Heritability of brain volume on MRI in middle to advanced age: A twin study of Japanese adults

Heritability of cerebellar subregion volumes in adolescent and young adult twins

Twin studies have found gross cerebellar volume to be highly heritable. However, whether fine-grained regional volumes within the cerebellum are similarly heritable is still being determined. Anatomical MRI scans from two independent datasets (QTIM: Queensland Twin IMaging, N = 798, mean age 22.1 years; QTAB: Queensland Twin Adolescent Brain, N = 396, mean age 11.3 years) were combined with an optimised and automated cerebellum parcellation algorithm to segment and measure 28 cerebellar regions. We show that the heritability of regional volumetric measures varies widely across the cerebellum (h 2 $$ {h}^2 $$ 47%-91%). Additionally, the good to excellent test-retest reliability for a subsample of QTIM participants suggests that non-genetic variance in cerebellar volumes is due primarily to unique environmental influences rather than measurement error. We also show a consistent pattern of strong associations between the volumes of homologous left and right hemisphere regions. Associations were predominantly driven by genetic effects shared between lobules, with only sparse contributions from environmental effects. These findings are consistent with similar studies of the cerebrum and provide a first approximation of the upper bound of heritability detectable by genome-wide association studies.

Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders

BACKGROUND

Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics.

METHODS

We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV).

RESULTS

Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction.

CONCLUSIONS

Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

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 Subcortical Grey Matter Structures

Background and Objectives: Subcortical grey matter structures play essential roles in cognitive, affective, social, and motoric functions in humans. Their volume changes with age, and decreased volumes have been linked with many neuropsychiatric disorders. The aim of our study was to examine the heritability of six subcortical brain volumes (the amygdala, caudate nucleus, pallidum, putamen, thalamus, and nucleus accumbens) and four general brain volumes (the total intra-cranial volume and the grey matter, white matter, and cerebrospinal fluid (CSF) volume) in twins. Materials and Methods: A total of 118 healthy adult twins from the Hungarian Twin Registry (86 monozygotic and 32 dizygotic; median age 50 ± 27 years) underwent brain magnetic resonance imaging. Two automated volumetry pipelines, Computational Anatomy Toolbox 12 (CAT12) and volBrain, were used to calculate the subcortical and general brain volumes from three-dimensional T1-weighted images. Age- and sex-adjusted monozygotic and dizygotic intra-pair correlations were calculated, and the univariate ACE model was applied. Pearson's correlation test was used to compare the results obtained by the two pipelines. Results: The age- and sex-adjusted heritability estimates, using CAT12 for the amygdala, caudate nucleus, pallidum, putamen, and nucleus accumbens, were between 0.75 and 0.95. The thalamus volume was more strongly influenced by common environmental factors (C = 0.45-0.73). The heritability estimates, using volBrain, were between 0.69 and 0.92 for the nucleus accumbens, pallidum, putamen, right amygdala, and caudate nucleus. The left amygdala and thalamus were more strongly influenced by common environmental factors (C = 0.72-0.85). A strong correlation between CAT12 and volBrain (r = 0.74-0.94) was obtained for all volumes. Conclusions: The majority of examined subcortical volumes appeared to be strongly heritable. The thalamus was more strongly influenced by common environmental factors when investigated with both segmentation methods. Our results underline the importance of identifying the relevant genes responsible for variations in the subcortical structure volume and associated diseases.

Identifying genes associated with brain volumetric differences through tissue specific transcriptomic inference from GWAS summary data

BACKGROUND

Brain volume has been widely studied in the neuroimaging field, since it is an important and heritable trait associated with brain development, aging and various neurological and psychiatric disorders. Genome-wide association studies (GWAS) have successfully identified numerous associations between genetic variants such as single nucleotide polymorphisms and complex traits like brain volume. However, it is unclear how these genetic variations influence regional gene expression levels, which may subsequently lead to phenotypic changes. S-PrediXcan is a tissue-specific transcriptomic data analysis method that can be applied to bridge this gap. In this work, we perform an S-PrediXcan analysis on GWAS summary data from two large imaging genetics initiatives, the UK Biobank and Enhancing Neuroimaging Genetics through Meta Analysis, to identify tissue-specific transcriptomic effects on two closely related brain volume measures: total brain volume (TBV) and intracranial volume (ICV).

RESULTS

As a result of the analysis, we identified 10 genes that are highly associated with both TBV and ICV. Nine out of 10 genes were found to be associated with TBV in another study using a different gene-based association analysis. Moreover, most of our discovered genes were also found to be correlated with multiple cognitive and behavioral traits. Further analyses revealed the protein-protein interactions, associated molecular pathways and biological functions that offer insight into how these genes function and interact with others.

