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Quantifying White Matter Structural Integrity With High-Definition Fiber Tracking in Traumatic Brain Injury Nora Presson, PhD*; Deepa Krishnaswamy, MS*; Lauren Wagener, BS*; William Bird, BS*; Kevin Jarbo, BS±§; Sudhir Pathak, MS*U; Ava M. Puccio, P hD f; Allison Borasso, M S f; Steven Benso, RN, B S N f; David O. Okonkwo, MD, P hD f; Walter Schneider, P hD *fj]**
ABSTRACT There is an urgent, unmet demand for definitive biological diagnosis of traumatic brain injury (TBI) to pinpoint the location and extent of damage. We have developed High-Definition Fiber Tracking, a 3 T magnetic resonance imaging-based diffusion spectrum imaging and tractography analysis protocol, to quantify axonal injury in military and civilian TBI patients. A novel analytical methodology quantified white matter integrity in patients with TBI and healthy controls. Forty-one subjects (23 TBI, 18 controls) were scanned with the High-Definition Fiber Tracking diffusion spectrum imaging protocol. After reconstruction, segmentation was used to isolate bilateral hemisphere homologues of eight major tracts. Integrity of segmented tracts was estimated by calculating homologue correlation and tract coverage. Both groups showed high correlations for all tracts. TBI patients showed reduced homologue correlation and tract spread and increased outlier count (correlations > 2.32 SD below control mean). On average, 6.5% of tracts in the TBI group were outliers with substantial variability among patients. Number and summed deviation of outlying tracts correlated with initial Glasgow Coma Scale score and 6-month Glasgow Outcome Scale-Extended score. The correlation metric used here can detect heterogeneous damage affecting a low proportion of tracts, presenting a potential mechanism for advancing TBI diagnosis.
INTRODUCTION Traumatic brain injury (TBI) is a major public health burden. TBI is the most common cause of death under age 45 in the United States and is a major cause of disability, especially in young adults.1 TBI is also a signature injury of recent mili tary conflicts.2,3 The diagnosis and treatment of TBI is hin dered by the lack of a definitive neuroimaging modality that visualizes and quantifies the injury. Current imaging methods (e.g., computed tomography [CT], diffusion tensor imaging [DTI], fluid attenuated inversion recovery [FLAIR]) often lead to inconclusive diagnostic information. This study lever
*Leaming Research and Development Center, University of Pittsburgh, 3939 O ’Hara Street, Pittsburgh, PA 15260. fDepartment of Neurological Surgery, University of Pittsburgh, UPMC Presbyterian, Suite B-400, 200 Lothrop Street, Pittsburgh, PA 15213. ^Psychology Department, Carnegie Mellon University, Baker Hall 342c, Pittsburgh, PA 15213. §Center for the Neural Basis of Cognition, 4400 Fifth Avenue, Pittsburgh, PA 15213. [Department of Bioengineering, University of Pittsburgh, 300 Technol ogy Drive, Pittsburgh, PA 15213. ^Department of Psychology, University of Pittsburgh, Sennott Square, 3rd Floor, 210 Bouquet Street, Pittsburgh, PA 15260. **Department of Radiology, University of Pittsburgh, 3950 Presbyterian Hospital, South Tower 200, Lothrop Street, Pittsburgh PA, 15213. This article was presented in poster format at the Medical Health System Research Symposium, Fort Lauderdale, Florida, August 12-15, 2013. The views, opinions, and/or findings contained in this presentation are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the U.S. Department of Defense. This study was approved by the University of Pittsburgh Institutional Review Board under the protocol number: PR008080168. doi: 10.7205/MILMED-D-14-00413
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ages the recent development of an advanced processing pipeline for diffusion spectrum imaging (DSI) called HighDefinition Fiber Tracking (HDFT).4 The HDFT method leads to high-quality models of diffusion, particularly at fiber crossings; HDFT generates tractography that can then be quantified to estimate white matter integrity in both patients with TBI and healthy controls. The goals of the HDFT analysis approach are to classify and quantify white matter injury in individual patients and correlate that injury to clin ical consequences. There are three key challenges facing imaging studies seeking to detect anatomical damage in mild TBI (mTBI). First, the expected effect size may be small; that is, a small absolute quantity of tissue injury ( 0.90, with four exceptions in the Z dimension) for both control and TBI groups. Figure 5 shows the distribution of correlations in all three dimensions for control and TBI groups. The left-right homologue correlation was lower for patients with TBI than controls (Fig. 5) in the Y (t(292) = 2.84,/? = 0.004) and Z (r(288) = 2.39, p - 0.018) dimensions, but not in the X dimension (t(297) = 1.06, p = 0.288). For statistical analysis, correlation scores (Pearson r coefficients) were transformed to z using the Fisher method. The lack of group correlation differences in the X dimension stems from the very high correlations for both groups in the X dimension, which may reflect the alignment procedure used to flip the right hemisphere tract for the calculation of the X correlation. In an analysis of the combination of the correlation and pro portion of voxels contacted metrics, Hotelling’s T2 showed an overall difference between control and patient groups (t2(4, 312) = 4.58,/? = 0.002). To rule out an artifact of differences among tracts, these results were replicated using hierarchical linear models (for X, Y, and Z dimension correlations and left and right hemi sphere proportion of voxels contacted) with random inter cepts for subject and tract and a single fixed effect predictor for group (TBI/control). For correlations, these models showed the same pattern of lower correlations for the TBI group in both Y (b = -0.19,/? = 0.018) and Z (b = -0.19,/? = 0.024) dimensions, but not in X (b = -0.07, p = 0.34), and when treating dimension as a fixed effect (b = -0.15, p = 0.024). For proportion of voxels contacted, however, there was no difference in hierarchical regression between TBI and control groups for either left (b = -0.25, p = 0.130) or right (b = -0.28, p = 0.130) hemispheres, or by treating hemisphere as a fixed effect (b = -0.13, p = 0.120). Two tracts showed significant differences in left-right homologue correlation in the Y dimension (CR and CC) and two showed marginal effects (/? < 0.1; GE and AR). No other tract showed evidence of group differences in correlation for any dimension.
Proportion of Voxels Contacted The average tract contacted approximately 4% of voxels in the skull-stripped brain mask (see Table I for descriptive statistics for proportion of voxels contacted). The number of voxels in the skull-stripped brain mask did not signifi cantly differ between TBI and control groups (1(28.9) = 0.03, p = 0.977). As predicted, the proportion of brain voxels contacted by each tract was significantly lower on average in patients with TBI compared to controls (Table I) in both the left (r(290) = 2.30, p = 0.022) and right (r(293) = 2.36, p = 0.019) hemi spheres. Across all tracts, the mean proportion of voxels contacted was 0.049 (SE = 0.004) for controls and 0.039 (SE = 0.002) for patients with TBI in the left hemisphere, and 0.045 (SE = 0.004) for controls and 0.034 (SE = 0.002) for patients in
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Quantifying White Matter Structural Integrity With HDFT in TBl
Corona Radiata A
Median Correlation (Y Dimension) TBI: r = .967
B
Median Correlation (Y Dimension) Control: r = .972
40
Voxel Position
Hemisphere -L e ft - R ig h t
0
20
40
60
Voxel Position
C
0
20
40
-L e ft
•• Right (Flipped)
60
Voxel Position
Minimum Correlation (Y Dimension) TBI: r = .263
Voxel Position
D
Minimum Correlation (Y Dimension) Control: r = .938
FIGURE 2. Count of left and right corona radiata streamlines crossing each projection point, used to calculate the correlation between homologues projected into the Y (anterior to posterior), Z (inferior to superior), and X (left to right) dimensions. Note the similar shape and magnitude in each case, except for panel C, where the correlation were very low (see arrow).
the right hemisphere. Interestingly, across tracts, average pro portion of voxels contacted was larger for the left hemisphere homologue than the right hemisphere homologue, both when examining controls alone (r( 134) = 7.16, p < 0.001) and when including the TBI group (f(309) = 9.83, p < 0.001). Although the mean proportion of voxels contacted was significantly reduced for the TBI group relative to the con trol group, this difference was not reflected in tract-level comparisons. Although mean differences were observed (e.g., in the left hemisphere, AR was reduced by 40% and Cl was reduced 50% in TBI relative to control), these could not be detected statistically because of the size of the sample (18 controls and 24 TBI) and the between-subject variability
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in tract spread. The fact that the correlation metric uses a within-subject comparison (left-right homologue) as its basis may explain the ability to detect some tract-level group dif ferences in correlation.
