Longitudinal white matter changes after traumatic axonal injury.
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ABSTRACT:
SUBMITTER: Perez AM
PROVIDER: S-EPMC4144365 | biostudies-literature | 2014 Sep
REPOSITORIES: biostudies-literature
Publications
Longitudinal white matter changes after traumatic axonal injury.
Perez Alison M AM Adler Justin J Kulkarni Nimay N Strain Jeremy F JF Womack Kyle B KB Diaz-Arrastia Ramon R Marquez de la Plata Carlos D CD
Journal of neurotrauma 20140721 17
Diffusion tensor imaging (DTI) has been useful in showing compromise after traumatic axonal injury (TAI) at the chronic stage; however, white matter (WM) compromise from acute stage of TAI to chronic stage is not yet well understood. This study aims to examine changes in WM integrity following TAI by obtaining DTI, on average, 1 d post injury and again approximately seven months post-injury. Sixteen patients with complicated mild to severe brain injuries consistent with TAI were recruited in the ...[more]
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Project description:ObjectivesTo identify structural connectivity change occurring during the first 6 months after traumatic brain injury and to evaluate the utility of diffusion tensor tractography for predicting long-term outcome.MethodsThe participants were 28 patients with mild to severe traumatic axonal injury and 20 age- and sex-matched healthy control subjects. Neuroimaging was obtained 0-9 days postinjury for acute scans and 6-14 months postinjury for chronic scans. Long-term outcome was evaluated on the day of the chronic scan. Twenty-eight fiber regions of 9 major white matter structures were reconstructed, and reliable tractography measurements were determined and used.ResultsAlthough most (23 of 28) patients had severe brain injury, their long-term outcome ranged from good recovery (16 patients) to moderately (5 patients) and severely disabled (7 patients). In concordance with the diverse outcome, the white matter change in patients was heterogeneous, ranging from improved structural connectivity, through no change, to deteriorated connectivity. At the group level, all 9 fiber tracts deteriorated significantly with 7 (corpus callosum, cingulum, angular bundle, cerebral peduncular fibers, uncinate fasciculus, and inferior longitudinal and fronto-occipital fasciculi) showing structural damage acutely and 2 (fornix body and left arcuate fasciculus) chronically. Importantly, the amount of change in tractography measurements correlated with patients' long-term outcome. Acute tractography measurements were able to predict patients' learning and memory performance; chronic measurements also determined performance on processing speed and executive function.ConclusionsDiffusion tensor tractography is a valuable tool for identifying structural connectivity changes occurring between the acute and chronic stages of traumatic brain injury and for predicting patients' long-term outcome.
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Project description:In the largest sample studied to date, white matter microstructural trajectories and their relation to persistent symptoms were examined after pediatric mild traumatic brain injury (mTBI). This prospective, longitudinal cohort study recruited children aged 8-16.99 years with mTBI or mild orthopedic injury (OI) from five pediatric emergency departments. Children's pre-injury and 1-month post-injury symptom ratings were used to classify mTBI with or without persistent symptoms. Children completed diffusion-weighted imaging at post-acute (2-33 days post-injury) and chronic (3 or 6 months via random assignment) post-injury assessments. Mean diffusivity (MD) and fractional anisotropy (FA) were derived for 18 white matter tracts in 560 children (362 mTBI/198 OI), 407 with longitudinal data. Superior longitudinal fasciculus FA was higher in mTBI without persistent symptoms relative to OI, d (95% confidence interval) = 0.31 to 0.37 (0.02, 0.68), across time. In younger children, MD of the anterior thalamic radiations was higher in mTBI with persistent symptoms relative to both mTBI without persistent symptoms, 1.43 (0.59, 2.27), and OI, 1.94 (1.07, 2.81). MD of the arcuate fasciculus, -0.58 (-1.04, -0.11), and superior longitudinal fasciculus, -0.49 (-0.90, -0.09) was lower in mTBI without persistent symptoms relative to OI at 6 months post-injury. White matter microstructural changes suggesting neuroinflammation and axonal swelling occurred chronically and continued 6 months post injury in children with mTBI, especially in younger children with persistent symptoms, relative to OI. White matter microstructure appears more organized in children without persistent symptoms, consistent with their better clinical outcomes.
