes associated with the risk gene information. To this end, we present a hypergraph-regularized multimodal learning by graph diffusion (HMGD) for joint association learning and outcome prediction. Specifically, we first present a graph diffusion method for enhancing similarity measures among subjects given from multi-modality phenotypes, which fully uses multiple input similarity graphs and integrates them into a unified graph with valuable geometric structures among different imaging phenotypes. Then, we employ the unified graph to represent the high-order similarity relationships among subjects, and enforce a hypergraph-regularized term to incorporate both inter- and cross-modality information for selecting the imaging phenotypes associated with the risk single nucleotide polymorphism (SNP). Finally, a multi-kernel support vector machine (MK-SVM) is adopted to fuse such phenotypic features selected from different modalities for the final diagnosis and prediction. The proposed approach is experimentally explored on brain imaging genetic data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets. Relevant results present that the proposed approach is superior to several competing algorithms, and realizes strong associations and discovers significant consistent and robust ROIs across different imaging phenotypes associated with the genetic risk biomarkers to guide disease interpretation and prediction.

Genetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders

Imaging genetics provides an opportunity to discern associations between genetic variants and brain imaging phenotypes. Historically, the field has focused on adults and adolescents; very few imaging genetics studies have focused on brain development in infancy and early childhood (from birth to age 6 years). This is an important knowledge gap because developmental changes in the brain during the prenatal and early postnatal period are regulated by dynamic gene expression patterns that likely play an important role in establishing an individual's risk for later psychiatric illness and neurodevelopmental disabilities. In this review, we summarize findings from imaging genetics studies spanning from early infancy to early childhood, with a focus on studies examining genetic risk for neuropsychiatric disorders. We also introduce the Organization for Imaging Genomics in Infancy (ORIGINs), a working group of the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium, which was established to facilitate large-scale imaging genetics studies in infancy and early childhood.

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.

Identify connectome between genotypes and brain network phenotypes via deep self-reconstruction sparse canonical correlation analysis

MOTIVATION

As a rising research topic, brain imaging genetics aims to investigate the potential genetic architecture of both brain structure and function. It should be noted that in the brain, not all variations are deservedly caused by genetic effect, and it is generally unknown which imaging phenotypes are promising for genetic analysis.

RESULTS

In this work, genetic variants (i.e. the single nucleotide polymorphism, SNP) can be correlated with brain networks (i.e. quantitative trait, QT), so that the connectome (including the brain regions and connectivity features) of functional brain networks from the functional magnetic resonance imaging data is identified. Specifically, a connection matrix is firstly constructed, whose upper triangle elements are selected to be connectivity features. Then, the PageRank algorithm is exploited for estimating the importance of different brain regions as the brain region features. Finally, a deep self-reconstruction sparse canonical correlation analysis (DS-SCCA) method is developed for the identification of genetic associations with functional connectivity phenotypic markers. This approach is a regularized, deep extension, scalable multi-SNP-multi-QT method, which is well-suited for applying imaging genetic association analysis to the Alzheimer's Disease Neuroimaging Initiative datasets. It is further optimized by adopting a parametric approach, augmented Lagrange and stochastic gradient descent. Extensive experiments are provided to validate that the DS-SCCA approach realizes strong associations and discovers functional connectivity and brain region phenotypic biomarkers to guide disease interpretation.

AVAILABILITY AND IMPLEMENTATION

The Matlab code is available at https://github.com/meimeiling/DS-SCCA/tree/main.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Functional ultrasound imaging: A useful tool for functional connectomics?

Functional ultrasound (fUS) is a hemodynamic-based functional neuroimaging technique, primarily used in animal models, that combines a high spatiotemporal resolution, a large field of view, and compatibility with behavior. These assets make fUS especially suited to interrogating brain activity at the systems level. In this review, we describe the technical capabilities offered by fUS and discuss how this technique can contribute to the field of functional connectomics. First, fUS can be used to study intrinsic functional connectivity, namely patterns of correlated activity between brain regions. In this area, fUS has made the most impact by following connectivity changes in disease models, across behavioral states, or dynamically. Second, fUS can also be used to map brain-wide pathways associated with an external event. For example, fUS has helped obtain finer descriptions of several sensory systems, and uncover new pathways implicated in specific behaviors. Additionally, combining fUS with direct circuit manipulations such as optogenetics is an attractive way to map the brain-wide connections of defined neuronal populations. Finally, technological improvements and the application of new analytical tools promise to boost fUS capabilities. As brain coverage and the range of behavioral contexts that can be addressed with fUS keep on increasing, we believe that fUS-guided connectomics will only expand in the future. In this regard, we consider the incorporation of fUS into multimodal studies combining diverse techniques and behavioral tasks to be the most promising research avenue.

