MILD TRAUMATIC BRAIN INJURY DETECTION: IMPLEMENTING DATA MINING SOFTWARE

Emily ReinhardEmory University

Oak Ridge National Laboratory (ORNL)Research Alliance in Math and Science (RAMS)

Mentor: Barbara BeckermanComputational Sciences and Engineering Division

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AbstractTraumatic brain injury (TBI) is one of the most common injuries of combat in Iraq and Afghanistan. Mild traumatic brain injuries (mTBI) seem to make up the majority of these brain injuries. Due to the difficulty of detection, the prevalence of mTBI is uncertain, because many cases are not recognized and go untreated. Many mTBI sufferers also have post-traumatic stress disorders (PTSD), which has overlapping symptoms with mTBI. Thus, determining if a patient has mTBI alone or a combination of PTSD and mTBI can be a challenge. The objectives of this project were to more accurately define and differentiate mTBI and PTSD, present recent advancements made in m TBI detection and diagnosis, and to perform trends analysis. A literary search was conducted to find current articles related to mTBI and PTSD. Then a data mining software tool developed at Oak Ridge National Laboratory (ORNL) called Piranha was used for visual and text analysis. A more detailed analysis was conducted to perform advanced knowledge discovery. Trends were then analyzed and conclusions were drawn. It was found that diffuse axonal injury (DAI) is a main mechanism of TBI and diffusion tensor imaging (DTI) is uniquely equipped to detect DAI. Using four metrics, DTI looks at the diffusion of water in and through tissue. One of these metrics, fractional anisotropy (FA), was found to decrease over time in mild TBI patients, possibly explained by cytotoxic edema. In comparing mild and moderate TBI, a variation in injury location in the corpus callosum, specifically the genu and splenium, was found. Injury to only the genu was seen in mTBI patients only. More DTI longitudinal studies across the TBI severity spectrum are needed, as well as consideration of trends in the other DTI metrics.

Keywords: mild traumatic brain injury, post-traumatic stress disorder, diffusion tensor imaging, corpus callosum, fractional anisotropy

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Mild Traumatic Brain Injury Detection: Implementing Data Mining Software

I. IntroductionTraumatic brain injury (TBI) is defined by the Department of Veteran Affairs (VA)

to be “any traumatically induced structural injury and/or physiological disruption of brain function as a result of an external force, indicated by a loss or decrease in consciousness, loss of memory of events surrounding the injury, alteration of mental state at the time of the injury, neurological deficits, or intracranial lesions” [1]. This injury, previously called shell shock, has sparked major interest in recent years with the new understanding that both emotional distress and physical trauma were occurring [2]. TBI has a range of severity, including mild up to severe. There are currently four recognized measures to differentiate these severities. They are the Glasgow Coma Scale (GCS), length of coma, length of period altered consciousness (PAC), and length of post-traumatic amnesia. Depending on the test score or length of symptom, a severity is assigned. Mild TBI (mTBI) is classified by a GCS score between thirteen and fifteen, a coma lasting under thirty minutes, and a PAC and amnesia length not exceeding one day [1].

Post-traumatic stress disorder (PTSD) is a psychiatric disorder that develops after surviving a life-threatening traumatic event. Normally a trauma survivor recovers fully after some time. However some people have persistent stress reactions post-injury, which eventually develop in to PTSD. There are three main PTSD symptom categories. They are reliving the trauma, feeling isolated or numb, and being easily startled or guarded [3].

II. Background Mild TBI occurrences have exponentially increased in recent years, causing it to

be deemed the signature injury of Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF). This can be partially explained by the growing use of explosives over guns in warfare, as well as the improvement in head protection [4]. In 2010, 78% of combat injuries in OIF/OEF were from explosives [1].This increased prevalence has led to a search for the best ways to treat these patients. However, before treatment comes diagnosis. Two issues make the diagnosis of mTBI far from easy, leaving many injuries untreated. First, mTBI is hard to detect with the current methods available. Most mild TBI patients have normal CT and/or MRI scans, unlike the more severe cases of TBI [5]. The other form of diagnosis, including the four measures mentioned above, relies heavily on descriptions from the patients and the duration of certain symptoms. This “historical” diagnosis leaves room for much error, especially if the patient is disoriented and/or did not report the injury right away [1]. Second, mild TBI is often seen in combination with PTSD. In 2008, the percentage of soldiers with both mTBI and PTSD symptoms was 43.9% [4]. The overlap of symptoms includes irritability, depressive symptoms, and cognitive difficulties [6]. The difficulty in diagnosing paired with the increasing prevalence leaves an important gap to be filled.

