Cerebral atrophy after traumatic white matter injury: Correlation with acute neuroimaging and outcome

Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas 75390-9036, USA.
Journal of Neurotrauma (Impact Factor: 3.97). 12/2008; 25(12):1433-40. DOI: 10.1089/neu.2008.0683
Source: PubMed

ABSTRACT Traumatic brain injury (TBI) is a pathologically heterogeneous disease, including injury to both neuronal cell bodies and axonal processes. Global atrophy of both gray and white matter is common after TBI. This study was designed to determine the relationship between neuroimaging markers of acute diffuse axonal injury (DAI) and cerebral atrophy months later. We performed high-resolution magnetic resonance imaging (MRI) at 3 Tesla (T) in 20 patients who suffered non-penetrating TBI, during the acute (within 1 month after the injury) and chronic stage (at least 6 months after the injury). Volume of abnormal fluid-attenuated inversion-recovery (FLAIR) signal seen in white matter in both acute and follow-up scans was quantified. White and gray matter volumes were also quantified. Functional outcome was measured using the Functional Status Examination (FSE) at the time of the chronic scan. Change in brain volumes, including whole brain volume (WBV), white matter volume (WMV), and gray matter volume (GMV), correlates significantly with acute DAI volume (r = -0.69, -0.59, -0.58, respectively; p <0.01 for all). Volume of acute FLAIR hyperintensities correlates with volume of decreased FLAIR signal in the follow-up scans (r = -0.86, p < 0.001). FSE performance correlates with acute hyperintensity volume and chronic cerebral atrophy (r = 0.53, p = 0.02; r = -0.45, p = 0.03, respectively). Acute axonal lesions measured by FLAIR imaging are strongly predictive of post-traumatic cerebral atrophy. Our findings suggest that axonal pathology measured as white matter lesions following TBI can be identified using MRI, and may be a useful measure for DAI-directed therapies.

