Associations Between Screen-Based Media Use and Brain White Matter Integrity in Preschool-Aged Children | Neurology | JAMA Pediatrics | JAMA Network
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Figure 1.  Scatterplots of ScreenQ Survey Total Scores vs Cognitive Assessment Scores
Scatterplots of ScreenQ Survey Total Scores vs Cognitive Assessment Scores

Solid blue line represents least squares fit; dashed blue lines, 95% CI bounds of slope; dashed orange lines, 95% prediction interval for new observations. CTOPP-2 indicates Comprehensive Test of Phonological Processing, Second Edition, Rapid Object Naming subtest; EVT, Expressive Vocabulary Test, Second Edition; GRTR, Get Ready to Read! assessment.

Figure 2.  Diffusion Tensor Imaging (DTI) Parameter Maps for Whole-Brain Analysis
Diffusion Tensor Imaging (DTI) Parameter Maps for Whole-Brain Analysis

White matter voxels exhibit a statistically significant correlation between screen-based media use (ScreenQ scores) and lower fractional anisotropy (FA; A) as well as higher radial diffusivity (RD; B) in whole-brain analysis, controlling for child age and household income level (P < .05, familywise error–corrected). Color indicates the slope or magnitude of correlation (ie, change in the DTI parameter for every point increase in ScreenQ score).

Figure 3.  Tract-Based Spatial Statistical Analysis in the Left Hemisphere
Tract-Based Spatial Statistical Analysis in the Left Hemisphere

The tract-based spatial statistical analysis involves these 3 language- and literacy-associated tracts in the left hemisphere: arcuate fasciculus (white), inferior longitudinal fasciculus (tan), uncinate fasciculus (brown). A, Blue represents statistically significantly lower fractional anisotropy (FA) with higher ScreenQ scores. B, Red represents statistically significantly higher radial diffusivity (RD) with higher ScreenQ scores. Both analyses controlled for child age and household income level (P < .05, familywise error–corrected).

Table 1.  Participant Demographics
Participant Demographics
Table 2.  Extent of Tracts With Statistically Significant Correlations Between ScreenQ Scores and Diffusion Tensor Imaging Parameters in Whole-Brain Analysis
Extent of Tracts With Statistically Significant Correlations Between ScreenQ Scores and Diffusion Tensor Imaging Parameters in Whole-Brain Analysis
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    7 Comments for this article
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    Was prenatal alcohol exposure excluded in these healthy children?
    barry stanley, MBChB, FRCS(C) | N/A
    There is nothing to indicate that pae was excluded, or accounted for, in the above Abstract. Could the authors say how such an omission might impact their conclusions and recommendations?
    Barry Stanley
    CONFLICT OF INTEREST: None Reported
    ScreenQ details
    Eric Osika, MD | Pediatric Department, Hopital Saint Camille, Bry Sur Marne France
    ScreenQ is a screen-based media use assessment that could be useful for the studies of other colleagues. Where could we find the details of that score? For example, how to reach a score of 26 with 15 items?
    CONFLICT OF INTEREST: None Reported
    What type of screens were the kids exposed too?
    Amir Borenstein, Electronic Practical Eng | www.norad4u.com
    Hello and congratulate on the study.
    I would like to ask if you have some data on the type of screens the kids were exposed to?
    Were they smart phones, wireless tablets, wireless laptops, wired computers, wireless smart TV or other?
    The reason I am asking is that there might be a link to the exposure of these kids to Radio Frequency radiation from the screens (if wireless). Past studies showed that RF radiation can cause changes and damage to the myelin layer over the nerves.
    https://www.ncbi.nlm.nih.gov/pubmed/25205214
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5247706/
    Thanks in advance.
    Amirb
    CONFLICT OF INTEREST: I develop and sell EMF measurement and protection products.
    Control
    Sarah Wheeler |
    What was controlled for here other than SES and she? Couldn't this association be more about the third factors that would exist in a home where a child was in front of a TV all day? Poverty? Parental neglect? Trauma?
    CONFLICT OF INTEREST: None Reported
    Technology Safety Training for Clinicians and Patients
    Cecelia Doucette, Master Tech Writing | The international non-profit Wireless Education; Understanding EMFs; EMF Conference
    After spending years doing fundraising to bring technology into our public schools in Ashland, MA for what we were hearing was needed for the 21st Century Classroom, I discovered all wireless technology came to market with no safety testing, and transmits our data via two-way microwave radiation signals.

