Multidimensional heritability analysis of neuroanatomical shape

Bayesian mixed model inference for genetic association under related samples with brain network phenotype

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.

Heritability of movie-evoked brain activity and connectivity

The neural bases of sensory processing are conserved across people, but no two individuals experience the same stimulus in exactly the same way. Recent work has established that the idiosyncratic nature of subjective experience is underpinned by individual variability in brain responses to sensory information. However, the fundamental origins of this individual variability have yet to be systematically investigated. Here, we establish a genetic basis for individual differences in sensory processing by quantifying (1) the heritability of high-dimensional brain responses to movies and (2) the extent to which this heritability is grounded in lower-level aspects of brain function. Specifically, we leverage 7T fMRI data collected from a twin sample to first show that movie-evoked brain activity and connectivity patterns are heritable across the cortex. Next, we use hyperalignment to decompose this heritability into genetic similarity in where vs. how sensory information is processed. Finally, we show that the heritability of brain activity patterns can be partially explained by the heritability of the neural timescale, a one-dimensional measure of local circuit functioning. These results demonstrate that brain responses to complex stimuli are heritable, and that this heritability is due, in part, to genetic control over stable aspects of brain function.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

BACKGROUND

Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state functional magnetic resonance imaging (rs-fMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains.

METHODS

rs-fMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron emission tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients.

RESULTS

Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta-power. Exploratory analyses revealed a close statistical relationship between LEN and positive symptom severity in patients.

CONCLUSION

Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies

The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images. Validated in three independent longitudinal healthy cohorts (40, 23, and 60 healthy participants, respectively) with two common brain atlases, we found that the vTSN could robustly resolve inter-subject variability with high test-retest reliability. In contrast to the regional-based texture similarity networks (rTSNs) that calculate radiomic features based on region-of-interest information, vTSNs had higher inter- and intra-subject variability ratios and test-retest reliability in connectivity strength and network topological properties. Moreover, the Spearman correlation indicated a stronger association of the gene expression similarity network (GESN) with vTSNs than with rTSNs (vTSN: r = 0.600, rTSN: r = 0.433, z = 39.784, P < 0.001). Hierarchical clustering identified 3 vTSN subnets with differential association patterns with 13 coexpression modules, 16 neurotransmitters, 7 electrophysiology, 4 metabolism, and 2 large-scale structural and 4 functional organization maps. Moreover, these subnets had unique biological hierarchical organization from the subcortex-limbic system to the ventral neocortex and then to the dorsal neocortex. Based on 424 unrelated, qualified healthy subjects from the Human Connectome Project, we found that vTSNs could sensitively represent sex differences, especially for connections in the subcortex-limbic system and between the subcortex-limbic system and the ventral neocortex. Moreover, a multivariate variance component model revealed that vTSNs could explain a significant proportion of inter-subject behavioral variance in cognition (80.0 %) and motor functions (63.4 %). Finally, using 494 healthy adults (aged 19-80 years old) from the Southwest University Adult Lifespan Dataset, the Spearman correlation identified a significant association between aging and vTSN strength, especially within the subcortex-limbic system and between the subcortex-limbic system and the dorsal neocortex. In summary, our proposed vTSN is robust in uncovering individual variability and neurobiological brain processes, which can serve as biologically plausible measures for linking biological processes and human behavior.

Enhanced cerebral blood flow similarity of the somatomotor network in chronic insomnia: Transcriptomic decoding, gut microbial signatures and phenotypic roles

Chronic insomnia (CI) is a complex disease involving multiple factors including genetics, gut microbiota, and brain structure and function. However, there lacks a unified framework to elucidate how these factors interact in CI. By combining data of clinical assessment, sleep behavior recording, cognitive test, multimodal MRI (structural, functional, and perfusion), gene, and gut microbiota, this study demonstrated that enhanced cerebral blood flow (CBF) similarities of the somatomotor network (SMN) acted as a key mediator to link multiple factors in CI. Specifically, we first demonstrated that only CBF but not morphological or functional networks exhibited alterations in patients with CI, characterized by increases within the SMN and between the SMN and higher-order associative networks. Moreover, these findings were highly reproducible and the CBF similarity method was test-retest reliable. Further, we showed that transcriptional profiles explained 60.4 % variance of the pattern of the increased CBF similarities with the most correlated genes enriched in regulation of cellular and protein localization and material transport, and gut microbiota explained 69.7 % inter-individual variance in the increased CBF similarities with the most contributions from Negativicutes and Lactobacillales. Finally, we found that the increased CBF similarities were correlated with clinical variables, accounted for sleep behaviors and cognitive deficits, and contributed the most to the patient-control classification (accuracy = 84.4 %). Altogether, our findings have important implications for understanding the neuropathology of CI and may inform ways of developing new therapeutic strategies for the disease.

Cortical Morphological Networks Differ Between Gyri and Sulci

This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.

