Autism-related dietary preferences mediate autism-gut microbiome associations.

There is increasing interest in the potential contribution of the gut microbiome to autism spectrum disorder (ASD). However, previous studies have been underpowered and have not been designed to address potential confounding factors in a comprehensive way. We performed a large autism stool metagenomics study (n = 247) based on participants from the Australian Autism Biobank and the Queensland Twin Adolescent Brain project. We found negligible direct associations between ASD diagnosis and the gut microbiome. Instead, our data support a model whereby ASD-related restricted interests are associated with less-diverse diet, and in turn reduced microbial taxonomic diversity and looser stool consistency. In contrast to ASD diagnosis, our dataset was well powered to detect microbiome associations with traits such as age, dietary intake, and stool consistency. Overall, microbiome differences in ASD may reflect dietary preferences that relate to diagnostic features, and we caution against claims that the microbiome has a driving role in ASD.

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.

Persistence of Anxiety/Depression Symptoms in Early Adolescence: A Prospective Study of Daily Life Stress, Rumination, and Daytime Sleepiness in a Genetically Informative Cohort.

In this prospective study of mental health, we examine the influence of three interrelated traits - perceived stress, rumination, and daytime sleepiness - and their association with symptoms of anxiety and depression in early adolescence. Given the known associations between these traits, an important objective is to determine the extent to which they may independently predict anxiety/depression symptoms. Twin pairs from the Queensland Twin Adolescent Brain (QTAB) project were assessed on two occasions (N = 211 pairs aged 9-14 years at baseline and 152 pairs aged 10-16 years at follow-up). Linear regression models and quantitative genetic modeling were used to analyze the data. Prospectively, perceived stress, rumination, and daytime sleepiness accounted for 8-11% of the variation in later anxiety/depression; familial influences contributed strongly to these associations. However, only perceived stress significantly predicted change in anxiety/depression, accounting for 3% of variance at follow-up after adjusting for anxiety/depression at baseline, although it did not do so independently of rumination and daytime sleepiness. Bidirectional effects were found between all traits over time. These findings suggest an underlying architecture that is shared, to some degree, by all traits, while the literature points to hypothalamic-pituitary-adrenal (HPA) axis and/or circadian systems as potential sources of overlapping influence and possible avenues for intervention.

ErbB Signaling Pathway Genes Are Differentially Expressed in Monozygotic Twins Discordant for Sports-Related Concussion.

Transcriptional changes involved in neuronal recovery after sports-related concussion (SRC) may be obscured by inter-individual variation in mRNA expression and nonspecific changes related to physical exertion. Using a co-twin study, the objective of this study was to identify important differences in mRNA expression among a single pair of monozygotic (MZ) twins discordant for concussion. A pair of MZ twins were enrolled as part of a larger study of concussion biomarkers among collegiate athletes. During the study, Twin A sustained SRC, allowing comparison of mRNA expression to the nonconcussed Twin B. Twin A clinically recovered by Day 7. mRNA expression was measured pre-injury and at 6 h and 7 days postinjury using Affymetrix HG-U133 Plus 2.0 microarray. Changes in mRNA expression from pre-injury to each postinjury time point were compared between the twins; differences >1.5-fold were considered important. Kyoto Encyclopedia of Genes and Genomes identified biologic networks associated with important transcripts. Among 38,000 analyzed genes, important changes were identified in 153 genes. The ErbB (epidermal growth factor receptor) signaling pathway was identified as the top transcriptional network from pre-injury to 7 days postinjury. Genes in this pathway with important transcriptional changes included epidermal growth factor (2.41), epiregulin (1.73), neuregulin 1 (1.54) and mechanistic target of rapamycin (1.51). In conclusion, the ErbB signaling pathway was identified as a potential regulator of clinical recovery in a MZ twin pair discordant for SRC. A co-twin study design may be a useful method for identifying important gene pathways associated with concussion recovery.

Genetic Specificity of Hippocampal Subfield Volumes, Relative to Hippocampal Formation, Identified in 2148 Young Adult Twins and Siblings.

