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Gene expression data of the Allen Human Brain Atlas were mapped onto the 12 genetically based cortical regions in the MR space. A) Resulting volume registration between FreeSurfer surface (fsaverage) and Allen brain MNI coordinates displayed as a point cloud, with a slice of the MRI imaging at the bottom (colin27). B) After the volume registration, gene expression data points are mapped to FreeSurfer surface vertices by assigning each surface vertex the gene expression of the closest (Euclidean distance) Allen brain data point using nearest neighbor interpolation. If two vertices have the same closest Allen brain data point, they belong to the same patch and the patch id is displayed as color. Thus, the color patches illustrate the local density of data points. The color patches with similar sizes across the cortex represent an even distribution of Allen brain data points and their surface correspondences. Colors of the dots in both (A) and (B) panels represent cortical regions to which they were assigned, corresponding to the color schemes in Fig 1B.
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The many subcomponents of the human cortex are known to follow an anatomical pattern and functional relationship that appears to be highly conserved between individuals. This suggests that this pattern and the relationship among cortical regions are important for cortical function and likely shaped by genetic factors, although the degree to which g...
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... further investigate the phenotypic and genetic relationships of cortical surface areas, we compared the inter-regional genetic correlations with the gene co-expression profiles using a third independent data set: data from the publicly available Allen Human Brain Atlas [17,18]. We developed a spatial mapping between the neuroanatomical subdivisions of the transcrip- tome data and the locations focused on in our brain imaging analysis (Fig 4). An aggregated transcriptome profile was derived for each of the 12 cortical regions we considered in the twin and genotype-based analyses. ...
Context 2
... registered Free- Surfer space to MNI space by using FreeSurfer's fsaverage T1 atlas, which was rigidly registered to an MNI T1 brain atlas (colin27). Fig 4 shows the resulting registration between FreeSurfer surface (fsaverage), MNI T1 atlas (colin27 displayed as slice) and Allen brain MNI coordinates displayed as a point cloud. After the registration, gene expression data defined at each point is mapped to FreeSurfer surface vertices by assigning each surface vertex the gene expression of the closest (Euclidean distance) Allen brain atlas coordinate using nearest neighbor interpola- tion. ...
Citations
... For completeness, we also looked at the phenotypic correlations between all 24 regions comparing GWASr and GWASg+r, which can be found in Fig. 4B. We have previously published the phenotypic correlations for GWASr (11,12,19). Notably, much more uniform phenotypic correlation structures emerge when globals are not regressed out, likely due to the shared global genetic variants underlying many regions. ...
... The gene-based P-value was calculated based on the mean of the summary statistic (χ 2 statistic) of GWAS for the SNPs in a gene (70). The P-value significance threshold was determined using the Bonferroni method, 0.05 divided by the number of genes (19,241), which is 2.6 × 10 −6 . The genes for GWASr and the global measures have been previously reported in Makowski et al. (12). ...
Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e., total surface area, mean cortical thickness) using a genetically informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, where 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recovered loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant genetic overlap of global and dorsolateral prefrontal modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.
... Beyond the brain imaging measures themselves, it will be important for the field to also replicate atlases in different populations. For instance, the gross cortical patterning captured by the genetic atlases we define largely remain the same with age, 47,48 but the extent to which fine-scale regional boundaries differ by factors such as age, sex or ancestry still remain unknown. Further, probabilistic methods (e.g., "fuzzy" atlases) can be employed in capturing intersubject variations or likelihood of regional boundaries. ...
... Whole brain complexity in women predicts cognitive ability (Fig. 1). This relationship is true even for childhood intelligence (Fig. 1c), and thus brain structural complexity can be considered a conservation of distinct genetically mediated human cortical patterns 44 and as a consequence a biomarker of cognitive resilience for use in epidemiological studies. Prenatal conditions have an influence on brain complexity 45 and also environmental factors such as paternal education and maternal ethnicity also intervene during pregnancy on cortex development 46 and early childhood environmental factors might have their impact too, which we plan to investigate in future studies. ...
Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences.
... For completion we also looked at the phenotypic correlations between all 24 regions comparing GWASr and GWASg+r, which can be found in Figure 4B. We have previously published the phenotypic correlations for GWASr 10,11,16 . Notably, much more uniform phenotypic correlation structures emerge when globals are not regressed out, likely due to the shared global genetic variants underlying many regions. ...
Human cortical expansion has occurred non-uniformly across the brain. We assessed the genetic architecture of cortical global expansion and regionalization by comparing two sets of genome-wide association studies of 24 cortical regions with and without adjustment for global measures (i.e. total surface area, mean cortical thickness) using a genetically-informed parcellation in 32,488 adults. We found 393 and 756 significant loci with and without adjusting for globals, respectively, among which 8% and 45% loci were associated with more than one region. Results from analyses without adjustment for globals recounted loci associated with global measures. Genetic factors that contribute to total surface area of the cortex particularly expand anterior/frontal regions, whereas those contributing to thicker cortex predominantly increase dorsal/frontal-parietal thickness. Interactome-based analyses revealed significant overlap of global and regional genetic modules, enriched for neurodevelopmental and immune system pathways. Consideration of global measures is important in understanding the genetic variants underlying cortical morphology.
... The cerebrum attains its folding structures through a complex orchestrated set of systematic mechanisms including differential proliferation, mechanical buckling, and differential expansion (Richman et al., 1975;Van Essen et al., 2018;Llinares-Benadero and Borrell, 2019). Previous studies have suggested that the development of cortical anatomy is dominated by genetic factors rather than random convolutions (Peng et al., 2016). These genetic factors could contribute to changes of cortical structure throughout the lifespan (Fjell et al., 2015;Ronan and Fletcher, 2015). ...