Heritability of functional gradients in the human subcortico-cortical connectivity

The human subcortex plays a pivotal role in cognition and is widely implicated in the pathophysiology of many psychiatric disorders. However, the heritability of functional gradients based on subcortico-cortical functional connectivity remains elusive. Here, leveraging twin functional MRI (fMRI) data from both the Human Connectome Project (n = 1023) and the Adolescent Brain Cognitive Development study (n = 936) datasets, we construct large-scale subcortical functional gradients and delineate an increased principal functional gradient pattern from unimodal sensory/motor networks to transmodal association networks. We observed that this principal functional gradient is heritable, and the strength of heritability exhibits a heterogeneous pattern along a hierarchical unimodal-transmodal axis in subcortex for both young adults and children. Furthermore, employing a machine learning framework, we show that this heterogeneous pattern of the principal functional gradient in subcortex can accurately discern the relationship between monozygotic twin pairs and dizygotic twin pairs with an accuracy of 76.2% (P < 0.001). The heritability of functional gradients is associated with the anatomical myelin proxied by MRI-derived T1-weighted/T2-weighted (T1w/T2w) ratio mapping in subcortex. This study provides new insights into the biological basis of subcortical functional hierarchy by revealing the structural and genetic properties of the subcortical functional gradients.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

Background

Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state fMRI (rfMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains.

Methods

rfMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron-Emission Tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients.

Results

Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta power. Exploratory analyses revealed a close statistical relationship between LEN and positive PSD symptoms.

Conclusion

Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

CRediT Authorship Contribution Statement

Fabian Hirsch: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization; Ângelo Bumanglag: Methodology, Software, Formal analysis, Writing - Review & Editing; Yifei Zhang: Methodology, Software, Formal analysis, Writing - Review & Editing; Afra Wohlschlaeger: Methodology, Writing - Review & Editing, Supervision, Project administration.

Using a deep generation network reveals neuroanatomical specificity in hemispheres

Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.

A multiscale characterization of cortical shape asymmetries in early psychosis

Psychosis has often been linked to abnormal cortical asymmetry, but prior results have been inconsistent. Here, we applied a novel spectral shape analysis to characterize cortical shape asymmetries in patients with early psychosis across different spatial scales. We used the Human Connectome Project for Early Psychosis dataset (aged 16-35), comprising 56 healthy controls (37 males, 19 females) and 112 patients with early psychosis (68 males, 44 females). We quantified shape variations of each hemisphere over different spatial frequencies and applied a general linear model to compare differences between healthy controls and patients with early psychosis. We further used canonical correlation analysis to examine associations between shape asymmetries and clinical symptoms. Cortical shape asymmetries, spanning wavelengths from about 22 to 75 mm, were significantly different between healthy controls and patients with early psychosis (Cohen's d = 0.28-0.51), with patients showing greater asymmetry in cortical shape than controls. A single canonical mode linked the asymmetry measures to symptoms (canonical correlation analysis r = 0.45), such that higher cortical asymmetry was correlated with more severe excitement symptoms and less severe emotional distress. Significant group differences in the asymmetries of traditional morphological measures of cortical thickness, surface area, and gyrification, at either global or regional levels, were not identified. Cortical shape asymmetries are more sensitive than other morphological asymmetries in capturing abnormalities in patients with early psychosis. These abnormalities are expressed at coarse spatial scales and are correlated with specific symptom domains.

Heritability estimation of subcortical volumes in a multi-ethnic multi-site cohort study

Heritability of regional subcortical brain volumes (rSBVs) describes the role of genetics in middle and inner brain development. rSBVs are highly heritable in adults but are not characterized well in adolescents. The Adolescent Brain Cognitive Development study (ABCD), taken over 22 US sites, provides data to characterize the heritability of subcortical structures in adolescence. In ABCD, site-specific effects co-occur with genetic effects which can bias heritability estimates. Existing methods adjusting for site effects require additional steps to adjust for site effects and can lead to inconsistent estimation. We propose a random-effect model-based method of moments approach that is a single step estimator and is a theoretically consistent estimator even when sites are imbalanced and performs well under simulations. We compare methods on rSBVs from ABCD. The proposed approach yielded heritability estimates similar to previous results derived from single-site studies. The cerebellum cortex and hippocampus were the most heritable regions (> 50%).

Static and dynamic fMRI-derived functional connectomes represent largely similar information

Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

Single-subject cortical morphological brain networks: Phenotypic associations and neurobiological substrates

Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.

Subcortical Brain Alterations in Carriers of Genomic Copy Number Variants

OBJECTIVE

Copy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disorders (NPDs), including autism (ASD) and schizophrenia. Little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, the authors investigated gross volume, vertex-level thickness, and surface maps of subcortical structures in 11 CNVs and six NPDs.

METHODS

Subcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (CNVs at 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2; age range, 6-80 years; 340 males) and 782 control subjects (age range, 6-80 years; 387 males) as well as ENIGMA summary statistics for ASD, schizophrenia, attention deficit hyperactivity disorder, obsessive-compulsive disorder, bipolar disorder, and major depression.

RESULTS

All CNVs showed alterations in at least one subcortical measure. Each structure was affected by at least two CNVs, and the hippocampus and amygdala were affected by five. Shape analyses detected subregional alterations that were averaged out in volume analyses. A common latent dimension was identified, characterized by opposing effects on the hippocampus/amygdala and putamen/pallidum, across CNVs and across NPDs. Effect sizes of CNVs on subcortical volume, thickness, and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and schizophrenia.