The hippocampus is a complex brain structure with key roles in cognitive and emotional processing and with subregion abnormalities associated with a range of disorders and psychopathologies. Here we combine data from two large independent young adult twin/sibling cohorts to obtain the most accurate estimates to date of genetic covariation between hippocampal subfield volumes and the hippocampus as a single volume. The combined sample included 2148 individuals, comprising 1073 individuals from 627 families (mean age = 22.3 years) from the Queensland Twin IMaging (QTIM) Study, and 1075 individuals from 454 families (mean age = 28.8 years) from the Human Connectome Project (HCP). Hippocampal subfields were segmented using FreeSurfer version 6.0 (CA4 and dentate gyrus were phenotypically and genetically indistinguishable and were summed to a single volume). Multivariate twin modeling was conducted in OpenMx to decompose variance into genetic and environmental sources. Bivariate analyses of hippocampal formation and each subfield volume showed that 10%-72% of subfield genetic variance was independent of the hippocampal formation, with greatest specificity found for the smaller volumes; for example, CA2/3 with 42% of genetic variance being independent of the hippocampus; fissure (63%); fimbria (72%); hippocampus-amygdala transition area (41%); parasubiculum (62%). In terms of genetic influence, whole hippocampal volume is a good proxy for the largest hippocampal subfields, but a poor substitute for the smaller subfields. Additive genetic sources accounted for 49%-77% of total variance for each of the subfields in the combined sample multivariate analysis. In addition, the multivariate analyses were sufficiently powered to identify common environmental influences (replicated in QTIM and HCP for the molecular layer and CA4/dentate gyrus, and accounting for 7%-16% of total variance for 8 of 10 subfields in the combined sample). This provides the clearest indication yet from a twin study that factors such as home environment may influence hippocampal volumes (albeit, with caveats).

Are Sex Differences in Human Brain Structure Associated With Sex Differences in Behavior?

On average, men and women differ in brain structure and behavior, raising the possibility of a link between sex differences in brain and behavior. But women and men are also subject to different societal and cultural norms. We navigated this challenge by investigating variability of sex-differentiated brain structure within each sex. Using data from the Queensland Twin IMaging study (n = 1,040) and Human Connectome Project (n = 1,113), we obtained data-driven measures of individual differences along a male-female dimension for brain and behavior based on average sex differences in brain structure and behavior, respectively. We found a weak association between these brain and behavioral differences, driven by brain size. These brain and behavioral differences were moderately heritable. Our findings suggest that behavioral sex differences are, to some extent, related to sex differences in brain structure but that this is mainly driven by differences in brain size, and causality should be interpreted cautiously.

Region-specific sex differences in the hippocampus.

The hippocampus is a brain region critical for learning and memory, and is also implicated in several neuropsychiatric disorders that show sex differences in prevalence, symptom expression, and mean age of onset. On average, males have larger hippocampal volumes than females, but findings are inconclusive after adjusting for overall brain size. Although the hippocampus is a heterogenous structure, few studies have focused on sex differences in the hippocampal subfields - with little consensus on whether there are regionally specific sex differences in the hippocampus after adjusting for brain size, or whether it is important to adjust for total hippocampal volume (HPV). Here, using two young adult cohorts from the Queensland Twin IMaging study (QTIM; N â€‹= â€‹727) and the Human Connectome Project (HCP; N â€‹= â€‹960), we examined differences between males and females in the volumes of 12 hippocampal subfields, extracted using FreeSurfer 6.0. After adjusting the subfield volumes for either HPV or brain size (brain segmentation volume (BSV)) using four controlling methods (allometric, covariate, residual and matching), we estimated the percentage difference of the sex effect (males versus females) and Cohen's d using hierarchical general linear models. Males had larger volumes compared to females in the parasubiculum (up to 6.04%; Cohen's d â€‹= â€‹0.46) and fimbria (up to 8.75%; d â€‹= â€‹0.54) after adjusting for HPV. These sex differences were robust across the two cohorts and multiple controlling methods, though within cohort effect sizes were larger for the matched approach, due to the smaller sub-sample. Additional sex effects were identified in the HCP cohort and combined (QTIM and HCP) sample (hippocampal fissure (up to 6.79%), presubiculum (up to 3.08%), and hippocampal tail (up to -0.23%)). In contrast, no sex differences were detected for the volume of the cornu ammonis (CA)2/3, CA4, Hippocampus-Amygdala Transition Area (HATA), or the granule cell layer of the dentate gyrus (GCDG). These findings show that, independent of differences in HPV, there are regionally specific sex differences in the hippocampus, which may be most prominent in the fimbria and parasubiculum. Further, given sex differences were less consistent across cohorts after controlling for BSV, adjusting for HPV rather than BSV may benefit future studies. This work may help in disentangling sex effects, and provide a better understanding of the implications of sex differences for behaviour and neuropsychiatric disorders.

