JOURNAL OF NEUROTRAUMA Volume 24, Number 5, 2007 © Mary Ann Liebert, Inc. Pp. 753–765 DOI: 10.1089/neu.2006.0208
Diffuse Axonal Injury in Severe Traumatic Brain Injury Visualized Using High-Resolution Diffusion Tensor Imaging JIAN XU,1 INGE-ANDRE RASMUSSEN, JR.,1 JIM LAGOPOULOS,2,3 and ASTA HÅBERG1
ABSTRACT Traumatic brain injury (TBI) is the most common cause of death and disability in young people. The functional outcome in patients with TBI cannot be explained by focal pathology alone, and diffuse axonal injury (DAI) is considered a major contributor to the neurocognitive deficits experienced by this group. The aim of the present study was to investigate whether diffusion tensor imaging (DTI) offers additional information as to the extent of damage not visualized with standard magnetic resonance imaging (MRI) in patients with severe TBI. Nine chronic male TBI patients and 11 matched healthy controls were recruited. Results of the voxel-based analysis of fractional anisotropy (FA) maps and apparent diffusion coefficient (ADC) maps revealed significant differences in anisotropy in major white matter tracts, including the corpus callosum (CC), internal and external capsule, superior and inferior longitudinal fascicles, and the fornix in the TBI group. The FA and ADC measurements offered superior sensitivity compared to conventional MRI diagnosis of DAI. Region-of-interest (ROI) analyses confirmed these results in the investigated regions. The findings of this study support the hypothesis that severe TBI is accompanied by DAI. The DTI changes were more prominent on the right side that contained the focal pathology in most of the patients and accurately reflected differences in both hemispheres. In conclusion, DTI holds great promise as a diagnostic tool to identify and quantify the degree of white matter injury in TBI patients. Key words: apparent diffusion coefficient; brain damage; DTI; fractional anisotropy; Glasgow Coma Scale (GCS); motor vehicle accident; region of interest analysis; ROI; voxel-based analysis; white matter INTRODUCTION
T
(TBI) results when the brain sustains physical damage as a result of a forceful blow to the head. In situations of closed head injury, such as when the head hits a solid object, a key mechanism in RAUMATIC BRAIN INJURY
the ensuing damage is diffuse axonal injury (DAI) (Arfanakis et al., 2002a; Maxwell et al., 1997; Adams et al., 1982). This type of injury is commonly induced by sudden acceleration-deceleration or rotational forces and the subsequent tissue injury is characterized by axonal stretching, disruption and eventual separation of nerve
1Department of Circulation and Medical Imaging, Faculty of Medicine, Norwegian University of Science and Technology, Trondheim, Norway. 2Discipline of Psychological Medicine, Northern Clinical School, University of Sydney, NSW, Australia. 3CADE Clinic, Royal North Shore Hospital, St. Leonards, Australia.
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XU ET AL. fibers (Adams et al., 1989; Basser and Pierpaoli, 1996; Povlishock, 2001; Gennarelli et al., 1982; Strich, 1956). DAI is linked to coma of immediate onset and associated with low Glasgow Coma Scale (GCS) scores (Jennett et al., 1981; Teasdale and Jennett, 1974). The anatomical sites commonly affected are corticomedullary junctions, located in frontal and temporal regions as well as the corpus callosum (CC), upper brainstem, and deep gray matter (Adams et al., 1982, 1989). Early and exact identification of the extent of axonal injury is a major diagnostic challenge, because these injuries are seldom visible on computed tomography (CT) scans, or conventional T1- and T2-weighted magnetic resonance imaging (MRI) (Adams et al., 1985). Upon admission to hospital, only 10% of DAI patients demonstrate classic CT findings associated with DAI characterized by hemorrhagic punctuate lesions of the CC and the gray-white matter junction in the cerebrum and the pontine-mesencephalic junction adjacent to the superior cerebellar peduncles (Blumbergs et al., 1994, 1995). Often, the radiological findings develop over time, and the most prominent feature encountered is the enlargement of the ventricles, which can be misinterpreted as hydrocephalus. Late radiological findings may be relatively normal except for generalized atrophy of deep white matter structures and further ventricular enlargement (DiazMarchan, 1996; Whyte, 1993). Fluid-attenuated inversion recovery (FLAIR) imaging of white matter injury offers some additional diagnostic properties (Ashikaga et al., 1997; Parizel et al., 1998), but definitive diagnosis of DAI is only reliably given after autopsy. It has been repeatedly demonstrated that CT and conventional MRI scans are poor predictors of the functional outcome of patients with TBI, and it is believed that this is