CONCLUSIONS

The findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions, as well distinct effects, with some CNVs clustering with adult-onset conditions and others with ASD. These findings provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD and why a single CNV increases the risk for a diverse set of NPDs.

Heritability of cognitive abilities and regional brain structures in middle-aged to elderly East Asians

This study examined the single-nucleotide polymorphism heritability and genetic correlations of cognitive abilities and brain structural measures (regional subcortical volume and cortical thickness) in middle-aged and elderly East Asians (Korean) from the Gwangju Alzheimer's and Related Dementias cohort study. Significant heritability was found in memory function, caudate volume, thickness of the entorhinal cortices, pars opercularis, superior frontal gyri, and transverse temporal gyri. There were 3 significant genetic correlations between (i) the caudate volume and the thickness of the entorhinal cortices, (ii) the thickness of the superior frontal gyri and pars opercularis, and (iii) the thickness of the superior frontal and transverse temporal gyri. This is the first study to describe the heritability and genetic correlations of cognitive and neuroanatomical traits in middle-aged to elderly East Asians. Our results support the previous findings showing that genetic factors play a substantial role in the cognitive and neuroanatomical traits in middle to advanced age. Moreover, by demonstrating shared genetic effects on different brain regions, it gives us a genetic insight into understanding cognitive and brain changes with age, such as aging-related cognitive decline, cortical atrophy, and neural compensation.

Heritability of human "directed" functional connectome

INTRODUCTION

The functional connectivity patterns in the brain are highly heritable; however, it is unclear how genetic factors influence the directionality of such "information flows." Studying the "directionality" of the brain functional connectivity and assessing how heritability modulates it can improve our understanding of the human connectome.

METHODS

Here, we investigated the heritability of "directed" functional connections using a state-space formulation of Granger causality (GC), in conjunction with blind deconvolution methods accounting for local variability in the hemodynamic response function. Such GC implementation is ideal to explore the directionality of functional interactions across a large number of networks. Resting-state functional magnetic resonance imaging data were drawn from the Human Connectome Project (total n = 898 participants). To add robustness to our findings, the dataset was randomly split into a "discovery" and a "replication" sample (each with n = 449 participants). The two cohorts were carefully matched in terms of demographic variables and other confounding factors (e.g., education). The effect of shared environment was also modeled.

RESULTS

The parieto- and prefronto-cerebellar, parieto-prefrontal, and posterior-cingulate to hippocampus connections showed the highest and most replicable heritability effects with little influence by shared environment. In contrast, shared environmental factors significantly affected the visuo-parietal and sensory-motor directed connectivity.

CONCLUSION

We suggest a robust role of heritability in influencing the directed connectivity of some cortico-subcortical circuits implicated in cognition. Further studies, for example using task-based fMRI and GC, are warranted to confirm the asymmetric effects of genetic factors on the functional connectivity within cognitive networks and their role in supporting executive functions and learning.

Subcortical brain alterations in carriers of genomic copy number variants

Objectives

Copy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disorders (NPDs) including autism (ASD) and schizophrenia (SZ). Overall, little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, we investigated gross volume, and vertex level thickness and surface maps of subcortical structures in 11 different CNVs and 6 different NPDs.

Methods

Subcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (at the following loci: 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2) and 782 controls (Male/Female: 727/730; age-range: 6-80 years) as well as ENIGMA summary-statistics for ASD, SZ, ADHD, Obsessive-Compulsive-Disorder, Bipolar-Disorder, and Major-Depression.

Results

Nine of the 11 CNVs affected volume of at least one subcortical structure. The hippocampus and amygdala were affected by five CNVs. Effect sizes of CNVs on subcortical volume, thickness and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and SZ. Shape analyses were able to identify subregional alterations that were averaged out in volume analyses. We identified a common latent dimension - characterized by opposing effects on basal ganglia and limbic structures - across CNVs and across NPDs.

Conclusion

Our findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions. We also observed distinct effects with some CNVs clustering with adult conditions while others clustered with ASD. This large cross-CNV and NPDs analysis provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD, as well as why a single CNV increases the risk for a diverse set of NPDs.

A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.

Heritability estimation for a linear combination of phenotypes via ridge regression

MOTIVATION

The joint analysis of multiple phenotypes is important in many biological studies, such as plant and animal breeding. The heritability estimation for a linear combination of phenotypes is designed to account for correlation information. Existing methods for estimating heritability mainly focus on single phenotypes under random-effect models. These methods also require some stringent conditions, which calls for a more flexible and interpretable method for estimating heritability. Fixed-effect models emerge as a useful alternative.

RESULTS

In this paper, we propose a novel heritability estimator based on multivariate ridge regression for linear combinations of phenotypes, yielding accurate estimates in both sparse and dense cases. Under mild conditions in the high-dimensional setting, the proposed estimator appears to be consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is promising under different scenarios. Compared with independently combined heritability estimates in the case of multiple phenotypes, the proposed method significantly improves the performance by considering correlations among those phenotypes. We further demonstrate its application in heritability estimation and correlation analysis for the Oryza sativa rice dataset.