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.

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.

Identify Consistent Cross-Modality Imaging Genetic Patterns via Discriminant Sparse Canonical Correlation Analysis

Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. However, the traditional SCCA algorithm has been designed to seek a linear correlation between the SNP genotype and brain imaging phenotype, ignoring the discriminant similarity information between within-class subjects in brain imaging genetics association analysis. In addition, multi-modality brain imaging phenotypes are extracted from different perspectives and imaging markers from the same region consistently showing up in multimodalities may provide more insights for the mechanistic understanding of diseases. In this paper, a novel multi-modality discriminant SCCA algorithm (MD-SCCA) is proposed to overcome these limitations as well as to improve learning results by incorporating valuable discriminant similarity information into the SCCA algorithm. Specifically, we first extract the discriminant similarity information between within-class subjects by the sparse representation. Second, the discriminant similarity information is enforced within SCCA to construct a discriminant SCCA algorithm (D-SCCA). At last, the MD-SCCA algorithm is adopted to fully explore the relationships among different modalities of different subjects. In experiments, both synthetic dataset and real data from the Alzheimer's Disease Neuroimaging Initiative database are used to test the performance of our algorithm. The empirical results have demonstrated that the proposed algorithm not only produces improved cross-validation performances but also identifies consistent cross-modality imaging genetic biomarkers.

Identify Complex Imaging Genetic Patterns via Fusion Self-Expressive Network Analysis

In the brain imaging genetic studies, it is a challenging task to estimate the association between quantitative traits (QTs) extracted from neuroimaging data and genetic markers such as single-nucleotide polymorphisms (SNPs). Most of the existing association studies are based on the extensions of sparse canonical correlation analysis (SCCA) for the identification of complex bi-multivariate associations, which can take the specific structure and group information into consideration. However, they often take the original data as input without considering its underlying complex multi-subspace structure, which will deteriorate the performance of the following integrative analysis. Accordingly, in this paper, the self-expressive property is exploited for the reconstruction of the original data before the association analysis, which can well describe the similarity structure. Specifically, we first apply the within-class similarity information to construct self-expressive networks by sparse representation. Then, we use the fusion method to iteratively fuse the self-expressive networks from multi-modality brain phenotypes into one network. Finally, we calculate the imaging genetic association based on the fused self-expressive network. We conduct the experiments on both single-modality and multi-modality phenotype data. Related experimental results validate that our method can not only better estimate the potential association between genetic markers and quantitative traits but also identify consistent multi-modality imaging genetic biomarkers to guide the interpretation of Alzheimer's disease.

Multimodal Neurophysiological and Neuroimaging Evidence of Genetic Influence on Motor Control: A Case Report of Monozygotic Twins

Considering genetic influence on brain structure and function, including motor control, we report a case of right-handed monozygotic twins with atypical organization of fine motor movement control that might imply genetic influence. Structural and functional organization of the twins' motor function was assessed using transcranial magnetic stimulation (TMS), fMRI with a motor-task paradigm, and diffusion tensor imaging (DTI) tractography. TMS revealed that both twins presented the same unexpected activation and inhibition of both motor cortices during volitional unilateral fine hand movement. The right ipsilateral corticospinal tract was weaker than the left contralateral one. The motor-task fMRI identified activation in the left primary motor cortex and bilateral secondary motor areas during right-hand (dominant) movement and activation in the bilateral primary motor cortex and secondary motor areas during left-hand movement. Based on DTI tractography, both twins showed a significantly lower streamline count (number of fibers) in the right corticospinal tract compared with a control group, which was not the case for the left corticospinal tract. Neither twin reported any difficulty in conducting fine motor movements during their activities of daily living. The combination of TMS and advanced neuroimaging techniques identified an atypical motor control organization that might be influenced by genetic factors. This combination emphasizes that activation of the unilateral uncrossed pyramidal tract represents an alternative scheme to a "failure" of building a standard pattern but may not necessarily lead to disability.

Heritability of individualized cortical network topography

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and is predictive of behavior, it is not yet clear to what extent genetic factors underlie interindividual differences in network topography. Here, leveraging a nonlinear multidimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n = 1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2 : M = 0.34, SD = 0.070), relative to unimodal sensory/motor cortex (h2 : M = 0.40, SD = 0.097). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multidimensional estimation of heritability (h2-multi; M = 0.14, SD = 0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.

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.

