Elsevier

NeuroImage

Volume 185, 15 January 2019, Pages 140-153
NeuroImage

Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study

https://doi.org/10.1016/j.neuroimage.2018.10.040Get rights and content

Highlights

  • Screen media activity is a common recreational activity in children and adolescents.

  • The manuscript focuses on how screen media activity is related to structural brain characteristics.

  • Structural correlation networks were identified supporting the maturational coupling hypothesis.

  • Some networks were associated with for externalizing psychopathology, fluid and crystallized intelligence.

Abstract

The adolescent brain undergoes profound structural changes which is influenced by many factors. Screen media activity (SMA; e.g., watching television or videos, playing video games, or using social media) is a common recreational activity in children and adolescents; however, its effect on brain structure is not well understood. A multivariate approach with the first cross-sectional data release from the Adolescent Brain Cognitive Development (ABCD) study was used to test the maturational coupling hypothesis, i.e. the notion that coordinated patterns of structural change related to specific behaviors. Moreover, the utility of this approach was tested by determining the association between these structural correlation networks and psychopathology or cognition. ABCD participants with usable structural imaging and SMA data (N = 4277 of 4524) were subjected to a Group Factor Analysis (GFA) to identify latent variables that relate SMA to cortical thickness, sulcal depth, and gray matter volume. Subject scores from these latent variables were used in generalized linear mixed-effect models to investigate associations between SMA and internalizing and externalizing psychopathology, as well as fluid and crystalized intelligence. Four SMA-related GFAs explained 37% of the variance between SMA and structural brain indices. SMA-related GFAs correlated with brain areas that support homologous functions. Some but not all SMA-related factors corresponded with higher externalizing (Cohen's d effect size (ES) 0.06–0.1) but not internalizing psychopathology and lower crystalized (ES: 0.08–0.1) and fluid intelligence (ES: 0.04–0.09). Taken together, these findings support the notion of SMA related maturational coupling or structural correlation networks in the brain and provides evidence that individual differences of these networks have mixed consequences for psychopathology and cognitive performance.

Introduction

Brain structure undergoes remarkable changes in the second decade of life (Pfefferbaum et al., 2016), characterized by a reduction of gray matter and an increase in white matter (Giedd et al., 2015), with enduring impacts on cognition (Walhovd et al., 2016). Specifically, coordinated cortical thinning (Ducharme et al., 2016) is governed by evolutionary novelty and functional specialization (Sotiras et al., 2017), showing regional and temporal specificity with development (Houston et al., 2014). Evidence from several recent studies is consistent with the hypothesis that changes of brain structure are correlated across areas with similar function that recapitulate functional networks (Geng et al., 2017), which has been termed maturational coupling or structural correlation networks (SCNs), and has been proposed as a putative region-specific biomarker for developmental psychopathology (Saggar et al., 2015). Thus, brain regions that change together, i.e. increase or decrease in volume at the same rate over the course of years in the same individual, show structural covariance (Vandekar et al., 2015) or anatomical connectivity across individuals, reflecting synchronized developmental change in distributed cortical regions (Alexander-Bloch et al., 2013). For example, developmental changes in maturational coupling within the default-mode network (DMN) align with developmental changes in structural and functional DMN connectivity (Khundrakpam et al., 2017). These structural changes can also be affected by environmental characteristics, such as childhood abuse (Gold et al., 2016) or urban upbringing (Besteher et al., 2017), and have direct implications for brain functions such as general cognitive ability (Vuoksimaa et al., 2016), behavioral inhibition (Sylvester et al., 2016), and subjective ratings of empathy (Bernhardt et al., 2014). Finally, these maturational differences seem to be triggered by regional variation of gene expression having a direct impact on cortical thickness (Fjell et al., 2015). Together, structural brain changes are a consequence of a coordinated process that reflects an interaction between environment and genes that impact specific neural functions.