    The peer-reviewed, published scientific literature indicates this non-ionizing, low-level, non-thermal radiation is very toxic at levels well below what the FCC continues to allow.

    Thousands of studies link today's wireless radiation exposures to our skyrocketing rates of infertility, Alzheimer's, autism, genotoxicity, neurotoxicity, child and adult cancers, insomnia, headaches,
    nausea, nosebleeds, irregular heartbeats, skin abnormalities, behavior issues, cognitive impairment, memory deficits, learning disabilities, pain, anger, anxiety, depression, suicidal ideation and more.

    After investigating, I helped our school district become the first in the nation to begin taking precautionary measures until public policy catches up to the science.

    I have also educated legislators, and helped Massachusetts lead the nation with 20 bills to address man-made radiation. NH and OR have just passed laws to investigate health and environmental impacts, especially in light of 5G which puts cell antennas outside our homes.

    I was honored to be asked to help plan the recent EMF Conference to train health care providers to recognize, diagnose, treat and prevent illnesses from wireless technology. The impact on brain development was widely discussed.

    Dr. Victoria Dunkley, author of Reset Your Child's Brain, lectured on Excessive Screen Syndrome (ESS) and the four-week protocol she has developed for digital detox.

    Other experts from around the world and U.S. flew in to educate on other detrimental impacts wireless technology is having on our health.

    The EMF Conference (electromagnetic fields of radiation) offered 16 CMEs and the conference videos are expected to be available this fall. Please see https://emfconference.com/.

    To quickly understand the scope of this issue, please consider taking a course from the non-profit Wireless Education. The Schools & Families Course and the Corporate Induction Safety Awareness Course can each be completed in about a half hour on-line. There is a nominal fee to help keep the non-profit afloat.

    You will learn the science, risks, what other countries are already doing to protect their public, and medically recommended best practices for safe technology use. A printable tip sheet is available at the end with handy reminders. https://www.wirelesseducation.org/store/l2/

    You may also wish to consider watching the award-winning film Generation Zapped. You will hear directly from leading scientists and doctors, several of whom presented at the EMF Conference in September. https://generationzapped.com/#gen-trailer.

    Dr. Delaney Ruston has also produced two documentaries being shown in middle and high schools today. She investigates the social, emotional and addiction impact of today's technology on teenagers and offers stress resilience strategies. Please see https://www.screenagersmovie.com/.

    Please share these resources widely with loved ones, colleagues, patients, schools and your own public servants as this information is not typically covered in mainstream media and most are in the dark. Fox25 in Boston did a JAMA follow-on story last night: https://www.boston25news.com/news/plugged-in-too-much-screen-time-could-be-harming-children-s-brains/1005520155.

    Thank you for your time and consideration.
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Blue light or RF? Flicker or Magnetic Field?
    Samantha Vilppu, BS ARCE (engineering) | EMF Consultant and Course Creator
    As a couple of the other comments on this page have pointed out, the current technology that was used in this study needs to be clearly described, since the factors within the construction of such devices has been changing quite rapidly over the past several years. Television screens from 20 years ago are not the same as TV screens that are currently available, and so it helps to zero in on the actual factors of causality by being more specific. The factors that current technology has can include RF (radio frequency/microwave) wireless radiation, as well as excessive blue frequencies from the visible spectrum, and also flicker. There is also the electric and magnetic field that one is exposed to when in close proximity to the device which can also affect our biology negatively. If these factors are not controlled for, then it becomes impossible to separate out which of (or if all) these factors are actually causing these developmental defects to occur. I believe all of these to be an issue, but rather than blanketing them all together in the study, it is important to differentiate.
    CONFLICT OF INTEREST: None Reported
    READ MORE
    ScreenQ survey
    Melissa Workman | West Virginia University Child Development Laboratory School
    This article is very interesting to my early childhood colleagues. Where can we find the ScreenQ tool? We would like to use this survey with our Pre-K and primary school parents.
    CONFLICT OF INTEREST: None Reported
    Original Investigation
    November 4, 2019

    Associations Between Screen-Based Media Use and Brain White Matter Integrity in Preschool-Aged Children

    Author Affiliations
    • 1Division of General and Community Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
    • 2Reading and Literacy Discovery Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
    • 3Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio
    • 4Educational Neuroimaging Center, Biomedical Engineering, Technion, Israel
    • 5Medpace Inc, Cincinnati, Ohio
    JAMA Pediatr. 2020;174(1):e193869. doi:10.1001/jamapediatrics.2019.3869
    Key Points

    Question  Is screen-based media use associated with differences in the structural integrity of brain white matter tracts that support language and literacy skills in preschool-aged children?

