Heritability of brain network topology in 853 twins and siblings

2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015

Genetic and environmental factors affect white matter connectivity in the normal brain, and they also influence diseases in which brain connectivity is altered. Little is known about genetic influences on brain connectivity, despite wide variations in the brain's neural pathways. Here we applied the "DICCCOL" framework to analyze structural connectivity, in 261 twin pairs (522 participants, mean age: 21.8 y ± 2.7SD). We encoded connectivity patterns by projecting the white matter (WM) bundles of all "DICCCOLs" as a tracemap (TM). Next we fitted an A/C/E structural equation model to estimate additive genetic (A), common environmental (C), and unique environmental/error (E) components of the observed variations in brain connectivity. We found 44 "heritable DICCCOLs" whose connectivity was genetically influenced (2 >1%); half of them showed significant heritability (2 >20%). Our analysis of genetic influences on WM structural connectivity suggests high heritability for some WM projection patterns, yielding new targets for genome-wide association studies.

NeuroImage, 2012

Complex networks Diffusion weighted MRI Topological analysis Hierarchical analysis False discovery rate Sex and kinship brain network differences Modern non-invasive brain imaging technologies, such as diffusion weighted magnetic resonance imaging (DWI), enable the mapping of neural fiber tracts in the white matter, providing a basis to reconstruct a detailed map of brain structural connectivity networks. Brain connectivity networks differ from random networks in their topology, which can be measured using small worldness, modularity, and high-degree nodes (hubs). Still, little is known about how individual differences in structural brain network properties relate to age, sex, or genetic differences. Recently, some groups have reported brain network biomarkers that enable differentiation among individuals, pairs of individuals, and groups of individuals. In addition to studying new topological features, here we provide a unifying general method to investigate topological brain networks and connectivity differences between individuals, pairs of individuals, and groups of individuals at several levels of the data hierarchy, while appropriately controlling false discovery rate (FDR) errors. We apply our new method to a large dataset of high quality brain connectivity networks obtained from High Angular Resolution Diffusion Imaging (HARDI) tractography in 303 young adult twins, siblings, and unrelated people. Our proposed approach can accurately classify brain connectivity networks based on sex (93% accuracy) and kinship (88.5% accuracy). We find statistically significant differences associated with sex and kinship both in the brain connectivity networks and in derived topological metrics, such as the clustering coefficient and the communicability matrix.

bioRxiv (Cold Spring Harbor Laboratory), 2021

Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies (GWAS) of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index (BMI) in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.

Brain structure & function, 2016

Understanding the genetic factors underlying brain structural connectivity is a major challenge in imaging genetics. Here, we present results from genome-wide association studies (GWASs) of whole-brain white matter (WM) fractional anisotropy (FA), an index of microstructural coherence measured using diffusion tensor imaging. Data from independent GWASs of 355 Swedish and 250 Norwegian healthy adults were integrated by meta-analysis to enhance power. Complementary GWASs on behavioral data reflecting processing speed, which is related to microstructural properties of WM pathways, were performed and integrated with WM FA results via multimodal analysis to identify shared genetic associations. One locus on chromosome 17 (rs145994492) showed genome-wide significant association with WM FA (meta P value = 1.87 × 10(-08)). Suggestive associations (Meta P value <1 × 10(-06)) were observed for 12 loci, including one containing ZFPM2 (lowest meta P value = 7.44 × 10(-08)). This locus was al...

Human brain mapping, 2018

We measured and compared heritability estimates for measures of functional brain connectivity extracted using the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) rsfMRI analysis pipeline in two cohorts: the genetics of brain structure (GOBS) cohort and the HCP (the Human Connectome Project) cohort. These two cohorts were assessed using conventional (GOBS) and advanced (HCP) rsfMRI protocols, offering a test case for harmonization of rsfMRI phenotypes, and to determine measures that show consistent heritability for in-depth genome-wide analysis. The GOBS cohort consisted of 334 Mexican-American individuals (124M/210F, average age = 47.9 ± 13.2 years) from 29 extended pedigrees (average family size = 9 people; range 5-32). The GOBS rsfMRI data was collected using a 7.5-min acquisition sequence (spatial resolution = 1.72 × 1.72 × 3 mm ). The HCP cohort consisted of 518 twins and family members (240M/278F; average age = 28.7 ± 3.7 years). rsfMRI data was collected using 2...

2016

One of the grand challenges faced by neuroscience is to delineate the determinants of interindividual variation in the comprehensive structural and functional connection matrices that comprise the human connectome. At present, this endeavor appears most tractable at the macroanatomic scale, where intrinsic brain activity exhibits robust patterns of synchrony that recapitulate core functional circuits at the individual level. Here, we use a classical twin study design to examine the heritability of intrinsic functional network properties in 101 twin pairs, including network activity (i.e., variance of a network's specific temporal fluctuations) and internetwork coherence (i.e., correlation between networks' specific temporal fluctuations). Five of 7 networks exhibited significantly heritable (23.3-65.2%) network activity, 6 of the 21 internetwork coherences were significantly heritable (25.6-42.0%), and 11 of the 21 internetwork coherences were significantly influenced by common environmental factors (18.0-47.1%). These results suggest that the source of interindividual variation in functional connectome has a modular architecture: individual modules represented by intrinsic connectivity networks are genetic controlled, while environmental factors influence the interplays between the modules. This work further provides network-specific hypotheses for discovery of the specific genetic and environmental factors influencing functional specialization and integration of the human brain.

Frontiers in human neuroscience, 2015

We examined network properties of genetic covariance between average cortical thickness (CT) and surface area (SA) within genetically-identified cortical parcellations that we previously derived from human cortical genetic maps using vertex-wise fuzzy clustering analysis with high spatial resolution. There were 24 hierarchical parcellations based on vertex-wise CT and 24 based on vertex-wise SA expansion/contraction; in both cases the 12 parcellations per hemisphere were largely symmetrical. We utilized three techniques-biometrical genetic modeling, cluster analysis, and graph theory-to examine genetic relationships and network properties within and between the 48 parcellation measures. Biometrical modeling indicated significant shared genetic covariance between size of several of the genetic parcellations. Cluster analysis suggested small distinct groupings of genetic covariance; networks highlighted several significant negative and positive genetic correlations between bilateral p...

eLife, 2017

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

Behavior Genetics, 2000

Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2012

Human brain connectivity is disrupted in a wide range of disorders - from Alzheimer's disease to autism - but little is known about which specific genes affect it. Here we conducted a genome-wide association for connectivity matrices that capture information on the density of fiber connections between 70 brain regions. We scanned a large twin cohort (N=366) with 4-Tesla high angular resolution diffusion imaging (105-gradient HARDI). Using whole brain HARDI tractography, we extracted a relatively sparse 70×70 matrix representing fiber density between all pairs of cortical regions automatically labeled in co-registered anatomical scans. Additive genetic factors accounted for 1-58% of the variance in connectivity between 90 (of 122) tested nodes. We discovered genome-wide significant associations between variants and connectivity. GWAS permutations at various levels of heritability, and split-sample replication, validated our genetic findings. The resulting genes may offer new lead...