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00001199-201007000-0000500001199_2010_25_267_kou_biomarkers_4article< 162_0_23_13 >Journal of Head Trauma Rehabilitation© 2010 Lippincott Williams & Wilkins, Inc.Volume 25(4)July/August 2010p 267–282The Role of Advanced MR Imaging Findings as Biomarkers of Traumatic Brain Injury[ARTICLE]Kou, Zhifeng PhD; Wu, Zhen MD, PhD; Tong, Karen A. MD; Holshouser, Barbara PhD; Benson, Randall R. MD; Hu, Jiani PhD; Mark Haacke, E. PhDSection Editor(s): Bazarian, Jeffrey J. MD, MPHDepartments of Biomedical Engineering and Radiology (Dr Kou), Department of Neurology, School of Medicine (Dr Benson), and MR Research Facility, Department of Radiology, School of Medicine (Drs Hu and Haacke), Wayne State University, Detroit, Michigan; School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada (Drs Wu and Haacke); and Department of Radiology, Loma Linda University Medical Center, Loma Linda, California (Drs Tong and Holshouser).Corresponding Author: E. Mark Haacke, PhD, MR Research Facility, Department of Radiology, School of Medicine, Wayne State University, 3990 John R St, Detroit, MI 48201 ( Mark Haacke has federal funding via NIH 62983 on susceptibility-weighted imaging and a contract with Siemens Medical Systems on susceptibility-weighted imaging. The authors thank Jie Yang, PhD, for assistance in editing; Ramtilak Gattu, MS, for DTI image processing; and Zahid Latif, RT, and Yang Xuan, BS, for data acquisition.AbstractTreatment of traumatic brain injury (TBI) requires proper classification of the pathophysiology. Clinical classifiers and conventional neuroimaging are limited in TBI detection, outcome prediction, and treatment guidance. Advanced magnetic resonance imaging (MRI) techniques such as susceptibility weighted imaging, diffusion tensor imaging, and magnetic resonance spectroscopic imaging are sensitive to microhemorrhages, white matter injury, and abnormal metabolic activities, respectively, in brain injury. In this article, we reviewed these 3 advanced MRI methods and their applications in TBI and report some new findings from our research. These MRI techniques have already demonstrated their potential to improve TBI detection and outcome prediction. As such, they have demonstrated the capacity of serving as a set of biomarkers to reveal the heterogeneous and complex nature of brain injury in a regional and temporal manner. Further longitudinal studies using advanced MRI in a synergistic approach are expected to provide insight in understanding TBI and imaging implications for treatment.TRAUMATIC BRAIN INJURY (TBI) is a leading cause of death and disability in young people and people at the most productive time of their lives.1 Each year, 1.4 million civilians in the United States sustain TBI, as reported by the Centers for Disease Control and Prevention (CDC).2 TBI has been referred to as “a silent epidemic.”3 Approximately 5.3 million Americans live with long-term disability as a result of TBI.4 The economic impact of TBI on the nation is also enormous, according to the CDC, with an estimated total direct and indirect cost of $60 billion per year in 2000.5Despite the severity and prevalence of TBI, our understanding of the problem is still limited, and there is no effective treatment for TBI. The current treatment and management plans for TBI are mostly symptom-based instead of curing fundamental brain injury. Previous clinical trials related to pharmaceutical treatment have consistently failed, despite promising findings in experimental animal models. Those animal models often addressed a single spectrum of brain pathology at certain locations, unlike most brain injuries, which are heterogeneous in nature. A recent National Institute for Neurological Disorder and Stroke workshop attributed the failure of many clinical trials to the use of Glasgow Coma Scale (GCS) score as a single classifier for patient inclusion. The panel proposed that neuroimaging should play a larger role to better classify TBI for targeted therapy by properly categorizing TBI patients into more homogeneous groups according to their pathoanatomical information.6 Before imaging could impact TBI treatment, our in-depth understanding of how imaging improves the detection of brain injury pathophysiology and the prediction of TBI patients' outcome would be the first step. Although computed tomography (CT) has been used to screen patients for large hemorrhages at the acute stage or other lesions that require urgent surgical intervention,7 CT assessment remains insensitive to many other primary and secondary injuries, some of which can be detected with conventional magnetic resonance imaging (MRI).8 However, for most moderate and even more severe diffuse axonal injuries, the neurological and neurobehavioral symptoms are not readily explained by CT or conventional MRI. In addition, most mild TBI patients (mTBI), which consists of more than 90% of the TBI population, have negative CT or conventional MRI findings.ADVANCED MRI FINDINGS AS BIOMARKERS OF BRAIN INJURYTBI involves a heterogeneous and complex spectrum of pathologies including hemorrhage, axonal shear injury, and ischemic/hypoxic injury, among others.9 No single imaging method, including MRI, is able to capture the whole complex phenomena of TBI. The past 3 decades have witnessed tremendous advances in MRI methodology and its applications in medicine. Today, advanced MRI methods are uniquely suited to detect and localize many of the pathologic and pathophysiologic alterations resulting from TBI. These advanced methods include susceptibility-weighted imaging (SWI) for hemorrhage detection,10 diffusion-weighted imaging and diffusion tensor imaging (DWI/DTI) for edema quantification11 and axonal injury identification,12 magnetic resonance spectroscopy (MRS) for metabolite measurement,13 perfusion-weighted imaging to measure abnormal cerebral blood supply and perfusion after injury,14 functional MRI for detection of altered cortical activation patterns in performing certain cognitive demanding tasks after injury,15,16 and functional connectivity MRI to measure phase coherence of spontaneous blood oxygen level–dependent (BOLD) fluctuations between functionally related regions after injury at resting state.17–20 In this article, we will focus on the first 3 techniques through their basic principles and clinical applications in TBI.Susceptibility-weighted imagingAlthough CT is important for initial assessment of large hemorrhages that may require surgical intervention, it is insensitive to small hemorrhages, whether from early contusion or from diffuse axonal injury (DAI). Some investigators have suggested that the presence of hemorrhage in DAI is predictive of poor outcome.21 SWI22 has been used to evaluate TBI patients since 2003 by Tong et al.10 It is a high-resolution, fully velocity compensated, 3-dimensional gradient echo imaging sequence that is extremely sensitive to blood products in hemorrhage and deoxyhemoglobin in venous blood. Haacke et al have given a detailed technical description of SWI,23 and Kou et al have given a systematic review on the role of SWI in brain trauma.24 Studies by Tong et al have shown that SWI is more sensitive than conventional gradient echo imaging (GRE) in detecting suspected hemorrhagic lesions in children.10,25 SWI has been shown not only to detect tiny hemorrhages that may be the only abnormal finding but also to document the presence of brain injury, which may change management of the patient. In addition, lesion number and volume identified by SWI is negatively associated with patients' outcome25 and neuropsychological functions.26,27 Using SWI will allow clinicians to detect intracranial microhemorrhages in an unparalleled manner that to date has been accomplished only in autopsy examination, thereby directing appropriate management and therapeutic interventions.SWI lesion locationsConventional CT and MRI studies have reported that traumatic hemorrhagic lesions in humans are found in cortical gray matter (GM); subcortical white matter (WM); major WM tracts, including corpus callosum and internal capsule; brainstem; and in the ventricles.28 