    Findings  In this cross-sectional study of 47 healthy prekindergarten children, screen use greater than that recommended by the American Academy of Pediatrics guidelines was associated with (1) lower measures of microstructural organization and myelination of brain white matter tracts that support language and emergent literacy skills and (2) corresponding cognitive assessments.

    Meaning  These findings suggest a need for further study into the association between screen-based media use and the developing brain, particularly during early childhood.

    Abstract

    Importance  The American Academy of Pediatrics (AAP) recommends limits on screen-based media use, citing its cognitive-behavioral risks. Screen use by young children is prevalent and increasing, although its implications for brain development are unknown.

    Objective  To explore the associations between screen-based media use and integrity of brain white matter tracts supporting language and literacy skills in preschool-aged children.

    Design, Setting, and Participants  This cross-sectional study of healthy children aged 3 to 5 years (n = 47) was conducted from August 2017 to November 2018. Participants were recruited at a US children’s hospital and community primary care clinics.

    Exposures  Children completed cognitive testing followed by diffusion tensor imaging (DTI), and their parent completed a ScreenQ survey.

    Main Outcomes and Measures  ScreenQ is a 15-item measure of screen-based media use reflecting the domains in the AAP recommendations: access to screens, frequency of use, content viewed, and coviewing. Higher scores reflect greater use. ScreenQ scores were applied as the independent variable in 3 multiple linear regression models, with scores in 3 standardized assessments as the dependent variable, controlling for child age and household income: Comprehensive Test of Phonological Processing, Second Edition (CTOPP-2; Rapid Object Naming subtest); Expressive Vocabulary Test, Second Edition (EVT-2; expressive language); and Get Ready to Read! (GRTR; emergent literacy skills). The DTI measures included fractional anisotropy (FA) and radial diffusivity (RD), which estimated microstructural organization and myelination of white matter tracts. ScreenQ was applied as a factor associated with FA and RD in whole-brain regression analyses, which were then narrowed to 3 left-sided tracts supporting language and emergent literacy abilities.

    Results  Of the 69 children recruited, 47 (among whom 27 [57%] were girls, and the mean [SD] age was 54.3 [7.5] months) completed DTI. Mean (SD; range) ScreenQ score was 8.6 (4.8; 1-19) points. Mean (SD; range) CTOPP-2 score was 9.4 (3.3; 2-15) points, EVT-2 score was 113.1 (16.6; 88-144) points, and GRTR score was 19.0 (5.9; 5-25) points. ScreenQ scores were negatively correlated with EVT-2 (F2,43 = 5.14; R2 = 0.19; P < .01), CTOPP-2 (F2,35 = 6.64; R2 = 0.28; P < .01), and GRTR (F2,44 = 17.08; R2 = 0.44; P < .01) scores, controlling for child age. Higher ScreenQ scores were correlated with lower FA and higher RD in tracts involved with language, executive function, and emergent literacy abilities (P < .05, familywise error–corrected), controlling for child age and household income.

    Conclusions and Relevance  This study found an association between increased screen-based media use, compared with the AAP guidelines, and lower microstructural integrity of brain white matter tracts supporting language and emergent literacy skills in prekindergarten children. The findings suggest further study is needed, particularly during the rapid early stages of brain development.