Educational attainment polygenic scores are associated with cortical total surface area and regions important for language and memory.

It is well established that higher cognitive ability is associated with larger brain size. However, individual variation in intelligence exists despite brain size and recent studies have shown that a simple unifactorial view of the neurobiology underpinning cognitive ability is probably unrealistic. Educational attainment (EA) is often used as a proxy for cognitive ability since it is easily measured, resulting in large sample sizes and, consequently, sufficient statistical power to detect small associations. This study investigates the association between three global (total surface area (TSA), intra-cranial volume (ICV) and average cortical thickness) and 34 regional cortical measures with educational attainment using a polygenic scoring (PGS) approach. Analyses were conducted on two independent target samples of young twin adults with neuroimaging data, from Australia (N â€‹= â€‹1097) and the USA (N â€‹= â€‹723), and found that higher EA-PGS were significantly associated with larger global brain size measures, ICV and TSA (R2 â€‹= â€‹0.006 and 0.016 respectively, p â€‹< â€‹0.001) but not average thickness. At the regional level, we identified seven cortical regions-in the frontal and temporal lobes-that showed variation in surface area and average cortical thickness over-and-above the global effect. These regions have been robustly implicated in language, memory, visual recognition and cognitive processing. Additionally, we demonstrate that these identified brain regions partly mediate the association between EA-PGS and cognitive test performance. Altogether, these findings advance our understanding of the neurobiology that underpins educational attainment and cognitive ability, providing focus points for future research.

A unified framework for association and prediction from vertex-wise grey-matter structure.

The recent availability of large-scale neuroimaging cohorts facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. Here, we investigate the association (previously coined morphometricity) of a phenotype with all 652,283 vertex-wise measures of cortical and subcortical morphology in a large data set from the UK Biobank (UKB; N = 9,497 for discovery, N = 4,323 for replication) and the Human Connectome Project (N = 1,110). We used a linear mixed model with the brain measures of individuals fitted as random effects with covariance relationships estimated from the imaging data. We tested 167 behavioural, cognitive, psychiatric or lifestyle phenotypes and found significant morphometricity for 58 phenotypes (spanning substance use, blood assay results, education or income level, diet, depression, and cognition domains), 23 of which replicated in the UKB replication set or the HCP. We then extended the model for a bivariate analysis to estimate grey-matter correlation between phenotypes, which revealed that body size (i.e., height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the morphometricity (confirmed using a conditional analysis), providing possible insight into previous MRI case-control results for psychiatric disorders where case status is associated with body mass index. Our LMM framework also allowed to predict some of the associated phenotypes from the vertex-wise measures, in two independent samples. Finally, we demonstrated additional new applications of our approach (a) region of interest (ROI) analysis that retain the vertex-wise complexity; (b) comparison of the information retained by different MRI processings.

Genetic Complexity of Cortical Structure: Differences in Genetic and Environmental Factors Influencing Cortical Surface Area and Thickness.

Quantifying the genetic architecture of the cerebral cortex is necessary for understanding disease and changes to the brain across the lifespan. Prior work shows that both surface area (SA) and cortical thickness (CT) are heritable. However, we do not yet understand the extent to which region-specific genetic factors (i.e., independent of global effects) play a dominant role in the regional patterning or inter-regional associations across the cortex. Using a population sample of young adult twins (N = 923), we show that the heritability of SA and CT varies widely across regions, generally independent of measurement error. When global effects are controlled for, we detected a complex pattern of genetically mediated clusters of inter-regional associations, which varied between hemispheres. There were generally weak associations between the SA of different regions, except within the occipital lobe, whereas CT was positively correlated within lobar divisions and negatively correlated across lobes, mostly due to genetic covariation. These findings were replicated in an independent sample of twins and siblings (N = 698) from the Human Connectome Project. The different genetic contributions to SA and CT across regions reveal the value of quantifying sources of covariation to appreciate the genetic complexity of cortical structures.