In addition to these “traditional” locations reported using conventional imaging techniques, SWI has been shown to visualize microhemorrhagic lesions at the boundary of GM/WM as well as at the junction of branching vessels, especially the veins.24 See Figure 1 for lesion locations found with SWI in a severe TBI case.Figure 1. Typical hemorrhagic lesions identified by susceptibility-weighted imaging (SWI) in an example of a severe traumatic brain injury case. (Imaging order: from left to right columns are T2 (A), fluid-attenuated inversion recovery (FLAIR) (B), and SWI (C) images; from top to bottom, rows are 3 different slice locations for a single individual. Each column represents 1 sequence; each row represents 1 slice location of the brain across different sequences. Images at the same column belong to same sequence, and images at the same row belong to same slice of the brain.) A 27-year-old male, injured in a motor vehicle crash had a Glasgow Coma Scale score of 6 and had posttraumatic amnesia for 3 weeks. Initial computed tomography (not shown) demonstrated bilateral subarachnoid hemorrhage and petechial hemorrhage in the frontal regions. Magnetic resonance imaging was performed 10 months after injury. In the upper images (A1, B1, C1), T2 showed hemorrhagic lesions along the falx (white arrow head), gray matter (GM) (dashed white arrow), and gray-white matter (GM/WM) junctions (solid white arrow); SWI identified the same type of hemorrhagic lesions, but to a much larger extent (C1); whereas FLAIR showed associated hyperintense edema (white arrow) in frontoparietal WM. In the middle slices (A2, B2 C2), T2 identified hemorrhagic lesions at the genu and splenium of the corpus callosum (CC) (white arrowheads) as well as petechial hemorrhages in the GM; in addition to genu and splenium of CC. SWI also detected hemorrhages in the body of CC (arrowhead), perivascular hemorrhage along the course of transmedullary veins (solid arrow), and subarachnoid hemorrhage on both sides of the brain (dashed arrows); FLAIR showed associated hyperintense edema in the body of the CC (white arrowhead). In the lowest slices (A3, B3 C3), SWI showed linear hemorrhages in the frontal periventricular WM (solid arrow) in addition to bifrontal petechial GM hemorrhages (dashed arrows) that are not shown on T2 or FLAIR. All MRI sequences demonstrated mildly enlarged ventricles and sulci of the brain, suggesting the presence of brain atrophy 10 months after injury.SWI versus conventional GRETong et al were the first to compare the ability of SWI to detect hemorrhage with conventional GRE; they retrospectively studied 7 children and adolescents with TBI and presumed DAI admitted to an intensive care unit.10 MRI (1.5T) was taken within 11 days of injury. They reported a 3-to 6-fold increase in lesions detected by SWI and a 2-fold increase in the total apparent volume of hemorrhagic DAI lesions in SWI images compared with that in GRE images. The difference in lesion count was greatest in the brainstem/cerebellum, corpus callosum, and less so in frontal and parietal-temporal-occipital GM/WM.SWI detection of traumatic subarachnoid hemorrhageA recent study by Wu et al evaluated 20 acute TBI patients with subarachnoid hemorrhage (SAH) using CT and SWI.29 Two neuroradiologists analyzed the CT and SWI data to decide whether there was SAH in 8 anatomical parts of the subarachnoid space. Fifty-five areas with SAH were identified by both CT and SWI. Ten areas were identified by CT only, and 13 by SWI only. SAH was recognized on SWI by its very dark signal surrounded by cerebrospinal fluid signal in the sulci or cisterns. Compared with the smooth appearance of veins, SAH tended to have a rough margins and inhomogeneous signal intensity. In many instances, blood in the sulcus left an area of signal loss that had a “triangle” shape. The authors concluded that SWI is better than CT in detecting intraventricular hemorrhage and small amounts of SAH but not good at detecting basilar cistern SAH. Overall, SWI has the potential to provide complementary information to CT in imaging SAH. See Figure 2 for a comparison of CT and SWI in their ability to demonstrate sulcal SAH and intraventricular hemorrhage.Figure 2. (A) A computed tomography (CT) image shows curvilinear hyperdensity in the sulci consistent with subarachnoid hemorrhage (SAH). (B) Susceptibility-weighted imaging (SWI) phase image showing the same areas of abnormality (arrows) and other areas of sulcal and parenchymal hemorrhage. The broadened hypointensity within the sulci may represent blood in the sulci or iron uptake in the adjacent parenchyma after traumatic subarachnoid hemorrhage.83 A second case with intraventricular bleeding with a CT image (C) and SWI image (D). CT (C) does not show intraventricular hemorrhage, but only 2 calcified choroid plexus. SWI (D) shows a very small amount of hemorrhage in both posterior horns of the lateral ventricle (arrows). Because CT has thicker slices (4.5 mm) than SWI (2.0 mm), and the patient's head is tilted at different angle, slice position on the 2 imaging modality is not the same. We have viewed a few neighbor slices on CT and did not find intraventricular hemorrhage. The CT image we put in this figure is the slice that covers most part of the posterior lateral ventricle. In the third case, both CT (E) and SWI (F) show hemorrhage in the prepontine cistern (arrows), whereas in the fourth case, CT (G) shows high density in the pontine cistern (white arrow). SWI (H) does not show any hemorrhage signal in the corresponding region.SWI versus patient outcomeTong et al evaluated 40 children and adolescents with TBI and DAI (mean age 12 years; MRI obtained 7 ± 4 days after injury).25 The number and volume of hemorrhagic lesions were correlated with long-term neurologic outcome indicated by the Pediatric Cerebral Performance Category Scale score. Children with lower GCS scores (≤8, n = 30) or prolonged coma (>4 days, n = 20) had significantly greater average number and volume of hemorrhagic lesions. Children with normal outcomes or mild neurologic disability (n = 30) at 6 to 12 months after injury had significantly fewer number and volume of hemorrhagic DAI lesions than those who were moderately or severely disabled or in a vegetative state. The authors also determined that there were regional differences in DAI injury. By dividing the brain into 9 regions, they reported that more than 90% of patients had lesions in frontal WM, parietal-temporal-occipital WM, and parieto-temporal-occipital GM. Four regions were less commonly affected (ie, <65% of patients): thalami, brainstem, cerebellum, and basal ganglia. Only patients with involvement of 7 or more regions had poor outcomes. Fourteen patients had lesions in 6 or fewer regions, and all had good outcomes at 6 to 12 months.In another study of 38 adult TBI patients who were scanned at an average of 5.6 days after trauma,30 the same group of researchers reported that SWI was the most sensitive modality for smaller lesions. This sensitivity allowed SWI to detect a larger number of lesions and to define smaller areas of damage. SWI detected a much greater number of lesions per patient in both good and poor outcomes than did CT, T2-weighted imaging, or fluid-attenuated inversion recovery (FLAIR) imaging. SWI was less consistent in discriminating outcome in this population sample, compared with T2 and FLAIR, possibly from the sheer sensitivity of SWI and the limitations of a dichotomized outcome criteria. Although the detection of more lesions may not mean a better prediction in this study, it does give clinicians a better idea of the injury extent and injury locations.Initial attempt to classify TBI using SWIWu made the first attempt to develop a SWI classification scheme to predict moderate to severe TBI patients' outcome measured by Glasgow Outcome Scale (GOS).31 SWI data were collected in 63 moderate to severe TBI patients with conventional MRI data between 1 to 30 days after injury, along with CT within a week of injury. In this classification scheme, brain injuries were identified as 2 general types: focal (hematoma and contusion) and DAI lesions. DAI