Birth weight is associated with adolescent brain development: A multimodal imaging study in monozygotic twins

Previous research has shown that the prenatal environment, commonly indexed by birth weight (BW), is a predictor of morphological brain development. We previously showed in monozygotic (MZ) twins associations between BW and brain morphology that were independent of genetics. In the present study, we employed a longitudinal MZ twin design to investigate whether variations in prenatal environment (as indexed by discordance in BW) are associated with resting‐state functional connectivity (rs‐FC) and with structural connectivity. We focused on the limbic and default mode networks (DMNs), which are key regions for emotion regulation and internally generated thoughts, respectively. One hundred and six healthy adolescent MZ twins (53 pairs; 42% male pairs) followed longitudinally from birth underwent a magnetic resonance imaging session at age 15. Graph theoretical analysis was applied to rs‐FC measures. TrackVis was used to determine track count as an indicator of structural connectivity strength. Lower BW twins had less efficient limbic network connectivity as compared to their higher BW co‐twin, driven by differences in the efficiency of the right hippocampus and right amygdala. Lower BW male twins had fewer tracks connecting the right hippocampus and right amygdala as compared to their higher BW male co‐twin. There were no associations between BW and the DMN. These findings highlight the possible role of unique prenatal environmental influences in the later development of efficient spontaneous limbic network connections within healthy individuals, irrespective of DNA sequence or shared environment.

An open database of resting-state fMRI in awake rats

Rodent models are essential to translational research in health and disease. Investigation in rodent brain function and organization at the systems level using resting-state functional magnetic resonance imaging (rsfMRI) has become increasingly popular. Due to this rapid progress, publicly shared rodent rsfMRI databases can be of particular interest and importance to the scientific community, as inspired by human neuroscience and psychiatric research that are substantially facilitated by open human neuroimaging datasets. However, such databases in rats are still rare. In this paper, we share an open rsfMRI database acquired in 90 rats with a well-established awake imaging paradigm that avoids anesthesia interference. Both raw and preprocessed data are made publicly available. Procedures in data preprocessing to remove artefacts induced by the scanner, head motion and non-neural physiological noise are described in details. We also showcase inter-regional functional connectivity and functional networks obtained from the database.

Environmental Factors Shape Sleep EEG Connectivity During Early Adolescence

Quantifying the degree to which genetic and environmental factors shape brain network connectivity is critical to furthering our understanding of the developing human brain. Sleep, a state of sensory disengagement, provides a unique opportunity to study brain network activity noninvasively by means of sleep electroencephalography (EEG) coherence. We conducted a high-density sleep EEG study in monozygotic (MZ; n = 38; mean age = 12.46; 20 females) and dizygotic (DZ; n = 24; mean age = 12.50; 12 females) twins to assess the heritability of sleep EEG coherence in early adolescence—a period of significant brain rewiring. Structural equation modeling was used to estimate three latent factors: genes, environmental factors shared between twins and environmental factors unique to each twin. We found a strong contribution of unique environmental factors (66% of the variance) and moderate genetic influence (19% of the variance) on sleep EEG coherence across frequencies and sleep states. An exception to this was sleep spindle activity, an index of the thalamocortical network, which showed on average a genetic contribution of 48% across connections. Furthermore, we observed high intraindividual stability of coherence across two consecutive nights suggesting that despite only a modest genetic contribution, sleep EEG coherence is like a trait. Our findings in adolescent humans are in line with earlier findings in animals that show the primordial cerebral map and its connections are plastic and it is through interaction with the environment that the pattern of brain network connectivity is shaped. Therefore, even in twins living together, small differences in the environment may cascade into meaningful differences in brain connectivity.

An Embodied Neurocomputational Framework for Organically Integrating Biopsychosocial Processes: An Application to the Role of Social Support in Health and Disease

OBJECTIVE

Two distinct perspectives-typically referred to as the biopsychosocial and biomedical models-currently guide clinical practice. Although the role of psychosocial factors in contributing to physical and mental health outcomes is widely recognized, the biomedical model remains dominant. This is due in part to (a) the largely nonmechanistic focus of biopsychosocial research and (b) the lack of specificity it currently offers in guiding clinicians to focus on social, psychological, and/or biological factors in individual cases. In this article, our objective is to provide an evidence-based and theoretically sophisticated mechanistic model capable of organically integrating biopsychosocial processes.

METHODS

To construct this model, we provide a narrative review of recent advances in embodied cognition and predictive processing within computational neuroscience, which offer mechanisms for understanding individual differences in social perceptions, visceral responses, health-related behaviors, and their interactions. We also review current evidence for bidirectional influences between social support and health as a detailed illustration of the novel conceptual resources offered by our model.

RESULTS

When integrated, these advances highlight multiple mechanistic causal pathways between psychosocial and biological variables.

CONCLUSIONS

By highlighting these pathways, the resulting model has important implications motivating a more psychologically sophisticated, person-specific approach to future research and clinical application in the biopsychosocial domain. It also highlights the potential for quantitative computational modeling and the design of novel interventions. Finally, it should aid in guiding future research in a manner capable of addressing the current criticisms/limitations of the biopsychosocial model and may therefore represent an important step in bridging the gap between it and the biomedical perspective.

