Whole-brain white matter organization, intelligence, and educational attainment

Abstract

General cognitive ability, sometimes referred to as intelligence, is associated with educational attainment throughout childhood. Most studies that have explored the neural correlates of intelligence in childhood focus on individual brain regions. This analytical approach is designed to identify restricted sets of voxels that overlap across participants. By contrast, we explored the relationship between white matter connectome organization, intelligence, and education. In both a sample of typically-developing children (N = 63) and a sample of struggling learners (N = 139), the white matter connectome efficiency was strongly associated with intelligence and educational attainment. Further, intelligence partially mediated the relationship between connectome efficiency and educational attainment. In contrast, a canonical voxel-wise analysis failed to identify any significant relationships. The results emphasize the importance of distributed brain network properties for cognitive or educational ability in childhood. Our findings are interpreted in the context of a developmental theory, which emphasizes the interaction between different subsystems over developmental time.

Keywords: Child development; Connectome; Education; Intelligence; White matter.

Fig. 1

Overview of processing steps to derive the FA-weighted white matter connectome from diffusion- and T1-weighted neuroimaging data.

Fig. 2

Exploratory factor analysis (EFA) of cognitive (a,c) and educational attainment measures (b,d) in the ACE (a,b) and CALM sample (c,d). The left panel of each plot shows a matrix of correlations between measures for each factor. The right panel shows the eigenvalues for each principal component (red) compared to scrambled data (grey). Abbreviations: MR=matrix reasoning, DR = Digit Recall, DM = Dot Matrix, BD = Backward Digit Recall, MX = Mr. X, SP = Spelling, RE = Reading, MA = Maths.

Fig. 3

(A) Visualization of the group-average connectome in the ACE sample (B) Group-average connectome in the CALM sample. (C) Regression of factor scores for educational attainment, cognition, and global efficiency of the white matter network and mediation relationships for the ACE sample. The shaded area in the regression results shows the 5–95%ile confidence interval based on bootstrap sample with 500 permutations. (D) Regression and mediation results in the CALM sample. Legend: β: direct effect,  β’: indirect effect,  ***p<0.001, **p<0.01, *p<0.05.

Fig. 4

Regression coefficients for the regional associations between local efficiency (E_j) and the cognitive factor scores (A, C) and the learning factor scores (B, D) in the ACE and CALM sample. The top panel shows native associations and the bottom panel shows associations corrected for the grey matter volume of each region.

Fig. 5

Results of voxel-wise analysis using tract-based spatial statistics (TBSS). There were no significant positive or negative associations between voxel FA values and cognitive or educational attainment measures in the ACE or CALM sample (p_corrected > 0.1). Only maps of positive associations are shown.