lesions were further categorized into 3 levels (mild, moderate, and severe) and focal lesions into 2 levels (large and small). The brain was divided into 15 regions and lesion characteristics in each region were recorded. An SWI imaging score was calculated by adding up the number of regions affected, weighted by different coefficient (0.5, 1, or 2), depending on the level of lesions. The SWI imaging scores were then correlated with GCS and GOS scores. The results showed that 21 cases had only diffuse lesions, 24 cases had only focal lesions, 11 had both types of injuries, and 7 cases were normal. For the 24 cases that only had focal lesions, these patients all had a good outcome. For the 32 cases that had diffuse lesions, 17 had good outcomes and 15 had unfavorable outcomes. All 15 cases that had unfavorable outcome had brainstem lesions. The scatter plot between SWI imaging scores and long-term outcome (6-month GOS score) is shown in Figure 3. This plot reveals that there was no overlap in SWI scores between the group of patients that had good outcome and the group of patients that did not have good outcome. In particular, an SWI score of 8 could be used as a threshold value to predict whether the patients will have a favorable or unfavorable outcome, whereas the initial or discharge GCS score failed to distinguish outcomes. The extent of hemorrhages on SWI can serve as a biomarker of injury, with practical implications for clinical management.Figure 3. Correlation of susceptibility-weighted imaging (SWI) score with outcome (6-month Glasgow Outcome Scale [GOS] score). There is no overlap of the SWI score between the favorable outcomes and unfavorable outcomes (horizontal line). The points above the threshold of 8 represent all 15 of the poor outcome patients. The interpolation line shows the trend that the lower the SWI score, the better of the outcome. (The black dots represent number of patients at each point with larger dots representing more patients.)Diffusion tensor imagingDiffusion imaging sequences are sensitive to traumatic axonal injury secondary to stretch and shear forces. DTI measures the bulk motion of water molecular diffusion in brain tissue. It is sensitive to the spatial orientation of diffusion of water, such as in long axons. Histological data also validated the usefulness of DTI characterization of brain injury pathology by using either a focal injury model32 or a DAI model.33 When axons are injured, as in acceleration/deceleration injuries, which commonly occur in motor vehicle crash accidents, normal anisotropy decreases because of restricted axoplasmic flow and possibly increased flow across the axonal membrane. Two scalars derived from DTI have been applied to TBI: apparent diffusion coefficient (ADC) and fractional anisotropy (FA).34,35 ADC is an estimate of the average magnitude of water movement in a voxel, whereas FA describes the spatial inhomogeneity of water diffusion within a voxel. ADC increases with vasogenic edema, when water flows out of capillaries into the interstitial space, and decreases with cytotoxic edema, when diffusion is restricted by ischemic swollen cells. FA has been widely used as a composite index of water diffusivity changes in each voxel at any directions after tissue damage. FA in WM is highest when fibers are long (relative to voxel dimension) and uniformly oriented within a voxel and lowest when fibers are not collinear (eg, crossing fibers) or have been damaged. In general, many pathologies of WM injury will result in FA decrease, including edema, impaired axonal transportation, and axonal disruption. ADC, FA, and DTI directional diffusivities could be used together to characterize brain injury pathologies. Changed FA in association with ADC changes could differentiate the type of edema: decreased FA in association with increased ADC means vasogenic edema, whereas increased FA in association with decreased ADC in acute stage means cytotoxic edema. Decreased FA in association with decreased longitudinal water diffusivity could mean an impaired axonal transportation. Biomechanical forces such as stretch and shear force exceeding the elastic limit for an axon will result in a change in the morphology of the axon over hours to months, and characterized by swelling and shortening of fibers and eventual fiber loss (Wallerian degeneration). It should be noted that, microscopically, one typically observes damaged, distorted fibers interspersed among normal appearing fibers; this intermixing of normal and abnormal axons would also be represented in a single voxel. Thus, the greater the proportion of damaged distorted axons in a voxel, the lower the FA for the voxel. In the more chronic stage, fiber disruption occurs resulting in lower fiber density (causing decreased FA) and atrophy of WM structures on anatomical imaging.Global approach to WM injury detectionThe underlying hypothesis of this approach is that axonal injury is diffuse, or more precisely multifocal, throughout the WM of the brain. This hypothesis is based on a large number of pathological studies of postmortem brains and a large amount of data from animal models of TBI.36–41 These studies demonstrate axonal stretch and shear injuries in a number of commissural, supratentorial, and infratentorial WM fasciculi with more extensive involvement with increasing biomechanical forces. This hypothesis was supported by our study of a heterogeneous group of 21 nonpenetrating TBI patients and 14 approximately age-matched healthy controls. The patients varied considerably in severity of injury (GCS score 3–15), age (11–57), and interval to scan (3 days–15 years). A histogram analysis of the WM in the whole brain was used, showing that TBI was associated with a global decrease in FA as well as a change in the shape of the distribution (peak, skew, and leptokurtosis) for TBI patients12 (Fig 4).Figure 4. Fractional anisotropy (FA) histograms by subject. Gray curves are healthy subjects, and black curves are traumatic brain injury (TBI) subjects. Note the low variance for the controls (FA mean 0.43 and standard deviation 0.013) and the greater variance for TBI subjects (FA mean 0.38 and standard deviation 0.025). Also, note the higher peaks and the leftward shifted curves for TBI subjects (see text). The histogram kurtosis, skewedness, mean FA, and peak value all reached statistical significance (P < .05) in group comparisons by using Student t-tests.Using a Student's t-test, the global FA mean was found to distinguish the TBIs from controls better than global ADC, radial and axial diffusivities, trace, kurtosis, skew, or peak. Even the 6 mTBI patients had lower FA means than all 14 of the healthy controls, suggesting the sensitivity of a global approach. In addition, correlation of the various DTI indices with early clinical indicators of TBI severity (GCS score and post-traumatic amnesia) revealed that FA mean was the best predictor of severity of injury (Spearman r = 0.47 and r = 0.64, respectively). The validity of the histogram approach has been confirmed by another group who performed the work concurrently with us.42 A study of pediatric TBI revealed that FA is correlated with cognitive and functional outcome in moderate to severely injured children.43 The shifted global histogram of TBI in comparison with control group suggests that brain injury is multifocal. The histogram approach provides an overall picture of brain injury extent. However, it lacks the specificity of brain injury locations, which could be answered by either regional approach or voxel-based morphometry approach as described in the following sections.Field strength and voxel size effects on FAFA is calculated from the eigenvalues of the major, intermediate, and minor diffusivities and is thus limited by the signal-to-noise ratio inherent in the image acquisition. Both voxel size and field strength (1.5 and 3.0T) effects on FA have been found44 and should be properly accounted for to avoid falsely attributing a change in FA to injury or evolution. Smaller voxels (138 to 8 mm3) increased FA in a log-linear fashion (5% per 2× reduction of voxel volume) (Fig 5), whereas higher