Mapping the regional influence of genetics on brain structure variability--a tensor-based morphometry study

Abstract

Genetic and environmental factors influence brain structure and function profoundly. The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8+/-1.8 SD years). All 92 twins' 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject's anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions that have a more protracted maturational time-course.

Fig. 1

top row: Intraclass correlation maps are shown for the monozygotic twins (rMZ; left panel) and for the dizygotic twins (rDZ; right panel); middle row: An anatomical image (left) shows the sections for which statistics are displayed; maps of Falconer’s heritability estimate (h²) show high heritability in subcortical regions; bottom row: Maps show the p-values (significance) of the intraclass correlation in monozygotic twins (icc MZ) and dizygotic twins (icc DZ).

Fig. 2

Variance component maps for additive genetic (a² - top left), common (c² - top right), and unique environmental (e² - bottom left) factors for the unscaled data. Bottom right: Color-coded maps representing the model choice at each voxel- Light blue (yellow and red, respectively) indicates that the best fitting model is obtained with ACE (AE and CE, respectively). The corresponding anatomical sections (a-g) are shown in Figure 5 - middle left.

Fig. 3

Variance component maps for additive genetic (a² - top left), common (c² - top right), and unique environmental (e² - bottom left) factors for the scaled data. Bottom right: Color-coded maps representing the model choice at each voxel- Light blue (yellow and red, respectively) indicates that the best fitting model fit is obtained with ACE (AE and CE, respectively). The corresponding anatomical sections (a-g) are shown in Figure 5 - middle left.

Fig. 4

Variance of the Jacobian across the population for the unscaled (left) and the scaled data (right) displayed as the percentage of the mean at each voxel. Here, blue indicates a small variance in the trait (0%), whereas red indicates a higher variance (5%). It is worth noting that values in the unscaled white matter are higher than values in the scaled white matter except for the subcortical regions (1% versus 0.3%).