predominately due to DAI that is not adequately detected (Diaz-Marchan, 1996; McLellan, 1990). Recent studies have suggested that diffusion tensor imaging (DTI) may be useful in identifying early signs of axonal injury in TBI (Huisman et al., 2004; Lee et al., 2006). DTI is an imaging technique based on the microscopic Brownian motion of water through tissues, where displacement distances are comparable with cell dimensions (Basser, 1995; Basser and Pierpaoli, 1996). Thus, diffusion MRI can be used to visualize pathology at a microscopic level that is not evident using conventional MRI or other non-invasive methods. Currently, the most established scalar invariants of the tensor and one-dimensional reductions of diffusivity are fractional anisotropy (FA) and apparent diffusion coefficient (ADC) (Basser and Pierpaoli, 1998) The FA index varies between 0 (representing a symmetrical anisotropic medium where there is no direc-
tionality of the diffusion, for instance in water) and 1 (representing maximum anisotropy). The ADC varies inversely with the FA, where a high value represents low anisotropy. The FA index is reportedly a more reliable rotationally invariant scalar metric for measuring diffusion anisotropy compared to other one-dimensional reductions of the diffusion tensor (Pierpaoli and Basser, 1996; Sorensen et al., 1999). The utility of DTI in TBI has been successfully applied in several studies (Arfanakis et al., 2002a; Gupta et al., 2005; Inglese et al., 2005; Salmond et al., 2006), and the method has great potential in providing better understanding and improved diagnosis of DAI. DTI studies in TBI patients have reported patterns of reduced FA in major white matter tracts in the central areas of the brain (Arfanakis et al., 2002b; Gupta et al., 2005; Inglese et al., 2005; Lee et al., 2006; Salmond et al., 2006). The aim of this study was to investigate the quantitative diffusion characteristics of patients with severe TBI employing DTI using a 3-Tesla (T) MRI scanner with a high-performance gradient system at high spatial and gradient-directional resolution, as an aid to improved diagnosis and classification of diffuse axonal injuries.
METHODS Subjects Nine chronic TBI patients, (average age 26.4 years, range 21–36 years; average time post-injury 4 years, range 2–6 years) were recruited from an outpatient clinic specializing in the rehabilitation of TBI patients (Munkvoll Rehabilitation Unit, St. Olav’s Hospital, Trondheim, Norway). Severity of injury on admission was graded using GCS, and all had a score of 7 or lower, indicating severe injury and coma. Eight patients were injured in motor vehicle accidents (two motorbike accidents and six car accidents), and one patient suffered injuries from a fall. The patients had a variety of focal brain damage, and DAI was diagnosed with conventional MR imaging in six of the patients using established criteria (Gentry, 1994). Details of patient demographics and conventional MRI-based diagnostics are summarized in Table 1, and the functional level and structural brain pathology in the TBI group are listed in Table 2. Eleven healthy controls (average age 26 years, range 22–33 years) of comparable socio-intellectual levels were also recruited. All were male and in the same age group as the investigated patients. None of the controls had a history of trauma to the head or neurological disorders such as epilepsy, cerebrovascular disease, mental retardation, neurodegenerative disorders, significant systemic medical illness, nor a history of axis I diagnosis of psy-
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DIFFUSION TENSOR IMAGING IN SEVERE TBI TABLE 1. DEMOGRAPHIC DATA
Patient no. 1 2 3 4 5 6 7 8 9
AND
ACCIDENT-RELATED INFORMATION
FOR
TRAUMATIC BRAIN INJURY GROUP
Patient age (years)
Time from accident (years)
Accident mechanism
GCS at admission
PTA (weeks)
LOS neurosurgical ward (days)
21 24 30 26 22 29 36 22 28
4 2 6 4 4 6 4 3 6
MVA, moped MVA, car MVA, motorbike MVA, car MVA, car MVA, car MVA, car MVA, car Fall, 5–6 m
3 7 5 7 7 7 7 0 7
1 4 5 6 5 5 2 38 5
24 30 15 36 51 28 10 49 16
LOS—primary rehabilitation (days) 0 93 126 136 95 94 73 109
GCS, Glasgow Coma Scale; MVA, motor vehicle accident; LOS, length of stay; PTA, posttraumatic amnesia.
chiatric illness (including substance abuse). None of the participants were using psychopharmaceuticals of any kind upon recruitment. All controls had normal findings on conventional diagnostic MRI examinations. This study was approved by the Regional Committee for Medical Research Ethics at Norwegian University of Science and Technology and St. Olav’s Hospital, Norway. All patients and healthy controls gave their written informed consent after the procedure had been carefully explained and after they had the opportunity to ask questions about the research.