field strength increased FA by about 8% for the same subjects and the same imaging parameters.Figure 5. Relationship between voxel volume and fractional anisotropy (FA). Global white matter FA is plotted for 7 voxel sizes for 5 healthy volunteers at 1.5T. As might be expected, the higher resolution approach yields a higher FA value. Sub, subject.Aging effect on FASeveral groups, including our own, have demonstrated a decrease in FA with aging (Fig 6). This appears to be the result of an increase in radial diffusivity and not a decrease in axial diffusivity.45 Nearly all regions demonstrated a negative effect of aging on FA, although it was strongest for the fornix (−0.02 FA/decade) and genu of the corpus callosum (−0.01 FA/decade). The aging effect is significant when one considers that the standard deviation of FA mean for 50 controls was 0.02 for global WM and 0.03 for regions. Figure 7 illustrates the importance of accounting for age in mild TBI. FA global WM mean is plotted by age for 50 non-TBI controls subjects and 5 TBI patients and their initial GCS score. Note that for the TBI patients with GCS scores of 3 to 13, they have markedly lower FA than the controls. As an example of how patients with different ages should be compared with these data, we consider 2 patients, A and B, with a GCS score of 15. Patient B has a lower FA than patient A, suggesting greater axonal injury. When the effect of age (37 years difference) is accounted for, however, both patients A and B are roughly equidistant from the regression line and each within the 95% confidence interval.Figure 6. Aging effect on fractional anisotropy (FA). Global white matter mean FA is plotted against age for 50 non–traumatic brain injury normal volunteers between 19 and 81 years of age. A similar relation was obtained for nearly all white matter regions, although the right tapetum showed the strongest effect of age at 0.022 FA units/decade (Pearson R = 0.45). Dashed lines are 95% confidence interval of mean FA linear modeling, and 2 outside solid lines are 95% confidence interval of all cases.Figure 7. Importance of accounting for age in fractional anisotropy (FA) analyses. Fifty non–traumatic brain injury (TBI) controls and 5 TBI patients with legend revealing initial Glasgow Coma Scale (GCS) scores of the patients. Patients A and B both had a GCS score of 15. Note that without accounting for the linear decline of FA with age, patient B might be considered more severe than patient A. The regression line is equidistant from the FA values for patients A and B, suggesting that there is little difference between them. Comparatively, age effect is less important for more severe TBI (GCS score = 3, 7, 13). WM, white matter.Regional approaches to WM injury detectionAlthough the global approach has demonstrated the ability to predict injury severity and 1-year general neurocognitive outcome, detection of lesions in specific anatomical regions might be expected to (1) improve detection of injury in mTBI; (2) improve prognostication of specific neurological and cognitive impairments in individual patients; and (3) provide needed lesion localization information that should improve biomechanical models of TBI. We have used 2 regional approaches: (1) an atlas-based regional method, which divides the WM into 50 standardized regions (Fig 8 and Table 1); and (2) a voxel-based method (Fig 9). Both of these methods initially transform the images into a standard reference space (International Consortium of Brain Mapping) using statistical parametric mapping8 (SPM8) to derive statistical maps computed from comparing “cases” and non-TBI controls.Figure 8. Atlas-based regional approach: atlas with regions denoted by color to show major white matter tracts as regions of interest.Figure 9. Voxel-based result for a 43-year-old woman who was rear ended by a truck. Patient had no loss of consciousness but had whiplash, stuttering, cognitive slowing, loss of fine motor skills, and emotional irritability. Findings included corticospinal and left temporal white matter (WM) low fractional anisotropy (FA). Voxels in red and yellow have FA Z score of −2 or less and are smoothed (full width at half-maximum [FWHM] = 8 mm). Blue circle: left temporal WM “lesion,” which is deep to a posterior language region and may be related to patient's stuttering. DTI, diffusion tensor imaging.TABLE 1 Example result table for a putative mild TBI patientIs DTI-FA sensitive to mTBI?As detailed previously, we have applied both global and voxel-based approaches to mTBI, but our experience with acute mTBI is limited. We have seen a number of mTBI patients with normal global WM mean FA who have reduced FA in the voxel-based analysis (Fig 10). A few other groups have addressed the sensitivity of DTI-FA in mTBI. A recent study showed that, compared with matched controls, reduced FA could be detected in WM tracts of mTBI patients by 2 weeks after injury and persisted for at least 1 year.46 In addition, Kraus et al reported that mTBI patients (n = 20) had reduced FA in a number of tracts. They also found that the number of tracts with reduced FA correlated with deficits in executive function, attention, and memory.47 Two other groups have found associations between reduced FA in WM and neuropsychological abnormalities.46,48Figure 10. Mild traumatic brain injury patient scanned on the day of injury and 6 weeks later. A 31-year-old male fell 10 ft off a ladder, striking the back of his head with brief loss of consciousness and confusion. The patient developed persistent mild cognitive symptoms 6 weeks after injury. Note the same location of reduced fractional anisotropy (FA) in left corona radiata. Similar finding in splenium of the corpus callosum (not shown). Global white matter FA mean was within normal.DTI-FA: Increase or decrease?All DTI studies of moderate to severe TBI patients12,42,43 and subacute/chronic mTBI patients47–53 report FA decreases, which are associated with clinical or neuropsychological measures. However, there are seemingly contradictory findings in mTBI in the acute stage (within 1 week after injury) in the literature: Inglese et al (scanned 20 patients at 1 to 10 days after injury)50 and Arfanakis et al (scanned 5 patients within 24 hours of injury)53 both reported FA decreases; whereas Wilde et al (scanned 10 pediatric patients 1 to 6 days after injury)54 and Bazarian et al (scanned 6 adult patients within 72 hours after injury)55 reported FA increases and decreased radial diffusivity. It has been suggested that increased FA acutely may reflect cytotoxic edema,55 which would shunt extracellular fluid into swollen cells. This could have the effect of reducing interaxonal free water and therefore increasing anisotropy. Only a few investigators have looked at the time dependence of FA. Sidaros et al studied 23 adults with severe TBI at 8 weeks and 12 months and found partial recovery of initially depressed FA values in internal capsule and centrum semiovale, which predicted favorable outcomes. Kumar et al studied 16 moderate TBI patients within 2 weeks, 6 months, and 24 months and found persistently reduced FA except in the genu where it increased from the early time point.In summary, DTI has been demonstrated in virtually all studies of TBI to be a more sensitive measure of diffuse axonal injury compared with conventional imaging. DTI metrics, including FA, are affected by imaging variables such as field strength and resolution as well as subject variables including age and so must be accounted for to reduce false positives and false negatives. In addition, FA has demonstrated the ability to predict global and neurocognitive outcome, whereas preliminary results indicate serial changes in FA may be predictive of neuropsychological recovery. DTI and FA appear to be promising biomarkers for TBI but require further study to more fully determine their utility clinically and in research.In vivo 1H MRSCellular changes to neurons and glial cells after TBI are complex and dynamic. Proton (1H) MRSI has the advantage of measuring brain metabolites in vivo and is able to detect various biochemical processes of brain injury such