Detecting axonal injury in individual patients after traumatic brain injury.
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Published version
Author(s)
Type
Journal Article
Abstract
Poor outcomes after traumatic brain injury (TBI) are common yet remain difficult to predict. Diffuse axonal injury is important for outcomes, but its assessment remains limited in the clinical setting. Currently, axonal injury is diagnosed based on clinical presentation, visible damage to the white matter or via surrogate markers of axonal injury such as microbleeds. These do not accurately quantify axonal injury leading to misdiagnosis in a proportion of patients. Diffusion tensor imaging provides a quantitative measure of axonal injury in vivo, with fractional anisotropy often used as a proxy for white matter damage. Diffusion imaging has been widely used in TBI but is not routinely applied clinically. This is in part because robust analysis methods to diagnose axonal injury at the individual level have not yet been developed. Here, we present a pipeline for diffusion imaging analysis designed to accurately assess the presence of axonal injury in large white matter tracts in individuals. Average fractional anisotropy is calculated from tracts selected on the basis of high test-retest reliability, good anatomical coverage and their association to cognitive and clinical impairments after TBI. We test our pipeline for common methodological issues such as the impact of varying control sample sizes, focal lesions and age-related changes to demonstrate high specificity, sensitivity and test-retest reliability. We assess 92 patients with moderate-severe TBI in the chronic phase (≥6 months post-injury), 25 patients in the subacute phase (10 days to 6 weeks post-injury) with 6-month follow-up and a large control cohort (n = 103). Evidence of axonal injury is identified in 52% of chronic and 28% of subacute patients. Those classified with axonal injury had significantly poorer cognitive and functional outcomes than those without, a difference not seen for focal lesions or microbleeds. Almost a third of patients with unremarkable standard MRIs had evidence of axonal injury, whilst 40% of patients with visible microbleeds had no diffusion evidence of axonal injury. More diffusion abnormality was seen with greater time since injury, across individuals at various chronic injury times and within individuals between subacute and 6-month scans. We provide evidence that this pipeline can be used to diagnose axonal injury in individual patients at subacute and chronic time points, and that diffusion MRI provides a sensitive and complementary measure when compared to susceptibility weighted imaging, which measures diffuse vascular injury. Guidelines for the implementation of this pipeline in a clinical setting are discussed.
Date Issued
2021-01
Date Acceptance
2020-08-17
Citation
Brain: a journal of neurology, 2021, 144 (1), pp.92-113
ISSN
0006-8950
Publisher
Oxford University Press (OUP)
Start Page
92
End Page
113
Journal / Book Title
Brain: a journal of neurology
Volume
144
Issue
1
Copyright Statement
© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse,distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Sponsor
Guarantors of Brain
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
The Royal British Legion
National Institute for Health Research
UK DRI Ltd
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/33257929
PII: 6012956
Grant Number
N/A
RDA03
RDC04 79560
RDA03_79560
RDC04
Centre for Blast Injury Studie
NIHR-RP-011-048
'CR & T IMP'
Subjects
diagnostic pipeline
diffuse axonal injury
diffusion tensor imaging
traumatic brain injury
Publication Status
Published online
Coverage Spatial
England
Date Publish Online
2020-11-30