Magnetic Resonance Imaging All scanning was performed on a Philips Intera 3-T scanner (Philips Medical, Best, The Netherlands) with a Quasar Dual gradient system (maximum amplitude 80 mT/m) and a SENSE head-coil (MRI Devices, Orlando, FL). DTI was performed with the use of a single-shot spin echo, echo planar imaging sequence with 32 diffusion gradients applied along non-collinear directions. DTI scanning parameters were as follows: field of view 230 230; matrix size 128 128; 55 contiguous axial slices with slice thickness 1.72 mm, giving isotropic voxels of 5.6 mm3, TE 50 msec; bvalue 800 sec/mm2; partial Fourier acquisition 61.5% and a SENSE reduction factor 1.5. Cardiac triggering was applied with a systolic trigger delay of 150 msec with a repetition time (TR) of 15 R-R intervals giving a TR of 13–16 sec, varying slightly between individuals with variations in heart rate (Skare and Andersson, 2001). For each slice, one image without diffusion gradient was acquired (b 0). Additional conventional diagnostic scanning included high-resolution T1weighted images, FLAIR images, and gradient-echo T2weighted image series.
Image Processing The diffusion tensor images were corrected for headmotion and eddy-currents using FLIRT in Functional Software Library (FSL) package (Analysis Group, FMRIB, Oxford, UK), with b 0 as reference. Brain masks was created from b 0 images using the Brain Extraction Tool (BET) in FSL to exclude voxels outside the brain. Thereafter, all sets were manually fine-segmented using the MRIcro software (Rorden and Brett, 2000) to provide accurate isolation of brain voxels for analysis. FA and ADC were calculated at each voxel from the diffusion-weighted images (Basser et al., 1994) using the DtiStudio software [Laboratory of Brain Anatomical MRI, John Hopkins Medical Institute, Baltimore, MD (Jiang et al., 2006)].
Statistical Parametric Map Analysis of Anisotropy Maps The FA and ADC maps were analyzed using SPM2 software (Wellcome Department of Imaging Neuroscience, London, UK; www.fil.ion.ucl.ac.uk/spm). In MRIcro, all visible lesions on the reference images of the TBI patients were masked out before template creation. Before voxel-wise statistical analysis, the images from all subjects were transformed to a standard space by normalizing them to a common template. Custom templates were created by first normalizing b 0 images of all healthy and masked injured subjects to a standardized T2 template using non-linear transformation (Ashburner and Friston, 2000). The resulting transformation matrices were then applied to the FA images. All the normalized FA maps were averaged and smoothed using an 8-mm full with at half maximum (FWHM) isotropic Gaussian kernel to create a new customized template.
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TABLE 2. FUNCTIONAL LEVEL Patient no.
Motor deficits
AND
STRUCTURAL BRAIN PATHOLOGY
Main cognitive problema
Disabled/working
1
No
Easily fatigued, reduced cognitive capacity
Work 100%
2
No
Vocational rehabilitation
3
No
4
No
5
No
6
No
Memory problems, impaired awareness of deficits, reduced concentration Memory problems, disturbed regulation of affects, reduced stress tolerance Impaired awareness of deficits, indifferent behavior, reduced initiative, reduced social cognition Memory problems, impaired awareness of deficits, reduced concentration Extensive memory problems, impaired awareness of deficits Impaired awareness of deficits, minor personality change Reduced initiative, memory problems, irritability Reduced social cognition, impaired attention, impaired judgment, impaired awareness of deficits
7
Minor right hand
8
Minor left sided hemiparesis
9
No
Disabled
IN
TRAUMATIC BRAIN INJURY GROUP Structural pathology on MRI Right temporal lobe grey and white matter loss. Cystic lesions and atrophy of posterior corpus callosum. Large left white matter lesion in occipital lobe. Left frontoparietal lesions in grey/white matter junction. Minor hemorrhagic lesion in right frontal lobe. Contusion in right frontoparietal cortex. Hemorrhagic lesion in right thalamus. Cystic lesion in right middle frontal lobe.
Disabled
Large white matter lesion in right frontal lobe, smaller white matter changes in right temporal lobe and left frontal lobe. Grey matter loss in right parietal cortex.
Disabled
Bilateral temporal contusion. Right frontal lobe white matter lesion. Hemorrhagic lesion in right caudate nucleus.