T ra c t S e v e rity S c o re
Figure 6 shows the left-right homologue correlation for con trol and TBI groups. Importantly, although there is a close match of the median scores (reflecting the difficulty of detect ing tract-level significant group differences described above), there are more outliers, or observations that lie far below the central tendency of the data, in the TBI group.
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Corpus Callosum A
Median Correlation (Y Dimension) TBI:/-=.961
C Minimum Correlation (Y Dimension) TBI: r = .805
B
Median Correlation (Y Dimension) Control: r = .974
D Minimum Correlation (Y Dimension) Control: r= .888
FIGURE 3. Segmented tractography of the left and right halves of the corpus callosum for the median and minimum observed Y correlation in TBI (left panels) and control groups (right panels). Note the smaller effect size of the difference between minimum and median TBI cases than in the corona radiata.
Our goal was to capture the extent to which TBI patients deviated from the distribution of control correlations. We decided to measure the number of outlying tracts and the extent of their abnormality at the subject level. This metric is, thus, an outlier statistic, not concerned with overall aver age damage but rather with extreme damage to any region, which makes it conceptually similar to the Injury Severity Score (ISS). The ISS is defined as the square of the three highest Abbreviated Injury Scale scores recorded for six body regions (head, face, chest, abdomen, extremities [including pelvis], and external). Only the highest Abbreviated Injury Scale score in each body region is used. That is, if the heart is severely injured, lack of damage to the face should not reduce the total ISS. For each patient, we calculated two types of Tract Severity Score (TSS). First, the number of outlying tracts was calcu lated for each participant as the number of tracts for which the symmetry correlation fell below a threshold of 2.33 SD
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below the mean of the control sample (capturing roughly the bottom 1% of control data). Second, the cumulative z score was calculated for each participant as the sum of the z scores for all outlying tracts (tracts that exceeded the control thresh old). For example, a case with z scores o f-1 , -3 , and - 4 would have a cumulative score of -7 , because only the last two scores exceeded the -2.33 threshold. The average number of outlying tracts for a patient with TBI was 1.48 (SD = 1.9). Among tracts, the proportion of TBI patients with a specific white matter pathway more than 2.3 SD below the control mean ranged from 13% (FO) to 1% (AR). As a logical check, a t test (Fig. 2) indicates that the number of outlying tracts is larger for the TBI group than the control group whether collapsing across dimension (mean of 0.12 for controls, 1.63 for TBI, r(25) = 3.63, p = 0.001) or treating each dimension as a separate observation (mean of 0.04 for controls, 0.54 for TBI, r(79) = 4.37, p < 0.001). However, by setting the thresholds based on the
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Quantifying White Matter Structural Integrity With HDFT in TBI
Corpus Callosum A
Median Correlation (Y Dimension) TBI: r = .961
B
Median Correlation (Y Dimension) Control: r = .974
C
Minimum Correlation (Y Dimension) TBI: r= .805
D
Minimum Correlation (Y Dimension) Control: r = .888
Voxel Position
Voxel Position
Voxel Position
Voxel Position
FIGURE 4. Count of streamlines in left and right halves of the corpus callosum crossing each projection point, used to calculate the correlation between homologues projected onto all three dimensions. Note the similar shape and magnitude in each case, except for panel C, showing reduced correlation (see arrow).
TABLE I.
Descriptive Statistics for Homologue Correlations and Volume of Contacted Voxels for Control and T B I Groups Volume of Contacted Voxels
Left-Right Homologue Correlation Y (Anterior to Posterior) Control
TBI
Z (Inferior to Superior) Control
TBI
X (Left to Right, Shifted) Control
TBI
Left Hemisphere Control
TBI
0.984 85.2 Arcuate Fasciculus 0.965 0.958 0.798 0.809 0.98 62.7 + 186.2 Corpus Callosum 0.968 0.941 + 0.989 0.963 0.986 0.986 149.3 44 Cingulum 0.951 0.955 0.954 0.952 0.983 0.97 31 Corona Radiata 0.974 0.924 0.988 0.988 0.98 161.4 126.2 0.976 50.4 Corticospinal Tract 0.969 0.941 * 0.967 0.945 0.959 0.958 67.5 42 Fronto-Occipital Fasc 0.952 0.925 0.885 0.975 0.958 + 51.3 0.953 * 57.5 Genu 0.994 0.98 0.98 0.935 + 0.961 0.958 45.6 54.1 Splenium 0.989 0.982 0.982 0.981 0.985 41.4 0.891 7Q ** Mean 0.97 0.954 ** 0.939 0.921 ** 0.977 0.973 88.7 f-test f(292) = 2.84, p = 0.005 1(288) = 2.39, p = 0.017 t( 297) = 1.06, p = 0.288 1(290) = 2.30, p = 0.022
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Right Hemisphere Control
TBI
82.6 58.8 167.6 138.7 35.7 21.6 152.8 107.4 56.8 41.2 55.1 39.1 50 41.2 49.3 37.6 81.4 61.9 ** t(293) = 2.36,p = 0.019
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G roup Ej3 TBI E5I Control
G roup TBI £-9 Control
G roup E^3 TBI (“9 Control
Distribution of correlation scores for each tract and dimension in control and TBI groups. Whiskers extend to capture the data point closest to 1.58 times the interquartile range.
FIGURE 5.
controls only, the control group should by definition be unlikely to exceed that threshold (i.e., reduced the probability of false positives).
shows that a quantification of tractography may potentially be used to predict functional outcome.
Correlation With GCS and GOS-E
The goal of this study was to quantify white matter integrity with a HDFT approach, using quantification methods to detect structural white matter change in patients with TBI. Although overall group differences were not large, the TBI group showed consistently lower left-right correlations and lower proportion of voxels contacted compared to the con trol group. The results from this pilot study support our hypotheses. The first hypothesis was that healthy volunteer and TBI sub jects would consistently show high correlations between left and right hemispheres white matter tract homologues. Corre lations between homologues were high for all tracts exam ined in the study in all dimensions (r > 0.92 in the Y dimension, r > 0.79 in the Z dimension, r > 0.95 in the X
DISCUSSION Two additional tests were performed on the TBI group only by correlating number of white matter fiber tracts below the 2.3 SD threshold and cumulative z score with GCS and GOS-E scores. The number of white matter tracts below threshold for left-right homologue correlation (in all 3 dimensions) was marginally correlated with both GCS (r(69) = -0.26, p = 0.07) and GOS-E (r(69) = -0.26, p = 0.07). The cumulative z score was significantly correlated with both GCS (r(69) = 0.34, p = 0.01) and GOS-E (r(69) = -0.29, p = 0.03). The direction of the correlations is as expected; that is, the more tracts below the threshold or the greater the decrease relative to controls, the lower the GCS or GOS-E observed (the greater the impairment). Most importantly, this analysis
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Group ♦ TBI # Control
G roup TBI • Control
FIGURE 6. Tract severity score metrics (top). Number of tracts falling below 2.32 SD of the control mean (bottom). Summed z score for tracts falling below 2.32 SD of the control mean.
