White Matter Disruption in Pediatric Traumatic Brain Injury: Results from ENIGMA Pediatric Moderate to Severe Traumatic Brain Injury

Neurology. 2021 May 28;97(3):e298-e309. doi: 10.1212/WNL.0000000000012222. Online ahead of print.

Abstract

Objective: Our study addressed aims: (1) test the hypothesis that moderate-severe TBI in pediatric patients is associated with widespread white matter (WM) disruption; (2) test the hypothesis that age and sex impact WM organization after injury; and (3) examine associations between WM organization and neurobehavioral outcomes.

Methods: Data from ten previously enrolled, existing cohorts recruited from local hospitals and clinics were shared with the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Pediatric msTBI working group. We conducted a coordinated analysis of diffusion MRI (dMRI) data using the ENIGMA dMRI processing pipeline.

Results: Five hundred and seven children and adolescents (244 with complicated mild to severe TBI [msTBI] and 263 controls) were included. Patients were clustered into three post-injury intervals: acute/subacute - <2 months, post-acute - 2-6 months, chronic - 6+ months. Outcomes were dMRI metrics and post-injury behavioral problems as indexed by the Child Behavior Checklist (CBCL). Our analyses revealed altered WM diffusion metrics across multiple tracts and all post-injury intervals (effect sizes ranging between d=-0.5 to -1.3). Injury severity is a significant contributor to the extent of WM alterations but explained less variance in dMRI measures with increasing time post-injury. We observed a sex-by-group interaction: females with TBI had significantly lower fractional anisotropy in the uncinate fasciculus than controls (𝞫=0.043), which coincided with more parent-reported behavioral problems (𝞫=-0.0027).

Conclusions: WM disruption after msTBI is widespread, persistent, and influenced by demographic and clinical variables. Future work will test techniques for harmonizing neurocognitive data, enabling more advanced analyses to identify symptom clusters and clinically-meaningful patient subtypes.