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III. ObjectivesThe objectives of this project are to: Collect and organize new information on detection and treatment of mTBI

using text data analytic software More accurately define and differentiate mTBI from PTSD Present recent advancements made in the detection and diagnosis of mTBI Perform trend analysis Identify results and draw conclusions

IV. Methodology Various approaches were taken in this project. First, a literature search was

conducted to collect a data set of current mTBI and PTSD literature. Then, Piranha, a data mining software tool, was used to perform visual and text analysis on the collected data set. After analysis, the trends found were tested and conclusions were drawn.

a. Literature SearchUsing Google Scholar and PubMed, as well as other sources, a literature search

was performed to obtain approximately one hundred mTBI and PTSD articles. Search terms included mild TBI in the military, detection methodologies for both mTBI and PTSD, and mild TBI with PTSD. Julie C. Chapman, PhD from the VA highlighted diffusion tensor imaging (DTI) as a topic of interest and indicated that DTI is of great use to the field of mTBI. She provided an additional hundred documents on the topic of DTI. These articles were then imported into Piranha, a text data analytic software tool developed at Oak Ridge National Laboratory (ORNL), for data mining, visual clustering, and text analysis.

b. Visual and Text Analysis using PiranhaPiranha allows large amounts of text data to be

clustered according to how they relate to each other. This data mining software tool provides a search screen to restrict the data being clustered, a visual analysis screen to visually organize the documents by how they relate to each other and what the top words and phrases are, and a text analysis screen to view all the top words and phrases in the documents. Piranha also offers several features to manipulate the data. Thresholds can be set by the user to increase or decrease confidence in the clustering relationships. Stop words allow the user to remove common terms that would not be helpful in the analysis. Piranha categories allow the user to organize the data in their own way. A combination of these features was used to conduct visual and text analysis.

In general, with each new clustering, unexpected top words consistently appearing on the nodes were

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Fig. 1. text analysis screen showing documents with axon/axonal

Fig. 2. initial Piranha cluster showing flower formation

noted, as well as any interesting organization of the documents in the clustering. To determine the significance of words and phrases that frequently appeared, the text analysis screen was used to look at the words and phrases inside the documents (Fig.1). Words and phrases determined to be of relevant significance were used to revise the search and cluster a new, smaller document set.

After the first initial unstructured clustering of the entire document set with the default thresholds (Fig 2.), the thresholds were increased because all the documents were already fairly similar, being from TBI or PTSD searches, and were clustering under one node. To further remove the base level similarity, stop words, including “TBI” and “PTSD,” were applied (Fig. 3). From these changes, “axon/axonal” was discovered as a top word (Fig.4), which is connected to diffuse axonal injury (DAI).

To investigate how this injury manifests itself in TBI, DAI was used as a search term to extract new knowledge from this concentrated document set. Diffuse axonal injury and its variations were also added as stop words to allow the mechanisms and detection of DAI to appear in the clustering. The new clustering revealed, through the “tensor” cluster (Fig. 5), a growing imaging technique called diffusion tensor imaging (DTI). Diffuse tensor imaging has four metrics: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) [7]. (A summary of what each metric measures is highlighted in Fig. 6.) Surprisingly, only one of the DTI metrics, FA, appeared in the clustering as a branch of the “tensor” node (Fig. 5), suggesting its possible importance over the other metrics.

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Fig. 3. first stop words list

Fig. 4. unstructured search cluster of all documents showing axon/axonal cluster

Fig. 5. DAI search cluster showing tensor and FA

c. Trend AnalysisAfter

examining the documents containing the significant words and phrases found in Piranha, some were determined to have possible significance over time. To test these trends in the current data set, several methods were employed. In one case, Piranha categories were utilized (Fig. 7). Charts, bar graphs, linear regression, and scatterplots were also used to visually evaluate the possible trend.