AVAILABILITY AND IMPLEMENTATION

An R package implementing the proposed method is available at https://github.com/xg-SUFE1/MultiRidgeVar, where covariance estimates are also given together with heritability estimates.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A cognitive neurogenetic approach to uncovering the structure of executive functions

One central mission of cognitive neuroscience is to understand the ontology of complex cognitive functions. We addressed this question with a cognitive neurogenetic approach using a large-scale dataset of executive functions (EFs), whole-brain resting-state functional connectivity, and genetic polymorphisms. We found that the bifactor model with common and shifting-specific components not only was parsimonious but also showed maximal dissociations among the EF components at behavioral, neural, and genetic levels. In particular, the genes with enhanced expression in the middle frontal gyrus (MFG) and the subcallosal cingulate gyrus (SCG) showed enrichment for the common and shifting-specific component, respectively. Finally, High-dimensional mediation models further revealed that the functional connectivity patterns significantly mediated the genetic effect on the common EF component. Our study not only reveals insights into the ontology of EFs and their neurogenetic basis, but also provides useful tools to uncover the structure of complex constructs of human cognition.

A review of SNP heritability estimation methods

Over the past decade, statistical methods have been developed to estimate single nucleotide polymorphism (SNP) heritability, which measures the proportion of phenotypic variance explained by all measured SNPs in the data. Estimates of SNP heritability measure the degree to which the available genetic variants influence phenotypes and improve our understanding of the genetic architecture of complex phenotypes. In this article, we review the recently developed and commonly used SNP heritability estimation methods for continuous and binary phenotypes from the perspective of model assumptions and parameter optimization. We primarily focus on their capacity to handle multiple phenotypes and longitudinal measurements, their ability for SNP heritability partition and their use of individual-level data versus summary statistics. State-of-the-art statistical methods that are scalable to the UK Biobank dataset are also elucidated in detail.

Multivariate genome-wide association study on tissue-sensitive diffusion metrics highlights pathways that shape the human brain

The molecular determinants of tissue composition of the human brain remain largely unknown. Recent genome-wide association studies (GWAS) on this topic have had limited success due to methodological constraints. Here, we apply advanced whole-brain analyses on multi-shell diffusion imaging data and multivariate GWAS to two large scale imaging genetic datasets (UK Biobank and the Adolescent Brain Cognitive Development study) to identify and validate genetic association signals. We discover 503 unique genetic loci that have impact on multiple regions of human brain. Among them, more than 79% are validated in either of two large-scale independent imaging datasets. Key molecular pathways involved in axonal growth, astrocyte-mediated neuroinflammation, and synaptogenesis during development are found to significantly impact the measured variations in tissue-specific imaging features. Our results shed new light on the biological determinants of brain tissue composition and their potential overlap with the genetic basis of neuropsychiatric disorders.

Dynamic trajectories of connectome state transitions are heritable

The brain’s functional connectome is dynamic, constantly reconfiguring in an individual-specific manner. However, which characteristics of such reconfigurations are subject to genetic effects, and to what extent, is largely unknown. Here, we identified heritable dynamic features, quantified their heritability, and determined their association with cognitive phenotypes. In resting-state fMRI, we obtained multivariate features, each describing a temporal or spatial characteristic of connectome dynamics jointly over a set of connectome states. We found strong evidence for heritability of temporal features, particularly, Fractional Occupancy (FO) and Transition Probability (TP), representing the duration spent in each connectivity configuration and the frequency of shifting between configurations, respectively. These effects were robust against methodological choices of number of states and global signal regression. Genetic effects explained a substantial proportion of phenotypic variance of these features (h² = 0.39, 95% CI = [.24,.54] for FO; h² = 0. 43, 95% CI = [.29,.57] for TP). Moreover, these temporal phenotypes were associated with cognitive performance. Contrarily, we found no robust evidence for heritability of spatial features of the dynamic states (i.e., states’ Modularity and connectivity pattern). Genetic effects may therefore primarily contribute to how the connectome transitions across states, rather than the precise spatial instantiation of the states in individuals. In sum, genetic effects impact the dynamic trajectory of state transitions (captured by FO and TP), and such temporal features may act as endophenotypes for cognitive abilities.

Estimating SNP heritability in presence of population substructure in biobank-scale datasets

Single nucleotide polymorphism heritability of a trait is measured as the proportion of total variance explained by the additive effects of genome-wide single nucleotide polymorphisms. Linear mixed models are routinely used to estimate single nucleotide polymorphism heritability for many complex traits, which requires estimation of a genetic relationship matrix among individuals. Heritability is usually estimated by the restricted maximum likelihood or method of moments approaches such as Haseman-Elston regression. The common practice of accounting for such population substructure is to adjust for the top few principal components of the genetic relationship matrix as covariates in the linear mixed model. This can get computationally very intensive on large biobank-scale datasets. Here, we propose a method of moments approach for estimating single nucleotide polymorphism heritability in presence of population substructure. Our proposed method is computationally scalable on biobank datasets and gives an asymptotically unbiased estimate of heritability in presence of discrete substructures. It introduces the adjustments for population stratification in a second-order estimating equation. It allows these substructures to vary in their single nucleotide polymorphism allele frequencies and in their trait distributions (means and variances) while the heritability is assumed to be the same across these substructures. Through extensive simulation studies and the application on 7 quantitative traits in the UK Biobank cohort, we demonstrate that our proposed method performs well in the presence of population substructure and much more computationally efficient than existing approaches.

Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies

With the advent of high throughput genetic data, there have been attempts to estimate heritability from genome-wide SNP data on a cohort of distantly related individuals using linear mixed model (LMM). Fitting such an LMM in a large scale cohort study, however, is tremendously challenging due to its high dimensional linear algebraic operations. In this paper, we propose a new method named PredLMM approximating the aforementioned LMM motivated by the concepts of genetic coalescence and Gaussian predictive process. PredLMM has substantially better computational complexity than most of the existing LMM based methods and thus, provides a fast alternative for estimating heritability in large scale cohort studies. Theoretically, we show that under a model of genetic coalescence, the limiting form of our approximation is the celebrated predictive process approximation of large Gaussian process likelihoods that has well-established accuracy standards. We illustrate our approach with extensive simulation studies and use it to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.

The individuality of shape asymmetries of the human cerebral cortex

Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales (~65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences.

A Novel Bayesian Semi-parametric Model for Learning Heritable Imaging Traits

Heritability analysis is an important research topic in brain imaging genetics. Its primary motivation is to identify highly heritable imaging quantitative traits (QTs) for subsequent in-depth imaging genetic analyses. Most existing studies perform heritability analyses on regional imaging QTs using predefined brain parcellation schemes such as the AAL atlas. However, the power to dissect genetic underpinnings under QTs defined in such an unsupervised fashion is largely deteriorate with inner partition noise and signal dilution. To bridge the gap, we propose a new semi-parametric Bayesian heritability estimation model to construct highly heritable imaging QTs. Our method leverages the aggregate of genetic signals to imaging QT construction by developing a new brain parcellation driven by voxel-level heritability. To ensure biological plausibility and clinical interpretability of the resulting brain heritability parcellations, hierarchical sparsity and smoothness, coupled with structural connectivity of the brain, are properly imposed on genetic effects to induce spatial contiguity of heritable imaging QTs. Using the ADNI imaging genetic data, we demonstrate the strength of our proposed method, in comparison with the standard GCTA method, in identifying highly heritable and biologically meaningful new imaging QTs.

Polygenic Architecture of Human Neuroanatomical Diversity

We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured <one fourth of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG ~ 0.45. Genetic correlations were similar to phenotypic and environmental correlations; however, genetic correlations were often larger than phenotypic correlations for the left/right volumes of the same region. The heritability of differences in left/right volumes was generally not statistically significant, suggesting an important influence of environmental causes in the variability of brain asymmetry. Our code is available athttps://github.com/neuroanatomy/genomic-architecture.

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.

Ridge-penalized adaptive Mantel test and its application in imaging genetics

We propose a ridge-penalized adaptive Mantel test (AdaMant) for evaluating the association of two high-dimensional sets of features. By introducing a ridge penalty, AdaMant tests the association across many metrics simultaneously. We demonstrate how ridge penalization bridges Euclidean and Mahalanobis distances and their corresponding linear models from the perspective of association measurement and testing. This result is not only theoretically interesting but also has important implications in penalized hypothesis testing, especially in high-dimensional settings such as imaging genetics. Applying the proposed method to an imaging genetic study of visual working memory in healthy adults, we identified interesting associations of brain connectivity (measured by electroencephalogram coherence) with selected genetic features.

Unpaired data empowers association tests

Motivation

There is growing interest in the biomedical research community to incorporate retrospective data, available in healthcare systems, to shed light on associations between different biomarkers. Understanding the association between various types of biomedical data, such as genetic, blood biomarkers, imaging, etc. can provide a holistic understanding of human diseases. To formally test a hypothesized association between two types of data in Electronic Health Records (EHRs), one requires a substantial sample size with both data modalities to achieve a reasonable power. Current association test methods only allow using data from individuals who have both data modalities. Hence, researchers cannot take advantage of much larger EHR samples that includes individuals with at least one of the data types, which limits the power of the association test.

Results

We present a new method called the Semi-paired Association Test (SAT) that makes use of both paired and unpaired data. In contrast to classical approaches, incorporating unpaired data allows SAT to produce better control of false discovery and to improve the power of the association test. We study the properties of the new test theoretically and empirically, through a series of simulations and by applying our method on real studies in the context of Chronic Obstructive Pulmonary Disease. We are able to identify an association between the high-dimensional characterization of Computed Tomography chest images and several blood biomarkers as well as the expression of dozens of genes involved in the immune system.

Availability and implementation

Code is available on https://github.com/batmanlab/Semi-paired-Association-Test.

Supplementary information

Supplementary data are available at Bioinformatics online.

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 Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies

Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (P ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population and common genetic variants can explain up to 80% of their variabilities (median heritability 34.8%). We observed omnigenic impact across the genome and examined the enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks, exhibit a symmetric pattern across left and right hemispheres, and are consistent in females and males (correlation = 0.638). We repeated the main analysis in Alzheimer's Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, which demonstrated that more stable estimates can be obtained from the UK Biobank.

