Common and heritable components of white matter microstructure predict cognitive function at 1 and 2 y

Abstract

Previous studies indicate that the microstructure of individual white matter (WM) tracts is related to cognitive function. More recent studies indicate that the microstructure of individual tracts is highly correlated and that a property common across WM is related to overall cognitive function in adults. However, little is known about whether these common WM properties exist in early childhood development or how they are related to cognitive development. In this study, we used diffusion tensor imaging (DTI) to investigate common underlying factors in 12 fiber tracts, their relationship with cognitive function, and their heritability in a longitudinal sample of healthy children at birth (n = 535), 1 y (n = 322), and 2 y (n = 244) of age. Our data show that, in neonates, there is a highly significant correlation between major WM tracts that decreases from birth to 2 y of age. Over the same period, the factor structure increases in complexity, from one factor at birth to three factors at age 2 y, which explain 50% of variance. The identified common factors of DTI metrics in each age group are significantly correlated with general cognitive scores and predict cognitive ability in later childhood. These factors are moderately heritable. These findings illustrate the anatomical differentiation of WM fiber from birth to 2 y of age that correlate with cognitive development. Our results also suggest that the common factor approach is an informative way to study WM development and its relationship with cognition and is a useful approach for future imaging genetic studies.

Keywords: DTI; cognition; factor analysis; heritability; white matter.

Fig. 1.

(A) Heat maps of the intertract correlation matrices obtained from tract-level AD, RD, and FA and (B) mean correlation coefficients of overall (OT), homologous (HT), and nonhomologous tracts (NHT). *P < 0.05 compared with a group of 1-y-olds; †, P < 0.05 compared with a group of 2-y-olds; ‡, P < 0.05 compared with a group of 2-y-olds. Bilateral cingulate cinguli, CGC; callosal genu, GENU; callosal splenium, SPLN; uncinate, UNC.

Fig. 2.

Factor loadings of 12 tracts on extracted factors in three age groups. Refer to Fig. 1 for abbreviations.

Fig. S1.

Scree plots illustrating variance explained for EFA outlined in this work.

Fig. S2.

Factor loading of 12 fibers on extracted factors within subgroups (cotwin-1 vs. cotwin-2). Factor loading for neonates (A), 1-year-olds (B), and 2-year-olds (C). Twins are randomly divided into two groups of same size. Then, two expanded groups, cotwin-1 and cotwin-2, were made by adding all singletons and nontwins to each twin group. Both cotwin groups have the same number of 408 for neonates, 244 for 1-year-olds, and 185 for 2-year-olds.

Fig. S3.

Comparison of factor analysis results between the 6-direction and the 42-direction data. (A) Neonates had similar factor structures across all DTI metrics between the two datasets. (B) Subjects at age 1 y had similar factor structure for FA, less similar for RD, and a different factor structure for AD between the two datasets. We projected the 6-direction data onto factors from the 42-direction data set and computed projection scores. Most of these scores are nonzero (Dataset S1), indicating that the 42-direction factors account for a large proportion of the variability of the 6-direction data, and the difference between them is likely due to the small sample size of the 6-direction data. (C) At age 2 y, the factor structure for RD is similar, and the first two factors for FA are similar; the factor structure for AD is different between two datasets. Projection analysis found most of these scores to be nonzero (Dataset S1), indicating the 6-direction data are related to the 42-direction factors and that the difference is likely due to the small sample size for 6-direction data. Circled numbers represent the order of factors extracted.

Fig. S4.

Scatterplots of Mullen ELC score (y) against DTI factor (x). Both scores on the x and y axes were transformed into z scores. Regression lines represent the estimate curve (solid) with a 95% confidence band (dashed).

Fig. S5.

The number of participants who had DTI scans or cognitive data at each age.