The trends tested were:1. fractional anisotropy (FA), a DTI

metric, changes over time in mild TBI cases2. mean diffusivity (MD), another DTI metric, changes over time in mild TBI

cases3. genu and splenium of the CC injury in mild versus moderate TBI

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Fig. 7. DAI search cluster with higher FA/lower FA categories showing CC and splenium

Fig. 6. Summary of explanations of DTI metrics changes in mild TBI

Fig. 9. Graph showing counts of documents with FA values higher and lower than controls and weeks post-injury (22 people average per study)

In the set of documents mentioning mTBI FA values, one widely recognized paper [7], which reviewed twenty-three mild TBI DTI studies, recognized a disagreement as to whether FA was higher or lower than controls in mTBI patients. The average number of mTBI patients per study was twenty and the control group in most cases was made up of healthy individuals matched to patients by sex and age. The explanation given by Niogi & Mukherjee for the FA changes was that acute mild TBI showed higher FA and chronic mild TBI showed lower FA [7]. FA measures how free

diffusion is. A score of zero means free diffusion in all directions and a score of one means restricted diffusion to one direction. To test this trend in the current data set, a “lower FA” and “higher FA” Piranha category were

created (Fig. 7). This provided an initial confirmation of the trend with all higher FA documents being acute or semi-acute studies. To further confirm the possible trend in FA values, average FA values compared to controls and time post-injury were considered with an average patient size of twenty-two per study (Fig. 8, 9). The acute time frame showed varying results. However, after about thirty days all documents found reported FA values lower than controls. In order to reduce the confounding variable of each article’s individual control group, actual FA values, when given, were also considered, which led to the discovery of a trend over time.

Mean diffusivity was also evaluated for a possible trend over time, due to the frequency of its use alongside FA [7], [8], [9] & [10]. Mean diffusivity measures the average diffusion rate. Average MD values compared to controls and time post-injury were considered (Fig. 10).

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Fig. 8. FA value in mTBI white matter compared to controls according to time after injury

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Fig. 10. MD Value in mTBI white matter compared to controls according to time post-injury

Fig. 12. Trends of FA values in mild and moderate TBI in the genu and splenium of the CC

Besides two documents that found no difference, mTBI MD values were found to be consistently higher than controls.

Injury location in the CC was evaluated as a possible differentiator for mild and moderate TBI. While testing the FA trend, Figure 7 presented an unexpected top word, “callosum” (Fig. 7). It was determined from the documents in this cluster that the genu of the corpus callosum is one of the most frequently damaged white matter areas. Rutgers et al. 2008a addressed this issue more specifically. This study found that less than three months post-injury, mild TBI patients showed lower FA than controls in the genu of the CC and moderate TBI patients showed lower FA than controls in the genu and splenium of the CC (Fig. 11), presenting the splenium as a possible differentiator for mild and moderate TBI. Splenium was also seen in Figure 7. In addition, Kumar et al. 2010b mentioned that over time in moderate TBI, the splenium FA values stayed the same, suggesting more permanent injury [11]. To test the possible genu and splenium injury trend across mild and moderate TBI in the current document data set, average FA values compared to controls in mild and moderate TBI, injury location, time post-injury, and any trends that existed over time were considered (Fig. 12). Not all the experts in these studies agreed on genu and splenium injury in mild versus moderate TBI. Consequently trend analysis was reconsidered. A general separation is seen between mild and moderate TBI, with all moderate TBI documents showing genu/splenium injury (Fig. 12). Mild TBI documents show only genu, only splenium, and genu/splenium injury. Thus splenium injury could not be deemed the difference between mild and moderate TBI. However, injury to only the genu of the CC was isolated to mTBI.

V. ResultsThrough the prevalence of “axon/axonal” in the visual clustering, DAI (Fig. 4) was found to be a common injury of TBI patients and could potentially help to differentiate the varying severities of TBI. With a unique approach involving diffusion, DTI is uniquely equipped to evaluate axon integrity and thus detect DAI. Diffusion tensor imaging is a form of MRI that is sensitive to axonal injury [12]. It looks at the movement of water in

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Fig. 11. Parts of the Corpus Callosum

Fig. 13. FA Value vs. Average Time Post-Injury in mTBI Patients

Fig. 14. Graph of injury in corpus callosum for mild and moderate TBI (30 people average per study)

and through tissue. Changes in measurements are what constitute injury [13]. Two potentially useful trends were also discovered out of the three investigated. These were that mTBI FA values decrease over time and the area of injury in the CC vary among mild and moderate TBI. Mild TBI MD values were found to have no significance over time.