as loss (or dysfunction) of neuronal cells. TBI is known to induce changes in N-acetylaspartate (NAA) (a neuronal marker), creatine (Cr) (a marker for energy metabolism), choline (Cho) (a marker for membrane disruption, synthesis, or repair), and lactate (Lac) (associated with anaerobic glycolysis).56 Several techniques are commonly used to acquire spectroscopic data. Single-voxel spectroscopy allows acquisition of a single spectrum from 1 volume element (voxel) typically 8 cc or more, whereas 2-or 3-dimensional magnetic resonance spectroscopic imaging (2-dimensional-MRSI/3-dimensional-MRSI), also called chemical shift imaging, allows for the simultaneous acquisition of multiple spectra from smaller adjacent voxels through multiple sections of the brain. MRSI has an inherent advantage over single-voxel spectroscopy because it is better able to evaluate regional distributions of neurochemical alterations. A complication for all MRSI studies of the brain, however, is the multiple contributions to metabolite signal arising from both GM and WM within the same voxel, as well as cerebrospinal fluid, which, if present, causes what appears to be a reduction in overall metabolites. However, segmentation57 and linear regression58 techniques using MRI to estimate contributions from various tissues contained in MRS/MRSI voxels have been employed to account for partial volume problems. An additional issue of any short echo time MRS study of the brain is the presence of lipid signal arising from the skull. This is particularly challenging for MRSI studies that sample near the brain surface and more so in infants and neonates with small brain sizes. Investigators have reported acquisition of spectra with little lipid contamination by using outer volume suppression pulses that saturate subcutaneous lipid tissue57 or by using lipid nulling and k-space extrapolation.58,59MRS studies have been used to follow TBI; however, comparison of results is complicated by the variability in the study designs (ie, ranges of severity of injury, time after injury, types and location of spectral acquisitions, and the outcome measures used to monitor recovery). Nonetheless, many studies of metabolite changes after TBI have proven MRS to be a sensitive tool to predict neurologic13,60–64 cognitive,65–69 and functional outcome.70Using single-voxel spectroscopy, significant decreases of NAA and increases of Cho have been observed in “normal appearing” WM or GM after TBI.64–66,71–73 Reduction of NAA in visibly injured brain is often caused by the primary impact, whereas reduction of NAA in normal-appearing brain may reflect DAI and Wallerian degeneration.72 Elevated Cho may be detected in WM as a breakdown product after shearing of myelin74 and the presence of lactate in brain suggests hypoxic/ischemic injury. Other investigators have used 2-dimensional-MRSI to study TBI. Macmillan and colleagues studied normal and abnormal areas of brain as seen on T2-weighted images in patients with TBI (both with and without SAH) and found low NAA levels in both areas compared with controls.75 One longitudinal study demonstrated a uniform global reduction of NAA in severely head-injured patients that returned to normal values at 40 days after injury in those patients who made a good recovery by 6 months.76 Another longitudinal study in severely injured adult patients also reported global NAA/Cr reductions and Cho/Cr increases compared with controls when reevaluated at 6 to 12 months after injury in patients with poor outcomes.13 Figure 11 compares 2 MRSI spectra (point-resolved spectroscopy [PRESS], repetition time/echo time = 3000/144 ms, 1.5 T) from a severely injured patient who shows decreased NAA in the corpus callosum (Fig 11B) on the initial study that remained lower than normal on a follow-up study done 7 months after injury (Fig 11C). Although the patient had a good outcome, he experienced mild sensory motor deficits at 1 year after injury. Another study using volumetric 3-dimensional MRSI to evaluate 14 subjects with mBI reported significant reductions in NAA/Cr and NAA/Cho and increases in Cho/Cr ratios compared with controls in brain regions that appeared normal on conventional MRI.77 These studies illustrate that MRS is a sensitive technique to measure posttraumatic neurometabolite abnormalities in injured brain when conventional neuroimaging appears normal and has the potential to improve the prediction of outcome. In summary, the reduction of NAA has been shown to be a dynamic process after injury that remains low in patients with poor recovery suggesting neuronal loss, whereas recovery of NAA in patients with good outcomes suggests recovery of mitochondrial function and/or neuronal repair.Figure 11. Spectra (magnetic resonance spectroscopy imaging [MRSI] point-resolved spectroscopy [PRESS] sequence, repetition time/echo time = 3000/144 ms, 1.5T, 10-mm slab taken through the level of the corpus callosum) from an 18-year-old, severely injured male struck by a motor vehicle. (A) Spectrum taken 3 days after injury shows normal levels of metabolites from the right frontal white matter. (B) Another spectrum from the same acquisition taken from the anterior corpus callosum shows decreased N-acetylaspartate (NAA). (C) A spectrum from an MRSI, repeated at 7 months after injury, shows a residual decrease of NAA in the anterior corpus callosum. The patient showed mild sensory motor deficits at 1-year follow-up. Cho, choline; Cr, creatine.Future TBI studies will benefit from a multimodal approach to imaging. We found that MRS combined with SWI improved prognostic value. In a group of severely injured patients combining imaging parameters such as lesion number or volume of DAI lesions with NAA/Cr metabolite measurements improved outcome prediction and correlated with neurologic disability and impairments of global intelligence, memory, and attention.78 Another investigator used DTI and MRS to show that FA and NAA/Cr measurements achieved higher sensitivity and specificity than variables used from either technique alone.79 Each technique provides additional information that together will be more sensitive to injury and provide clinicians with improved diagnostics to make decisions on patient care. Figure 12 provides a comparison of DTI, SWI, and MRSI from a TBI patient in which MRSI shows more extensive injury than DTI or SWI.Figure 12. (A) Diffusion tensor imaging fractional anisotropy (FA) maps, (B) SWI image, and (C) 1H MRSI (point-resolved spectroscopy [PRESS] sequence, repetition time/echo time = 1700/144 ms, 10 mm thick, 3T) spectral map from a 14-year-old, severely injured traumatic brain injury patient taken 8 days after injury. (A, B) Arrows in the left frontal white matter point to abnormalities in the FA map and hemorrhagic lesions. (C) Spectra in the magnetic resonance spectroscopy imaging slab shows a more diffuse pattern of injury, in which N-acetylaspartate (NAA) is decreased below normal limits for this patient's age.PROGRESSION OF BRAIN PATHOLOGYImpact to the brain, whether direct or indirect, triggers a cascade of pathophysiological events that can extend over long periods. Brain injury itself is a collection of dynamically changing pathologies instead of a single disease.9 Imaging can also provide a means to better evaluate the dynamic changes of the brain to improve patient management. There are several recent studies showing the ability of advanced MRI techniques to measure specific changes in brain injury over time.Susceptibility-weighted imagingTong et al performed a follow-up analysis of SWI lesions in 25 patients from 16 to 80 years (mean = 26) imaged within 2 to 23 days (mean = 6) of injury. Most patients had severe injury as measured by the GCS score (22 had GCS scores of ≤8). Motor vehicle accident was the most common mechanism of trauma. Follow-up SWI was performed within the first year after trauma. The authors observed a total of 4766 hemorrhagic lesions with a total volume of 1709 136 mm3 in our 25 patients. The total number of lesions decreased by 41.2% to 2802 lesions 1 year later. The total volume of lesions decreased by 51.4% to 831 440 mm3. Patients were divided