dimension), including inter-hemispheric (CC, GE, Cl, SP), intra-hemispheric (FO, AR), and subcortical (CR, CS) tracts. High correlations were present in all three dimensions in both control and TBI participants, as predicted, supporting the accepted model of substantial brain symmetry. The second hypothesis was that TBI damage could be detected by examining tract homologue correlation and pro portion of voxels contacted. Across tracts, the TBI group showed a small (difference in r between 0.01 and 0.02) but statistically reliable reduction in con-elation across tracts, though only the Y (anterior to posterior) and Z (inferior to superior) dimensions showed evidence of this reduction. Like wise, across tracts, there were significant reductions in the proportion of voxels contacted in the TBI group. However, because of high variability and low sample size, this study could only detect two significant tract-level differences, for Y correlations in the CR and the CC. Future studies should focus on detecting such tract-level differences, as detecting damage at the tract level is essential to linking tractography data to relevant behavioral and cognitive outcomes. The third hypothesis was that quantifying the number of tracts in patients that deviated from the distribution of con trols (more than 2.32 SD below the control mean), and the cumulative deviance of those tracts, would provide a more sensitive metric of structural white matter change than the mean proportion of voxels contacted or homologue correla tion. We found that the cumulative deviance metric produced a larger effect size with the same number of obser
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vations, suggesting greater sensitivity than the mean differ ence metrics. The fourth hypothesis was that the outlier statistics would correlate with initial injury severity (GCS) and functional performance (GOS-E) at 6 months after injury. The cumula tive deviation in this study was moderately correlated with GCS (r = 0.34) and GOS-E (r = 0.29). Raw homologue correlation and proportion of voxels contacted did not signif icantly predict GCS or GOS-E rating. However, it is impor tant to note one limitation of using the proportion of voxels contacted, that multiple tracts occupy the same volume as they cross in a voxel. We are developing quantitative methods designed to model and remove interstitial water volume as well as appropriately model volume in crossing tracts.25 but this analysis is restricted to the proportion of the voxels contacted by a tract (which will consistently overesti mate tract coverage). The fifth hypothesis was that TBI damage would be het erogeneous, with no one white matter pathway accounting for the majority of the observed change. At the tract level, two tracts showed significant reductions in the TBI group relative to controls, and four tracts showed marginal reductions. The average number of outlying tracts for a patient with TBI was 1.48 (SD = 1.9). Among tracts, the proportion of TBI patients with a specific white matter pathway more than 2.3 SD below the control mean ranged from 13% (FO) to 1% (AR), show ing additional variability among tracts. These results sup port the interpretation that the tract damage is heterogeneous,
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and that an outlier approach can capture such heteroge neous damage. The results also support two research predictions: tractbased metrics might detect TBI damage where mean gFA would not and the median tract in TBI would be in the range of the median tract of the control group. This was expected given the expectation of low incidence of tract damage of any specific tract and because damage location may vary between TBI cases. As expected, gFA measurements were not signif icantly different between the TBI and control group, whereas the tract symmetry and outlier metrics were. With substan tially larger sample size, we would expect the gFA measure ments to show mean group differences as well. We note that in clinical diagnostic imaging, the goal is to identify the presence, location, and amount of damage for an individual case to inform clinical actions for that patient. There is a need for metrics that on an individual case can identify the probability of damage. In this data set, the gFA measures were less successful than tract-based symmetry and outlier metrics, and the difference could be clearly visualized in individual cases (see Fig. 2). There are a number of limitations in this study to be addressed in ongoing and future research. This study has a limited set of observations, especially given the prevalence of mild cases in the TBI sample. Although the groups in this study did not differ in age, greater coverage in the young adult age range in future research would improve the generalizability of the data to a military sample. Another limitation is the lack of detailed neuropsychological assessment; GCS and GOS-E are blunt measures of overall functional deficits and do not make tract-specific damage predictions. The abil ity to discover links between reductions in specific tracts and concrete behavioral and cognitive impairments will become much stronger with a sample that includes more controls and more moderate and severely injured patients in particular. One of our ongoing goals is to analyze data from a substantial number of subjects with evidence of reductions in one com mon tract, to determine what quantitative metrics can best predict functional outcomes and how well those predictions can generalize to new data. We are currently engaged in a larger enrollment study to begin to test for these specific links. Nevertheless, the results of this study are an important step forward in quantitative analytics of neuroimaging to address the heterogeneity of TBI. No two patients have an identical TBI; on the “milder” end of the TBI scale, structural injury is rare, diffuse, and in unpredictable locations, such that tradi tional grouped mean statistics are not sensitive enough to detect change. The development of a TSS may improve our future ability to use quantitative estimates of heterogeneous damage for analysis of individual patient data. The challenge of detecting patient-level TBI damage is that the expected amount of damage is small, damage is embedded in brains with high levels of between-subject variability; most brain tissue is “normal” even in those with severe TBI, and damage is heterogeneous among patients.
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The clinical requirement in TBI is to have a confident and unambiguous diagnosis on a single patient that presents with suspected injury. The requirement illustrates a mismatch between the analytic goals of basic research and application to clinical practice. Although basic research studies can include hundreds of subjects and look for very small differ ences between groups, in clinical settings, the individual patient data must show much larger deviations from a com parable healthy sample before a diagnosis can be clear for that patient. Our long-term goal is to visualize and quantify white matter tracts with enough resolution that structural white matter change is as easy to detect as a fractured bone. The homologue correlation metric has the advantage of being a within-subject comparison; much of the variability that makes between-subject comparison difficult (e.g., genetics, age, life history) is constant within a patient for both brain hemispheres, reducing variance as a result of noise. We have observed cases of dramatic mismatches in visual and quanti tative homologue correlation (z scores below -5 relative to controls). We believe that the TSS for TBI has the potential to be a sensitive measure of overall damage severity, as the ISS has been in body trauma. The field will continue to improve the quality of imaging and refine and invent metrics to accurately quantify structural injury from TBI. For example, this study used a high-end DSI sequence requiring a 45-minute scan. Newer pulse sequences and acquisition methods developed within the last year, such as multiband and compressed sensing,26 provide high-quality data in 5 minutes (a 90% reduction is scanning time) using advanced coils, gradients, and analysis. In the future, clinics will be able to collect advanced diffusion imaging data (such as HDFT) within a 30-minute clinical session (including T1 structural, FLAIR, and susceptibility-weighted imaging) to provide a more comprehensive clinical scan. This advanced imaging data can then be used to improve the precision of inferences about tract integrity compared to healthy controls. An important limitation of the current analysis approach is the human time and judgment required to accurately draw the ROIs used to restrict streamlines during tractography. To improve the validity and reliability of quantification metrics, we are developing automated tract segmentation protocols for cortical and subcortical ROIs and ROAs. Most impor tantly, as techniques for registration of severely damaged brains improve,23 such an automated protocol will allow reg istration of control and patient data to a common template space and application of a standard set of ROIs to all brains. On the other hand, one strength of the method in this study was the lack of human trimming of streamlines that are ana tomically incorrect (e.g., false continuations). This approach made the analysis more conservative, as quantitative metrics were not fully restricted to streamlines in the tract of interest. Future studies can reduce variability in metrics (and improve detection of damage) by removing noise through trimming, either by hand or using automated rules reflecting anatomical properties of the human brain.
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Finally, we are also currently developing semiautomated and automated methods to write a comprehensive case report based on quantitative and qualitative data generated from HDFT imaging, containing relevant information about each tract. We have created an iPad 3-dimensional case report viewer27 that can present the report interactively and dynam ically, and also produce an article including the same infor mation. With progressively improving data, metrics, and understanding of control tracts, the HDFT Case Report is designed to inform both the clinical team and the patient about which tracts may show evidence of damage and which do not, to aid diagnosis, support behavioral data, and better target treatment to individual patients. ACKNOWLEDGMENT This article was funded by CDMRP PT110773 (W81XH-12-2-0140) and U.S. Army 12342013 (W81XWH-12-2-0319).