Functional Connectivity Fingerprints at Rest Are Similar across Youths and Adults and Vary with Genetic Similarity

Distinguishing individuals from brain connectivity, and studying the genetic influences on that identification across different ages, improves our basic understanding of functional brain network organization. We applied support vector machine classifiers to two datasets of twins (adult, pediatric) and two datasets of repeat-scan individuals (adult, pediatric). Classifiers were trained on resting state functional connectivity magnetic resonance imaging (rs-fcMRI) data and used to predict individuals and co-twin pairs from independent data. The classifiers successfully identified individuals from a previous scan with 100% accuracy, even when scans were separated by months. In twin samples, classifier accuracy decreased as genetic similarity decreased. Our results demonstrate that classification is stable within individuals, similar within families, and contains similar representations of functional connections over a few decades of life. Moreover, the degree to which these patterns of connections predict siblings' data varied by genetic relatedness, suggesting that genetic influences on rs-fcMRI connectivity are established early in life.

Brain Imaging Genomics: Integrated Analysis and Machine Learning

Brain imaging genomics is an emerging data science field, where integrated analysis of brain imaging and genomics data, often combined with other biomarker, clinical and environmental data, is performed to gain new insights into the phenotypic, genetic and molecular characteristics of the brain as well as their impact on normal and disordered brain function and behavior. It has enormous potential to contribute significantly to biomedical discoveries in brain science. Given the increasingly important role of statistical and machine learning in biomedicine and rapidly growing literature in brain imaging genomics, we provide an up-to-date and comprehensive review of statistical and machine learning methods for brain imaging genomics, as well as a practical discussion on method selection for various biomedical applications.

Functional network dynamics in a neurodevelopmental disorder of known genetic origin

Dynamic connectivity in functional brain networks is a fundamental aspect of cognitive development, but we have little understanding of the mechanisms driving variability in these networks. Genes are likely to influence the emergence of fast network connectivity via their regulation of neuronal processes, but novel methods to capture these rapid dynamics have rarely been used in genetic populations. The current study redressed this by investigating brain network dynamics in a neurodevelopmental disorder of known genetic origin, by comparing individuals with a ZDHHC9-associated intellectual disability to individuals with no known impairment. We characterised transient network dynamics using a Hidden Markov Model (HMM) on magnetoencephalography (MEG) data, at rest and during auditory oddball stimulation. The HMM is a data-driven method that captures rapid patterns of coordinated brain activity recurring over time. Resting-state network dynamics distinguished the groups, with ZDHHC9 participants showing longer state activation and, crucially, ZDHHC9 gene expression levels predicted the group differences in dynamic connectivity across networks. In contrast, network dynamics during auditory oddball stimulation did not show this association. We demonstrate a link between regional gene expression and brain network dynamics, and present the new application of a powerful method for understanding the neural mechanisms linking genetic variation to cognitive difficulties.

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.

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.

Salience network connectivity is reduced by a meal and influenced by genetic background and hypothalamic gliosis

Background/Objectives:

The salience network (SN) comprises brain regions that evaluate cues in the external environment in light of internal signals. We examined the SN response to meal intake and potential genetic and acquired influences on SN function.

Subjects/Methods:

Monozygotic (MZ; 40 pairs) and dizygotic (15 pairs) twins had body composition and plasma metabolic profile evaluated (glucose, insulin, leptin, ghrelin and GLP-1). Twins underwent resting-state functional magnetic resonance imaging (fMRI) scans before and after a standardized meal. The strength of SN connectivity was analyzed pre- and post-meal and the percentage change elicited by a meal was calculated. A multi-echo T2 MRI scan measured T2 relaxation time, a radiologic index of gliosis, in the mediobasal hypothalamus (MBH) and control regions. Statistical approaches included intraclass correlations (ICC) to investigate genetic influences and within-pair analyses to exclude genetic confounders.

Results:

SN connectivity was reduced by meal ingestion (β=−0.20; P<0.001). Inherited influences on both pre- and post-meal connectivity were present (ICC MZ twins 26%, P<0.05 and 47%, P<0.001, respectively), but not percentage change in response to the meal. SN connectivity in response to a meal did not differ between participants with obesity and of normal weight (χ2(1)=0.93; P=0.33). However, when participants were classified as having high or low signs of MBH gliosis, the high MBH gliosis group failed to reduce the connectivity in response to a meal (z=−1.32; P=0.19). Excluding genetic confounders, the percentage change in SN connectivity by a meal correlated to body fat percentage (r=0.24; P<0.01).