Non-linear realignment improves hippocampus subfield segmentation reliability.

Participant movement can deleteriously affect MR image quality. Further, for the visualization and segmentation of small anatomical structures, there is a need to improve image quality, specifically signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), by acquiring multiple anatomical scans consecutively. We aimed to ameliorate movement artefacts and increase SNR in a high-resolution turbo spin-echo (TSE) sequence acquired thrice using non-linear realignment in order to improve segmentation consistency of the hippocampus subfields. We assessed the method in 29 young healthy participants, 11 Motor Neuron Disease patients, and 11 age matched controls at 7T, and 24 healthy adolescents at 3T. Results show improved image segmentation of the hippocampus subfields when comparing template-based segmentations with individual segmentations with Dice overlaps N = 75; ps < 0.001 (Friedman's test) and higher sharpness ps < 0.001 in non-linearly realigned scans as compared to linearly, and arithmetically averaged scans.

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.

A Fast Method for Estimating Statistical Power of Multivariate GWAS in Real Case Scenarios: Examples from the Field of Imaging Genetics.

In GWAS of imaging phenotypes (e.g., by the ENIGMA and CHARGE consortia), the growing number of phenotypes considered presents a statistical challenge that other fields are not experiencing (e.g. psychiatry and the Psychiatric Genetics Consortium). However, the multivariate nature of MRI measurements may also be an advantage as many of the MRI phenotypes are correlated and multivariate methods could be considered. Here, we compared the statistical power of a multivariate GWAS versus the current univariate approach, which consists of multiple univariate analyses. To do so, we used results from twin models to estimate pertinent vectors of SNP effect sizes on brain imaging phenotypes, as well as the residual correlation matrices, necessary to estimate analytically the statistical power. We showed that for subcortical structure volumes and hippocampal subfields, a multivariate GWAS yields similar statistical power to the current univariate approach. Our analytical approach is as accurate but ~ 1000 times faster than simulations and we have released the code to facilitate the investigation of other scenarios, may they be outside the field of imaging genetics.

Genetic influences on individual differences in longitudinal changes in global and subcortical brain volumes: Results of the ENIGMA plasticity working group.

Structural brain changes that occur during development and ageing are related to mental health and general cognitive functioning. Individuals differ in the extent to which their brain volumes change over time, but whether these differences can be attributed to differences in their genotypes has not been widely studied. Here we estimate heritability (h2 ) of changes in global and subcortical brain volumes in five longitudinal twin cohorts from across the world and in different stages of the lifespan (N = 861). Heritability estimates of brain changes were significant and ranged from 16% (caudate) to 42% (cerebellar gray matter) for all global and most subcortical volumes (with the exception of thalamus and pallidum). Heritability estimates of change rates were generally higher in adults than in children suggesting an increasing influence of genetic factors explaining individual differences in brain structural changes with age. In children, environmental influences in part explained individual differences in developmental changes in brain structure. Multivariate genetic modeling showed that genetic influences of change rates and baseline volume significantly overlapped for many structures. The genetic influences explaining individual differences in the change rate for cerebellum, cerebellar gray matter and lateral ventricles were independent of the genetic influences explaining differences in their baseline volumes. These results imply the existence of genetic variants that are specific for brain plasticity, rather than brain volume itself. Identifying these genes may increase our understanding of brain development and ageing and possibly have implications for diseases that are characterized by deviant developmental trajectories of brain structure. Hum Brain Mapp 38:4444-4458, 2017. © 2017 Wiley Periodicals, Inc.

Genetics and brain morphology.

A wealth of empirical evidence is accumulating on the genetic mediation of brain structure phenotypes. This comes from twin studies that assess heritability and genetic covariance between traits, candidate gene associations, and genome-wide association studies (GWAS) that can identify specific genetic variants. Here we review the major findings from each of these approaches and consider how they inform on the genetic architecture of brain structure. The findings from twin studies show there is a strong genetic influence (heritability) on brain structure, and overlap of genetic effects (pleiotropy) between structures, and between structure and cognition. However, there is also evidence for genetic specificity, with distinct genetic effects across some brain regions. Candidate gene associations show little convergence; most have been under powered to detect effect sizes of the magnitude now expected. GWAS have identified 19 genetic variants for brain structure, though no replicated associations account for more than 1% of the variance. Together these studies are revealing new insights into the genetic architecture of brain morphology. As the scope of inquiry broadens, including measures that capture the complexity of the brain, along with larger samples and new analyses, such as genome-wide common trait analysis (GCTA) and polygenic scores, which combine variant effects for a phenotype, as well as whole-genome sequencing, more genetic variants for brain structure will be identified. Increasingly, large-scale multi-site studies will facilitate this next wave of studies, and promise to enhance our understanding of the etiology of variation in brain morphology, as well as brain disorders.

Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI.

Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).

The Queensland Twin Adolescent Brain Project, a longitudinal study of adolescent brain development.

We describe the Queensland Twin Adolescent Brain (QTAB) dataset and provide a detailed methodology and technical validation to facilitate data usage. The QTAB dataset comprises multimodal neuroimaging, as well as cognitive and mental health data collected in adolescent twins over two sessions (session 1: N = 422, age 9-14 years; session 2: N = 304, 10-16 years). The MRI protocol consisted of T1-weighted (MP2RAGE), T2-weighted, FLAIR, high-resolution TSE, SWI, resting-state fMRI, DWI, and ASL scans. Two fMRI tasks were added in session 2: an emotional conflict task and a passive movie-watching task. Outside of the scanner, we assessed cognitive function using standardised tests. We also obtained self-reports of symptoms for anxiety and depression, perceived stress, sleepiness, pubertal development measures, and risk and protective factors. We additionally collected several biological samples for genomic and metagenomic analysis. The QTAB project was established to promote health-related research in adolescence.

Temporo-basal sulcal connections: a manual annotation protocol and an investigation of sexual dimorphism and heritability.

The temporo-basal region of the human brain is composed of the collateral, the occipito-temporal, and the rhinal sulci. We manually rated (using a novel protocol) the connections between rhinal/collateral (RS-CS), collateral/occipito-temporal (CS-OTS) and rhinal/occipito-temporal (RS-OTS) sulci, using the MRI of nearly 3400 individuals including around 1000 twins. We reported both the associations between sulcal polymorphisms as well with a wide range of demographics (e.g. age, sex, handedness). Finally, we also estimated the heritability, and the genetic correlation between sulcal connections. We reported the frequency of the sulcal connections in the general population, which were hemisphere dependent. We found a sexual dimorphism of the connections, especially marked in the right hemisphere, with a CS-OTS connection more frequent in females (approximately 35-40% versus 20-25% in males) and an RS-CS connection more common in males (approximately 40-45% versus 25-30% in females). We confirmed associations between sulcal connections and characteristics of incomplete hippocampal inversion (IHI). We estimated the broad sense heritability to be 0.28-0.45 for RS-CS and CS-OTS connections, with hints of dominant contribution for the RS-CS connection. The connections appeared to share some of their genetic causing factors as indicated by strong genetic correlations. Heritability appeared much smaller for the (rarer) RS-OTS connection.

Interactions between the lipidome and genetic and environmental factors in autism.

Autism omics research has historically been reductionist and diagnosis centric, with little attention paid to common co-occurring conditions (for example, sleep and feeding disorders) and the complex interplay between molecular profiles and neurodevelopment, genetics, environmental factors and health. Here we explored the plasma lipidome (783 lipid species) in 765 children (485 diagnosed with autism spectrum disorder (ASD)) within the Australian Autism Biobank. We identified lipids associated with ASD diagnosis (n = 8), sleep disturbances (n = 20) and cognitive function (n = 8) and found that long-chain polyunsaturated fatty acids may causally contribute to sleep disturbances mediated by the FADS gene cluster. We explored the interplay of environmental factors with neurodevelopment and the lipidome, finding that sleep disturbances and unhealthy diet have a convergent lipidome profile (with potential mediation by the microbiome) that is also independently associated with poorer adaptive function. In contrast, ASD lipidome differences were accounted for by dietary differences and sleep disturbances. We identified a large chr19p13.2 copy number variant genetic deletion spanning the LDLR gene and two high-confidence ASD genes (ELAVL3 and SMARCA4) in one child with an ASD diagnosis and widespread low-density lipoprotein-related lipidome derangements. Lipidomics captures the complexity of neurodevelopment, as well as the biological effects of conditions that commonly affect quality of life among autistic people.