Mild TBI FA values were found to have a negative correlation with time post-injury (Fig. 13). A plausible explanation of this FA trend was cytotoxic edema coupled with axonal injury [14], [15], [16]. Edema is the excess accumulation of fluid. The derangement of cellular metabolism causes faulty functioning of the sodium and potassium pump in the glial cell membrane, which leads to excess retention of sodium and water [17]. In cytotoxic edema, the blood brain barriers remain intact. Thus while mTBI is acute, edema is present, which would cause larger axons filled with excess fluid. This would in turn crowd the extraxonal space, forcing most of the diffusion to take place along the axonal axis and raising the FA value. Then once mTBI becomes chronic, the edema subsides and the axonal injury is left. This axonal damage seen in the infrastructure of the axon would lead to much freer diffusion and a lower FA value. This result also helps to define a possible lifespan of cytotoxic edema in mTBI injuries. The linear regression line in Figure 13 reaches the control of .5, provided by Susumu Mori, PhD from Johns Hopkins, at fifty-five days [18]. A mild TBI specific time frame for FA values would be valuable for diagnosis.

Injury location in the CC was found to vary among mild and moderate TBI, specifically in the genu and splenium areas. Contrary to initial predictions, splenium injury was not found to be a differentiator between mild and moderate TBI. Instead, injury to only the genu area of the CC was isolated to mTBI and could potentially be helpful in diagnosis (Fig. 14).

Mild TBI MD values were found to have no significant change over time. This is in agreement with what MD measures. With the axon integrity lower than normal, fewer barriers exist to keep diffusion along the axon axis. Therefore flow would be freer and the average diffusion rate would increase.

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FA Value vs. Avg Time Post-Injury in mTBI Patients

FA Value

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VI. DiscussionAs such a common component of TBI, DAI could potentially help to differentiate

PTSD and mTBI. As knowledge of the four DTI metrics and what they represent grows, DTI may become a key component to identifying mild TBI and thus finally offer a way to diagnose with imaging. More understanding of how DAI distributes across the various TBI severities, as well as conclusive evidence that DAI is not present PTSD, is needed before definitive definitions can be claimed.

The course of the four DTI metrics over time may help to identify how DAI attributes to mild, moderate, and severe TBI. A trend was already observed in mTBI FA values. If specific trends with set time frames were identified for the DTI metrics, mild TBI could be identified using DTI as the imaging tool that can find what CT and MRI cannot. Explanations offered in the current data set of some of the DTI metric values compared to controls are summarized in Fig. 6.

Another tool to aid in distinguishing mild and more severe forms of TBI may be the locations of injury, particularly in the corpus callosum (Fig. 15). In Fig. 14, injury to the genu of the CC is shown only in mTBI cases. The initial trend seen was injury to the genu for mild TBI and injury to the genu and splenium for moderate TBI. However, the graphical representation showed that splenium injury cannot be isolated to moderate TBI.

Future studies focused on CC injury including all TBI severities would help to further investigate this observation. DTI would be the most helpful tool in finding these injured areas. General DTI longitudinal studies involving all three TBI severities would allow for discovery of more differences between the injuries. DTI is the preferred imaging method because of its sensitivity to DAI. A trend was seen in mTBI FA values over time. Extending the time frame of observation to several years post-injury would give a fuller picture of this trend. This same method would also be valuable to instigate with the other DTI metrics. In general, all trends found need to be investigated with a larger data set.

VII. Acknowlegements

The Research Alliance in Math and Science program is sponsored by the Office of Advanced Scientific Computing Research, U.S. Department of Energy. The work was performed at the Oak Ridge National Laboratory, which is managed by UT Battelle, LLC under Contract No. De-AC05-00OR22725. This work has been authored by a contractor

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Fig. 15. Genu and splenium of the corpus callosum injury in mild TBI

of the U.S. Government, accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes. Thanks to Barbara Beckerman and Robert Patton, PhD for their help and guidance and to Julie C. Chapman, PhD from the Department of Veteran Affairs War-Related Illness and Injury Study Center (WRIISC) for providing information on TBI and for guiding my research.

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