APPENDIX: TRACKING AND ROI PLACEMENT The following tracts were tracked using the specified ROIs and ROAs: Left/Right Arcuate Fasciculus (AR: ROIs: Left/ Right Prefrontal Cortex, Left/Right Temporal-ParietalOccipital Junction, ROAs: Mid-Sagittal Slice, Left/Right FOF Neck, Left/Right External Capsule), Corpus Callosum (CC; ROIs: Corpus Callosum, ROAs: Temporal-Putamen, Left and Right Internal Capsule, Left and Right External Capsule, Left and Right Crus Cerebri), Left/Right Cingulum (Cl; ROIs: Left/Right Posterior Papez, ROAs: Mid-Sagittal Slice), Left/Right Corona Radiata (CR; ROIs: Left/Right Internal Capsule, ROAs: Mid-Sagittal Slice, Left/Right FOF Neck, Arcuate Slice), Left/Right Corticospinal Tract (CS; ROIs: Left/Right Internal Capsule, Left/Right Crus Cerebri, ROAs: Mid-Sagittal Slice, Arcuate Slice), Left/Right FrontoOccipital Fasciculus (FO: ROIs: Left/Right Occipital Cortex, Left/Right FO Neck, ROAs: Mid-Sagittal Slice), Genu (GE; ROIs: Genu, ROAs: Posterior Genu Slice), Splenium (SP; ROIs: Splenium, ROAs: Temporal-Putamen). ROIs and ROAs were placed according to the following rules: Corpus Callosum: In the mid-sagittal slice, a planar ROI highlighting all voxels with x-traveling fibers in the white matter of the corpus callosum. Genu: Subset of the Corpus Callosum ROI. All fibers anterior to the anterior infe rior drop of the corpus callosum. Includes the rostrum. Poste rior Genu Slice: Corona slice immediately posterior to the Genu ROI. Rectangular planar ROI encompassing the entire slice. Splenium: Subset of the Corpus Callosum ROI. All fibers posterior to the posterior inferior drop of the corpus callosum. Mid-Sagittal Slice: In the mid-sagittal slice, high light a rectangular planar ROI encompassing the entire corpus callosum, with one spare voxel in each direction (superior, inferior, anterior, posterior). Left/Right Internal Capsule: Axial slice displaying the internal capsule as separate from the exter nal capsule. Highlight all internal capsule white matter voxels in each hemisphere separately, as planar regions. Go no further
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anterior or posterior than the corpus callosum. Left/Right External Capsule: Same slice as the Internal Capsule ROI. Highlight all external capsule white matter voxels in each hemisphere separately, as planar regions. Arcuate Slice: Same axial slice as the Internal Capsule ROI. Highlight a rectangular planar ROI of all voxels posterior to the Internal Capsule, for both hemispheres. Left/Right Crus Cerebri: Axial slice show ing the white matter of the cms cerebri. Planar ROIs highlight ing each hemisphere’s crus cerebi’s white matter separately. Left/Right Posterior Papez: Coronal slice just anterior to the Splenium ROI. Highlight all voxels with y-travelling fibers just superior to the corpus callosum, in each hemisphere sepa rately, with a planar ROI. Left/Right Prefrontal Cortex: Sagit tal slice showing the c-shaped curve of the arcuate white matter. Place a spherical ROI in the prefrontal region, not extending into the temporal lobe, in each hemisphere sepa rately. Left/Right Temporal-Parietal-Occipital Junction: Same sagittal slice as the Prefrontal Cortex ROI. Place a spherical ROI at the curve of the white matter of the acuate, where the fibers are traveling in the z-direction, in each hemisphere sepa rately, no wider than the arcuate itself. Left/Right Occipital Cortex: Axial slice between the Crus Cerebri ROI and Internal Capsule ROI. Place spherical ROIs encompassing the occipital lobe in each hemisphere separately. The spheres will cross over into the contralateral hemisphere partially. Temporal-Putamen: Coronal slice displaying the putamen. Planar ROI highlighting all temporal voxels in both hemispheres, including any voxels in between, without crossing into the frontal or parietal lobes. Left/Right FOF Neck: Coronal slice displaying the white mat ter tract of the FOF as it crosses from the temporal lobe to the frontal lobe. It should be just anterior to the Temporal-Putamen ROI slice. Place rectangular planar ROIs over these white matter tracts in each hemisphere separately.
REFERENCES 1. Koehler R, Wilhelm E, Shoulson I: Cognitive Rehabilitation Therapy for Traumatic Brain Injury: Model Study Protocols and Frameworks to Advance the State of the Science: Workshop Summary. Washington, DC, The National Academies Press, 2013. 2. Risling M: Blast Induced Brain Injuries—A Grand Challenge in TBI Research. Front Neurol 2010; 1: 1. 3. Warden D: Military TBI during the Iraq and Afghanistan wars. J Head Trauma Rehabil 2006; 21(5): 398-402. 4. Femandez-Miranda JC, et al: High-definition fiber tractography of the human brain: neuroanatomical validation and neurosurgical applica tions. Neurosurgery 2012; 71(2): 430-53. 5. Catani M, Thiebaut de Schotten M: Atlas of Human Brain Connections. New York, Oxford University Press, 2012. 6. Saatman KE, et al: Classification of traumatic brain injury for targeted therapies. J Neurotrauma 2008; 25(7): 719-38. 7. Emmett JE, Breck LW: A review and analysis of 11,000 fractures seen in a private practice of orthopaedic surgery 1937-1956. J Bone Joint Surg Am 1958; 40(5): 1169-75. 8. MacDonald CL, et al: Detection of blast-related traumatic brain injury in U.S. military personnel. N Engl J Med 2011; 364(22): 2091-100. 9. O’Donnell LJ, Westin CF, Golby AJ: Tract-based morphometry for white matter group analysis. Neuroimage 2009; 45(3): 832-44.
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Quantifying White Matter Structural Integrity With HDFT in TBI 10. Catani M, Mesulam M: The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state. Cortex 2008; 44(8): 953-61. 11. Amunts K, Zilles K: Architecture and organizational principles of Broca’s region. Trends Cogni Sci 2012; 16(8): 418-26. 12. Wang Y, et al: Rethinking the role of the middle longitudinal fascicle in language and auditory pathways. Cereb Cortex 2013; 23(10): 2347-56. 13. Hess CP, Mukheijee P, Han ET, Xu D, Vigneron DB: Q-ball reconstruc tion of multimodal fiber orientations using the spherical harmonic basis. Magn Reson Med 2006; 56(1): 104-17. 14. Jian B, Vemuri BC: A unified computational framework for decon volution to reconstruct multiple fibers from diffusion weighted MRI. IEEE Trans Med Imaging 2007; 26(11): 1464-71. 15. Kaden E, Knosche TR, Anwander A: Parametric spherical decon volution: inferring anatomical connectivity using diffusion MR imaging. Neuroimage 2007; 37(2): 474-88. 16. Shin SS, et al: High-definition fiber tracking for assessment of neurolog ical deficit in a case of traumatic brain injury: finding, visualizing, and interpreting small sites of damage. J Neurosurg 2012; 116(5): 1062-9. 17. Kraus MF, et al: White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain 2007; 130: 2508-19. 18. Galaburda AM, LeMay M, Kemper TL, Geshwind N: Right-left asym metries in the brain. Science 1978; 199(4331): 852-6. 19. Zilles K, et al: Quantitative analysis of sulci in the human cerebral cortex: development, regional heterogeneity, gender difference, asym
MILITARY MEDICINE, Vol. 180, M arch Supplem ent 2015
20.
21. 22. 23.
24. 25.
26. 27.
metry, intersubject variability and cortical architecture. Hum Brain Mapp 1997; 5(4): 218-21. Khotanlou H, Colliot O, Ati J, Bloch I: 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Set Syst 2009; 160(10): 1457-3. Tuzikov AV, Colliot O, Bloch I: Evaluation of the symmetry plane in 3D MR brain images. Pattern Recogn Lett 2003; 24(14): 2219-33. Yeh FC, Wedeen VJ, Tseng WY: Generalized q-sampling imaging. IEEE Trans Med Imaging 2010; 29: 1626-35. Irimia A, et al: Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes. J Neurotrauma 2011; 28(11): 2287-306. Yeh FC, Wedeen VJ, Tseng WY: Generalized-Sampling Imaging. IEEE Trans Med Imag 2010; 29(9): 1626-35. Pathak S, Schneider W: Quantifying directional axonal volume in fiber tractography. Poster presented at Society for Neuroscience; November 14, 2011; Washington, DC. Available at http://www.abstractsonline.com/ Plan/ViewAbstract.aspx?mID=2773&sKey=f5bl lfc7-0771-4360-834d684cc259e309&cKey=9f8708a0-1967-483f-8al7-f694fe0717ea&mKey= 8334be29-8911 -4991 -8c31-32b32dd5e6c8; accessed October 14, 2014. Setsompop K, et al: Pushing the limits of in vivo diffusion MRI for the Human Connectome Project. Neuroimage 2013; 80: 220-33. Chmura J, Presson N, Benso S, et al: A high-definition fiber tracking brain report for patients with traumatic brain injury and their doctors. Mil Med 2015; 180(3 Suppl): 122-34.