Disabled
Bilateral frontoparietal contusions. Large bilateral frontal lobe white matter lesions. Left frontal lobe cyst.
Rehabilitation
Left parietal white matter lesion. Right thalamus lesion.
Disabled
Right cortical lesion. Cyst in right internal capsule.
Vocational rehabilitation
White matter lesion of entire right frontoparietal lobe.
DAI gradeb II
II
II
I
II
II
aMain cognitive problem based on neuropsychological tests and clinical observation by specialist in neuropsychology and consultant in rehabilitation medicine. bDiffuse axonal injury (DAI) graded by neuroradiologist based on structural magnetic resonance imaging (MRI) scans.
DIFFUSION TENSOR IMAGING IN SEVERE TBI All FA maps of both TBI patients and controls were then normalized using the new custom template. Normalization procedures utilized the following settings in SPM2: 25-mm cutoff for the basis functions, 16 nonlinear iterations, and medium regularization (0.1). The normalized FA maps were subsequently smoothed with a 4mm FWHM isotropic Gaussian kernel to improve the signal-to-noise ratio, to increase the validity of the statistical inferences, and to improve normalization. Finally, the FA maps were resampled to (2 mm3) isotropic voxels using trilinear interpolation. The transformation matrices of the FA normalization steps were then applied to the ADC maps, as these have low image contrast and are not suitable for direct normalization, while the FA maps have excellent image contrast-producing adequate registration. Before statistical inference, the voxels belonging to cerebrospinal fluid (CSF) and gray matter were excluded by making a mask of voxels with FA values below 0.15. This mask was applied to both the FA and ADC maps. To check the quality of the normalization procedure, the transformation matrices used were applied to the T2-weighted b 0 images from all subjects. The transformed b 0 images from the patient group were compared to those of the healthy control group using a one-sided t-test in a SPM2 analysis. The resulting map thresholded at a false discovery rate (FDR) (Genovese et al., 2002) of q(FDR) 0.05 showed no differences within white matter of the two groups. Minor registration errors on the medial side of the left thalamus were the only indicators of inaccurate normalization of the images. Statistical analysis. A one-sided t-test was used to detect whether each voxel had a higher or lower FA or ADC value in the TBI group compared with the group of controls. Correction for false positives in the statistical parametric maps (SPMs) was done using a q(FDR) 0.05 for the FA maps and q(FDR) 0.01 for the ADC maps.
and a significance level was set at p 0.05. In subject 9, the setting of the posterior CC (PCC)–ROI was impossible due to near complete atrophy of this region.
Description of ROI Settings in White Matter Anterior CC (ACC) was defined by placing the cross within in the genu of the CC starting in the mid-sagittal plane and centered in the transversal plane. Posterior CC (PCC) was defined by placing the cross within the splenium of the CC starting in the mid-sagittal plane and next centering it in the axial plane. Posterior limb of internal capsula (PLIC) was defined by first selecting the axial slice that contained the anterior commisure, then going to the posterior end of the pallidum to select the correct coronal slice, then placing the ROI just medially to the pallidum in the coronal slice. Deep frontal (DF) was defined by first selecting the axial slice that contains the most anterior part of the CC, then placing the ROI in the center of deep frontal white matter, directly diagonally from the tip of the anterior horn of the lateral ventricles in each hemisphere. Anterior periventricular (APV) and posterior periventricular (PPV) were defined by first selecting the axial slice that contains the most posterior or anterior part of the CC, then placing the ROIs approximately 5 mm from the cap of each horn. Medial orbitofrontal (MOF) was defined by first selecting the axial slice containing the most anterior part of the CC, then moving 10 mm anteriorly to determine the correct coronal slice, then placing the ROIs in white matter close to gyrus rectus medially just before branching. Occipital (OC) was defined by first selecting the axial slice containing the most posterior part of CC, and next placing the ROI in white matter in the occipital lobe before branching.
