
Young children who spent more time in front of screens than recommended by the American Academy of Pediatrics (AAP) had lower white-matter integrity on brain MR images and reduced language and literacy skills compared with other children in a study published online November 4 in JAMA Pediatrics.
The researchers from Cincinnati Children's Hospital Medical Center in Ohio examined 47 children between the ages of 3 and 5 who completed cognitive tests and underwent diffusion-tensor MR imaging (DTI-MRI). They found that children who spent the most time in front of a screen tended to have the worst cognitive function and lowest white-matter integrity.
White matter showing reduced structural integrity (blue) on the DTI-MR images of a child exposed to significant screen time. Image courtesy of Cincinnati Children's Hospital Medical Center.To be specific, the researchers identified a negative correlation between screen time and scores on language processing, vocabulary, and literacy tests (p < 0.01). They also found that high screen time correlated with a reduction in fractional anisotropy and increased radial diffusivity (p < 0.05), markers indicating microstructural organization and myelination of white-matter tracts on DTI-MR images.
"This study raises questions as to whether at least some aspects of screen-based media use in early childhood may provide suboptimal stimulation during this rapid, formative state of brain development," lead author Dr. John Hutton said in a statement.
Though the study was not able to confirm that increased screen time was the sole cause of these structural changes, the findings do warrant further investigation, as providers, policymakers, and parents aim to set appropriate limits for screen-based media, he concluded.















![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)