CONCLUSIONS

These results confirm that S-PrediXcan can identify genes with tissue-specific transcriptomic effects on complex traits. The analysis also suggested novel genes whose expression levels are related to brain volumetric traits. This provides important insights into the genetic mechanisms of the human brain.

Lobular Difference in Heritability of Brain Atrophy among Elderly Japanese: A Twin Study

Background and Objectives: Brain atrophy is related to cognitive decline. However, the heritability of brain atrophy has not been fully investigated in the Eastern Asian population. Materials and Methods: Brain imaging of 74 Japanese twins registered in the Osaka University Twin Registry was conducted with voxel-based morphometry SPM12 and was processed by individual voxel-based morphometry adjusting covariates (iVAC) toolbox. The atrophy of the measured lobes was obtained by comparing the focal volume to the average of healthy subjects. Classical twin analysis was used to measure the heritability of its z-scores. Results: The heritability of brain atrophy ranged from 0.23 to 0.97, depending upon the lobes. When adjusted to age, high heritability was reported in the frontal, frontal-temporal, and parietal lobes, but the heritability in other lobes was lower than 0.70. Conclusions: This study revealed a relatively lower heritability in brain atrophy compared to other ethnicities. This result suggests a significant environmental impact on the susceptibility of brain atrophy the Japanese. Therefore, environmental factors may have more influence on the Japanese than in other populations.

Nomograms of human hippocampal volume shifted by polygenic scores

Nomograms are important clinical tools applied widely in both developing and aging populations. They are generally constructed as normative models identifying cases as outliers to a distribution of healthy controls. Currently used normative models do not account for genetic heterogeneity. Hippocampal volume (HV) is a key endophenotype for many brain disorders. Here, we examine the impact of genetic adjustment on HV nomograms and the translational ability to detect dementia patients. Using imaging data from 35,686 healthy subjects aged 44-82 from the UK Biobank (UKB), we built HV nomograms using Gaussian process regression (GPR), which - compared to a previous method - extended the application age by 20 years, including dementia critical age ranges. Using HV polygenic scores (HV-PGS), we built genetically adjusted nomograms from participants stratified into the top and bottom 30% of HV-PGS. This shifted the nomograms in the expected directions by ~100 mm3 (2.3% of the average HV), which equates to 3 years of normal aging for a person aged ~65. Clinical impact of genetically adjusted nomograms was investigated by comparing 818 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database diagnosed as either cognitively normal (CN), having mild cognitive impairment (MCI) or Alzheimer's disease (AD) patients. While no significant change in the survival analysis was found for MCI-to-AD conversion, an average of 68% relative decrease was found in intra-diagnostic-group variance, highlighting the importance of genetic adjustment in untangling phenotypic heterogeneity.

Brain Volumes and Abnormalities in Adults Born Preterm at Very Low Birth Weight

OBJECTIVES

To assess radiographic brain abnormalities and investigate volumetric differences in adults born preterm at very low birth weight (<1500 g), using siblings as controls.

STUDY DESIGN

We recruited 79 adult same-sex sibling pairs with one born preterm at very low birth weight and the sibling at term. We acquired 3-T brain magnetic resonance imaging from 78 preterm participants and 72 siblings. A neuroradiologist, masked to participants' prematurity status, reviewed the images for parenchymal and structural abnormalities, and FreeSurfer software 6.0 was used to conduct volumetric analyses. Data were analyzed by linear mixed models.

RESULTS

We found more structural abnormalities in very low birth weight participants than in siblings (37% vs 13%). The most common finding was periventricular leukomalacia, present in 15% of very low birth weight participants and in 3% of siblings. The very low birth weight group had smaller absolute brain volumes (-0.4 SD) and, after adjusting for estimated intracranial volume, less gray matter (-0.2 SD), larger ventricles (1.5 SD), smaller thalami (-0.6 SD), caudate nuclei (-0.4 SD), right hippocampus (-0.4 SD), and left pallidum (-0.3 SD). We saw no volume differences in total white matter (-0.04 SD; 95% CI, -0.13 to 0.09).

CONCLUSIONS

Preterm very low birth weight adults had a higher prevalence of brain abnormalities than their term-born siblings. They also had smaller absolute brain volumes, less gray but not white matter, and smaller volumes in several gray matter structures.