Conventional Regions of Interest–Based Analysis To avoid bias, regions of interest (ROI) were defined using MRIcro on the non-diffusion-weighted measurements (b 0) in each dataset. These images are in perfect register with the FA and ADC maps. Each ROI consisted of seven voxels forming a three-dimensional cross. The total volume of each ROI was 39 mm3. The ROIs were positioned by identifying anatomical structures as visualized in MRICro, which was also used for the drawing the ROIs. A total of eight ROIs in each hemisphere were defined based on locations previously described (Salat et al., 2005). Locations and abbreviations of the ROIs are listed below. A one-sided t-test was applied to investigate differences in measured FA and ADC values between the TBI patient group and the control subjects,
Tractography-Based Analysis A novel approach to ROI setting was performed, where the CC was segmented using DTI-based fiber tractography using the software DTI-studio for calculations, segmentation, and visualization. Tractography was initiated using color-coded FA maps. In the mid-sagittal image, the fibers of the CC are predominantly directed in the right-left direction and very suitable for seed-pointing for white matter tractography. The images from all patients and controls were segmented using stringent criteria for starting and terminating tractography. Termination criteria were FA 0.15 or the main diffusion directions in consecutive steps differed by more than 30 degrees. Estimates of projections were computed using the fiber as-
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XU ET AL. signment by continuous tracking (FACT) algorithm (Mori et al., 1999). The rough tractograms were manually corrected by removing contaminating axonal projections belonging to other fiber bundles, such as the fornix, fibers from the fronto-occipital fasciculus, fibers connecting into the corticospinal tracts, and other abberants. The properties of the resulting segments of the CC were investigated visually for shape, thickness and extension. Statistical analysis. The FA and ADC values of the segments were normalized and plotted, and t-tests were performed on the resulting data.
RESULTS The voxel-based analysis of the FA maps revealed significant decreases in anisotropy for the TBI group in major white matter tracts such as the CC, the internal and external capsule, the superior and inferior longitudinal fascicles, the cingulate gyri, and the fornix. This pattern of fiber disruption was predominantly bilateral, but more prominent on the right side in the basal brain. There were no brain areas where the control group had lower FA values than the patient group. The peak differences (between patients and controls) in major white matter structures for the FA values are presented in Table 3. The voxel-based analysis of the ADC maps produced a more diffuse and widespread picture than the FA maps. Significant increases in ADC values were evident in most
of the white matter voxels. By tightening the statistical threshold to q(FDR) 0.01, the pattern of ADC increases became more similar in spatial distribution to the differences observed in the FA maps. The SPM of FA and ADC values are shown in Figure 1. The peak differences (between patients and controls) in major white matter structures for the ADC values are presented in Table 4. The results of the conventional ROI analysis indicated a significant decrease in FA values in several ROIs in the TBI group as compared to healthy controls. These areas included the ACC, PCC, PLIC bilateral, APV (right), and PPV (right). On the other hand, the ADC values showed more of a global increase in the TBI group, with significant increases in all investigated ROIs except the left PLIC. The averaged results for both the FA and ADC ROI analyses are summarized in Table 5. In the tractography-based analysis of the CC, the patient group showed earlier termination of fiber tracking and CC volumes that were significantly lower than in the healthy control group. All segmented CCs were normalized against a full white matter volume in the brain, measured as all voxels with an FA value above 0.15. For the healthy control group, the average size of the CC was 10% of the full white matter volume, whereas the size of the average TBI patient group CC was 6.7%. This difference in CC brain fraction was statistically significant at p 0.001. Three-dimensional rendered CCs are shown in Figure 2. Plotting the average FA and ADC values of the patients and the healthy controls showed a skewing of the patients FA values to the left (Fig. 3) and the ADC
TABLE 3. PEAK DIFFERENCES IN FRACTIONAL ANISOTROPY BETWEEN HEALTHY CONTROLS AND TRAUMATIC BRAIN INJURY PATIENTS IN MAJOR WHITE MATTER STRUCTURES MNI coordinates Anatomical location Superior corona radiata Superior longitudinal fasicle Superior longitudinal fasicle CC (body) CC (body) CC (genu) CC (genu) Thalamus Thalamus PLIC PLIC Mesencephalon/peduncle Mesencephalon/peduncle
Hemisphere
X
Y
Z
T
Right Left Right Left Right Left Right Left Right Left Right Left Right
18 43 40 15 13 13 14 4 6 30 30 24 24
26 7 18 10 1 39 32 19 12 26 25 22 24
54 30 31 35 38 5 15 5 2 6 8 6 6
7.86 8.30 5.66 5.71 6.71 7.41 5.34 7.60 6.86 5.55 6.71 6.03 13.31
The coordinates are in MNI space and correspond to the highest peak of the parametric map within major anatomical locations. MNI, Montreal Neurologic Institute; CC, corpus callosum; PLIC, posterior limb of internal capsula.