    Introduction

    In a single generation, through what has been described as a vast “uncontrolled experiment,”1 the landscape of childhood has been digitized, affecting how children play, learn, and form relationships. In addition to traditional programming, rapidly emerging technologies, particularly portable electronic devices, provide unprecedented access to a wide range of media.2,3 Use begins in infancy4 and increases with age, and it was recently estimated at more than 2 hours per day in children younger than 9 years, aside from use during childcare and school.3 Accompanying this rise are variables with potential risks and benefits, including access to screens (eg, in bedrooms), frequency of use, content, and grownup-child interaction (eg, coviewing).5,6 The American Academy of Pediatrics (AAP) recommends limits on screen-based media, citing developmental and health risks with excessive and inopportune use.3 These risks include language delay7,8; poor sleep2,9; impaired executive function10 and general cognition11; and decreased parent-child engagement, including reading together.12-14 The World Health Organization recently released even more restrictive recommendations for children younger than 5 years, discouraging screen time and advocating greater study of its implications for health and development.15

    Recent evidence suggests that screen-based media use poses neurobiological risks in children,16-19 yet its associations with early brain development are largely unknown, particularly during the dynamic span of development before kindergarten.10 Although sensory networks mature relatively early,11 those sensory networks for higher-order skills, such as language,12 executive function,20,21 multimodal association,13 and reading,22,23 exhibit protracted development11,14 and are dependent on constructive stimulation in the home. Specifically, the organization and myelination of white matter tracts, which enhance the efficiency of signal conduction within and between these networks, are highly sensitive to environmental factors.21,24-26

    Diffusion tensor imaging (DTI) is a powerful means to quantify white matter integrity in the brain and its various factors.27 Parameters of DTI include fractional anisotropy (FA) and radial diffusivity (RD), scalar values associated with microstructural organization (eg, bundling, packing), and myelination of white matter tracts.28 The aim of this study was to use DTI to explore the association between composite screen-based media use in the context of the domains cited in the AAP recommendations5 (access, frequency, content, and coviewing) and the indexes of white matter integrity in preschool-aged children, particularly major tracts involved with language, executive functions, and emergent literacy (arcuate fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus).29-34 Given the evidence of risks associated with screen time,5,7 we administered assessments of expressive language, speed of processing, and emergent literacy skills to serve as cognitive-behavioral correlates. Our hypothesis was that higher use would be associated with lower integrity in these tracts (ie, lower FA and higher RD) and with lower scores on corresponding cognitive measures.

    Methods
    Participants

    A total of 69 parent-child dyads were recruited through advertisement at a children’s medical center and surrounding primary care clinics. Inclusion criteria were as follows: children aged 3 to 5 years, born at at least 36 weeks’ gestation, living in a household of native English speakers, without a history of neurodevelopmental disorder conferring risk of language delays, no previous or current kindergarten attendance, and no contraindications to magnetic resonance imaging (MRI), such as metal implants. Written informed consent was obtained from a custodial parent, and families were compensated for their time and travel expenses. This study was approved by the institutional review board of Cincinnati Children's Hospital Medical Center and was conducted from August 2017 to November 2018.

    Screen-Based Media Use Assessment

    Research coordinators administered the ScreenQ35 survey to a custodial parent in a private room before or during the child’s MRI, and responses were entered into a REDCap (Research Electronic Data Capture) database.36 ScreenQ is a recently developed, 15-item composite measure of screen-based media use of children aged 3 to 9 years, reflecting the AAP recommendations for this age range.5,6 The conceptual model for ScreenQ is derived from the 4 domains of the AAP recommendations: access to screens, frequency of use, content viewed, and interactivity or coviewing. A preliminary version of the ScreenQ was tested and psychometrically refined in a previous study.37 Psychometric analyses for the current version included modern-theory Rasch methods and were favorable, including a range of item difficulty, reliability, internal consistency (Cronbach α = .74), and criterion-related validity referenced to external standards of child cognitive skills and home cognitive environment (StimQ-P38).35 The ScreenQ scoring range was 0 to 26 points, with higher scores reflecting greater use than the AAP recommendations.