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Project description:The purpose of this study was to analyze quantitative diffusion tensor imaging measures across the spectrum of traumatic brain injury severity and evaluate their trajectories in military service members. Participants were 96 U.S. military service members and veterans who had sustained a mild traumatic brain injury [including complicated mild traumatic brain injury (n = 16) and uncomplicated mild traumatic brain injury (n = 68)], moderate-severe traumatic brain injury (n = 12), and controls (with or without orthopaedic injury, n = 39). All participants had been scanned at least twice, with some receiving up to five scans. Both whole brain voxel-wise analysis and tract-of-interest analysis were applied to assess the group differences of diffusion tensor imaging metrics, and their trajectories between time points of scans and days since injury. Linear mixed modelling was applied to evaluate cross-sectional and longitudinal diffusion tensor imaging metrics changes within and between groups using both tract-of-interest and voxel-wise analyses. Participants with moderate to severe traumatic brain injury had larger white matter disruption both in superficial subcortical and deep white matter, mainly over the anterior part of cerebrum, than those with mild traumatic brain injury, both complicated and uncomplicated, and there was no evidence of recovery over the period of follow-ups in moderate-severe traumatic brain injury, but deterioration was possible. Participants with mild traumatic brain injury had white matter microstructural changes, mainly in deep central white matter over the posterior part of cerebrum, with more spatial involvement in complicated mild traumatic brain injury than in uncomplicated mild traumatic brain injury and possible brain repair through neuroplasticity, e.g. astrocytosis with glial processes and glial scaring. Our results did not replicate 'V-shaped' trajectories in diffusion tensor imaging metrics, which were revealed in a previous study assessing the sub-acute stage of brain injury in service members and veterans following military combat concussion. In addition, non-traumatic brain injury controls, though not demonstrating any evidence of sustaining a traumatic brain injury, might have transient white matter changes with recovery afterward. Our results suggest that white matter integrity following a remote traumatic brain injury may change as a result of different underlying mechanisms at the microstructural level, which can have a significant consequence on the long-term well beings of service members and veterans. In conclusion, longitudinal diffusion tensor imaging improves our understanding of the mechanisms of white matter microstructural changes across the spectrum of traumatic brain injury severity. The quantitative metrics can be useful as guidelines in monitoring the long-term recovery.
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Project description:New techniques for individualized assessment of white matter integrity are needed to detect traumatic axonal injury (TAI) and predict outcomes in critically ill patients with acute severe traumatic brain injury (TBI). Diffusion MRI tractography has the potential to quantify white matter microstructure in vivo and has been used to characterize tract-specific changes following TBI. However, tractography is not routinely used in the clinical setting to assess the extent of TAI, in part because focal lesions reduce the robustness of automated methods. Here, we propose a pipeline that combines automated tractography reconstructions of 40 white matter tracts with multivariate analysis of along-tract diffusion metrics to assess the presence of TAI in individual patients with acute severe TBI. We used the Mahalanobis distance to identify abnormal white matter tracts in each of 18 patients with acute severe TBI as compared to 33 healthy subjects. In all patients for which a FreeSurfer anatomical segmentation could be obtained (17 of 18 patients), including 13 with focal lesions, the automated pipeline successfully reconstructed a mean of 37.5 ± 2.1 white matter tracts without the need for manual intervention. A mean of 2.5 ± 2.1 tracts resulted in partial or failed reconstructions and needed to be reinitialized upon visual inspection. The pipeline detected at least one abnormal tract in all patients (mean: 9.1 ± 7.9) and accurately discriminated between patients and controls (AUC: 0.91). The number and neuroanatomic location of abnormal tracts varied across patients and levels of consciousness. The premotor, temporal, and parietal sections of the corpus callosum were the most commonly damaged tracts (in 10, 9, and 8 patients, respectively), consistent with prior histopathological studies of TAI. TAI measures were not associated with concurrent behavioral measures of consciousness. In summary, we provide proof-of-principle evidence that an automated tractography pipeline has translational potential to detect and quantify TAI in individual patients with acute severe TBI.
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Project description:Neuroimaging biomarkers that can detect white matter (WM) pathology after mild traumatic brain injury (mTBI) and predict long-term outcome are needed to improve care and develop therapies. We used diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to investigate WM microstructure cross-sectionally and longitudinally after mTBI and correlate these with neuropsychological performance. Cross-sectionally, early decreases of fractional anisotropy and increases of mean diffusivity corresponded to WM regions with elevated free water fraction on NODDI. This elevated free water was more extensive in the patient subgroup reporting more early postconcussive symptoms. The longer-term longitudinal WM changes consisted of declining neurite density on NODDI, suggesting axonal degeneration from diffuse axonal injury for which NODDI is more sensitive than DTI. Therefore, NODDI is a more sensitive and specific biomarker than DTI for WM microstructural changes due to mTBI that merits further study for mTBI diagnosis, prognosis, and treatment monitoring.