Conclusions:

SN connectivity was reduced by a meal, indicating potential participation of the SN in control of feeding. The strength of SN connectivity is inherited, but the degree to which SN connectivity is reduced by eating appears to be influenced by adiposity and the presence of hypothalamic gliosis.

A framework for linking resting-state chronnectome/genome features in schizophrenia: A pilot study

Multimodal, imaging-genomics techniques offer a platform for understanding genetic influences on brain abnormalities in psychiatric disorders. Such approaches utilize the information available from both imaging and genomics data and identify their association. Particularly for complex disorders such as schizophrenia, the relationship between imaging and genomic features may be better understood by incorporating additional information provided by advanced multimodal modeling. In this study, we propose a novel framework to combine features corresponding to functional magnetic resonance imaging (functional) and single nucleotide polymorphism (SNP) data from 61 schizophrenia (SZ) patients and 87 healthy controls (HC). In particular, the features for the functional and genetic modalities include dynamic (i.e., time-varying) functional network connectivity (dFNC) features and the SNP data, respectively. The dFNC features are estimated from component time-courses, obtained using group independent component analysis (ICA), by computing sliding-window functional network connectivity, and then estimating subject specific states from this dFNC data using a k-means clustering approach. For each subject, both the functional (dFNC states) and SNP data are selected as features for a parallel ICA (pICA) based imaging-genomic framework. This analysis identified a significant association between a SNP component (defined by large clusters of functionally related SNPs statistically correlated with phenotype components) and time-varying or dFNC component (defined by clusters of related connectivity links among distant brain regions distributed across discrete dynamic states, and statistically correlated with genomic components) in schizophrenia. Importantly, the polygenetic risk score (PRS) for SZ (computed as a linearly weighted sum of the genotype profiles with weights derived from the odds ratios of the psychiatric genomics consortium (PGC)) was negatively correlated with the significant dFNC component, which were mostly present within a state that exhibited a lower occupancy rate in individuals with SZ compared with HC, hence identifying a potential dFNC imaging biomarker for schizophrenia. Taken together, the current findings provide preliminary evidence for a link between dFNC measures and genetic risk, suggesting the application of dFNC patterns as biomarkers in imaging genetic association study.

Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzheimer's disease

MOTIVATION

Neuroimaging genetics is an emerging field to identify the associations between genetic variants [e.g. single-nucleotide polymorphisms (SNPs)] and quantitative traits (QTs) such as brain imaging phenotypes. However, most of the current studies focus only on the associations between brain structure imaging and genetic variants, while neglecting the connectivity information between brain regions. In addition, the brain itself is a complex network, and the higher-order interaction may contain useful information for the mechanistic understanding of diseases [i.e. Alzheimer's disease (AD)].

RESULTS

A general framework is proposed to exploit network voxel information and network connectivity information as intermediate traits that bridge genetic risk factors and disease status. Specifically, we first use the sparse representation (SR) model to build hyper-network to express the connectivity features of the brain. The network voxel node features and network connectivity edge features are extracted from the structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (fMRI), respectively. Second, a diagnosis-aligned multi-modality regression method is adopted to fully explore the relationships among modalities of different subjects, which can help further mine the relation between the risk genetics and brain network features. In experiments, all methods are tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results not only verify the effectiveness of our proposed framework but also discover some brain regions and connectivity features that are highly related to diseases.

AVAILABILITY AND IMPLEMENTATION

The Matlab code is available at http://ibrain.nuaa.edu.cn/2018/list.htm.

Body mass variability is represented by distinct functional connectivity patterns

Understanding weight-related differences in functional connectivity provides key insight into neurocognitive factors implicated in obesity. Here, we sampled three groups from human connectome project data: 1) 47 pairs of BMI-discordant twins (n = 94; average BMI-discordancy 6.7 ± 3.1 kg/m2), 2) 47 pairs of gender and BMI matched BMI-discordant, unrelated individuals, and 3) 47 pairs of BMI-similar twins, to test for body mass dependent differences in between network functional connectivity. Across BMI discordant samples, three networks appeared to be highly sensitive to weight status; specifically, a network comprised of gustatory processing regions, a visual processing network, and the default mode network (DMN). Further, in the BMI-discordant twin sample, twins with lower BMI had stronger connectivity between striatal/thalamic and prefrontal networks (pFWE = 0.04). We also observed that individuals with a higher BMI than their twin had stronger connectivity between cerebellar and insular networks (pFWE = 0.04). Connectivity patterns observed in the BMI-discordant twin sample were not seen in a BMI-similar sample, providing evidence that the results are specific to BMI discordance. Beyond the involvement of gustatory and visual networks and the DMN, little overlap in results were seen between the two BMI-discordant samples. In concordance with previous findings, we hypothesize that stronger cortical-striatal-thalamic connectivity associated with lower body mass in twins may facilitate increased regulation of hedonically motivated behaviors. In twins with higher body mass, increased cerebellar-insula connectivity may be associated with compromised satiation signaling, an interpretation dovetailing prior research. The lack of overlapping results between the two BMI discordant samples may be a function of higher study design sensitivity in the BMI-discordant twin sample, relative to the more generalizable results in the unrelated sample. These findings demonstrate that distinct connectivity patterns can represent weight variability, adding to mounting evidence that implicates atypical brain functioning with the accumulation and/or maintenance of elevated weight.