Bayesian sparse heritability analysis with high-dimensional neuroimaging phenotypes

Heritability analysis plays a central role in quantitative genetics to describe genetic contribution to human complex traits and prioritize downstream analyses under large-scale phenotypes. Existing works largely focus on modeling single phenotype and currently available multivariate phenotypic methods often suffer from scaling and interpretation. In this article, motivated by understanding how genetic underpinning impacts human brain variation, we develop an integrative Bayesian heritability analysis to jointly estimate heritabilities for high-dimensional neuroimaging traits. To induce sparsity and incorporate brain anatomical configuration, we impose hierarchical selection among both regional and local measurements based on brain structural network and voxel dependence. We also use a nonparametric Dirichlet process mixture model to realize grouping among single nucleotide polymorphism-associated phenotypic variations, providing biological plausibility. Through extensive simulations, we show the proposed method outperforms existing ones in heritability estimation and heritable traits selection under various scenarios. We finally apply the method to two large-scale imaging genetics datasets: the Alzheimer's Disease Neuroimaging Initiative and United Kingdom Biobank and show biologically meaningful results.

The Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology

Individual differences in educational attainment are linked to differences in intelligence, and predict important social, economic, and health outcomes. Previous studies have found common genetic factors that influence educational achievement, cognitive performance and total brain volume (i.e., brain size). Here, in a large sample of participants from the UK Biobank, we investigate the shared genetic basis between educational attainment and fine-grained cerebral cortical morphological features, and associate this genetic variation with a related aspect of cognitive ability. Importantly, we execute novel statistical methods that enable high-dimensional genetic correlation analysis, and compute high-resolution surface maps for the genetic correlations between educational attainment and vertex-wise morphological measurements. We conduct secondary analyses, using the UK Biobank verbal-numerical reasoning score, to confirm that variation in educational attainment that is genetically correlated with cortical morphology is related to differences in cognitive performance. Our analyses relate the genetic overlap between cognitive ability and cortical thickness measurements to bilateral primary motor cortex as well as predominantly left superior temporal cortex and proximal regions. These findings extend our understanding of the neurobiology that connects genetic variation to individual differences in educational attainment and cognitive performance.

Estimating the heritability of psychological measures in the Human Connectome Project dataset

The Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral and genotypic measures, as well as a biologically verified family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times (fingerprinting), and to identify identical twins significantly above chance. Standard heritability estimates of 37 behavioral measures were derived from twin correlations, and machine-learning models (univariate linear model, Ridge classifier and Random Forest model) were trained to classify monozygotic twins and dizygotic twins. Correlations between the standard heritability metric and each set of model weights ranged from 0.36 to 0.7, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further explored the heritability of a smaller number of latent factors extracted from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.05 to 0.43. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present a thorough investigation of the heritabilities of the behavioral measures in the HCP as a resource for other investigators, and illustrate the utility of machine-learning methods for qualitative characterization of the differential heritability across diverse measures.

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.

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.

Plasticity versus stability across the human cortical visual connectome

Whether and how the balance between plasticity and stability varies across the brain is an important open question. Within a processing hierarchy, it is thought that plasticity is increased at higher levels of cortical processing, but direct quantitative comparisons between low- and high-level plasticity have not been made so far. Here, we address this issue for the human cortical visual system. We quantify plasticity as the complement of the heritability of resting-state functional connectivity and thereby demonstrate a non-monotonic relationship between plasticity and hierarchical level, such that plasticity decreases from early to mid-level cortex, and then increases further of the visual hierarchy. This non-monotonic relationship argues against recent theory that the balance between plasticity and stability is governed by the costs of the "coding-catastrophe", and can be explained by a concurrent decline of short-term adaptation and rise of long-term plasticity up the visual processing hierarchy.

Resting brain dynamics at different timescales capture distinct aspects of human behavior

Linking human behavior to resting-state brain function is a central question in systems neuroscience. In particular, the functional timescales at which different types of behavioral factors are encoded remain largely unexplored. The behavioral counterparts of static functional connectivity (FC), at the resolution of several minutes, have been studied but behavioral correlates of dynamic measures of FC at the resolution of a few seconds remain unclear. Here, using resting-state fMRI and 58 phenotypic measures from the Human Connectome Project, we find that dynamic FC captures task-based phenotypes (e.g., processing speed or fluid intelligence scores), whereas self-reported measures (e.g., loneliness or life satisfaction) are equally well explained by static and dynamic FC. Furthermore, behaviorally relevant dynamic FC emerges from the interconnections across all resting-state networks, rather than within or between pairs of networks. Our findings shed new light on the timescales of cognitive processes involved in distinct facets of behavior.

Accelerated estimation and permutation inference for ACE modeling

There are a wealth of tools for fitting linear models at each location in the brain in neuroimaging analysis, and a wealth of genetic tools for estimating heritability for a small number of phenotypes. But there remains a need for computationally efficient neuroimaging genetic tools that can conduct analyses at the brain‐wide scale. Here we present a simple method for heritability estimation on twins that replaces a variance component model‐which requires iterative optimisation‐with a (noniterative) linear regression model, by transforming data to squared twin‐pair differences. We demonstrate that the method has comparable bias, mean squared error, false positive risk, and power to best practice maximum‐likelihood‐based methods, while requiring a small fraction of the computation time. Combined with permutation, we call this approach “Accelerated Permutation Inference for the ACE Model (APACE)” where ACE refers to the additive genetic (A) effects, and common (C), and unique (E) environmental influences on the trait. We show how the use of spatial statistics like cluster size can dramatically improve power, and illustrate the method on a heritability analysis of an fMRI working memory dataset.