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Project description:Finite element models (FEMs) are used increasingly in the traumatic brain injury (TBI) field to provide an estimation of tissue responses and predict the probability of sustaining TBI after a biomechanical event. However, FEM studies have mainly focused on predicting the absence/presence of TBI rather than estimating the location of injury. In this study, we created a multi-scale FEM of the pig brain with embedded axonal tracts to estimate the sites of acute (≤6 h) traumatic axonal injury (TAI) after rapid head rotation. We examined three finite element (FE)-derived metrics related to the axonal bundle deformation and three FE-derived metrics based on brain tissue deformation for prediction of acute TAI location. Rapid head rotations were performed in pigs, and TAI neuropathological maps were generated and colocalized to the FEM. The distributions of the FEM-derived brain/axonal deformations spatially correlate with the locations of acute TAI. For each of the six metric candidates, we examined a matrix of different injury thresholds (thx) and distance to actual TAI sites (ds) to maximize the average of two optimization criteria. Three axonal deformation-related TAI candidates predicted the sites of acute TAI within 2.5 mm, but no brain tissue metric succeeded. The optimal range of thresholds for maximum axonal strain, maximum axonal strain rate, and maximum product of axonal strain and strain rate were 0.08-0.14, 40-90, and 2.0-7.5 s-1, respectively. The upper bounds of these thresholds resulted in higher true-positive prediction rate. In summary, this study confirmed the hypothesis that the large axonal-bundle deformations occur on/close to the areas that sustained TAI.
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Project description:It has been estimated that 10%-20% of U.S. veterans of the wars in Iraq and Afghanistan experienced mild traumatic brain injury (TBI), mostly secondary to blast exposure. Diffusion tensor imaging (DTI) may detect subtle white matter changes in both the acute and chronic stages of mild TBI and thus has the potential to detect white matter damage in patients with TBI. The authors used DTI to examine white matter integrity in a relatively large group of veterans with a history of mild TBI.DTI images from 72 veterans of the wars in Iraq and Afghanistan who had mild TBI were compared with DTI images from 21 veterans with no exposure to TBI during deployment. Conventional voxel-based analysis as well as a method of identifying spatially heterogeneous areas of decreased fractional anisotropy ("potholes") were used. Veterans also underwent psychiatric and neuropsychological assessments.Voxel-based analysis did not reveal differences in DTI parameters between the veterans with mild TBI and those with no TBI. However, the veterans with mild TBI had a significantly higher number of potholes than those without TBI. The difference in the number of potholes was not influenced by age, time since trauma, a history of mild TBI unrelated to deployment, or coexisting psychopathology. The number of potholes was correlated with the severity of TBI and with performance in executive functioning tasks.Veterans who had blast-related mild TBI showed evidence of multifocal white matter abnormalities that were associated with severity of the injury and with relevant functional measures. Overall, white matter potholes may constitute a sensitive biomarker of axonal injury that can be identified in mild TBI at acute and chronic stages of its clinical course.
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Project description:White matter disruption is an important determinant of cognitive impairment after brain injury, but conventional neuroimaging underestimates its extent. In contrast, diffusion tensor imaging provides a validated and sensitive way of identifying the impact of axonal injury. The relationship between cognitive impairment after traumatic brain injury and white matter damage is likely to be complex. We applied a flexible technique-tract-based spatial statistics-to explore whether damage to specific white matter tracts is associated with particular patterns of cognitive impairment. The commonly affected domains of memory, executive function and information processing speed were investigated in 28 patients in the post-acute/chronic phase following traumatic brain injury and in 26 age-matched controls. Analysis of fractional anisotropy and diffusivity maps revealed widespread differences in white matter integrity between the groups. Patients showed large areas of reduced fractional anisotropy, as well as increased mean and axial diffusivities, compared with controls, despite the small amounts of cortical and white matter damage visible on standard imaging. A stratified analysis based on the presence or absence of microbleeds (a marker of diffuse axonal injury) revealed diffusion tensor imaging to be more sensitive than gradient-echo imaging to white matter damage. The location of white matter abnormality predicted cognitive function to some extent. The structure of the fornices was correlated with associative learning and memory across both patient and control groups, whilst the structure of frontal lobe connections showed relationships with executive function that differed in the two groups. These results highlight the complexity of the relationships between white matter structure and cognition. Although widespread and, sometimes, chronic abnormalities of white matter are identifiable following traumatic brain injury, the impact of these changes on cognitive function is likely to depend on damage to key pathways that link nodes in the distributed brain networks supporting high-level cognitive functions.
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