Predicting seizure outcome of vagus nerve stimulation using MEG-based network topology

Vagus nerve stimulation (VNS) is a low-risk surgical option for patients with drug resistant epilepsy, although it is impossible to predict which patients may respond to VNS treatment. Resting-state magnetoencephalography (rs-MEG) connectivity analysis has been increasingly utilized to investigate the impact of epilepsy on brain networks and identify alteration of these networks after different treatments; however, there is no study to date utilizing this modality to predict the efficacy of VNS treatment. We investigated whether the rs-MEG network topology before VNS implantation can be used to predict efficacy of VNS treatment. Twenty-three patients with epilepsy who had MEG before VNS implantation were included in this study. We also included 89 healthy control subjects from the Human Connectome Project. Using the phase-locking value in the theta, alpha, and beta frequency bands as a measure of rs-MEG functional connectivity, we calculated three global graph measures: modularity, transitivity, and characteristic path length (CPL). Our results revealed that the rs-MEG graph measures were significantly heritable and had an overall good test-retest reliability, and thus these measures may be used as potential biomarkers of the network topology. We found that the modularity and transitivity in VNS responders were significantly larger and smaller, respectively, than those observed in VNS non-responders. We also observed that the modularity and transitivity in three frequency bands and CPL in delta and beta bands were significantly different in controls than those found in responders or non-responders, although the values of the graph measures in controls were closer to those of responders than non-responders. We used the modularity and transitivity as input features of a naïve Bayes classifier, and achieved an accuracy of 87% in classification of non-responders, responders, and controls. The results of this study revealed that MEG-based graph measures are reliable biomarkers, and that these measures may be used to predict seizure outcome of VNS treatment.

Genetic influences on prefrontal activation during a verbal fluency task in children: A twin study using near-infrared spectroscopy

OBJECTIVE

The genetic and environmental influences on prefrontal function in childhood are underinvestigated due to the difficulty of measuring prefrontal function in young subjects, for which near-infrared spectroscopy (NIRS) is a suitable functional neuroimaging technique that facilitates the easy and noninvasive measurement of blood oxygenation in the superficial cerebral cortices.

METHOD

Using a two-channel NIRS arrangement, we measured changes in bilateral prefrontal blood oxygenation during a category version of the verbal fluency task (VFT) in 27 monozygotic twin pairs and 12 same-sex dizygotic twin pairs ages 5-17 years. We also assessed the participant's full-scale intelligence quotient (FIQ) and retrieved parental socioeconomic status (SES). Classical structured equation modeling was used to estimate the heritability.

RESULTS

The heritability of VFT-related brain activation was estimated to be 44% and 37% in the right and left prefrontal regions, respectively. We also identified a significant genetic contribution (74%) to FIQ, but did not to VFT task performance. Parental SES was not correlated with FIQ, task performance, or task-related prefrontal activation.

CONCLUSIONS

This finding provides further evidence that variance in prefrontal function has a genetic component since childhood and highlights brain function, as measured by NIRS, as a promising candidate for endophenotyping neurodevelopmental disorders.

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.

Heritability of Sleep EEG Topography in Adolescence: Results from a Longitudinal Twin Study

The topographic distribution of sleep EEG power is a reflection of brain structure and function. The goal of this study was to examine the degree to which genes contribute to sleep EEG topography during adolescence, a period of brain restructuring and maturation. We recorded high-density sleep EEG in monozygotic (MZ; n = 28) and dizygotic (DZ; n = 22) adolescent twins (mean age = 13.2 ± 1.1 years) at two time points 6 months apart. The topographic distribution of normalized sleep EEG power was examined for the frequency bands delta (1-4.6 Hz) to gamma 2 (34.2-44 Hz) during NREM and REM sleep. We found highest heritability values in the beta band for NREM and REM sleep (0.44 ≤ h2 ≤ 0.57), while environmental factors shared amongst twin siblings accounted for the variance in the delta to sigma bands (0.59 ≤ c2 ≤ 0.83). Given that both genetic and environmental factors are reflected in sleep EEG topography, our results suggest that topography may provide a rich metric by which to understand brain function. Furthermore, the frequency specific parsing of the influence of genetic from environmental factors on topography suggests functionally distinct networks and reveals the mechanisms that shape these networks.