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Quantifying White Matter Structural Integrity With High-Definition Fiber Tracking in Traumatic Brain Injury Nora Presson, PhD*; Deepa Krishnaswamy, MS*; Lauren Wagener, BS*; William Bird, BS*; Kevin Jarbo, BS±§; Sudhir Pathak, MS*U; Ava M. Puccio, P hD f; Allison Borasso, M S f; Steven Benso, RN, B S N f; David O. Okonkwo, MD, P hD f; Walter Schneider, P hD *fj]**
ABSTRACT There is an urgent, unmet demand for definitive biological diagnosis of traumatic brain injury (TBI) to pinpoint the location and extent of damage. We have developed High-Definition Fiber Tracking, a 3 T magnetic resonance imaging-based diffusion spectrum imaging and tractography analysis protocol, to quantify axonal injury in military and civilian TBI patients. A novel analytical methodology quantified white matter integrity in patients with TBI and healthy controls. Forty-one subjects (23 TBI, 18 controls) were scanned with the High-Definition Fiber Tracking diffusion spectrum imaging protocol. After reconstruction, segmentation was used to isolate bilateral hemisphere homologues of eight major tracts. Integrity of segmented tracts was estimated by calculating homologue correlation and tract coverage. Both groups showed high correlations for all tracts. TBI patients showed reduced homologue correlation and tract spread and increased outlier count (correlations > 2.32 SD below control mean). On average, 6.5% of tracts in the TBI group were outliers with substantial variability among patients. Number and summed deviation of outlying tracts correlated with initial Glasgow Coma Scale score and 6-month Glasgow Outcome Scale-Extended score. The correlation metric used here can detect heterogeneous damage affecting a low proportion of tracts, presenting a potential mechanism for advancing TBI diagnosis.
INTRODUCTION Traumatic brain injury (TBI) is a major public health burden. TBI is the most common cause of death under age 45 in the United States and is a major cause of disability, especially in young adults.1 TBI is also a signature injury of recent mili tary conflicts.2,3 The diagnosis and treatment of TBI is hin dered by the lack of a definitive neuroimaging modality that visualizes and quantifies the injury. Current imaging methods (e.g., computed tomography [CT], diffusion tensor imaging [DTI], fluid attenuated inversion recovery [FLAIR]) often lead to inconclusive diagnostic information. This study lever
*Leaming Research and Development Center, University of Pittsburgh, 3939 O ’Hara Street, Pittsburgh, PA 15260. fDepartment of Neurological Surgery, University of Pittsburgh, UPMC Presbyterian, Suite B-400, 200 Lothrop Street, Pittsburgh, PA 15213. ^Psychology Department, Carnegie Mellon University, Baker Hall 342c, Pittsburgh, PA 15213. §Center for the Neural Basis of Cognition, 4400 Fifth Avenue, Pittsburgh, PA 15213. [Department of Bioengineering, University of Pittsburgh, 300 Technol ogy Drive, Pittsburgh, PA 15213. ^Department of Psychology, University of Pittsburgh, Sennott Square, 3rd Floor, 210 Bouquet Street, Pittsburgh, PA 15260. **Department of Radiology, University of Pittsburgh, 3950 Presbyterian Hospital, South Tower 200, Lothrop Street, Pittsburgh PA, 15213. This article was presented in poster format at the Medical Health System Research Symposium, Fort Lauderdale, Florida, August 12-15, 2013. The views, opinions, and/or findings contained in this presentation are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the U.S. Department of Defense. This study was approved by the University of Pittsburgh Institutional Review Board under the protocol number: PR008080168. doi: 10.7205/MILMED-D-14-00413
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ages the recent development of an advanced processing pipeline for diffusion spectrum imaging (DSI) called HighDefinition Fiber Tracking (HDFT).4 The HDFT method leads to high-quality models of diffusion, particularly at fiber crossings; HDFT generates tractography that can then be quantified to estimate white matter integrity in both patients with TBI and healthy controls. The goals of the HDFT analysis approach are to classify and quantify white matter injury in individual patients and correlate that injury to clin ical consequences. There are three key challenges facing imaging studies seeking to detect anatomical damage in mild TBI (mTBI). First, the expected effect size may be small; that is, a small absolute quantity of tissue injury ( 0.90, with four exceptions in the Z dimension) for both control and TBI groups. Figure 5 shows the distribution of correlations in all three dimensions for control and TBI groups. The left-right homologue correlation was lower for patients with TBI than controls (Fig. 5) in the Y (t(292) = 2.84,/? = 0.004) and Z (r(288) = 2.39, p - 0.018) dimensions, but not in the X dimension (t(297) = 1.06, p = 0.288). For statistical analysis, correlation scores (Pearson r coefficients) were transformed to z using the Fisher method. The lack of group correlation differences in the X dimension stems from the very high correlations for both groups in the X dimension, which may reflect the alignment procedure used to flip the right hemisphere tract for the calculation of the X correlation. In an analysis of the combination of the correlation and pro portion of voxels contacted metrics, Hotelling’s T2 showed an overall difference between control and patient groups (t2(4, 312) = 4.58,/? = 0.002). To rule out an artifact of differences among tracts, these results were replicated using hierarchical linear models (for X, Y, and Z dimension correlations and left and right hemi sphere proportion of voxels contacted) with random inter cepts for subject and tract and a single fixed effect predictor for group (TBI/control). For correlations, these models showed the same pattern of lower correlations for the TBI group in both Y (b = -0.19,/? = 0.018) and Z (b = -0.19,/? = 0.024) dimensions, but not in X (b = -0.07, p = 0.34), and when treating dimension as a fixed effect (b = -0.15, p = 0.024). For proportion of voxels contacted, however, there was no difference in hierarchical regression between TBI and control groups for either left (b = -0.25, p = 0.130) or right (b = -0.28, p = 0.130) hemispheres, or by treating hemisphere as a fixed effect (b = -0.13, p = 0.120). Two tracts showed significant differences in left-right homologue correlation in the Y dimension (CR and CC) and two showed marginal effects (/? < 0.1; GE and AR). No other tract showed evidence of group differences in correlation for any dimension.
Proportion of Voxels Contacted The average tract contacted approximately 4% of voxels in the skull-stripped brain mask (see Table I for descriptive statistics for proportion of voxels contacted). The number of voxels in the skull-stripped brain mask did not signifi cantly differ between TBI and control groups (1(28.9) = 0.03, p = 0.977). As predicted, the proportion of brain voxels contacted by each tract was significantly lower on average in patients with TBI compared to controls (Table I) in both the left (r(290) = 2.30, p = 0.022) and right (r(293) = 2.36, p = 0.019) hemi spheres. Across all tracts, the mean proportion of voxels contacted was 0.049 (SE = 0.004) for controls and 0.039 (SE = 0.002) for patients with TBI in the left hemisphere, and 0.045 (SE = 0.004) for controls and 0.034 (SE = 0.002) for patients in
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Corona Radiata A
Median Correlation (Y Dimension) TBI: r = .967
B
Median Correlation (Y Dimension) Control: r = .972
40
Voxel Position
Hemisphere -L e ft - R ig h t
0
20
40
60
Voxel Position
C
0
20
40
-L e ft
•• Right (Flipped)
60
Voxel Position
Minimum Correlation (Y Dimension) TBI: r = .263
Voxel Position
D
Minimum Correlation (Y Dimension) Control: r = .938
FIGURE 2. Count of left and right corona radiata streamlines crossing each projection point, used to calculate the correlation between homologues projected into the Y (anterior to posterior), Z (inferior to superior), and X (left to right) dimensions. Note the similar shape and magnitude in each case, except for panel C, where the correlation were very low (see arrow).
the right hemisphere. Interestingly, across tracts, average pro portion of voxels contacted was larger for the left hemisphere homologue than the right hemisphere homologue, both when examining controls alone (r( 134) = 7.16, p < 0.001) and when including the TBI group (f(309) = 9.83, p < 0.001). Although the mean proportion of voxels contacted was significantly reduced for the TBI group relative to the con trol group, this difference was not reflected in tract-level comparisons. Although mean differences were observed (e.g., in the left hemisphere, AR was reduced by 40% and Cl was reduced 50% in TBI relative to control), these could not be detected statistically because of the size of the sample (18 controls and 24 TBI) and the between-subject variability
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in tract spread. The fact that the correlation metric uses a within-subject comparison (left-right homologue) as its basis may explain the ability to detect some tract-level group dif ferences in correlation.