    Behavioral Measures and Analyses

    Three standardized assessments of language and emergent literacy abilities were administered to the children before their MRI: (1) Expressive Vocabulary Test, Second Edition (EVT-2), a norm-referenced measure for children aged 2.5 years or older39; (2) Comprehensive Test of Phonological Processing, Second Edition (CTOPP-2), a comprehensive, norm-referenced instrument for assessing phonological processing abilities as prerequisites to reading fluency,40,41 with a Rapid Object Naming subtest evaluating the retrieval of information from memory and execution of operations quickly (speed of processing) by young children who have not yet mastered letters, numbers, or colors; and (3) Get Ready to Read! (GRTR), a norm-referenced assessment of core emergent literacy skills for children 3 to 6 years of age that is a factor associated with reading outcomes.42 ScreenQ total scores were applied as variables in a series of multiple linear regression models, with CTOPP-2, EVT-2, and GRTR scores as the dependent variables. Child age and household income level were applied in each model as covariates, with a threshold of statistical significance of P < .05. We used SAS software, version 9.4 (SAS Institute) for behavioral analyses.

    Diffusion Tensor Imaging

    Details of play-based acclimatization techniques used with children before the MRI are described by Vannest et al.43 All children were awake and not sedated during the MRI. The MRI protocol involved a T1-weighted anatomical scan that lasted approximately 6 minutes, 4 BOLD (blood oxygen level–dependent) functional MRI sequences that lasted approximately 5 minutes each (resting state and 3 active tasks), and DTI that lasted approximately 8 minutes; each procedure was separated by approximately 2 minutes to allow the child to rest. Children were allowed to watch a video of their choice during DTI.

    The MRI was conducted using a scanner equipped with a 32-channel head coil (3 Tesla Achieva; Philips). For DTI, 61 diffusion-weighted (b-value = 2000 seconds/mm2) images were acquired with a spin echo-planar imaging sequence with the following parameters: repetition time/echo time = 5000/88 ms, acquisition matrix: 96 × 96, 66 slices, slice thickness = 2 mm, 2 × 2 mm in-plane resolution, in-plane parallel-reduction factor = 1.5, and multiband factor = 3. Data were visually assessed for quality issues (eg, pervasive motion or artifact, inadequate coverage) before processing and analyses conducted using the FMRIB (Functional MRI of the Brain) Software Library (FSL), version 5.0.11. Diffusion-weighted images were corrected for subject movement and other artifacts using the eddy function of the FSL.44 Mean (SD) framewise movement across children was 0.54 (0.29) mm, and mean (SD) frequency of outlier slices was 1.3% (1.1%). Slices identified as outliers were replaced with estimates by the gaussian process (eddy’s “–repol” option with default parameters, except single-slice and groupwise outlier detection for multiband acquisition).45 The eddy-corrected diffusion-weighted images and rotated b-vectors were applied to fit a standard diffusion tensor model at each voxel using the dtifit function of the FSL. Maps of FA and RD were calculated from the principal eigenvalues of the diffusion tensor using standard methods.46

    Tract-Based Spatial Statistics

    We performed 2 analyses using FSL’s tract-based spatial statistics47 pipeline to test for associations between DTI measures and ScreenQ scores: (1) whole-brain analysis and (2) tract-specific analysis. First, individual FA maps were nonlinearly warped into alignment with the FMRIB58_FA template, and the same warp fields were applied to the RD maps to bring them into alignment with standard space. For the whole-brain analysis, an analysis mask was created by skeletonizing the group mean FA map after thresholding at a value of 0.2. Second, a new analysis mask was created by intersecting the whole-brain mask with binarized probability maps48 of the left arcuate fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus, which are 3 tracts associated with language and literacy abilities assessed through the selected cognitive measures (language, emergent literacy, rapid naming).29-34

    Left-sided tracts were selected given well-described structural and functional lateralization supporting these abilities at this age.49-51 A general linear model approach was used to test for correlations between ScreenQ scores and each of the 2 DTI measures at each voxel, with ScreenQ scores, demeaned age, and demeaned household income level composing the design matrix. The randomize function52 of the FSL was used to perform 10 000 random permutations of this multiple regression analysis, and familywise error (FWE) rate was controlled with threshold-free cluster enhancement.53 Substantial clusters in the whole-brain analysis were cross-referenced to known white matter tracts using the Johns Hopkins University white matter tractography atlas,54 including commissural tracts.

    Results
    Demographics

    A total of 47 of 69 children (68%) completed DTI (20 [43%] boys and 27 [57%] girls; mean [SD] age, 54.3 [7.5] months). Median household income category was $50 001 to $100 000 per year, and 37 mothers (78%) were college graduates or had higher educational status, reflecting a sample with middle to high socioeconomic status.55 Participant demographics are summarized in Table 1.