Development of brain-wide connectivity architecture in awake rats

Childhood and adolescence are both critical developmental periods, evidenced by complex neurophysiological changes the brain undergoes and high occurrence rates of neuropsychiatric disorders during these periods. Despite substantial progress in elucidating the developmental trajectories of individual neural circuits, our knowledge of developmental changes of whole-brain connectivity architecture in animals is sparse. To fill this gap, here we longitudinally acquired rsfMRI data in awake rats during five developmental stages from juvenile to adulthood. We found that the maturation timelines of brain circuits were heterogeneous and system specific. Functional connectivity (FC) tended to decrease in subcortical circuits, but increase in cortical circuits during development. In addition, the developing brain exhibited hemispheric functional specialization, evidenced by reduced inter-hemispheric FC between homotopic regions, and lower similarity of region-to-region FC patterns between the two hemispheres. Finally, we showed that whole-brain network development was characterized by reduced clustering (i.e. local communication) but increased integration (distant communication). Taken together, the present study has systematically characterized the development of brain-wide connectivity architecture from juvenile to adulthood in awake rats. It also serves as a critical reference point for understanding circuit- and network-level changes in animal models of brain development-related disorders. Furthermore, FC data during brain development in awake rodents contain high translational value and can shed light onto comparative neuroanatomy.

Variability in Resting State Network and Functional Network Connectivity Associated With Schizophrenia Genetic Risk: A Pilot Study

Imaging genetics posits a valuable strategy for elucidating genetic influences on brain abnormalities in psychiatric disorders. However, association analysis between 2D genetic data (subject × genetic variable) and 3D first-level functional magnetic resonance imaging (fMRI) data (subject × voxel × time) has been challenging given the asymmetry in data dimension. A summary feature needs to be derived for the imaging modality to compute inter-modality association at subject level. In this work, we propose to use variability in resting state networks (RSNs) and functional network connectivity (FNC) as potential features for purpose of association analysis. We conducted a pilot study to investigate the proposed features in a dataset of 171 healthy controls and 134 patients with schizophrenia (SZ). We computed variability in RSN and FNC in a group independent component analysis framework and tested three types of variability metrics, namely Euclidean distance, Pearson correlation and Kullback-Leibler (KL) divergence. Euclidean distance and Pearson correlation metrics more effectively discriminated controls from patients than KL divergence. The group differences observed with variability in RSN and FNC were highly consistent, indicating patients presenting increased deviation from the cohort-common pattern of RSN and FNC than controls. The variability in RSN and FNC showed significant associations with network global efficiency, the more the deviation, the lower the efficiency. Furthermore, the RSN and FNC variability were found to associate with individual SZ risk SNPs as well as cumulative polygenic risk score for SZ. Collectively the current findings provide preliminary evidence for variability in RSN and FNC being promising imaging features that may find applications as biomarkers and in imaging genetic association analysis.

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.

Overweight is associated with lower resting state functional connectivity in females after eliminating genetic effects: A twin study

Obesity is related to altered functional connectivity of resting state brain networks that are involved in reward and motivation. It is unknown to what extent these associations reflect genetic confounding and whether the obesity-related connectivity changes are associated with differences in dietary intake. In this study, resting state functional MRI was performed after an overnight fast in 16 female monozygotic twin pairs (aged 48.8 ± 9.8 years) with a mean BMI discordance of 3.96 ± 2.1 kg/m² (range 0.7-8.2). Functional connectivity of the salience, basal ganglia, default mode and anterior cingulate-orbitofrontal cortex networks was examined by independent component analysis. Dietary intake was assessed using 3-day 24-hour recalls. Results revealed that within the basal ganglia network, heavier versus leaner co-twins have decreased functional connectivity strength in bilateral putamen (P < 0.05, FWE-corrected). There were no differences in connectivity in the other networks examined. In the overall group, lower functional connectivity strength in the left putamen was correlated with higher intake of total fat (P < 0.01). It was concluded that, after eliminating genetic effects, overweight is associated with lower resting state functional connectivity in bilateral putamen in the basal ganglia network. The association between lower putamen connectivity and higher fat intake suggests an important role of the putamen in appetitive mechanisms. The cross-sectional nature of our study cannot discriminate cause and consequence, but the findings are compatible with an effect of lower putamen connectivity on increased BMI and associated higher fat intake. Hum Brain Mapp 38:5069-5081, 2017. © 2017 Wiley Periodicals, Inc.