T ra c t S e v e rity S c o re
Figure 6 shows the left-right homologue correlation for con trol and TBI groups. Importantly, although there is a close match of the median scores (reflecting the difficulty of detect ing tract-level significant group differences described above), there are more outliers, or observations that lie far below the central tendency of the data, in the TBI group.
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Corpus Callosum A
Median Correlation (Y Dimension) TBI:/-=.961
C Minimum Correlation (Y Dimension) TBI: r = .805
B
Median Correlation (Y Dimension) Control: r = .974
D Minimum Correlation (Y Dimension) Control: r= .888
FIGURE 3. Segmented tractography of the left and right halves of the corpus callosum for the median and minimum observed Y correlation in TBI (left panels) and control groups (right panels). Note the smaller effect size of the difference between minimum and median TBI cases than in the corona radiata.
Our goal was to capture the extent to which TBI patients deviated from the distribution of control correlations. We decided to measure the number of outlying tracts and the extent of their abnormality at the subject level. This metric is, thus, an outlier statistic, not concerned with overall aver age damage but rather with extreme damage to any region, which makes it conceptually similar to the Injury Severity Score (ISS). The ISS is defined as the square of the three highest Abbreviated Injury Scale scores recorded for six body regions (head, face, chest, abdomen, extremities [including pelvis], and external). Only the highest Abbreviated Injury Scale score in each body region is used. That is, if the heart is severely injured, lack of damage to the face should not reduce the total ISS. For each patient, we calculated two types of Tract Severity Score (TSS). First, the number of outlying tracts was calcu lated for each participant as the number of tracts for which the symmetry correlation fell below a threshold of 2.33 SD
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below the mean of the control sample (capturing roughly the bottom 1% of control data). Second, the cumulative z score was calculated for each participant as the sum of the z scores for all outlying tracts (tracts that exceeded the control thresh old). For example, a case with z scores o f-1 , -3 , and - 4 would have a cumulative score of -7 , because only the last two scores exceeded the -2.33 threshold. The average number of outlying tracts for a patient with TBI was 1.48 (SD = 1.9). Among tracts, the proportion of TBI patients with a specific white matter pathway more than 2.3 SD below the control mean ranged from 13% (FO) to 1% (AR). As a logical check, a t test (Fig. 2) indicates that the number of outlying tracts is larger for the TBI group than the control group whether collapsing across dimension (mean of 0.12 for controls, 1.63 for TBI, r(25) = 3.63, p = 0.001) or treating each dimension as a separate observation (mean of 0.04 for controls, 0.54 for TBI, r(79) = 4.37, p < 0.001). However, by setting the thresholds based on the
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Corpus Callosum A
Median Correlation (Y Dimension) TBI: r = .961
B
Median Correlation (Y Dimension) Control: r = .974
C
Minimum Correlation (Y Dimension) TBI: r= .805
D
Minimum Correlation (Y Dimension) Control: r = .888
Voxel Position
Voxel Position
Voxel Position
Voxel Position
FIGURE 4. Count of streamlines in left and right halves of the corpus callosum crossing each projection point, used to calculate the correlation between homologues projected onto all three dimensions. Note the similar shape and magnitude in each case, except for panel C, showing reduced correlation (see arrow).
TABLE I.
Descriptive Statistics for Homologue Correlations and Volume of Contacted Voxels for Control and T B I Groups Volume of Contacted Voxels
Left-Right Homologue Correlation Y (Anterior to Posterior) Control
TBI
Z (Inferior to Superior) Control
TBI
X (Left to Right, Shifted) Control
TBI
Left Hemisphere Control
TBI
0.984 85.2 Arcuate Fasciculus 0.965 0.958 0.798 0.809 0.98 62.7 + 186.2 Corpus Callosum 0.968 0.941 + 0.989 0.963 0.986 0.986 149.3 44 Cingulum 0.951 0.955 0.954 0.952 0.983 0.97 31 Corona Radiata 0.974 0.924 0.988 0.988 0.98 161.4 126.2 0.976 50.4 Corticospinal Tract 0.969 0.941 * 0.967 0.945 0.959 0.958 67.5 42 Fronto-Occipital Fasc 0.952 0.925 0.885 0.975 0.958 + 51.3 0.953 * 57.5 Genu 0.994 0.98 0.98 0.935 + 0.961 0.958 45.6 54.1 Splenium 0.989 0.982 0.982 0.981 0.985 41.4 0.891 7Q ** Mean 0.97 0.954 ** 0.939 0.921 ** 0.977 0.973 88.7 f-test f(292) = 2.84, p = 0.005 1(288) = 2.39, p = 0.017 t( 297) = 1.06, p = 0.288 1(290) = 2.30, p = 0.022
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Right Hemisphere Control
TBI
82.6 58.8 167.6 138.7 35.7 21.6 152.8 107.4 56.8 41.2 55.1 39.1 50 41.2 49.3 37.6 81.4 61.9 ** t(293) = 2.36,p = 0.019
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G roup Ej3 TBI E5I Control
G roup TBI £-9 Control
G roup E^3 TBI (“9 Control
Distribution of correlation scores for each tract and dimension in control and TBI groups. Whiskers extend to capture the data point closest to 1.58 times the interquartile range.
FIGURE 5.
controls only, the control group should by definition be unlikely to exceed that threshold (i.e., reduced the probability of false positives).
shows that a quantification of tractography may potentially be used to predict functional outcome.
Correlation With GCS and GOS-E
The goal of this study was to quantify white matter integrity with a HDFT approach, using quantification methods to detect structural white matter change in patients with TBI. Although overall group differences were not large, the TBI group showed consistently lower left-right correlations and lower proportion of voxels contacted compared to the con trol group. The results from this pilot study support our hypotheses. The first hypothesis was that healthy volunteer and TBI sub jects would consistently show high correlations between left and right hemispheres white matter tract homologues. Corre lations between homologues were high for all tracts exam ined in the study in all dimensions (r > 0.92 in the Y dimension, r > 0.79 in the Z dimension, r > 0.95 in the X
DISCUSSION Two additional tests were performed on the TBI group only by correlating number of white matter fiber tracts below the 2.3 SD threshold and cumulative z score with GCS and GOS-E scores. The number of white matter tracts below threshold for left-right homologue correlation (in all 3 dimensions) was marginally correlated with both GCS (r(69) = -0.26, p = 0.07) and GOS-E (r(69) = -0.26, p = 0.07). The cumulative z score was significantly correlated with both GCS (r(69) = 0.34, p = 0.01) and GOS-E (r(69) = -0.29, p = 0.03). The direction of the correlations is as expected; that is, the more tracts below the threshold or the greater the decrease relative to controls, the lower the GCS or GOS-E observed (the greater the impairment). Most importantly, this analysis
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Group ♦ TBI # Control
G roup TBI • Control
FIGURE 6. Tract severity score metrics (top). Number of tracts falling below 2.32 SD of the control mean (bottom). Summed z score for tracts falling below 2.32 SD of the control mean.