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    • "After the acute injury secondary processes including complex metabolic cascades, alterations in cerebral blood flow and raised intracranial pressure may occur contributing to the burden of injury. It is well recognised that complex pathophysiological processes including secondary Wallerian-type degeneration continue to occur months to years after the initial insult (Meythaler et al., 2001; Ding et al., 2008; Warner et al., 2010a). In order to improve treatment stratification and patient outcomes, as well as more accurately predict outcome, we need to better understand the complexity and heterogeneity of TBI both in the acute and chronic stages. "
    Medical Image Analysis 01/2015; 21(1):40-58. · 3.68 Impact Factor
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    • "The brainstem is notoriously involved in mTBI (Tomlinson 1970, Ommaya and Gennarelli 1974, Gennarelli 1986) but changes in other areas of the brain are also found. Overall reduction in white matter volume has been reported in association with mTBI (Ding et al. 2008, Levine et al. 2008, Schonberger et al. 2009), as well as white matter volume reduction at the level of the mesencephalon (midbrain) and the corona radiata (Holli et al. 2010). The corpus callosum (CC) also seems to be microdamaged in mTBI: Gale and Johnson (2005) showed a decreased CC volume in 36 patients with mTBI, which correlated with neuropsychological tests of motor function. "
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    ABSTRACT: Finite element (FE) models are often used to study the biomechanical effects of traumatic brain injury (TBI). Measures based on mechanical responses, such as principal strain or invariants of the strain tensor, are used as a metric to predict the risk of injury. However, the reliability of inferences drawn from these models depends on the correspondence between the mechanical measures and injury data, as well as the establishment of accurate thresholds of tissue injury. In the current study, a validated anisotropic FE model of the human head is used to evaluate the hypothesis that strain in the direction of fibers (axonal strain) is a better predictor of TBI than maximum principal strain (MPS), anisotropic equivalent strain (AESM) and cumulative strain damage measure (CSDM). An analysis of head kinematics-based metrics, such as head injury criterion (HIC) and brain injury criterion (BrIC), is also provided. Logistic regression analysis is employed to compare binary injury data (concussion/no concussion) with continuous strain/kinematics data. The threshold corresponding to 50% of injury probability is determined for each parameter. The predictive power (area under the ROC curve, AUC) is calculated from receiver operating characteristic (ROC) curve analysis. The measure with the highest AUC is considered to be the best predictor of mTBI. Logistic regression shows a statistical correlation between all the mechanical predictors and injury data for different regions of the brain. Peaks of axonal strain have the highest AUC and determine a strain threshold of 0.07 for corpus callosum and 0.15 for the brainstem, in agreement with previously experimentally derived injury thresholds for reversible axonal injury. For a data set of mild TBI from the national football league, the strain in the axonal direction is found to be a better injury predictor than MPS, AESM, CSDM, BrIC and HIC.
    Stapp car crash journal 11/2014; 58(2014-02):29-62.
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    • "Additionally, Bergeson et al. (2004) reported a correlation between frontal and temporal lobe atrophy and deficits in memory and executive function in patients with a range of severity from mild, to severe (GCS; 3-14). Overall reduction in white matter has also been reported (e.g., Ding et al. 2008; Levine et al. 2008; Schonberger et al. 2009), as well as white matter reduction at the level of the mesencephalon, corona radiata, centrum semiovale (Holli et al. 2010a and b), and corpus callosum (Holli et al. 2010a and b; Warner et al. 2010a; Yount et al. 2002). Ding and coworkers noted that the changes in white and gray matter over time were correlated with acute diffuse axonal injuries and the latter predicted post-injury cerebral atrophy. "
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    ABSTRACT: Mild traumatic brain injury (mTBI), also referred to as concussion, remains a controversial diagnosis because the brain often appears quite normal on conventional computed tomography (CT) and magnetic resonance imaging (MRI) scans. Such conventional tools, however, do not adequately depict brain injury in mTBI because they are not sensitive to detecting diffuse axonal injuries (DAI), also described as traumatic axonal injuries (TAI), the major brain injuries in mTBI. Furthermore, for the 15 to 30 % of those diagnosed with mTBI on the basis of cognitive and clinical symptoms, i.e., the "miserable minority," the cognitive and physical symptoms do not resolve following the first 3 months post-injury. Instead, they persist, and in some cases lead to long-term disability. The explanation given for these chronic symptoms, i.e., postconcussive syndrome, particularly in cases where there is no discernible radiological evidence for brain injury, has led some to posit a psychogenic origin. Such attributions are made all the easier since both posttraumatic stress disorder (PTSD) and depression are frequently co-morbid with mTBI. The challenge is thus to use neuroimaging tools that are sensitive to DAI/TAI, such as diffusion tensor imaging (DTI), in order to detect brain injuries in mTBI. Of note here, recent advances in neuroimaging techniques, such as DTI, make it possible to characterize better extant brain abnormalities in mTBI. These advances may lead to the development of biomarkers of injury, as well as to staging of reorganization and reversal of white matter changes following injury, and to the ability to track and to characterize changes in brain injury over time. Such tools will likely be used in future research to evaluate treatment efficacy, given their enhanced sensitivity to alterations in the brain. In this article we review the incidence of mTBI and the importance of characterizing this patient population using objective radiological measures. Evidence is presented for detecting brain abnormalities in mTBI based on studies that use advanced neuroimaging techniques. Taken together, these findings suggest that more sensitive neuroimaging tools improve the detection of brain abnormalities (i.e., diagnosis) in mTBI. These tools will likely also provide important information relevant to outcome (prognosis), as well as play an important role in longitudinal studies that are needed to understand the dynamic nature of brain injury in mTBI. Additionally, summary tables of MRI and DTI findings are included. We believe that the enhanced sensitivity of newer and more advanced neuroimaging techniques for identifying areas of brain damage in mTBI will be important for documenting the biological basis of postconcussive symptoms, which are likely associated with subtle brain alterations, alterations that have heretofore gone undetected due to the lack of sensitivity of earlier neuroimaging techniques. Nonetheless, it is noteworthy to point out that detecting brain abnormalities in mTBI does not mean that other disorders of a more psychogenic origin are not co-morbid with mTBI and equally important to treat. They arguably are. The controversy of psychogenic versus physiogenic, however, is not productive because the psychogenic view does not carefully consider the limitations of conventional neuroimaging techniques in detecting subtle brain injuries in mTBI, and the physiogenic view does not carefully consider the fact that PTSD and depression, and other co-morbid conditions, may be present in those suffering from mTBI. Finally, we end with a discussion of future directions in research that will lead to the improved care of patients diagnosed with mTBI.
    Brain Imaging and Behavior 03/2012; 6(2). DOI:10.1007/s11682-012-9156-5 · 4.60 Impact Factor
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