Human Connectome Project: heritability of brain volumes in young healthy adults

Here we report on the heritability and Intraclass Correlation Coefficients (ICCs) of brain volumes in 1,103 young healthy adults with mean age 29.2 years. Among them are: 153 monozygotic (MZ) twin pairs and 86 dizygotic (DZ) twin pairs, 133 non-twin siblings of MZ twins, 76 non-twin siblings of DZ twins, 335 siblings, and 81 unrelated individuals. ICCs were calculated between pairs of the following genetic groups: (1) MZ twins; (2) DZ twins; (3) MZ twins-their singleton siblings; (4) DZ twins-their singleton siblings; (5) siblings (SB); and (6) unrelated individuals (NR). We studied 4 brain groups: global, lobar, subcortical, and cortical brain regions. For each of 4 brain groups we found the same order of ICCs ranging from the highest values for MZ twins, statistically significantly smaller for the DZ twins and 3 sibling groups, and practically zero for NR. The DZ twins and 3 sibling groups were not different. No hemispheric difference was found in any genetic group. Among brain groups, the highest heritability was for the global regions, followed by lobar and subcortical groups. Only the cortical brain group heritability was statistically lower than other brain groups. We found less genetic control on the left hemisphere than on the right but no significant difference between hemispheres, and no hemispheric lateralization of heritability for any of the brain groups. These findings document substantial and systematic heritability of global and regional brain volumes.

Quantifying Genetic and Environmental Influence on Gray Matter Microstructure Using Diffusion MRI

Early neuroimaging work in twin studies focused on studying genetic and environmental influence on gray matter macrostructure. However, it is also important to understand how gray matter microstructure is influenced by genes and environment to facilitate future investigations of their influence in mental disorders. Advanced diffusion MRI (dMRI) measures allow more accurate assessment of gray matter microstructure compared with conventional diffusion tensor measures. To understand genetic and environmental influence on gray matter, we used diffusion and structural MRI data from a large twin and sibling study (N = 840) and computed advanced dMRI measures including return to origin probability (RTOP), which is heavily weighted toward intracellular and intra-axonal restricted spaces, and mean squared displacement (MSD), more heavily weighted to diffusion in extracellular space and large cell bodies in gray matter. We show that while macrostructural features like brain volume are mainly genetically influenced, RTOP and MSD can together tap into both genetic and environmental influence on microstructure.

Heritability of Structural Patterning in the Human Cerebral Cortex

Genetic influences that govern the spatial patterning of the human cortex and its structural variability are still incompletely known. We analyzed structural MR images in twins, siblings, and pairs of unrelated subjects. A comprehensive set of methods was employed to quantify properties of cortical features at different spatial scales. Measures were used to assess the influence of genetic similarity on structural patterning. Results indicated that: (1) Genetic effects significantly influence all structural features assessed here at all spatial resolutions, albeit at different strengths. (2) While strong genetic effects were found at the whole-brain and hemisphere level, effects were weaker at the regional and vertex level, depending on the measure under study. (3) Besides cortical thickness, sulcal (geodesic) depth was found to be under strong genetic control. The local pattern indicated that two axes along (a) the anterior-posterior direction (insula to parieto-occipital sulcus), and (b) superior-inferior direction (central sulcus to callosal sulcus) presumably determine the segregation of four quadrants in each hemisphere early in development. (4) While strong structural asymmetries were found at the regional level, genetic influences on laterality were relatively minor.

Update on Osaka University Twin Registry: An Overview of Multidisciplinary Research Resources and Biobank at Osaka University Center for Twin Research

The Osaka University Twin Registry was originally established as a registry of older twins but was subsequently expanded to include twins of all ages. The Center for Twin Research at Osaka University Graduate School of Medicine has been managing this registry, as well as collecting research information and bioresources from twin participants. Based on the resources, multidisciplinary research projects have been conducted in collaboration with researchers from institutions both inside and outside Japan. One of the main aims of the center is to collect research information as well as biological resources from registered twins, and to establish a biobank and databases of these data and bioresources. Although data availability may vary, the following data have been collected: physical data (e.g., height, body weight, blood pressure, theoretical visceral fat, pulse wave velocity and bone density); epidemiological data (e.g., medical history, lifestyle, quality of life, mood status, cognitive function and nutritional status); electrocardiography, ultrasonography (carotid artery and thyroid); dentistry, dermatological assessment; positron emission tomography; magnetoencephalographam; brain magnetic resonance imaging (MRI); and functional MRI. In addition to these in-person survey data, microbiome data have been collected from some participants. As for bioresources, peripheral blood is obtained from the participants for isolation of serum and extraction of DNA and RNA, then stored in deep freezers for further analyses. A variety of research projects are in progress and more are on the way both in Japan and internationally using these data.