    ScreenQ Survey and Cognitive Test Scores

    Mean (SD; range) ScreenQ score for children completing DTI was 8.6 (4.8; 1-19) points. Median (range) age for initiating screen use was 18 (0-36) months, and median (range) use was 1.5 (0-12) hours per day. Twenty-eight children (60%) had their own portable device, and 19 (41%) had a television or a portable device in their bedroom. Mean (SD; range) CTOPP-2 score was 9.4 (3.3; 2-15) points. Mean (SD; range) EVT-2 scaled score was 113.1 (16.6; 88-144) points, with 64% of scores in the mean range for age. Mean (SD; range) GRTR score was 19.0 (5.9; 5-25) points. Scores are summarized in Table 1.

    ScreenQ scores were negatively correlated with EVT-2 (F2,43 = 5.14; R2 = 0.19; P < .01), CTOPP-2 (F2,35 = 6.64; R2 = 0.28; P < .01), and GRTR (F2,44 = 17.08; R2 = 0.44; P < .01) scores, controlling for child age. These associations did not reach statistical significance when household income was included as a covariate, and this may be associated with the moderate collinearity between income level and ScreenQ scores (rρ = –0.54). Scatterplots of ScreenQ scores in comparison with EVT-2, CTOPP-2, and GRTR scores are shown in Figure 1.

    Diffusion Tensor Imaging

    Higher ScreenQ scores were statistically significantly correlated with lower FA and higher RD in extensive aspects of white matter tracts in the whole-brain analysis, controlling for child age and household income (P < .05, FWE-corrected). Figure 2 illustrates the extent and degree of these correlations, with parameter estimates from multiple regression analyses showing the adjusted change in FA or RD per unit increase in ScreenQ score. Correlations for FA (35% vs 28%) and RD (33% vs 21%) were generally more extensive in the left hemispheric tracts compared with their contralateral counterparts, as described in Table 2. Tracts with the most extensive associations were involved with language, visual processing or imagery, executive functions, and multimodal association. The arcuate fasciculus exhibited the greatest degree of left laterality (35% vs 10% of tract for FA; 44% vs 6% for RD), consistent with its well-described role in language.56

    In the hypothesis-driven analysis, higher ScreenQ scores were correlated with lower FA, covering 16% of the arcuate fasciculus, 29% of the inferior longitudinal fasciculus, and 55% of the uncinate fasciculus, controlling for child age and household income (P < .05, FWE-corrected). Higher ScreenQ scores were correlated with higher RD, spanning 20% of the arcuate fasciculus, 36% of the inferior longitudinal fasciculus, and 39% of the uncinate fasciculus, controlling for age and income (P < .05, FWE-corrected). These associations are shown in Figure 3.

    Discussion

    Unprecedented technological upheaval over the past few decades has largely outpaced the ability of scientists to characterize its fueling controversy and anxiety among parents, educators, and clinicians. To our knowledge, this study is the first to describe structural neurobiological correlates of screen-based media use in preschool-aged children. The findings are in the context of well-described measures of white matter microstructural integrity, FA and RD.57 Fractional anisotropy is most clearly associated with organization of white matter in parallel bundles, whereas RD is inversely associated with degree of myelination of such bundles57 as well as axonal packing and other microstructural processes. Increases in FA and decreases in RD are consistently observed across development in older children and in response to constructive environmental stimulation, such as language exposure.21,58-60 This study found higher FA and lower RD associated with more screen time in major fiber tracts supporting core language and executive and emergent literacy skills, raising questions as to whether at least some aspects of screen-based media use may provide suboptimal neurodevelopmental stimulation during early childhood.