dimension), including inter-hemispheric (CC, GE, Cl, SP), intra-hemispheric (FO, AR), and subcortical (CR, CS) tracts. High correlations were present in all three dimensions in both control and TBI participants, as predicted, supporting the accepted model of substantial brain symmetry. The second hypothesis was that TBI damage could be detected by examining tract homologue correlation and pro portion of voxels contacted. Across tracts, the TBI group showed a small (difference in r between 0.01 and 0.02) but statistically reliable reduction in con-elation across tracts, though only the Y (anterior to posterior) and Z (inferior to superior) dimensions showed evidence of this reduction. Like wise, across tracts, there were significant reductions in the proportion of voxels contacted in the TBI group. However, because of high variability and low sample size, this study could only detect two significant tract-level differences, for Y correlations in the CR and the CC. Future studies should focus on detecting such tract-level differences, as detecting damage at the tract level is essential to linking tractography data to relevant behavioral and cognitive outcomes. The third hypothesis was that quantifying the number of tracts in patients that deviated from the distribution of con trols (more than 2.32 SD below the control mean), and the cumulative deviance of those tracts, would provide a more sensitive metric of structural white matter change than the mean proportion of voxels contacted or homologue correla tion. We found that the cumulative deviance metric produced a larger effect size with the same number of obser
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vations, suggesting greater sensitivity than the mean differ ence metrics. The fourth hypothesis was that the outlier statistics would correlate with initial injury severity (GCS) and functional performance (GOS-E) at 6 months after injury. The cumula tive deviation in this study was moderately correlated with GCS (r = 0.34) and GOS-E (r = 0.29). Raw homologue correlation and proportion of voxels contacted did not signif icantly predict GCS or GOS-E rating. However, it is impor tant to note one limitation of using the proportion of voxels contacted, that multiple tracts occupy the same volume as they cross in a voxel. We are developing quantitative methods designed to model and remove interstitial water volume as well as appropriately model volume in crossing tracts.25 but this analysis is restricted to the proportion of the voxels contacted by a tract (which will consistently overesti mate tract coverage). The fifth hypothesis was that TBI damage would be het erogeneous, with no one white matter pathway accounting for the majority of the observed change. At the tract level, two tracts showed significant reductions in the TBI group relative to controls, and four tracts showed marginal reductions. The average number of outlying tracts for a patient with TBI was 1.48 (SD = 1.9). Among tracts, the proportion of TBI patients with a specific white matter pathway more than 2.3 SD below the control mean ranged from 13% (FO) to 1% (AR), show ing additional variability among tracts. These results sup port the interpretation that the tract damage is heterogeneous,
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and that an outlier approach can capture such heteroge neous damage. The results also support two research predictions: tractbased metrics might detect TBI damage where mean gFA would not and the median tract in TBI would be in the range of the median tract of the control group. This was expected given the expectation of low incidence of tract damage of any specific tract and because damage location may vary between TBI cases. As expected, gFA measurements were not signif icantly different between the TBI and control group, whereas the tract symmetry and outlier metrics were. With substan tially larger sample size, we would expect the gFA measure ments to show mean group differences as well. We note that in clinical diagnostic imaging, the goal is to identify the presence, location, and amount of damage for an individual case to inform clinical actions for that patient. There is a need for metrics that on an individual case can identify the probability of damage. In this data set, the gFA measures were less successful than tract-based symmetry and outlier metrics, and the difference could be clearly visualized in individual cases (see Fig. 2). There are a number of limitations in this study to be addressed in ongoing and future research. This study has a limited set of observations, especially given the prevalence of mild cases in the TBI sample. Although the groups in this study did not differ in age, greater coverage in the young adult age range in future research would improve the generalizability of the data to a military sample. Another limitation is the lack of detailed neuropsychological assessment; GCS and GOS-E are blunt measures of overall functional deficits and do not make tract-specific damage predictions. The abil ity to discover links between reductions in specific tracts and concrete behavioral and cognitive impairments will become much stronger with a sample that includes more controls and more moderate and severely injured patients in particular. One of our ongoing goals is to analyze data from a substantial number of subjects with evidence of reductions in one com mon tract, to determine what quantitative metrics can best predict functional outcomes and how well those predictions can generalize to new data. We are currently engaged in a larger enrollment study to begin to test for these specific links. Nevertheless, the results of this study are an important step forward in quantitative analytics of neuroimaging to address the heterogeneity of TBI. No two patients have an identical TBI; on the “milder” end of the TBI scale, structural injury is rare, diffuse, and in unpredictable locations, such that tradi tional grouped mean statistics are not sensitive enough to detect change. The development of a TSS may improve our future ability to use quantitative estimates of heterogeneous damage for analysis of individual patient data. The challenge of detecting patient-level TBI damage is that the expected amount of damage is small, damage is embedded in brains with high levels of between-subject variability; most brain tissue is “normal” even in those with severe TBI, and damage is heterogeneous among patients.
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The clinical requirement in TBI is to have a confident and unambiguous diagnosis on a single patient that presents with suspected injury. The requirement illustrates a mismatch between the analytic goals of basic research and application to clinical practice. Although basic research studies can include hundreds of subjects and look for very small differ ences between groups, in clinical settings, the individual patient data must show much larger deviations from a com parable healthy sample before a diagnosis can be clear for that patient. Our long-term goal is to visualize and quantify white matter tracts with enough resolution that structural white matter change is as easy to detect as a fractured bone. The homologue correlation metric has the advantage of being a within-subject comparison; much of the variability that makes between-subject comparison difficult (e.g., genetics, age, life history) is constant within a patient for both brain hemispheres, reducing variance as a result of noise. We have observed cases of dramatic mismatches in visual and quanti tative homologue correlation (z scores below -5 relative to controls). We believe that the TSS for TBI has the potential to be a sensitive measure of overall damage severity, as the ISS has been in body trauma. The field will continue to improve the quality of imaging and refine and invent metrics to accurately quantify structural injury from TBI. For example, this study used a high-end DSI sequence requiring a 45-minute scan. Newer pulse sequences and acquisition methods developed within the last year, such as multiband and compressed sensing,26 provide high-quality data in 5 minutes (a 90% reduction is scanning time) using advanced coils, gradients, and analysis. In the future, clinics will be able to collect advanced diffusion imaging data (such as HDFT) within a 30-minute clinical session (including T1 structural, FLAIR, and susceptibility-weighted imaging) to provide a more comprehensive clinical scan. This advanced imaging data can then be used to improve the precision of inferences about tract integrity compared to healthy controls. An important limitation of the current analysis approach is the human time and judgment required to accurately draw the ROIs used to restrict streamlines during tractography. To improve the validity and reliability of quantification metrics, we are developing automated tract segmentation protocols for cortical and subcortical ROIs and ROAs. Most impor tantly, as techniques for registration of severely damaged brains improve,23 such an automated protocol will allow reg istration of control and patient data to a common template space and application of a standard set of ROIs to all brains. On the other hand, one strength of the method in this study was the lack of human trimming of streamlines that are ana tomically incorrect (e.g., false continuations). This approach made the analysis more conservative, as quantitative metrics were not fully restricted to streamlines in the tract of interest. Future studies can reduce variability in metrics (and improve detection of damage) by removing noise through trimming, either by hand or using automated rules reflecting anatomical properties of the human brain.
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Quantifying White Matter Structural Integrity With HDFT in TBI
Finally, we are also currently developing semiautomated and automated methods to write a comprehensive case report based on quantitative and qualitative data generated from HDFT imaging, containing relevant information about each tract. We have created an iPad 3-dimensional case report viewer27 that can present the report interactively and dynam ically, and also produce an article including the same infor mation. With progressively improving data, metrics, and understanding of control tracts, the HDFT Case Report is designed to inform both the clinical team and the patient about which tracts may show evidence of damage and which do not, to aid diagnosis, support behavioral data, and better target treatment to individual patients. ACKNOWLEDGMENT This article was funded by CDMRP PT110773 (W81XH-12-2-0140) and U.S. Army 12342013 (W81XWH-12-2-0319).