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DIFFUSION TENSOR IMAGING IN SEVERE TBI
FIG. 1. Statistical parametric maps, showing regions of statistically significant lower fractional anisotropy (FA) values in the traumatic brain injury (TBI) patient group compared to healthy controls (p 0.05) and statistically significant higher apparent diffusion coefficient (ADC) values in the TBI-patient group compared to healthy controls (p 0.01). The axial images are overlaid on a customized template averaged from all participants and oriented in neurological convention. Changes are prominent in large fiber bundles such as the corpus callosum (CC), the superior longitudinal fascicle (SLF), internal capsule (IC), cingulum (CG), and around the ventricles. Spatial information is provided below the statistical maps in the form of directional maps.
values to the right (Fig. 4). The tractography-based analysis clearly depicts CC pathology in all TBI patients, even though only subjects 1, 2, 3, 6, and 9 were originally described with DAI II based on conventional MRI.
DISCUSSION In the present study, wide-ranging, bilateral changes in FA and ADC values were uncovered in the TBI group. Using a voxel-based morphometric approach, the results of this study indicated reduced FA values and increased ADC values for the TBI patients clustered in the large
white matter tracts of the telencephalon, including the CC, the internal and external capsules, periventricular white matter, and the superior and inferior longitudinal fascicles. A similar pattern was observed using a conventional ROI analysis approach where reduced FA values were detected in the TBI patients compared with the healthy controls in several of the investigated ROIs. The patients also showed an increase in ADC values, which were more widespread and more statistically significant than the changes in FA values. However, the variance of the measured FA values was larger in the TBI group than in the healthy controls, particularly noted in the anterior and posterior CC, which may be indicative of the vari-
759
XU ET AL. TABLE 4. PEAK DIFFERENCES IN APPARENT DIFFUSION COEFFICIENT BETWEEN HEALTHY CONTROLS AND TRAUMATIC BRAIN INJURY PATIENTS IN MAJOR WHITE MATTER STRUCTURES MNI coordinates Anatomical location
Hemisphere
X
Y
Z
T
Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Left Right Right
29 29 4 5 13 16 7 6 11 6 33 25 24 25 41 41 20 19 25
6 15 1 14 8 16 25 28 48 44 10 21 26 26 52 38 12 12 22
42 31 35 34 35 33 21 16 15 10 8 3 12 9 3 11 11 11 9
6.95 7.86 7.02 7.54 5.94 6.29 6.19 6.08 6.74 6.88 7.82 7.72 5.70 5.74 5.84 6.85 5.56 8.24 8.83
Superior longitudinal fasicle Superior longitudinal fasicle Cingulum Cingulum CC (body) CC (body) CC (genu) CC (genu) CC (tail) CC (tail) External capsule External capsule PLIC PLIC Inferior frontooccipital fasciculus Inferior frontooccipital fasciculus Mesencephalon/peduncle Mesencephalon/peduncle/anterior Mesencephalon/peduncle/posterior
The coordinates are in MNI space and correspond to the highest peak of the parametric map within ajor anatomical locations. MNI, Montreal Neurological Institute; CC, corpus callosum; PLIC, posterior limb of internal capsula.
TABLE 5. AVERAGED FA AND ADC VALUES IN FOURTEEN ROIS IN HEALTHY CONTROLS AND TBI PATIENTS WITH STANDARD DEVIATION (SD) AND ONE-SIDED P-VALUE ROI ACC APV_L APV_R DF_L DF_R MOF_L MOF_R OC_L OC_R PCC PLIC_L PLIC_R PPV_L PPV_R
HC-FA 0.72 0.31 0.33 0.35 0.31 0.51 0.046 0.43 0.47 0.78 0.69 0.70 0.55 0.54
( ( ( ( ( ( ( ( ( ( ( ( ( (
0.07) 0.06) 0.06) 0.07) 0.05) 10) 0.08) 0.08) 0.06) 0.05) 0.06) 0.07) 0.07) 0.07)
TBI-FA 0.57 0.27 0.26 0.33 0.28 0.45 0.44 0.43 0.42 0.67 0.65 0.60 0.50 0.42
( ( ( ( ( ( ( ( ( ( ( ( ( (
0.12) 0.07) 0.09) 0.06) 0.08) 0.11) 0.09) 0.08) 0.11) 0.11) 0.05) 0.08) 0.07) 0.06)
p-value 0.0025 0.079 0.020 0.27 0.16 0.12 0.32 0.46 0.10 0.020 0.0044 0.0076 0.081 0.00040
HD-ADC 10.50 8.55 8.53 8.40 8.25 8.55 8.44 7.48 7.55 7.83 7.90 7.41 8.05 8.33
( ( ( ( ( ( ( ( ( ( ( ( ( (
1.30) 0.63) 0.73) 0.52) 0.61) 0.76) 0.56) 0.65) 0.61) 0.72) 0.86) 0.60) 0.75) 0.65)
TBI-ADC 8.72 9.98 11.21 9.95 10.25 9.79 9.25 8.15 8.17 8.71 8.28 8.15 9.37 9.34
( ( ( ( ( ( ( ( ( ( ( ( ( (
0.66) 1.09) 3.43) 0.79) 2.45) 1.11) 1.37) 0.68) 0.71) 0.89) 0.15) 0.34) 0.67) 1.09)
p-value 0.0016 0.0023 0.024 0.00010 0.020 0.0063 0.0063 0.019 0.026 0.018 0.78 0.0010 0.015 0.015
The ROIs are based on those by Salat et al. (2005). ROI, region of interest; HC, healthy control; TBI, traumatic brain injury; FA, fractional anisotropy; ADC, apparent diffusion coefficient; ACC, anterior corpus callosum; APV, anterior periventricular; DF, deep frontal; MOF, medial orbitofrontal; OC, occipital; PCC, posterior corpus callosum; PLIC, posterior limb of internal capsula; PPV, posterior paraventricular.