into 3 follow-up groups; Group 1 had their follow-up MRI within 30 days of injury; Group 2 had their follow-up MRI between 31 and 200 days after injury; and Group 3 had their follow-up MRI beyond 200 days. The greatest percentage of decrease in lesion number/volume occurred after 30 days from injury. Hemorrhagic lesions were also categorized in 9 brain regions. The most significant decreases in lesion number/volume occurred in the frontal GM, parietal-temporal-occipital GM, followed by the frontal WM, parietal-temporal-occipital WM, and basal ganglia. This shows that SWI can be used to follow the evolution of traumatic brain hemorrhages, which may be useful in the management of TBI patients.Diffusion tensor imagingRecently, 2 studies revealed the possible progression of DTI FA over time after injury.52,80 Greenberg et al repeatedly scanned 13 moderate to severe TBI patients at 4.5 months to 2.5 years after injury.80 They found DTI FA significantly decreased in frontal and temporal tracts over time: right frontal (0.38 ± 0.06 to 0.30 ± 0.06; P < .005), left frontal (0.37 ± 0.06 to 0.32 ± 0.06; P < .05), right temporal (0.28 ± 0.05 to 0.22 ± 0.018; P < .005), and left temporal (0.28 ± 0.05 to 0.24 ± 0.02; P < .05) but did not find significant changes in the corpus callosum. Another article by Rutgers et al52 showed that the drop in FA increased with severity of the trauma in a group of 39 TBI patients with injuries ranging from mild to severe. They also showed a reduction in the number of WM tracts with increasing severity of injury. Finally, and of great interest, is that they showed a decrease in FA for mTBI patients within the first 3 months, but not afterward.Magnetic resonance spectroscopy imagingIn a longitudinal, prospective study of 42 severely injured adults,62 Shutter et al measured the time course of metabolite changes after injury by using short echo time–stimulated echo acquisition mode single-voxel MRS.62 They found that glutamate/glutamine (Glx) and Cho were significantly elevated in occipital GM and parietal WM early after injury in patients with poor long-term (6–12 months) outcomes. Glx and Cho/NAA ratios predicted long-term outcome with 94% accuracy and, when combined with the motor GCS score, provided the highest predictive accuracy (97%). Elevated Glx and Cho are sensitive indicators of injury and predictors of poor outcome when MRS is performed early after injury. This may be a reflection of early excitotoxic injury (ie, elevated Glx) and of injury associated with membrane disruption (ie, increased Cho) secondary to DAI. According to the literature, Glx levels most likely peak early after injury and fall rapidly81,82 and may therefore be more predictive of outcome if MRS is performed early after injury. In this same group of subjects, the authors showed the time course of NAA changes after injury. Subjects (all with severe injury) studied later after injury showed greater decreases of NAA compared with subjects studied early, suggesting that NAA changes may evolve more slowly and thus may be more predictive at the subacute time point.These findings provide a rationale for comparing imaging findings at acute and later time points to better understand the neuropathological changes after TBI and its association with patients' neurocognitive outcome.CONCLUSIONS AND FUTURE DIRECTIONSAdvanced MRI methods such as SWI, DTI, and MRSI can improve detection and outcome prediction of TBI. Complementary use of these MRI techniques reveals the multifaceted nature of brain injury. The various findings obtained by using the different MRI techniques can serve as biomarkers of the assorted pathologies in TBI, which will better allow clinicians to stratify patients into specific treatment groups and improve not only the results of clinical trials but also patient outcomes at any injury level from targeted therapies. In the future, longitudinal studies using such MRI techniques from the acute setting to the chronic stage, in correlation with neurocognitive assessment, are warranted to reveal the dynamic picture of brain injury and determine the optimal imaging modality and techniques at different timings for outcome prediction. In particular, the role of advanced MRI in the acute setting and intensive care stage needs further investigation to maximally impact patient treatment. Finally, advanced MRI methods have the potential to assess the effect of novel treatments and improve image classification schemes that may impact TBI treatment planning; each of these is an important direction to pursue and each is deserving of an intensive effort and further human studies.REFERENCES1. Langlois JA, Kegler SR, Butler JA, et al. Traumatic brain injury-related hospital discharges. Results from a 14-state surveillance system, 1997. MMWR Surveill Summ. 2003;52(4):1–20. [Context Link]2. Langlois JA, Rutland-Brown W, Thomas KE. Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations, and Deaths. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control; 2004. [Context Link]3. Goldstein M. Traumatic brain injury: a silent epidemic [editorial]. Ann Neurol. 1990;27:327. [CrossRef] [Medline Link] [Context Link]4. Thurman D, Alverson C, Dunn K, Guerrero J, Sniezek J. Traumatic brain injury in the United States: a public health perspective. J Head Trauma Rehabil. 1999;14(6):602–615. [CrossRef] [Full Text] [Medline Link] [Context Link]5. Finkelstein E, Corso P, Miller T, et al. The Incidence and Economic Burden of Injuries in the United States. New York: Oxford University Press; 2006. [Context Link]6. Saatman KE, Duhaime AC, Bullock R, Maas AIR, Valadka A, Manley GT and workshop scientific team and advisory panel members. Classification of traumatic brain injury for targeted therapies. J Neurotrauma. 2008;25:719–738. [CrossRef] [Medline Link] [Context Link]7. Stiell IG, Wells GA, Vandemheen K, et al. The Canadian CT head rule for patients with minor head injury. Lancet. 2001;357:1391–1396. [CrossRef] [Medline Link] [Context Link]8. Teasdale E, Hadley DM. Imaging the injury. In: Reilly P, Bullock R, eds. Head Injury. London: Chapman & Hall; 1997:167–207. [Context Link]9. Maas AIR, Marmarou A, Murry GD, Teasdale GM, Steyerberg EW. Prognosis and clinical trial design in traumatic brain injury: the IMPACT study. J Neurotrauma. 2007;24(2):232–238. [CrossRef] [Medline Link] [Context Link]10. Tong KA, Ashwal S, Holshouser BA, et al. Hemorrhagic shearing lesions in children and adolescents with posttraumatic diffuse axonal injury: improved detection and initial results. Radiology. 2003;227(2):332–339. [CrossRef] [Medline Link] [Context Link]11. Marmarou A, Signoretti S, Fatouros PP, Portella G, Aygok GA, Bullock MR. Predominance of cellular edema in traumatic brain swelling in patients with severe head injuries. J Neurosurg. 2006;104(5):720–730. [CrossRef] [Medline Link] [Context Link]12. Benson RR, Meda SA, Vasudevan S, et al. Global white matter analysis of diffusion tensor images is predictive of injury severity in TBI. J Neurotrauma. 2007;24(3):446–459. [CrossRef] [Medline Link] [Context Link]13. Holshouser BA, Tong KA, Ashwal S, et al. Prospective longitudinal proton magnetic resonance spectroscopy imaging in adult traumatic brain injury. J Magn Reson Imaging. 2006;24(1):33–40. [CrossRef] [Full Text] [Medline Link] [Context Link]14. Garnett MR, Blamire AM, Corkill RG, et al. Abnormal cerebral blood volume in regions of contused and normal appearing brain following traumatic brain injury using perfusion magnetic resonance imaging. J Neurotrauma. 2001;18(6):585–593. [CrossRef] [Medline Link] [Context Link]15. McAllister TW, Saykin AJ, Flashman LA, et al. Brain activation during working memory 1 month after mild traumatic brain injury: a functional MRI study. Neurology. 1999;53(6):1300–1308. [CrossRef] [Full Text] [Medline Link] [Context Link]16. McAllister TW, Flashman LA, McDonald BC, Saykin AJ. Mechanisms of working memory dysfunction after mild and moderate TBI: evidence from functional MRI and neurogenetics. J Neurotrauma. 