The Heritability of Cortical Folding: Evidence from the Human Connectome Project

Abstract

The mechanisms underlying cortical folding are incompletely understood. Prior studies have suggested that individual differences in sulcal depth are genetically mediated, with deeper and ontologically older sulci more heritable than others. In this study, we examine FreeSurfer-derived estimates of average convexity and mean curvature as proxy measures of cortical folding patterns using a large (N = 1096) genetically informative young adult subsample of the Human Connectome Project. Both measures were significantly heritable near major sulci and primary fissures, where approximately half of individual differences could be attributed to genetic factors. Genetic influences near higher order gyri and sulci were substantially lower and largely nonsignificant. Spatial permutation analysis found that heritability patterns were significantly anticorrelated to maps of evolutionary and neurodevelopmental expansion. We also found strong phenotypic correlations between average convexity, curvature, and several common surface metrics (cortical thickness, surface area, and cortical myelination). However, quantitative genetic models suggest that correlations between these metrics are largely driven by nongenetic factors. These findings not only further our understanding of the neurobiology of gyrification, but have pragmatic implications for the interpretation of heritability maps based on automated surface-based measurements.

Trial registration: ClinicalTrials.gov NCT00001246.

Keywords: FreeSurfer; MRI; average convexity; mean curvature; twin.

Published by Oxford University Press 2020.

Figures

Figure 1

Brain maps for average convexity (SULC) and mean curvature (CURV) estimate the proportion of phenotypic variance owed to genetic (a2), shared environmental (c2), and unique environmental (e2) components. FDR-corrected probability maps are also provided, testing the significance of genetic (Gen), shared environmental (S. Env), or both (Fam) effects on phenotypic variance. For reference, the vertex-level mean value for each metric (μ) is also shown at the top of the figure. On the right, heritability maps are also presented as flatmaps. Additional univariate results are provided in Supplementary Figures S1–S6 and in Tables S1 and S2.

Figure 2

Bivariate relationships between average convexity (SULC) and mean curvature (CURV). On left, brain maps display mean values for each metric, with a scatterplot showing the relationship between means at the vertex level. On the top right, regional variability in phenotypic (rP), genetic (rG), and environmental (rE) correlations are shown. As an alternative metric of the results from variance decomposition, the proportion of the phenotypic variance attributable to genetic (pcor) and environmental (ecor) variance is shown. At bottom, FDR-corrected probability maps for genetic (pA) and environmental (pE) covariance are provided.

Figure 3

Phenotypic correlations between average convexity (SULC) and common structural brain metrics of cortical thickness (CT), surface area (SA) and cortical myelination (CM). The corresponding FDR-corrected P values testing significance of the correlation (H0: r = 0) are also provided below. Probability maps are color coded based on whether the corresponding mean value of the folding measure is positive or negative at that vertex.

Figure 4

Shared genetic relationships between average convexity (SULC) and cortical thickness (CT), surface area (SA) and cortical myelination (CM). Genetic correlations (rG), the proportion of phenotypic variance attributable to the genetic factors (pcor), and FDR-corrected probability maps testing the significance of vertex-level genetic covariance (pA) are shown. Probability maps are color coded based on whether the mean value at that vertex was positive (red) or negative (blue).