Cranial and endocranial diversity in extant and fossil atelids (Platyrrhini: Atelidae): A geometric morphometric study

OBJECTIVES

Platyrrhines constitute a diverse clade, with the modern Atelidae exhibiting the most variation in cranial and endocast morphology. The processes responsible for this diversification are not well understood. Here, we present a geometric morphometric study describing variation in cranial and endocranial shape of 14 species of Alouatta, Ateles, Brachyteles, and Lagothrix and two extinct taxa, Cartelles and Caipora.

METHODS

We examined cranial and endocranial shape variation among species using images reconstructed from CT scans and geometric morphometric techniques based on three-dimensional landmarks and semilandmarks. Principal components analyses were used to explore variation, including the Procrustes shape coordinates, summing the logarithm of the Centroid Size, the common allometric component, and residual shape components.

RESULTS

Differences in endocranial shape are related to a relative increase or decrease in the volume of the neocortex region with respect to brainstem and cerebellum regions. The relative position of the brainstem varies from a posterior position in Alouatta to a more ventral position in Ateles. The shape of both the cranium and endocast of Caipora is within the observed variation of Brachyteles. Cartelles occupies the most differentiated position relative to the extant taxa, especially in regards to its endocranial shape.

CONCLUSIONS

The pattern of variation in the extant species in endocranial shape is similar to the variation observed in previous cranial studies, with Alouatta as an outlier. The similarities between Caipora and Brachyteles were unexpected and intriguing given the frugivorous adaptations inferred from the fossil's dentition. Our study shows the importance of considering both extant and fossil species when studying diversification of complex traits.

Global signal regression strengthens association between resting-state functional connectivity and behavior

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR.

Prenatal maternal anxiety and children's brain structure and function: A systematic review of neuroimaging studies

BACKGROUND

Maternal anxiety disorders are common during pregnancy and postpartum, and are associated with increased behaviour problems and risk of mental health difficulties in children. Understanding alterations in brain structure and function associated with maternal anxiety may help elucidate potential mechanisms via which high maternal anxiety could affect children.

METHODS

We conducted a systematic review of extant research studying the associations between prenatal anxiety and children's brain structure and function, as assessed through neuroimaging. Data were gathered in accordance with the PRISMA review guidelines.

RESULTS

Ten articles were identified, and all found a significant association between antenatal maternal anxiety and child neurodevelopment. Studies vary considerably in their methods with five studies employing electroencephalography (EEG), one using magnetoencephalography (MEG), and the rest employing magnetic resonance imaging (MRI).

LIMITATIONS

The heterogeneity of neuroimaging techniques undertaken by the reviewed studies precluded a meta-analysis from being performed. The applicability of this systematic review to clinical practice is also limited given that the studies examined children across a wide age range (neonates to 17 years).

CONCLUSIONS

From early infancy to late adolescence, findings suggest alterations of brain structure and function in frontal, temporal, and limbic areas in children born to mothers who experienced prenatal anxiety. These brain abnormalities may underlie associations between prenatal anxiety and children's behaviour, though more research incorporating neuroimaging and behavioural data is necessary to determine this.

Subcortical brain volumes, cortical thickness and cortical surface area in families genetically enriched for social anxiety disorder - A multiplex multigenerational neuroimaging study

BACKGROUND

Social anxiety disorder (SAD) is a disabling psychiatric condition with a genetic background. Brain alterations in gray matter (GM) related to SAD have been previously reported, but it remains to be elucidated whether GM measures are candidate endophenotypes of SAD. Endophenotypes are measurable characteristics on the causal pathway from genotype to phenotype, providing insight in genetically-based disease mechanisms. Based on a review of existing evidence, we examined whether GM characteristics meet two endophenotype criteria, using data from a unique sample of SAD-patients and their family-members of two generations. First, we investigated whether GM characteristics co-segregate with social anxiety within families genetically enriched for SAD. Secondly, heritability of the GM characteristics was estimated.

METHODS

Families with a genetic predisposition for SAD participated in the Leiden Family Lab study on SAD; T1-weighted MRI brain scans were acquired (n = 110, 8 families). Subcortical volumes, cortical thickness and cortical surface area were determined for a-priori determined regions of interest (ROIs). Next, associations with social anxiety and heritabilities were estimated.

FINDINGS

Several subcortical and cortical GM characteristics, derived from frontal, parietal and temporal ROIs, co-segregated with social anxiety within families (uncorrected p-level) and showed moderate to high heritability.

INTERPRETATION

These findings provide preliminary evidence that GM characteristics of multiple ROIs, which are distributed over the brain, are candidate endophenotypes of SAD. Thereby, they shed light on the genetic vulnerability for SAD. Future research is needed to confirm these results and to link them to functional brain alterations and to genetic variations underlying these GM changes. FUND: Leiden University Research Profile 'Health, Prevention and the Human Life Cycle'.