2006;23(10):1450–1467. [CrossRef] [Medline Link] [Context Link]17. Hampson M, Peterson BS, Skudlarski P, Gatenby JC, Gore JC. Detection of functional connectivity using temporal correlations in MR images. Hum Brain Mapp. 2001;15:247–262. [Context Link]18. Cordes D, Haughton VM, Arfanakis K, et al. Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. Am J Neuroradiol. 2001;22:1326–1333. [Medline Link] [Context Link]19. Lowe MJ, Mock BJ, Sorenson JA. Correlations in low-frequency BOLD fluctuations reflect cortico-cortical connections. Neuro Image. 1998;7:119–132. [Context Link]20. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34(4):537–541. [CrossRef] [Medline Link] [Context Link]21. Paterakis K, Karantanas AH, Komnos A, Volikas Z. Outcome of patients with diffuse axonal injury: the significance and prognostic value of MRI in the acute phase. J Trauma. 2000;49:1071–1075. [CrossRef] [Full Text] [Medline Link] [Context Link]22. Reichenbach JR, Venkatesan R, Schillinger DJ, Kido DK, Haacke EM. Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology. 1997;204:272–277. [Medline Link] [Context Link]23. Haacke EM, Mittal S, Wu Z, Neelavalli J, Chenc YC. Susceptibility-weighted imaging: technical aspects and clinical applications, part 1. AJNR Am J Neuroradiol. 2009;30(1):19–30. [CrossRef] [Medline Link] [Context Link]24. Kou Z, Benson RR, Haacke EM. Susceptibility weighted imaging in traumatic brain injury. In: Gillard J, Waldman A, Barker P, eds. Clinical MR Neuroimaging. 2nd ed. Cambridge, UK: Cambridge University Press; 2008. [Context Link]25. Tong KA, Ashwal S, Holshouser BA, et al. Diffuse axonal injury in children: clinical correlation with hemorrhagic lesions. Ann Neurol. 2004;56(1):36–50. [CrossRef] [Medline Link] [Context Link]26. Babikian T, Freier MC, Tong KA, et al. Susceptibility weighted imaging: neuropsychologic outcome and pediatric head injury. Pediatr Neurol. 2005;33(3):184–194. [CrossRef] [Medline Link] [Context Link]27. Babikian T, Tong KA, Galloway NR, Freier-Randall MC, Obenaus A, Ashwal S. Diffusion-weighted imaging predicts cognition in pediatric brain injury. Pediatr Neurol. 2009;41(6):406–412. [Context Link]28. Grossman RI, Yousem DM. Neuroradiology. Philadelphia, PA: Mosby; 2003. [Context Link]29. Wu Z, Li S, Lei J, An D, Haacke EM. Evaluation of traumatic subarachnoid hemorrhage using susceptibility weighted imaging comparing with CT. J Magn Reson Imaging. 2010. In press. [Context Link]30. Chastain C, Oyoyo U, Joo E, Shutter L, Ashwal S, Tong KA. Predicting outcomes of adult traumatic brain injury by imaging modality and brain region. J Invest Med. 2006;54:S142. [Full Text] [Medline Link] [Context Link]31. Zhen Wu. The application of susceptibility weighted imaging to traumatic brain injury. PhD. dissertation, Department of Biomedical Engineering. Hamilton, Ontario, Canada: McMaster University; 2009. [Context Link]32. Mac Donald CL, Dikranian K, Song SK, Bayly PV, Holtzman DM, Brody DL. Detection of traumatic axonal injury with diffusion tensor imaging in a mouse model of traumatic brain injury. Exp Neurol. 2007;205(2007):116–131. [Context Link]33. Kou Z, Shen Y, Zakaria N, et al. Correlation of fractional anisotropy with histology for diffuse axonal injury in a rat model. In: Proceedings of Joint Annual Meeting ISMRM-ESMRMB; 2007; Berlin, Germany. [Context Link]34. Shimony JS, McKinstry RC, Akbudak E, et al. Quantitative diffusion-tensor anisotropy brain MR imaging: normative human data and anatomic analysis. Radiology. 1999;212:770–784. [Context Link]35. Conturo TE, McKinstry RC, Akbudak E, Robinson BH. Encoding of anisotropic diffusion with tetrahedral gradients: a general mathematical diffusion formalism and experimental results. Magn Reson Med. 1996;35:399–412. [CrossRef] [Medline Link] [Context Link]36. Ommaya AK, Corrao P, Letcher FS. Head injury in the chimpanzee. 1. Biodynamics of traumatic unconsciousness. J Neurosurg. 1973;39:152–166. [CrossRef] [Medline Link] [Context Link]37. Adams JH, Mitchell DE, Graham DI, Doyle D. Diffuse brain damage of immediate impact type: its relationship to “primary brain-stem damage” in head injury. Brain. 1977;100:489–502. [CrossRef] [Medline Link] [Context Link]38. Christman CW, Povlishock JT. Use of retrograde peroxidase flooding as an effective tool for morphogenic analysis. Neurosci Lett. 1980;20(3):227–231. [CrossRef] [Medline Link] [Context Link]39. Gennarelli TA, Adams JH, Graham DI. Acceleration induced head injury in the monkey, I: the model, its mechanistic and physiological correlates. Acta Neuropathol. 1981;7(suppl):23–25. [Context Link]40. Gennarelli TA. Head injury in man and experimental animals: clinical aspects. Acta Neurochir Suppl (Wien). 1983;32:1–13. [Context Link]41. Povlishock JT, Becker DP, Cheng, CLY, Vaughan GW. Axonal change in minor head injury. J Neuropathol Exp Neurol. 1983;42:225–242. [CrossRef] [Full Text] [Medline Link] [Context Link]42. Newcombe VF, Williams GB, Nortje J, et al. Analysis of acute traumatic axonal injury using diffusion tensor imaging. Br J Neurosurg. 2007;21(4):340–348. [CrossRef] [Medline Link] [Context Link]43. Levin HS, Wilde EA, Chu Z, et al. Diffusion tensor imaging in relation to cognitive and functional outcome of traumatic brain injury in children. J Head Trauma Rehabil. 2008;23(4):197–208. [CrossRef] [Full Text] [Medline Link] [Context Link]44. Gattu R, Latif Z, Kou Z, Haacke EM, Benson RR. Effect of voxel size on DTI fractional anisotropy. In: Proceedings of International Society for Magnetic Resonance in Medicine; 2009; Honolulu, HI. [Context Link]45. Gattu R, Benson RR, Myrtheunjayan B, et al. Ageing and fractional anisotropy: global and regional results. In: Proceedings of International Society for Magnetic Resonance in Medicine; 2008; Toronto, Canada. [Context Link]46. Niogi SN, Ghajar J, et al. Structural dissociation of attentional control and memory in adults with and without mild traumatic brain injury. Brain. 2008;131(Pt 12):3209–3221. [CrossRef] [Full Text] [Medline Link] [Context Link]47. Kraus MF, Susmaras T, Caughlin BP, Walker CJ, Sweeney JA, Little DM. White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain. 2007;130(Pt 10):2508–2519. [CrossRef] [Full Text] [Medline Link] [Context Link]48. Niogi SN, Mukherjee PGhajar J, et al. Extent of microstructural white matter injury in postconcussive syndrome correlates with impaired cognitive reaction time: a 3T diffusion tensor imaging study of mild traumatic brain injury. AJNR Am J Neuroradiol. 2008;29(5):967–973. [CrossRef] [Medline Link] [Context Link]49. Kou Z, Gattu R, Benson RR, Raz N, Haacke EM. Region of interest analysis of DTI FA histogram differentiates mild traumatic brain injury from controls. In: Proceedings of International Society for Magnetic Resonance in Medicine; 2008; Toronto, Canada. [Context Link]50. Inglese M, Makani S, Johnson G, et al. Diffuse axonal injury in mild traumatic brain injury: a diffusion tensor imaging study. J Neurosurg. 2005;103:298–303. [CrossRef] [Medline Link] [Context Link]51. Wozniak JR, Krach L, Ward E, et al. Neurocognitive and neuroimaging correlates of pediatric traumatic brain injury: a diffusion tensor imaging (DTI) study. Arch Clin Neuropsychol. 2007;22(5):555–568. [CrossRef] [Medline Link] [Context Link]52. Rutgers DR, Fillard P, Paradot G, Tadié M, Lasjaunias P, Ducreux D. Diffusion tensor imaging characteristics of the corpus callosum in mild, moderate, and severe traumatic brain injury. AJNR Am J Neuroradiol. 2008;29(9):1730–1735. [CrossRef] [Medline Link] [Context Link]53. Arfanakis K, Haughton VM, Carew JD, Rogers BP, Dempsey RJ, Meyerand ME. Diffusion tensor MR imaging in diffuse axonal injury. Am J Neuroradiol. 2002;23:794–802. [Medline Link] [Context Link]54. Wilde EA, McCauley SR, Hunter JV, et al. Diffusion tensor imaging of acute mild traumatic brain injury in adolescents. Neurology. 2008;70(12):948–955. [CrossRef] [Full Text] [Medline Link] [Context Link]55. Bazarian JJ, Zhong J, Blyth B, Zhu T, Kavcic V, Peterson D. Diffusion tensor imaging detects clinically important axonal damage after mild traumatic brain injury: a pilot study. J Neurotrauma. 2007;24(9):1447–1459. [CrossRef] [Medline Link] [Context Link]56. Alessandri B, Doppenberg E, Zauner A, Woodward J, Choi S, Bullock R. Evidence for time-dependent glutamate-mediated glycolysis in head-injured patients: a microdialysis study. Acta Neurochir Suppl (Wien). 