Association of genetic and sulcal traits with executive function in congenital heart disease

Abstract

Objective: Persons with congenital heart disease (CHD) are at increased risk of neurodevelopmental disabilities, including impairments to executive function. Sulcal pattern features correlate with executive function in adolescents with single-ventricle heart disease and tetralogy of Fallot. However, the interaction of sulcal pattern features with genetic and participant factors in predicting executive dysfunction is unknown.

Methods: We studied sulcal pattern features, participant factors, and genetic risk for executive function impairment in a cohort with multiple CHD types using stepwise linear regression and machine learning.

Results: Genetic factors, including predicted damaging de novo or rare inherited variants in neurodevelopmental disabilities risk genes, apolipoprotein E genotype, and principal components of sulcal pattern features were associated with executive function measures after adjusting for age at testing, sex, mother's education, and biventricular versus single-ventricle CHD in a linear regression model. Using regression trees and bootstrap validation, younger participant age and larger alterations in sulcal pattern features were consistently identified as important predictors of decreased cognitive flexibility with left hemisphere graph topology often selected as the most important predictor. Inclusion of both sulcal pattern and genetic factors improved model fit compared to either alone.

Interpretation: We conclude that sulcal measures remain important predictors of cognitive flexibility, and the model predicting executive outcomes is improved by inclusion of potential genetic sources of neurodevelopmental risk. If confirmed, measures of sulcal patterning may serve as early imaging biomarkers to identify those at heightened risk for future neurodevelopmental disabilities.

Figure 1

Pearson correlation coefficient matrix between all sulcal similarity measures.

Figure 2

Regression trees predicting category switching accuracy with various candidate predictors. Tree (A) (n = 97, R ² = 0.12, RMSE = 3.08, cross‐validation RMSE = 3.12) considers only patient demographic characteristics and genetic variants while Tree (B) (n = 92, R ² = 0.30, RMSE = 2.76, cross‐validation RMSE = 3.12) additionally considers all sulcal measures. Each node indicates the mean category switching accuracy (top number) and sample size (bottom number) for participants with the characteristics along the paths above the node. For example, in tree A, 7 individuals aged <25 with an NDD LoF variant had the lowest mean score of 8.6 and 13 individuals aged >25 had the highest mean score of 13.2. LoF, loss‐of‐function; NDD, neurodevelopmental disability; RMSE, root mean squared error.