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

Neurology. 2021 Jul 19;97(3):e298-e309. doi: 10.1212/WNL.0000000000012222.

Abstract

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

Methods: Data from 10 previously enrolled, existing cohorts recruited from local hospitals and clinics were shared with the Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Pediatric Moderate/Severe TBI (msTBI) working group. We conducted a coordinated analysis of diffusion MRI (dMRI) data using the ENIGMA dMRI processing pipeline.

Results: Five hundred seven children and adolescents (244 with complicated msTBI and 263 controls) were included. Patients were clustered into 3 postinjury intervals: acute/subacute, <2 months; postacute, 2 to 6 months; and chronic, ≥6 months. Outcomes were dMRI metrics and postinjury behavioral problems as indexed by the Child Behavior Checklist. Our analyses revealed altered WM diffusion metrics across multiple tracts and all postinjury intervals (effect sizes range 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 after injury. We observed a sex-by-group interaction: female patients 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.