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DIFFUSION TENSOR IMAGING IN SEVERE TBI
FIG. 2. Three-dimensional renderings of the corpus callosum for all traumatic brain injury (TBI) patients and healthy controls. The patients results exhibit earlier termination of fibre tracking and have significantly lower volumes.
5
Percent of all voxels in CC
4 4 3 3 2 2 1 1 0 0.00
0.10
0.20
0.30
0.40 0.50 0.60 Fractional Anisotropy TBI
0.70
0.80
0.90
HC
FIG. 3. Plot of average fractional anisotropy (FA) values as a function of the percent voxels in the corpus callosum (CC). The healthy controls and traumatic brain injury (TBI) patients are depicted by the broken line and the hard line, respectively. The shaded regions correspond to areas of significant between group differences.
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XU ET AL. 8
Percent of all voxels in CC
7 6 5 4 3 2 1 0 0.00050
0.00060
0.00070
0.00080
0.00090 0.00100 0.00110 Apparent Diffusion Coefficient TBI
0.00120
0.00130
0.00140
HC
FIG. 4. Plot of average apparent diffusion coefficient (ADC) values as a function of the percent voxels in the corpus callosum (CC). The healthy controls and traumatic brain injury (TBI) patients are depicted by the broken line and the hard line respectively. The shaded regions correspond to areas of significant between group differences.
able degree of injury in the different patients. Inspection of individual sets of ROIs revealed that patterns of injury were heterogeneous as measured with FA value reductions and ADC value increases. Thinning of the CC made placement of the ROI troublesome and impossible in one patient, due to posterior CC atrophy. It should be appreciated that there was a trend of reduced FA values in all ROIs in the TBI group on the whole compared to healthy controls, even if all did not reach statistical significance. The voxel-based analysis of the ADC values showed a more global distribution of differences between the TBI group and the normal controls. By tightening the statistical threshold, the ADC SPM came to resemble the FA SPM. This may indicate that ADC is a more sensitive measure of changes in the brain after severe TBI. This is further reinforced in the ROI analysis, where greater statistical differences between the TBI patient group and the healthy controls were evident using the ADC approach— again pointing to ADC analysis as a more robust approach in quantifying white matter damage. In none of the ROIs did the healthy controls have lower FA values or higher ADC values than the TBI patients. In the tractography-based analysis of the CC, the patient fiber tracts showed a varying degree of early termination of fiber tracking in different regions. The damage to the CC was heterogeneous, as is evident in Figure 2. Different parts of the CC showed thinning and loss of fibers as measured with early termination of tractography. The patients also had different distribution of FA and ADC values compared to the healthy controls, indicating loss of structural integrity in the entire CC. Tract-
specific anisotropy measurements offer several advantages to conventional ROI analysis (Kanaan et al., 2006). Defining the CC in tractography is to a large degree automated, and thus less operator-dependent bias results compared with conventional ROI setting. It also offers analysis of a whole volume of white matter, with a much larger within-subject sample size, whilst conventional ROI’s investigate a two-dimensional (2D) reduction of the volume. With tractography, CC pathology in all TBI patients was clearly visualized. The CC tractogram conceptualizes DAI in one image, illustrating the impact that white matter injury has on brain integrity. The findings of this study support the hypothesis that severe TBI as defined by the admission characteristics measured by the GCS (Jennett et al., 1981; Teasdale and Jennett, 1974) is accompanied by DAI, even if this is not evident on conventional CT or MR imaging. Changes in diffusion anisotropy measures in cerebral white matter is of great value in evaluating axonal damage and may be of valuable as a biomarker of TBI severity. However, the ADC value appeared to be a more sensitive measurement for widespread white mater damage in TBI patients. Whilst the exact mechanisms underlying the changes in diffusion anisotropy are not fully understood, it has been suggested that they may reflect changes in the underlying microstructure of the axons. Moreover, in structures such as the myelin sheaths of densely packed white matter, any loss of structural order or integrity of the tissue results in a reduction in diffusion anisotropy (Arfanakis et al., 2002a; Huisman et al., 2004). The reduced FA values and increased ADC values identified in our study are
