Genetic and environmental architecture of executive functions in midlife

Abstract

Objective: Research on executive functions (EFs) has revealed evidence for general abilities that underlie performance across multiple EF tasks and domains. This Common EF factor is highly stable in adolescence through young adulthood, correlates with other important cognitive abilities, and is explained largely by genetic influences. However, little is known about Common EF beyond young adulthood. This study examines 3 hypotheses regarding the latent structure, genetic/environmental etiology, and cognitive correlates of Common EF in middle age.

Method: We examined data from 1,284 middle-aged twins (51-60 years) in the Vietnam Era Twin Study of Aging who completed 7 neuropsychological measures of EFs, as well as measures of general cognitive ability and processing speed.

Results: Our confirmatory factor analysis indicated that Common EF explained variation across all 7 EF tasks. Inhibition and shifting were subsumed entirely under the Common EF factor, and there was an additional working memory span-specific factor. Common EF was heritable in midlife (a2 = .46), with additional evidence for both shared environmental influences (c2 = .41) and nonshared environmental influences (e2 = .13). Higher Common EF was moderately associated with higher general cognitive ability, measured both in early adulthood and midlife, and faster processing speed in midlife. These correlations were primarily driven by shared genetic influences.

Conclusions: These results support the hypothesis that Common EF captures similar EF abilities in midlife as in adolescence and young adulthood. However, environmental influences may explain a larger portion of variance in this construct as individuals age. (PsycINFO Database Record

Figure B1

Three factor model of EF with correlated genetic and environmental influences on Inhibition (A_Inh, C_Inh, E_Inh), Shifting (A_Sh, C_Sh, E_Sh), and WM Span (A_WM, C_WM, E_WM). This model also fit the data well, and corresponds closely to the correlated factors model presented by Miyake et al. (2000). However, we favored the unity and diversity model presented in Figure 2 because it had the most parsimonious fit (see Table 2) and highlights Common EF vs. EF-specific variance components. Significant factor loadings and correlations are displayed with black text and black lines (p < .05).

Figure B2

Hierarchical genetic/environmental model in which Common EF is modeled as a hierarchical latent variable above Inhibition, Shifting, and WM Span. ACEs for EF-Specific processes (e.g., WM-Specific) are modeled directly on their latent variables (e.g., A_WM, C_WM, E_WM). This model also fit the data well, but we favored the unity and diversity model presented in Figure 2 because it had a more parsimonious fit (see Table 2) and corresponds to the parameterization used in current work (Friedman & Miyake, 2017; Miyake & Friedman, 2012). Significant factor loadings and correlations are displayed with black text and black lines (p < .05).

Figure 1

Unity and diversity model of EF tested in the current study. Ellipses represent latent variables, and rectangles represent measured variables. Based on previous work using this model, we expected that a Common EF factor would underlie performance on all seven neuropsychological tasks. Additionally, there would be orthogonal Shifting-Specific and WM-Specific factors explaining variation in the shifting tasks and WM span tasks (respectively) above and beyond the Common EF factor. As yet, there is no evidence for an Inhibition-Specific factor so we did not expect to observe it here. Task-specific residual variances are estimated but not shown.

Figure 2

Genetic unity and diversity model of Common EF and WM-Specific abilities. The ACE factors represent genetic influences (A), shared environmental influences (C), and nonshared environmental influences (E) on each latent construct and individual measure. Latent variables were not modeled for Inhibition-Specific or Shifting-Specific abilities because there was no evidence for these EF-Specific factors in this sample (see Table 2). Circles and ellipses represent latent variables, and rectangles represent measured variables. Variation explained by the latent factors can be computed by squaring the factor loadings. Significant factor loadings are displayed with black text and black lines (p < .05).

Figure 3

Correlations between the genetic (A), shared environmental (C), and nonshared environmental (E) variance components on Common EF and WM-Specific in midlife, and general cognitive ability in midlife (Panel A) or young adulthood (Panel B). Common EF and WM-Specific are not correlated, by definition. Circles and ellipses represent latent variables, and rectangles represent measured variables. Factor loadings and residual variance components for the individual EF tasks are not displayed here, but were nearly identical to those displayed in Figure 2. Significant factor loadings and correlations are displayed with black text and black lines (p < .05).

Figure 4

Correlations between the genetic (A), shared environmental (C), and nonshared environmental (E) variance components on Common EF, WM-Specific, and processing speed. Circles and ellipses represent latent variables, and rectangles represent measured variables (circles are not shown around residual ACE latent variables). Factor loadings and residual variance components for individual tasks are not displayed here. For EF tasks, they were nearly identical to those displayed in Figure 2. For processing speed, factor loadings were significant (.76 for simple RT; .88 for choice RT), and residual genetic/environmental components were estimated, but only significant for nonshared environmental components (residual paths: a = .27, c = .00, e = .62 for simple RT; a = .00, c = .21, e = .41, for choice RT). Significant factor loadings and correlations are displayed with black text and black lines (p < .05).