    Structural markers for language performance involving FA and RD have been described in full-term and preterm children as young as 3 years.31,61 The arcuate fasciculus, which connects receptive (Wernicke) and expressive (Broca) language brain areas,29,30 is associated with language skills,31,62,63 including phonological processing and vocabulary.31,59,64 In this study, increased screen time was associated with lower FA in 16% and higher RD in 20% of the arcuate fasciculus and also with significantly lower EVT-2 and CTOPP-2 scores, which are concordant neurocognitive results. Similar associations were also seen involving the uncinate fasciculus and inferior longitudinal fasciculus, which support other aspects of language and literacy, such as semantic processing, emotional integration, and incorporation of visual imagery.30,31,33,65,66 That these associations were left-lateralized (Table 2) is consistent with reliance on language areas in the left hemisphere that increases with age.51 Lower FA and higher RD in other tracts (eg, superior longitudinal fasciculus, anterior thalamic radiation, and forceps minor) are also consistent with the reliance of language processing on a distributed, bilateral network that becomes more specialized with age.31,59,61 Although these findings are correlational in nature and do not identify causality or long-term developmental risks, they align with findings in previous studies describing the detrimental outcomes of screen time for language abilities in children,7,67 warranting further investigation.

    Rapid automatized naming is a critical language and literacy skill that supports orthographic-phonological translation (eg, nonword reading) and semantic processing (eg, deriving meaning from objects, symbols, or letters) and is highly indicative of reading outcomes.68-70 Lesion studies have described major deficits in word and object naming involving the uncinate fasciculus, particularly in the left hemisphere.33,34 The inferior fronto-occipital fasciculus also plays a major role in rapid automatized naming, with higher FA estimating better performance.71 Substantial decreases in FA in the uncinate fasciculus (55% of the left, and 35% of the right) and fronto-occipital fasciculus (44% of the extent of the left, and 34% of the right) in the context of lower CTOPP-2 scores are concordant neurocognitive findings associated with greater screen use. In addition, rapid automatized naming is intimately associated with broader executive functions, including speed of processing and working memory,72,73 which are supported by other tracts associated with greater use in the whole-brain analyses (superior longitudinal fasciculus, forceps minor, and anterior thalamic tracts).74,75 Although broader executive functions were not directly assessed in this study, the associations between higher screen use and lower FA and higher RD for each of these tracts are consistent with deleterious associations that have been previously described in young children.10,19,76

    The children in this study were not yet attending school and not yet reading independently, but we believe these findings are highly relevant within the construct of emergent literacy, an incremental process beginning in infancy that involves skills, knowledge, and attitudes required for reading and writing.22,77 Emergent literacy development has been associated with increases in FA and decreases in RD in the arcuate fasciculus,62 whereas lower FA in the arcuate fasciculus, inferior longitudinal fasciculus, and superior longitudinal fasciculus has been associated with lower prereading (particularly phonological) skills.49,78 Neural microstructure involving frontal, thalamic, parietal, and temporal areas (eg, through arcuate fasciculus or superior longitudinal fasciculus) has also been shown to be a factor in reading outcomes.79-81 Associations between lower FA and higher RD in the arcuate fasciculus, inferior longitudinal fasciculus, and other tracts with higher ScreenQ scores, in the context of negative correlation between ScreenQ and GRTR scores, are concordant findings related to this multifaceted skill set.

    Although establishing causation is beyond the scope of this study, a critical question is whether neurobiological differences are directly associated with properties of screen-based media itself or indirectly associated with differences in human interactive (eg, shared reading) time, which tends to decrease with greater use.12-14 Aspects of both associations seem likely, as suggested by a recent MRI study of children aged 8 to 12 years that found lower functional connectivity within the emerging reading network was correlated with higher screen time and higher functional connectivity was correlated with more reading time.18 Given the prevalence of “learning” applications marketed for young children and the emphasis on technology in early childhood educational curricula,3 multiyear studies of emergent literacy outcomes are warranted to determine long-term implications.

    Limitations and Strengths

    This study has several limitations. The sample largely comprised families with high socioeconomic status, yet household income was accounted for in the DTI analyses. Associations between ScreenQ survey and cognitive testing scores did not meet the threshold for statistical significance when income was included in the model, which may be associated with the moderate collinearity between income level and ScreenQ scores that negatively affected statistical power, particularly given the small sample size. Both behavioral and DTI findings may have been even more robust in families with higher socioeconomic status compared with families with lower socioeconomic status, for whom language, literacy, and other cognitive disparities are well documented.82,83