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DIFFUSION TENSOR IMAGING IN SEVERE TBI consistent with large microstructural disorganization of the axonal system secondary to cerebral damage in patients with TBI. Both intra- and inter-hemipspheric fiber tracts were significantly affected, thus reflecting largescale changes in connectivity between cortical as well as subcortical regions, and between the two hemispheres. In the present study, the DTI changes were more prominent on the right side, which was consistent with similar rightsided patterns of focal pathology as observed on conventional MRI scans of these TBI patients. There are several limitations associated with the twofold analysis approach applied in this study. One issue is the large number of brains ideally needed to constitute the brain template used as an atlas. Next, factors such as age (Salat et al., 2005), gender (Westerhausen et al., 2004) and scanner variables all influence FA and ADC values, and whilst these were all controlled for in the present study, direct comparison of these indices with other studies becomes difficult. Comparison data should ideally be acquired using the same MRI scanner with identical application parameters, to minimize the impact of varying these variables, as FA and ADC values tend to vary with field strength, gradient performance, and a variety of parameters (Melhem et al., 2000). Furthermore, these factors also limit comparisons in observed FA and ADC values between studies without normalization. When the placement of a ROI is properly performed, the ROI analysis may yield accurate and reproducible results about individual patients. ROI positioning should be guided by anatomy and not by lesions. The method is straightforward, and may easily be done in a clinical setting without sophisticated software or tedious data processing procedures. From a practical point of view, the ROI analysis is limited to a few regions, and this may introduce possible bias. The ROIs need to be placed in areas not contaminated by grey matter, which would decrease the measured FA or ADC value, and also must be located to areas without susceptibility artifacts from the adjacent skull-base, where measurements are less reliable. The alternative approach to ROI analysis involves performing voxel-based morphometry (Ashburner and Friston, 2000), where a single patient image set can be compared with a group of controls. The advantage of this approach is that differences between a group and one individual can be determined with high spatial resolution in an unbiased manner. However, disadvantages of this method is that it requires the images to be spatially registered in a stringent fashion for the method to be reliable (Bookstein, 2001; Davatzikos, 2004). Good automated spatial registration is only possible if the brain has moderate pathology without major anatomic distortions. In the case of TBI patients, this is not always the case,
and the registration is expected to deteriorate with increasing severity of the injury. Great caution must therefore be exerted when performing voxel-based morphometry, carefully checking that the registration procedure is successful before inferring results. Macroscopic lesions should be masked out to minimize the impact on template creation. Since the data is often heavily smoothed to ensure that the statistics used are valid (Ashburner and Friston, 2000), the resulting parametric maps are of low resolution, and it is difficult to localize changes to specific white matter bundles. Data analysis is rather tedious, and requires the efforts of a dedicated image analyst, as fully automated image processing software is still unavailable.
CONCLUSION Our data demonstrate that DTI imaging of patients with severe TBI reveals a generalized pattern of white matter changes in major intra- and inter-hemispheric white matter tracts, consistent with DAI. In this study, these changes were more prominent on the right side of the brain, thus demonstrating that focal pathology is also a major contributor to changes in FA values. A generalized increase in ADC values were observed also outside the regions with FA reduction, possibly indicating that ADC may be an even more sensitive measurement than FA in widespread white matter damage. DTI and ADC mapping hold great promise as diagnostic tools to identify and quantify the degree of white matter injury in TBI patients. Future studies will ideally further explore whether early DTI/ADC abnormalities may be of value as a predictor of long-term clinical outcome in TBI patients and whether enhanced early diagnosis has any impact on treatment and rehabilitation.
ACKNOWLEDGMENTS We wish to thank Dr. Toril Skandsen for including patients, physicist Roar Sunde for helping out with keeping the hardware running, and Dr. Torgil R. Vangberg for valuable discussions concerning VBM analyses.
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