Original contributionSubject-specific changes in brain white matter on diffusion tensor imaging after sports-related concussion☆,☆☆
Introduction
Mild traumatic brain injury (mild TBI, also known as concussion) affects over 1.7 million Americans annually [1]. Approximately 300,000 concussions per year are due to sports or recreational activities [2]. A single concussion can result in a variety of adverse sequelae such as postconcussive symptoms and cognitive dysfunction [3]. Multiple concussions have been linked to chronic traumatic encephalopathy [4]. Although axonal injury is thought to be the anatomical abnormality that underlies these problems [5], there is currently no way to detect this in vivo and thus no way to treat it.
Diffusion tensor imaging (DTI) can potentially be used to diagnose axonal injury acutely. After mild TBI, fractional anisotropy (FA) decreases [6], [7], although several studies have documented areas of increased FA during the acute and subacute postinjury periods [8], [9]. While differing time intervals after injury and DTI methods have been cited to explain this discrepancy, the variation in injury forces and mechanisms is also a likely cause. The result is marked heterogeneity in the distribution of axonal injury from subject to subject.
Interindividual variation can be minimized by comparing the postinjury scan to the same person preinjury rather than to an unrelated control. Although this is not possible in most injury situations such as car accidents, athletes participating in contact sports with a high risk of concussion are uniquely suited to this purpose. However, direct comparison of a preinjury scan to a postinjury scan in a single individual in order to identify subject-specific changes would still not be possible using the current approaches to DTI analysis.
The three most commonly used analytical approaches — region of interest (ROI)-based analysis, tract-based spatial statistics and the whole-brain voxel-based [10] analysis — use traditional statistics to compare average DTI values (in an ROI or in a voxel location) in the TBI group to those in the control group. While they can robustly detect injuries in regions that are commonly vulnerable to concussions for most subjects, these group comparison-based approaches are not sensitive to injuries that only happen to one individual or a few individuals and, therefore, are unable to detect subject-dependent injury patterns after mild TBI.
None of these techniques permits a meaningful comparison of a single voxel value in one DTI scan (e.g., in a subject postinjury) to that in another scan (e.g., in the same subject preinjury or in an unrelated control). In other words, a standard statistical approach — which requires a comparison of two distributions of values — does not allow one to determine if a single index value in one voxel is statistically different from the value in the same voxel at a different point in time.
Wild bootstrapping (WB) is a novel statistical approach that has the potential to overcome this problem [11], [12]. Bootstrapping estimates the uncertainty of a given statistic (such as the FA value in a particular voxel) by generating a large number of samples from a small number of repeated measurements of the original data with replacements [13]. The precision level in the data can then be accurately estimated, and the true distribution of the population can be approximated. However, conventional bootstrapping requires at least five measurements to achieve reliable approximation of the true distribution of the population [14]. This would necessitate five or more separate consecutive DTI scans for each patient, which is prohibitive in most clinical DTI acquisitions. WB takes bootstrapping a step further and exploits the fact that the diffusion tensor is derived from linear regressions along six or more (60 in our data) noncollinear directions [11], [12]. The residuals from these linear regressions are used to generate a large number of bootstrap samples of the diffusion tensor, without the need for multiple DTI scans. Thus, an empirical distribution of the FA measurement is generated at each voxel location within the DTI data set of each time point (Supplementary Figure A) to allow a voxel-wise statistical comparison of FA values in a particular voxel location among longitudinal DTI data sets of the same subject.
In the current study, we sought to investigate the ability of WB analysis to detect subject-specific changes in DTI indices in a cohort of high school athletes before and after sports-related concussion. A secondary objective was to investigate the relationship between subconcussive head blows (SHBs) and subject-specific changes in white matter (WM) diffusion properties.
Section snippets
Methods
We performed a prospective cohort study of 10 high school athletes engaged in ice hockey (n=4) and football (n=6) and 5 controls. At the beginning of the 2006/2007 sports season, all subjects underwent DTI scanning, cognitive testing and concussion symptom checklist. Subjects were instructed in the use of a self-report diary to record the number of blows to the head during the season. Subjects were observed by the team athletic trainer during games and by coaches during practices. Subjects were
Results
Ten athletes and five nonathlete controls were initially recruited. One athlete quit the team during the first week of the season but agreed to continue participating in the study. This athlete was recategorized as a sixth control (Control 6), leaving nine athletes, as shown in Table 1. Only Athlete 9 suffered a concussion. The remaining athletes recorded between 26 and 399 SHBs during the sports season. The DTI scans of Athletes 7 and 8 were not analyzable due to noncorrectable image
Discussion
Using the WB analysis, we detected significant WM changes in the right corona radiata and right inferior longitudinal fasciculus of a single concussed athlete. Other researchers have also found WM abnormalities on DTI in these areas after mild TBI [20]. Damage in these areas could underlie this athlete's poor cognitive performance. The inferior longitudinal fasciculus is involved in semantics of language and verbal memory [21], [22]. The concussed athlete's performance on verbal memory was
Conclusion
WB analysis detected significantly changed WM in a single concussed athlete. Athletes with multiple SHB had significant changes in a percentage of their WM that was over three times higher than controls. If validated in larger cohorts, these results would have broad implications for the many youth and young adults who participate in contact sports such as football and hockey. Efforts to understand the significance of these WM changes and the relationship between these changes and head impact
References (33)
- et al.
Voxel-based morphometry — the methods
Neuroimage
(2000) - et al.
An optimized wild bootstrap method for evaluation of measurement uncertainties of DTI-derived parameters in human brain
Neuroimage
(2008) - et al.
The effect of filter size on VBM analyses of DT-MRI data
Neuroimage
(2005) - et al.
Distinct MRI pattern in lesional and perilesional area after traumatic brain injury in rat — 11 months follow-up
Exp Neurol
(2009) - et al.
Traumatic brain injury in the United States: emergency department visits, hospitalizations and deaths 2002–2006
(2010) - et al.
The epidemiology of sports-related traumatic brain injuries in the United States: recent developments
J Head Trauma Rehab
(1998) - et al.
Long-term neurologic outcomes after traumatic brain injury
J Head Trauma Rehab
(2009) - et al.
Chronic traumatic encephalopathy in a National Football League player
Neurosurgery
(2005) - et al.
Update of neuropathology and neurological recovery after traumatic brain injury
J Head Trauma Rehab
(2005) - et al.
Diffusion tensor MR imaging in diffuse axonal injury
AJNR Am J Neuroradiol
(2002)
Diffuse axonal injury in mild traumatic brain injury: a diffusion tensor imaging study
J Neurosurg
Diffusion tensor imaging detects clinically important axonal damage after mild traumatic brain injury: a pilot study
J Neurotrauma
Diffusion tensor imaging of acute mild traumatic brain injury in adolescents
Neurology
Using the wild bootstrap to quantify uncertainty in diffusion tensor imaging
Hum Brain Mapp
An introduction to the bootstrap
Magn Reson Med
Cited by (0)
- ☆
Support for this study was provided by National Institutes of Health grant 1R01HD051865 (Bazarian) and by a grant from the University of Rochester Health Sciences Center For Computational Innovation (Zhu).
- ☆☆
These data were presented in April 2010 at the 62nd Annual Meeting of the American Academy of Neurology in Toronto, Canada.