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Journal Article

Citation

Genc S, Anderson V, Ryan NP, Malpas CB, Catroppa C, Beauchamp MH, Silk TJ. J. Neurotrauma 2016; 34(4): 798-806.

Affiliation

Murdoch Childrens Research Institute, Developmental Imaging , Royal Childrens Hospital, Flemington Road, Parkville , Melbourne, Victoria, Australia , 3052 ; tim.silk@mcri.edu.au.

Copyright

(Copyright © 2016, Mary Ann Liebert Publishers)

DOI

10.1089/neu.2016.4584

PMID

27468807

Abstract

Previous studies in paediatric traumatic brain injury (TBI) have been variable in describing the effects of injury severity on white matter development. The present study used diffusion tensor imaging to investigate prospective sub-acute and longitudinal relationships between early clinical indicators of injury severity, diffusion metrics, and neuropsychological outcomes. Paediatric TBI patients underwent MR imaging (n = 78, M = 10.56, SD = 2.21 years of age) at the sub-acute stage following injury (M = 5.55, SD = 3.05 weeks), and typically developing children were also included and imaged (n = 30, M = 10.60, SD = 2.88 years of age). A subset of the TBI patients (n = 15) were followed up with MRI two years post-injury. Diffusion MRI images were acquired at sub-acute, and 2-year follow-up time points, and analysed using Tract-Based Spatial Statistics (TBSS). At the sub-acute stage, mean diffusivity (MD) and axial diffusivity (AxD) were significantly increased in the TBI group compared with matched controls (p <.05). TBI severity significantly predicted diffusion profiles at the sub-acute and 2-years post-injury MRI. Patients with more severe TBI also exhibited poorer information processing speed 6-months post-injury, which in turn correlated with their diffusion metrics. These findings highlight that the severity of the injury not only has an impact on white matter microstructure, it also impacts its recovery over time. Moreover, findings suggest that sub-acute microstructural changes may represent a useful prognostic marker to identify children at elevated risk for longer term deficits.


Language: en

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