1999;75:25–28. [Context Link]57. McLean MA, Woermann FG, Barker GJ, Duncan JS. Quantitative analysis of short echo time (1)H-MRSI of cerebral gray and white matter. Magn Reson Med. 2000;44(3):401–411. [CrossRef] [Medline Link] [Context Link]58. Schuff N, Ezekiel F, Gamst AC, et al. Region and tissue differences of metabolites in normally aged brain using multislice 1H magnetic resonance spectroscopic imaging. Magn Reson Med. 2001;45(5):899–907. [CrossRef] [Medline Link] [Context Link]59. Soher BJ, Vermathen P, Schuff N, et al. Short TE in vivo (1)H MR spectroscopic imaging at 1.5 T: acquisition and automated spectral analysis. Magn Reson Imaging. 2000;18(9):1159–1165. [CrossRef] [Medline Link] [Context Link]60. Holshouser BA, Ashwal S, Luh GY, et al. Proton MR spectroscopy after acute central nervous system injury: outcome prediction in neonates, infants, and children. Radiology. 1997;202(2):487–496. [Medline Link] [Context Link]61. Holshouser BA, Ashwal S, Shu S, Hinshaw DB Jr. Proton MR spectroscopy in children with acute brain injury: comparison of short and long echo time acquisitions. J Magn Reson Imaging. 2000;11(1):9–19. [CrossRef] [Medline Link] [Context Link]62. Shutter L, Tong KA, Holshouser BA. Proton MRS in acute traumatic brain injury: role for glutamate/glutamine and choline for outcome prediction. J Neurotrauma. 2004;21(12):1693–1705. [Context Link]63. Garnett MR, Blamire AM, Rajagopalan B, Styles P, Cadoux-Hudson TAD. Evidence of cellular damage in normal-appearing white matter correlates with injury severity in patients following traumatic brain injury: a magnetic resonance spectroscopy study. Brain. 2000;123:1403–1409. [CrossRef] [Medline Link] [Context Link]64. Garnett MR, Corkill RG, Blamire AM, et al. Altered cellular metabolism following traumatic brain injury: a magnetic resonance spectroscopy study. J Neurotrauma. 2001;18:231–240. [CrossRef] [Medline Link] [Context Link]65. Brooks WM, Stidley CA, Petropoulos H, et al. Metabolic and cognitive response to human traumatic brain injury: a quantitative proton magnetic resonance study. J Neurotrauma. 2000;17:629–640. [CrossRef] [Medline Link] [Context Link]66. Friedman SD, Brooks WM, Jung RE, Hart BL, Yeo RA. Proton MR spectroscopic findings correspond to neuropsychological function in traumatic brain injury. AJNR Am J Neuroradiol. 1998;19:1879–1885. [Medline Link] [Context Link]67. Brenner T, Freier MC, Holshouser BA, Burley T, Ashwal S. Predicting neuropsychologic outcome after traumatic brain injury in children. Pediatr Neurol. 2003;28(2):104–114. [CrossRef] [Medline Link] [Context Link]68. Babikian T, Freier MC, Ashwal S, Riggs ML, Burley T, Holshouser BA. MR spectroscopy: predicting long-term neuropsychological outcome following pediatric TBI. J Magn Reson Imaging. 2006;24(4):801–811. [CrossRef] [Full Text] [Medline Link] [Context Link]69. Yeo RA, Phillips JP, Jung RE, Brown AJ, Campbell RC, Brooks WM. Magnetic resonance spectroscopy detects brain injury and predicts cognitive functioning in children with brain injuries. J Neurotrauma. 2006;23(10):1427–1435. [CrossRef] [Medline Link] [Context Link]70. Yoon SJ, Lee JH, Kim ST, Chun MH. Evaluation of traumatic brain injured patients in correlation with functional status by localized 1H-MR spectroscopy. Clin Rehabil. 2005;19(2):209–215. [CrossRef] [Full Text] [Medline Link] [Context Link]71. Signoretti S, Marmarou A, Fatouros P, et al. Application of chemical shift imaging for measurement of NAA in head injured patients. Acta Neuochir Suppl. 2002;81:373–375. [Context Link]72. Garnett MR, Blamire AM, Corkill RG, Cadoux-Hudson TAD, Rajagopalan B, Styles P. Early proton magnetic resonance spectroscopy in normal-appearing brain correlates with outcome in patients following traumatic brain injury. Brain. 2000;123:2046–2054. [CrossRef] [Medline Link] [Context Link]73. Friedman SD, Brooks WM, Jung RE, et al. Quantitative proton MRS predicts outcome after traumatic brain injury. Neurology. 1999;52:1384–1391. [CrossRef] [Full Text] [Medline Link] [Context Link]74. Ross BD, Ernst T, Kreis R, et al. 1H MRS in acute traumatic brain injury. J Magn Reson Imag. 1998;8:829–840. [CrossRef] [Medline Link] [Context Link]75. Macmillan CSA, Wild JM, Wardlaw JM, Andrews PJ, Marshall I, Easton VJ. Traumatic brain injury and subarachnoid hemorrhage: in vivo occult pathology demonstrated by magnetic resonance spectroscopy may not be “ischaemic.” A primary study and review of the literature. Acta Neurochir (Wien). 2002;144(9):853–862; discussion 862. [Context Link]76. Signoretti S, Marmarou A, Aygok GA, Fatouros PP, Portella G, Bullock RM. Assessment of mitochondrial impairment in traumatic brain injury using high-resolution proton magnetic resonance spectroscopy. J Neurosurg. 2008;108(1):42–52. [CrossRef] [Medline Link] [Context Link]77. Govindaraju V, Gauger GE, Manley GT, Ebel A, Meeker M, Maudsley AA. Volumetric proton spectroscopic imaging of mild traumatic brain injury. AJNR Am J Neuroradiol. 2004;25(5):730–737. [Medline Link] [Context Link]78. Ashwal S, Babikian T, Gardner-Nichols J, Freier MC, Tong KA, Holshouser BA. Susceptibility-weighted imaging and proton magnetic resonance spectroscopy in assessment of outcome after pediatric traumatic brain injury. Arch Phys Med Rehabil. 2006;87(12) (suppl):50–58. [Context Link]79. Tollard E, Galanaud D, Perlbarg V, et al. Experience of diffusion tensor imaging and 1H spectroscopy for outcome prediction in severe traumatic brain injury: Preliminary results. Crit Care Med. 2009;37(4):1523–1524. [Context Link]80. Greenberg G, Mikulis DJ, Ng K, DeSouza D, Green RE. Use of diffusion tensor imaging to examine subacute white matter injury progression in moderate to severe traumatic brain injury. Arch Phys Med Rehabil. 2008;89(12) (suppl):s45–s50. [Context Link]81. Zhang H, Zhang X, Zhang T, Chen L. Excitatory amino acids in cerebrospinal fluid of patients with acute head injuries. Clin Chem. 2001;47(8):1458–1462. [Medline Link] [Context Link]82. Schumann MU, Stiller D, Thomas B, Brinker T, Samii M. 1H-MR spectroscopic monitoring of posttraumatic metabolism following controlled cortical impact injury: pilot study. Acta Neurochir Suppl. 2000;76:3–7. 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Link]|00001199-201007000-00005#xpointer(id(R70-5))|11065213||ovftdb|00012200-200502000-00012SL0001220020051920911065213P134[CrossRef]|00001199-201007000-00005#xpointer(id(R70-5))|11065404||ovftdb|00012200-200502000-00012SL0001220020051920911065404P134[Full Text]|00001199-201007000-00005#xpointer(id(R70-5))|11065405||ovftdb|00012200-200502000-00012SL0001220020051920911065405P134[Medline Link]|00001199-201007000-00005#xpointer(id(R72-5))|11065213||ovftdb|SL000024232000123204611065213P136[CrossRef]|00001199-201007000-00005#xpointer(id(R72-5))|11065405||ovftdb|SL000024232000123204611065405P136[Medline Link]|00001199-201007000-00005#xpointer(id(R73-5))|11065213||ovftdb|00006114-199904220-00015SL00006114199952138411065213P137[CrossRef]|00001199-201007000-00005#xpointer(id(R73-5))|11065404||ovftdb|00006114-199904220-00015SL00006114199952138411065404P137[Full Text]|00001199-201007000-00005#xpointer(id(R73-5))|11065405||ovftdb|00006114-199904220-00015SL00006114199952138411065405P137[Medline Link]|00001199-201007000-00005#xpointer(id(R74-5))|11065213||ovftdb|SL000025161998882911065213P138[CrossRef]|00001199-201007000-00005#xpointer(id(R74-5))|11065405||ovftdb|SL000025161998882911065405P138[Medline Link]|00001199-201007000-00005#xpointer(id(R76-5))|11065213||ovftdb|SL0000508820081084211065213P140[CrossRef]|00001199-201007000-00005#xpointer(id(R76-5))|11065405||ovftdb|SL0000508820081084211065405P140[Medline Link]|00001199-201007000-00005#xpointer(id(R77-5))|11065405||ovftdb|SL0000038820042573011065405P141[Medline Link]|00001199-201007000-00005#xpointer(id(R81-5))|11065405||ovftdb|SL00003030200147145811065405P145[Medline Link]11468237The Role of Advanced MR Imaging Findings as Biomarkers of Traumatic Brain InjuryKou, Zhifeng PhD; Wu, Zhen MD, PhD; Tong, Karen A. MD; Holshouser, Barbara PhD; Benson, Randall R. MD; Hu, Jiani PhD; Mark Haacke, E. PhDArticle425