Multi-modal brain fingerprinting: A manifold approximation based framework

This work presents an efficient framework, based on manifold approximation, for generating brain fingerprints from multi-modal data. The proposed framework represents images as bags of local features which are used to build a subject proximity graph. Compact fingerprints are obtained by projecting this graph in a low-dimensional manifold using spectral embedding. Experiments using the T1/T2-weighted MRI, diffusion MRI, and resting-state fMRI data of 945 Human Connectome Project subjects demonstrate the benefit of combining multiple modalities, with multi-modal fingerprints more discriminative than those generated from individual modalities. Results also highlight the link between fingerprint similarity and genetic proximity, monozygotic twins having more similar fingerprints than dizygotic or non-twin siblings. This link is also reflected in the differences of feature correspondences between twin/sibling pairs, occurring in major brain structures and across hemispheres. The robustness of the proposed framework to factors like image alignment and scan resolution, as well as the reproducibility of results on retest scans, suggest the potential of multi-modal brain fingerprinting for characterizing individuals in a large cohort analysis.

Heritability estimates of cortical anatomy: The influence and reliability of different estimation strategies

Twin study designs have been previously used to investigate the heritability of neuroanatomical measures, such as regional cortical volumes. Volume can be fractionated into surface area and cortical thickness, where both measures are considered to have independent genetic and environmental bases. Region of interest (ROI) and vertex-wise approaches have been used to calculate heritability of cortical thickness and surface area in twin studies. In our study, we estimate heritability using the Human Connectome Project magnetic resonance imaging dataset composed of healthy young twin and non-twin siblings (mean age of 29, sample size of 757). Both ROI and vertex-wise methods were used to compare regional heritability of cortical thickness and surface area. Heritability estimates were controlled for age, sex, and total ipsilateral surface area or mean cortical thickness. In both approaches, heritability estimates of cortical thickness and surface area were lower when accounting for average ipsilateral cortical thickness and total surface area respectively. When comparing both approaches at a regional level, the vertex-wise approach showed higher surface area and lower cortical thickness heritability estimates compared to the ROI approach. The calcarine fissure had the highest surface area heritability estimate (ROI: 44%, vertex-wise: 50%) and posterior cingulate gyrus had the highest cortical thickness heritability (ROI: 50%, vertex-wise 40%). We also observed that limitations in image processing and variability in spatial averaging errors based on regional size may make obtaining true estimates of cortical thickness and surface area challenging in smaller regions. It is important to identify which approach is best suited to estimate heritability based on the research hypothesis and the size of the regions being investigated.

Neuroimaging genomics in psychiatry-a translational approach

Neuroimaging genomics is a relatively new field focused on integrating genomic and imaging data in order to investigate the mechanisms underlying brain phenotypes and neuropsychiatric disorders. While early work in neuroimaging genomics focused on mapping the associations of candidate gene variants with neuroimaging measures in small cohorts, the lack of reproducible results inspired better-powered and unbiased large-scale approaches. Notably, genome-wide association studies (GWAS) of brain imaging in thousands of individuals around the world have led to a range of promising findings. Extensions of such approaches are now addressing epigenetics, gene–gene epistasis, and gene–environment interactions, not only in brain structure, but also in brain function. Complementary developments in systems biology might facilitate the translation of findings from basic neuroscience and neuroimaging genomics to clinical practice. Here, we review recent approaches in neuroimaging genomics—we highlight the latest discoveries, discuss advantages and limitations of current approaches, and consider directions by which the field can move forward to shed light on brain disorders.

Voxel-based morphometry multi-center mega-analysis of brain structure in social anxiety disorder

Social anxiety disorder (SAD) is a prevalent and disabling mental disorder, associated with significant psychiatric co-morbidity. Previous research on structural brain alterations associated with SAD has yielded inconsistent results concerning the direction of the changes in gray matter (GM) in various brain regions, as well as on the relationship between brain structure and SAD-symptomatology. These heterogeneous findings are possibly due to limited sample sizes. Multi-site imaging offers new opportunities to investigate SAD-related alterations in brain structure in larger samples.

An international multi-center mega-analysis on the largest database of SAD structural T1-weighted 3T MRI scans to date was performed to compare GM volume of SAD-patients (n = 174) and healthy control (HC)-participants (n = 213) using voxel-based morphometry. A hypothesis-driven region of interest (ROI) approach was used, focusing on the basal ganglia, the amygdala-hippocampal complex, the prefrontal cortex, and the parietal cortex. SAD-patients had larger GM volume in the dorsal striatum when compared to HC-participants. This increase correlated positively with the severity of self-reported social anxiety symptoms. No SAD-related differences in GM volume were present in the other ROIs. Thereby, the results of this mega-analysis suggest a role for the dorsal striatum in SAD, but previously reported SAD-related changes in GM in the amygdala, hippocampus, precuneus, prefrontal cortex and parietal regions were not replicated. Our findings emphasize the importance of large sample imaging studies and the need for meta-analyses like those performed by the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium.

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Multi-center mega-analysis on gray matter (GM) in social anxiety disorder (SAD)

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Largest sample available for analysis to date: 174 SAD-patients vs 213 controls

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Larger GM volume in the right putamen in SAD-patients

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No SAD-related alterations in amygdala-hippocampal, prefrontal or parietal regions

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Results stress need for larger samples and meta-analyses - cf. ENIGMA Consortium