APPENDIX: TRACKING AND ROI PLACEMENT The following tracts were tracked using the specified ROIs and ROAs: Left/Right Arcuate Fasciculus (AR: ROIs: Left/ Right Prefrontal Cortex, Left/Right Temporal-ParietalOccipital Junction, ROAs: Mid-Sagittal Slice, Left/Right FOF Neck, Left/Right External Capsule), Corpus Callosum (CC; ROIs: Corpus Callosum, ROAs: Temporal-Putamen, Left and Right Internal Capsule, Left and Right External Capsule, Left and Right Crus Cerebri), Left/Right Cingulum (Cl; ROIs: Left/Right Posterior Papez, ROAs: Mid-Sagittal Slice), Left/Right Corona Radiata (CR; ROIs: Left/Right Internal Capsule, ROAs: Mid-Sagittal Slice, Left/Right FOF Neck, Arcuate Slice), Left/Right Corticospinal Tract (CS; ROIs: Left/Right Internal Capsule, Left/Right Crus Cerebri, ROAs: Mid-Sagittal Slice, Arcuate Slice), Left/Right FrontoOccipital Fasciculus (FO: ROIs: Left/Right Occipital Cortex, Left/Right FO Neck, ROAs: Mid-Sagittal Slice), Genu (GE; ROIs: Genu, ROAs: Posterior Genu Slice), Splenium (SP; ROIs: Splenium, ROAs: Temporal-Putamen). ROIs and ROAs were placed according to the following rules: Corpus Callosum: In the mid-sagittal slice, a planar ROI highlighting all voxels with x-traveling fibers in the white matter of the corpus callosum. Genu: Subset of the Corpus Callosum ROI. All fibers anterior to the anterior infe rior drop of the corpus callosum. Includes the rostrum. Poste rior Genu Slice: Corona slice immediately posterior to the Genu ROI. Rectangular planar ROI encompassing the entire slice. Splenium: Subset of the Corpus Callosum ROI. All fibers posterior to the posterior inferior drop of the corpus callosum. Mid-Sagittal Slice: In the mid-sagittal slice, high light a rectangular planar ROI encompassing the entire corpus callosum, with one spare voxel in each direction (superior, inferior, anterior, posterior). Left/Right Internal Capsule: Axial slice displaying the internal capsule as separate from the exter nal capsule. Highlight all internal capsule white matter voxels in each hemisphere separately, as planar regions. Go no further
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anterior or posterior than the corpus callosum. Left/Right External Capsule: Same slice as the Internal Capsule ROI. Highlight all external capsule white matter voxels in each hemisphere separately, as planar regions. Arcuate Slice: Same axial slice as the Internal Capsule ROI. Highlight a rectangular planar ROI of all voxels posterior to the Internal Capsule, for both hemispheres. Left/Right Crus Cerebri: Axial slice show ing the white matter of the cms cerebri. Planar ROIs highlight ing each hemisphere’s crus cerebi’s white matter separately. Left/Right Posterior Papez: Coronal slice just anterior to the Splenium ROI. Highlight all voxels with y-travelling fibers just superior to the corpus callosum, in each hemisphere sepa rately, with a planar ROI. Left/Right Prefrontal Cortex: Sagit tal slice showing the c-shaped curve of the arcuate white matter. Place a spherical ROI in the prefrontal region, not extending into the temporal lobe, in each hemisphere sepa rately. Left/Right Temporal-Parietal-Occipital Junction: Same sagittal slice as the Prefrontal Cortex ROI. Place a spherical ROI at the curve of the white matter of the acuate, where the fibers are traveling in the z-direction, in each hemisphere sepa rately, no wider than the arcuate itself. Left/Right Occipital Cortex: Axial slice between the Crus Cerebri ROI and Internal Capsule ROI. Place spherical ROIs encompassing the occipital lobe in each hemisphere separately. The spheres will cross over into the contralateral hemisphere partially. Temporal-Putamen: Coronal slice displaying the putamen. Planar ROI highlighting all temporal voxels in both hemispheres, including any voxels in between, without crossing into the frontal or parietal lobes. Left/Right FOF Neck: Coronal slice displaying the white mat ter tract of the FOF as it crosses from the temporal lobe to the frontal lobe. It should be just anterior to the Temporal-Putamen ROI slice. Place rectangular planar ROIs over these white matter tracts in each hemisphere separately.
REFERENCES 1. Koehler R, Wilhelm E, Shoulson I: Cognitive Rehabilitation Therapy for Traumatic Brain Injury: Model Study Protocols and Frameworks to Advance the State of the Science: Workshop Summary. Washington, DC, The National Academies Press, 2013. 2. Risling M: Blast Induced Brain Injuries—A Grand Challenge in TBI Research. Front Neurol 2010; 1: 1. 3. Warden D: Military TBI during the Iraq and Afghanistan wars. J Head Trauma Rehabil 2006; 21(5): 398-402. 4. Femandez-Miranda JC, et al: High-definition fiber tractography of the human brain: neuroanatomical validation and neurosurgical applica tions. Neurosurgery 2012; 71(2): 430-53. 5. Catani M, Thiebaut de Schotten M: Atlas of Human Brain Connections. New York, Oxford University Press, 2012. 6. Saatman KE, et al: Classification of traumatic brain injury for targeted therapies. J Neurotrauma 2008; 25(7): 719-38. 7. Emmett JE, Breck LW: A review and analysis of 11,000 fractures seen in a private practice of orthopaedic surgery 1937-1956. J Bone Joint Surg Am 1958; 40(5): 1169-75. 8. MacDonald CL, et al: Detection of blast-related traumatic brain injury in U.S. military personnel. N Engl J Med 2011; 364(22): 2091-100. 9. O’Donnell LJ, Westin CF, Golby AJ: Tract-based morphometry for white matter group analysis. Neuroimage 2009; 45(3): 832-44.
MILITARY MEDICINE, Vol. 180, March Supplement 2015
Quantifying White Matter Structural Integrity With HDFT in TBI 10. Catani M, Mesulam M: The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state. Cortex 2008; 44(8): 953-61. 11. Amunts K, Zilles K: Architecture and organizational principles of Broca’s region. Trends Cogni Sci 2012; 16(8): 418-26. 12. Wang Y, et al: Rethinking the role of the middle longitudinal fascicle in language and auditory pathways. Cereb Cortex 2013; 23(10): 2347-56. 13. Hess CP, Mukheijee P, Han ET, Xu D, Vigneron DB: Q-ball reconstruc tion of multimodal fiber orientations using the spherical harmonic basis. Magn Reson Med 2006; 56(1): 104-17. 14. Jian B, Vemuri BC: A unified computational framework for decon volution to reconstruct multiple fibers from diffusion weighted MRI. IEEE Trans Med Imaging 2007; 26(11): 1464-71. 15. Kaden E, Knosche TR, Anwander A: Parametric spherical decon volution: inferring anatomical connectivity using diffusion MR imaging. Neuroimage 2007; 37(2): 474-88. 16. Shin SS, et al: High-definition fiber tracking for assessment of neurolog ical deficit in a case of traumatic brain injury: finding, visualizing, and interpreting small sites of damage. J Neurosurg 2012; 116(5): 1062-9. 17. Kraus MF, et al: White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain 2007; 130: 2508-19. 18. Galaburda AM, LeMay M, Kemper TL, Geshwind N: Right-left asym metries in the brain. Science 1978; 199(4331): 852-6. 19. Zilles K, et al: Quantitative analysis of sulci in the human cerebral cortex: development, regional heterogeneity, gender difference, asym
MILITARY MEDICINE, Vol. 180, M arch Supplem ent 2015
20.
21. 22. 23.
24. 25.
26. 27.
metry, intersubject variability and cortical architecture. Hum Brain Mapp 1997; 5(4): 218-21. Khotanlou H, Colliot O, Ati J, Bloch I: 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Set Syst 2009; 160(10): 1457-3. Tuzikov AV, Colliot O, Bloch I: Evaluation of the symmetry plane in 3D MR brain images. Pattern Recogn Lett 2003; 24(14): 2219-33. Yeh FC, Wedeen VJ, Tseng WY: Generalized q-sampling imaging. IEEE Trans Med Imaging 2010; 29: 1626-35. Irimia A, et al: Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes. J Neurotrauma 2011; 28(11): 2287-306. Yeh FC, Wedeen VJ, Tseng WY: Generalized-Sampling Imaging. IEEE Trans Med Imag 2010; 29(9): 1626-35. Pathak S, Schneider W: Quantifying directional axonal volume in fiber tractography. Poster presented at Society for Neuroscience; November 14, 2011; Washington, DC. Available at http://www.abstractsonline.com/ Plan/ViewAbstract.aspx?mID=2773&sKey=f5bl lfc7-0771-4360-834d684cc259e309&cKey=9f8708a0-1967-483f-8al7-f694fe0717ea&mKey= 8334be29-8911 -4991 -8c31-32b32dd5e6c8; accessed October 14, 2014. Setsompop K, et al: Pushing the limits of in vivo diffusion MRI for the Human Connectome Project. Neuroimage 2013; 80: 220-33. Chmura J, Presson N, Benso S, et al: A high-definition fiber tracking brain report for patients with traumatic brain injury and their doctors. Mil Med 2015; 180(3 Suppl): 122-34.
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