The Dynamic Associations Between Cortical Thickness and General Intelligence are Genetically Mediated

Abstract

The neural substrates of intelligence represent a fundamental but largely uncharted topic in human developmental neuroscience. Prior neuroimaging studies have identified modest but highly dynamic associations between intelligence and cortical thickness (CT) in childhood and adolescence. In a separate thread of research, quantitative genetic studies have repeatedly demonstrated that most measures of intelligence are highly heritable, as are many brain regions associated with intelligence. In the current study, we integrate these 2 streams of prior work by examining the genetic contributions to CT-intelligence relationships using a genetically informative longitudinal sample of 813 typically developing youth, imaged with high-resolution MRI and assessed with Wechsler Intelligence Scales (IQ). In addition to replicating the phenotypic association between multimodal association cortex and language centers with IQ, we find that CT-IQ covariance is nearly entirely genetically mediated. Moreover, shared genetic factors drive the rapidly evolving landscape of CT-IQ relationships in the developing brain.

Trial registration: ClinicalTrials.gov NCT00001246.

Keywords: MRI; cortical thickness; genetics; intelligence; neurodevelopment.

© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Figures

Figure 1.

Age distribution of the sample. Each point represents a high-resolution MRI, with connected points indicating data from the same subject. Subjects are color-coded based on study group (MZ = red, DZ = green, siblings of twins = blue, singletons = black).

Figure 2.

Simplified path diagram. Rectangles denote observed measures of full scale IQ and cortical thickness (measured at up to 8 timepoints), with circles indicating latent variables. Changes with time are modeled with a latent growth curves, allowing for both linear and nonlinear effects with age. Variance and CT–IQ covariance were decomposed into genetic (A), environmental (E), and error (ε) components. Paths in red were constrained to unity, green paths were defined as age at timepoint i, purple paths were set to age2 at timepoint i, and α represents the degree of kinship between 2 family members. The remaining paths were freely estimated. The paths in blue contribute to genetically mediated CT–IQ covariance. While only 2 related individuals are shown, the model included up to 5 members per family.

Figure 3.

FDR-corrected probability maps of CT–IQ covariance. Main effects of phenotypic, genetic, and environmental factors on CT–IQ covariance, as well as effects of genetic and environmental factors on changes in covariance over time. Similar maps with discrete significance thresholds are also provided in Figure S1.

Figure 4.

Dynamic changes between cortical thickness and full scale IQ over childhood and adolescence. Maximum likelihood estimates of the phenotypic correlation (rP), genetic correlations (rG), and the genetic contribution to covariance (pcorG) shown for ages 6–17. Changes with time can be viewed dynamically in the Supplementary Movies. Additional views are also provided in Figure S1.

Figure 5.

Genetics of adult cortical thickness and cognition in the Human Connectome Project (HCP) dataset. Heritability of cortical thickness (a2) and corresponding FDR-corrected probability maps (pa2) are provided, as well as the genetic correlation (rG) and genetic contribution to covariance (pcorG) between cortical thickness and the NIH Toolbox construct of total cognition; genetic covariances were not statistically significant after correction for multiple testing.