Screen media activity (SMA) (e.g., watching television or videos, playing video games, or using social media) is among the most common recreational activity in children and adolescents (Kenney and Gortmaker, 2017; Loprinzi and Davis, 2016). As many as 99% of adolescents use the internet, approximately 85% engage in electronic gaming (Rikkers et al., 2016), and nearly 97% of US youth have at least one electronic item in their bedroom (Hale and Guan, 2015). Relatively few studies have examined the relationship between SMA and brain structure or function. In one study with 18 year-old college students, individuals with internet gaming addiction showed less gray matter volume in bilateral anterior cingulate cortex, precuneus, supplementary motor area, superior parietal cortex, left dorsal lateral prefrontal cortex, left insula, and bilateral cerebellum (Wang et al., 2015) than matched controls. Among young adult female habitual internet users, more gray matter volume of bilateral putamen and right nucleus accumbens and lower gray matter volume of orbitofrontal cortex were associated with more frequent use (Altbacker et al., 2016). Functional neuroimaging studies have provided some evidence that those with internet addiction fail to recruit frontal-basal pathways that are important in inhibiting unwanted actions (Li et al., 2014). However, there are no large studies in youth in general, and prepubescent adolescents in particular, focused on SMA and structural or functional brain characteristics.

There is some controversy about whether excessive SMA is associated with problematic outcomes among youth and adolescents. Whereas some have reported that frequent SMA is associated with internalizing psychopathology including depression (Goldfield et al., 2016) and anxiety (Holfeld and Sukhawathanakul, 2017), externalizing psychopathology (Cerniglia et al., 2016), greater risk behaviors (Fischer et al., 2011), and even suicide (Twenge et al., 2017), others have not found evidence for an association between SMA and problematic outcomes (Ferguson, 2015, (Ferguson, 2017)). Even fewer studies have examined the relationship between different types of SMA and brain structure in healthy youth. In a recent cross-sectional study of healthy children ages 8–12, time spent reading was positively correlated with higher functional connectivity between the Brodmann Area 37 and left-sided language, visual, and cognitive control regions, but screen time was related to lower connectivity between the left visual word form area and regions related to language and cognitive control (Horowitz-Kraus and Hutton, 2017). However, this study had a small number of participants. The goal of this investigation was to determine whether SMA is related to specific SCN, i.e. whether exposure to SMA correlates to specific brain areas across cortical thickness, volume, and sulcal depth. Moreover, the second goal was to determine whether such SCN can be related to individual differences in psychopathology and cognition. Based on the maturational coupling hypothesis (Alexander-Bloch et al., 2013; Raznahan et al., 2011), i.e. coordinated patterns of structural change related to specific behaviors, we hypothesized that those individuals engaged in significant SMA relative to those with less SMA exposure would show greater maturity in sensorimotor areas, i.e. lower cortical thickness associated with SMA in primary sensory and motor areas. Moreover, based on the emerging findings of disorganized SCNs in psychiatric populations (Wang et al., 2016; Xia et al., 2018), we hypothesized that SMA-related SCNs related to mismatch between sensorimotor and executive and value-based processing areas are associated with psychopathology or cognitive performance.

Section snippets

Materials and methods

The Adolescent Brain and Cognitive Development Study (ABCD) is a multi-site, longitudinal neuroimaging study following 9-10 year-old youth through adolescence. The ABCD study team employed a rigorous epidemiologically informed school-based recruitment strategy, designed with consideration of the demographic composition of the 21 ABCD sites and the US as a whole (Volkow et al., 2017). The total sample size for the ABCD Study is projected to be 11,500; the first data release (February 2018)

Demographics and sample characteristics

Table 1 shows general demographics of the sample by quartiles of youth-reported total SMA. First, there was no difference in average age across the quartiles of screen time. Second, males were more frequently in the higher quartiles. Third, youth in the higher quartile had a higher BMI. Fourth, Black and Hispanic youth reported significantly more screen time use than White and Asian youth. Fifth, parents of youth in the higher quartiles were slightly younger, less well educated, were less

Discussion

This investigation applied a multivariate exploratory approach to the first data release of the ABCD study (1) to parse the relationship between SMA and structural brain indices (i.e., cortical thickness, sulcal depth, and gray matter volume) and (2) to evaluate its impact on psychopathology and overall cognitive functioning. First, the Group Factor Analysis extracted four SMA-related factors that integrated across cortical thickness, sulcal depth, and gray matter volume. Second, these factors

Conflicts of interest

The authors have no conflicts of interest to declare.

Acknowledgements

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041025, U01DA041028, U01DA041048,

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