    Although exhibiting strong psychometric properties,35 ScreenQ is a new measure reliant on parent reporting and thus is subject to social desirability bias. However, no similar composite measure or direct assessment currently exists, and ScreenQ reflects domains in the current AAP recommendations.5 Although DTI is a powerful method for studying white matter in the brain, it is vulnerable to artifacts attributable to nonuniform directionality of fiber tracts, such as crossing fibers.28 However, the analyses involved well-defined tracts and a hypothesis-driven approach, and FA and RD findings were consistent with results in the current behavioral and neuroimaging literature and with each other. Diffusion-weighted images were acquired at the end of the MRI session, resulting in a lower success rate (68%) than from recent work,17 possibly conferring bias toward older children with higher attention skills. However, DTI is a task-free paradigm that is not affected by performance factors. Although important, the cross-sectional nature of this study precludes the discernment of causation or whether the results are attributable to screen time or an indirect effect of differences in reading, language, or other constructive experiences.3,28

    This study also has strengths. The sample of preschool-aged children is remarkable given the difficulty of successfully conducting MRI at this age. The DTI results survived stringent correction for motion and multiple comparisons, controlling for child age and household income, and were concordant with behavioral results controlling for child age. Rather than apply a single screen time variable, such as minutes per day or violent content as is often the case,84,85 ScreenQ incorporated domains of use into a coherent composite, providing a more holistic view of use.35 The analyses were driven by a hypothesis based on recent neuroimaging evidence and AAP guidelines, including characterization of associations for a broad range of tracts supporting relevant cognitive abilities and focused analyses with increased statistical power. The DTI findings aligned with those on validated cognitive measures, providing compelling insights into the implications for child development, preschool education, and further research.

    Although preliminary, this study fills a gap in evidence and provides a novel, neurobiological lens through which to view the recommendations of the AAP1,3 and World Health Organization15 in terms of readiness of brain networks to constructively process screen-based media and the marketing of such media to children. Given the results of this study, we can speculate that before age 5 years, when brain networks are developing rapidly,86,87 caution is warranted, noting the postulate attributed to Donald Hebb: “neurons that fire together, wire together.”42 We believe multiyear studies are needed to explore whether the associations observed in this study, related to language and literacy abilities, reflect longer-term risks and outcomes. Such studies are critical in the context of a child’s early cognitive ecosystem, in which unprecedented technological shifts may change how children live, connect, and learn.

    Conclusions

    In this study of 47 preschool-aged children, increased use of screen-based media in the context of the AAP guidelines was associated with lower microstructural integrity of brain white matter tracts that support language, executive functions, and emergent literacy skills, controlling for child age and household income. Screen use was also associated with lower scores on corresponding behavioral measures, controlling for age. Given that screen-based media use is ubiquitous and increasing in children in home, childcare, and school settings, these findings suggest the need for further study to identify the implications for the developing brain, particularly during stages of dynamic brain growth in early childhood.

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    Article Information

    Accepted for Publication: August 4, 2019.

    Published Online: November 4, 2019. doi:10.1001/jamapediatrics.2019.3869

    Correction: This article was corrected on March 23, 2020, to change the article to open access status.

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Hutton JS et al. JAMA Pediatrics.

    Corresponding Author: John S. Hutton, MS, MD, Division of General and Community Pediatrics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 15008, Cincinnati, OH 45229 (john1.hutton@cchmc.org).

    Author Contributions: Drs Hutton and Dudley had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Hutton, Horowitz-Kraus, DeWitt, Holland.

    Acquisition, analysis, or interpretation of data: Hutton, Dudley, Horowitz-Kraus, Holland.

    Drafting of the manuscript: Hutton, Dudley, Horowitz-Kraus, Holland.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Hutton, Dudley, Horowitz-Kraus.

    Obtained funding: Hutton.

    Supervision: Hutton, Horowitz-Kraus, DeWitt, Holland.

    Conflict of Interest Disclosures: Dr DeWitt reported serving as chair of the national Reach Out and Read Board of Directors, an organization that advocates and supports an early literacy program working with pediatric health care clinicians. No other disclosures were reported.

    Funding/Support: This study was funded by a Procter Scholar Award from the Cincinnati Children’s Research Foundation (Dr Hutton).

    Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: Research coordinators Arielle Wilson, BA, Cincinnati Children’s Hospital Medical Center, and Amy Kerr, MEd, Cincinnati Children's Hospital Reading and Literacy Discovery Center, oversaw the data collection, data entry, and quality control for this study, with no compensation outside of their normal salaries.

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