Clinical Concepts Emerging from fMRI Functional Connectomics

Recent advances in connectomics have led to a synthesis of perspectives regarding the brain's functional organization that reconciles classical concepts of localized specialization with an appreciation for properties that emerge from interactions across distributed functional networks. This provides a more comprehensive framework for understanding neural mechanisms of normal cognition and disease. Although fMRI has not become a routine clinical tool, research has already had important influences on clinical concepts guiding diagnosis and patient management. Here we review illustrative examples. Studies demonstrating the network plasticity possible in adults and the global consequences of even focal brain injuries or disease both have had substantial impact on modern concepts of disease evolution and expression. Applications of functional connectomics in studies of clinical populations are challenging traditional disease classifications and helping to clarify biological relationships between clinical syndromes (and thus also ways of extending indications for, or "re-purposing," current treatments). Large datasets from prospective, longitudinal studies promise to enable the discovery and validation of functional connectomic biomarkers with the potential to identify people at high risk of disease before clinical onset, at a time when treatments may be most effective. Studies of pain and consciousness have catalyzed reconsiderations of approaches to clinical management, but also have stimulated debate about the clinical meaningfulness of differences in internal perceptual or cognitive states inferred from functional connectomics or other physiological correlates. By way of a closing summary, we offer a personal view of immediate challenges and potential opportunities for clinically relevant applications of fMRI-based functional connectomics.

Cognitive genomics: Linking genes to behavior in the human brain

Correlations of genetic variation in DNA with functional brain activity have already provided a starting point for delving into human cognitive mechanisms. However, these analyses do not provide the specific genes driving the associations, which are complicated by intergenic localization as well as tissue-specific epigenetics and expression. The use of brain-derived expression datasets could build upon the foundation of these initial genetic insights and yield genes and molecular pathways for testing new hypotheses regarding the molecular bases of human brain development, cognition, and disease. Thus, coupling these human brain gene expression data with measurements of brain activity may provide genes with critical roles in brain function. However, these brain gene expression datasets have their own set of caveats, most notably a reliance on postmortem tissue. In this perspective, I summarize and examine the progress that has been made in this realm to date, and discuss the various frontiers remaining, such as the inclusion of cell-type-specific information, additional physiological measurements, and genomic data from patient cohorts.

Relationship of a common OXTR gene variant to brain structure and default mode network function in healthy humans

A large body of research suggests that oxytocin receptor (OXTR) gene polymorphisms may influence both social behaviors and psychiatric conditions related to social deficits, such as autism spectrum disorders (ASDs), schizophrenia, and mood and anxiety disorders. However, the neural mechanism underlying these associations is still unclear. Relative to controls, patients with these psychiatric conditions show differences in brain structure, and in resting state fMRI (rs-fMRI) signal synchronicity among default mode network (DMN) regions (also known as functional connectivity). We used a stepwise imaging genetics approach in 328 healthy young adults to test the hypothesis that 10 SNPs in OXTR are associated with differences in DMN synchronicity and structure of some of the associated brain regions. As OXTR effects may be sex-dependent, we also tested whether our findings were modulated by sex. OXTR rs2254298 A allele carriers had significantly lower rsFC with PCC in a cluster extending from the right fronto-insular cortex to the putamen and globus pallidus, and in bilateral dorsal anterior cingulate cortex (dACC) compared to individuals with the GG genotype; all observed effects were found only in males. Moreover, compared to the male individuals with GG genotype ofrs2254298, the male A allele carriers demonstrated significantly thinner cortical gray matter in the bilateral dACC. Our findings suggest that there may be sexually dimorphic mechanisms by which a naturally occurring variation of the OXTR gene may influence brain structure and function in DMN-related regions implicated in neuropsychiatric disorders.

Environmental factors linked to depression vulnerability are associated with altered cerebellar resting-state synchronization

Hosting nearly eighty percent of all human neurons, the cerebellum is functionally connected to large regions of the brain. Accumulating data suggest that some cerebellar resting-state alterations may constitute a key candidate mechanism for depressive psychopathology. While there is some evidence linking cerebellar function and depression, two topics remain largely unexplored. First, the genetic or environmental roots of this putative association have not been elicited. Secondly, while different mathematical representations of resting-state fMRI patterns can embed diverse information of relevance for health and disease, many of them have not been studied in detail regarding the cerebellum and depression. Here, high-resolution fMRI scans were examined to estimate functional connectivity patterns across twenty-six cerebellar regions in a sample of 48 identical twins (24 pairs) informative for depression liability. A network-based statistic approach was employed to analyze cerebellar functional networks built using three methods: the conventional approach of filtered BOLD fMRI time-series, and two analytic components of this oscillatory activity (amplitude envelope and instantaneous phase). The findings indicate that some environmental factors may lead to depression vulnerability through alterations of the neural oscillatory activity of the cerebellum during resting-state. These effects may be observed particularly when exploring the amplitude envelope of fMRI oscillations.

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.