The cerebral cortex is a highly convoluted structure with distinct morphologic features, namely the gyri and sulci, which are associated with the functional segregation or integration in the human brain. During the lifespan, the brain atrophy that is accompanied by cognitive decline is a well-accepted aging phenotype. However, the detailed patterns of cortical folding change during aging, especially the changing age-dependencies of gyri and sulci, which is essential to brain functioning, remain unclear. In this study, we investigated the morphology of the gyral and sulcal regions from pial and white matter surfaces using MR imaging data of 417 healthy participants across adulthood to old age (21-92 years). To elucidate the age-related changes in the cortical pattern, we fitted cortical thickness and intrinsic curvature of gyri and sulci using the quadratic model to evaluate their age-dependencies during normal aging. Our findings show that comparing to gyri, the sulcal thinning is the most prominent pattern during the aging process, and the gyrification of pial and white matter surfaces were also affected differently, which implies the vulnerability of functional segregation during aging. Taken together, we propose a morphological model of aging that may provide a framework for understanding the mechanisms underlying gray matter degeneration.
... One, the bivariate twin analysis has a limited level of detail by using a lobar segmentation of the cortex. Although genetic clustering of the cortex appears to largely conform to anatomical boundaries defined by sulci and gyri, the genetically optimal configuration has a more refined subdivision of the cortical lobes Peng et al. 2016;Wen et al. 2016). As a result, the use of a lobar segmentation may result in a mixture of different gene pools for each lobar region, which may impact our ability to detect distinct genetic factors between regions. ...
... One strategy is to scan through fine-resolution imaging measures and group these based on estimated genetic effects. Genetic correlations estimated from twin studies have been used to cluster T1 imaging measures (49) and show that the clustering patterns are highly consistent with gene expression and SNP correlations (19,50). Figure 2 demonstrates an extension of this approach, using summary statistics of cortical surface GWAS to identify clusters that shared consistent GWAS signals. ...
Structural neuroimaging measures based on magnetic resonance imaging (MRI) have been at the forefront of imaging genetics. Global efforts to ensure homogeneity of measurements across study sites have enabled large-scale imaging genetic projects, accumulating nearly 50K samples for genome-wide association studies (GWAS). However, not many novel genetic variants have been identified by these GWAS, despite the high heritability of structural neuroimaging measures. Here, we discuss the limitations of using heritability as a guidance for assessing statistical power of GWAS, and highlight the importance of discoverability - which is the power to detect genetic variants for a given phenotype depending on its unique genomic architecture and GWAS sample size. Further, we present newly developed methods that boost genetic discovery in imaging genetics. By redefining imaging measures independent of traditional anatomical conventions, it is possible to improve discoverability, enabling identification of more genetic effects. Moreover, by leveraging enrichment priors from genomic annotations and independent GWAS of pleiotropic traits, we can better characterize effect size distributions, and identify reliable and replicable loci associated with structural neuroimaging measures. Statistical tools leveraging novel insights into the genetic discoverability of human traits, promises to accelerate the identification of genetic underpinnings underlying brain structural variation.
... One, the bivariate twin analysis has a limited level of detail by using a lobar segmentation of the cortex. Although genetic clustering of the cortex appears to largely conform to anatomical boundaries defined by sulci and gyri, the genetically optimal configuration has a more refined subdivision of the cortical lobes 2013;Peng et al. 2016;Wen et al. 2016). As a result, the use of a lobar segmentation may result in a mixture of different gene pools for each lobar region, which may impact our ability to detect distinct genetic factors between regions. ...
Previous studies have demonstrated that cortical thickness (CT) is under strong genetic control across the life span. However, little is known about genetic influences that cause changes in cortical thickness (ΔCT) during brain development. We obtained 482 longitudinal MRI scans at ages 9, 12, and 17 years from 215 twins and applied structural equation modeling to estimate genetic influences on (1) cortical thickness between regions and across time, and (2) changes in cortical thickness between ages. Although cortical thickness is largely mediated by the same genetic factor throughout late childhood and adolescence, we found evidence for influences of distinct genetic factors on regions across space and time. In addition, we found genetic influences for cortical thinning during adolescence that is mostly due to fluctuating influences from the same genetic factor, with evidence of local influences from a second emerging genetic factor. This fluctuating core genetic factor and emerging novel genetic factor might be implicated in the rapid cognitive and behavioral development during childhood and adolescence, and could potentially be targets for investigation into the manifestation of psychiatric disorders that have their origin in childhood and adolescence.
... However, when whole brain mean CT was included as a covariate, regionally specific patterns of genetic influence emerged, showing there is genetic specificity, with distinct genetic effects across brain regions. Genetic covariance across SA measures of neuroanatomical ROIs has not been examined, though a wide range of genetic correlations across genetically identified cortical regions (corrected for total SA) has been reported (Peng et al. 2016). Perhaps surprisingly, the genetic factors that affect SA and CT are largely independent (Panizzon et al. 2009;Winkler et al. 2010), and this may be due to differences in the cellular processes that influence each measure during corticogenesis (Rakic 2009). ...
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.
... Using a multivariate dimensionality reduction technique (partial least squares), they identified associations between the expression patterns of all genes (*20,000) and four MRI-based variables. Peng et al. (2016) investigated whether the relationships among cortical regions can be explained from genetic factors using genotype data from twins and unrelated individuals. In addition, they reported high concordance between inter-regional genetic correlations (based on genotype) and the inter-regional similarity of expression profiles using data from the Allen Human Brain Atlas, further confirming the genetic basis of cortical patterning. ...
Abstract The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data. We provide a comprehensive overview of the various computational methods used to analyze brain transcriptome atlases.
