**Transcript of Cyberseminar**

**Mild TBI Diagnosis and Management Strategies**

**Advances in Quantitative Susceptibility Mapping to Assess Mild Traumatic Brain Injury**

**Presented by****Harvey Levin, PhD, and  Rajendra Morey, M.D.**

**December 17, 2013**

*This is an unedited transcript of this session. As such, it may contain omissions or errors due to sound quality or misinterpretation. For clarification or verification of any points in the transcript, please refer to the audio version posted at www.hsrd.research.va.gov/cyberseminars/catalog-archive.cfm or contact**Harvey.Levin@va.gov**or**Rajendra.Morey@va.gov**.*

*Dr. Ralph DePalma:*  Today we have a very interesting presentation on the use of diagnostic neuroimaging for DTI and a special presentation by Rajendra Morey on “Advances in Quantitative Susceptibility Mapping to Assess mTBI.”  The conference will open with a general presentation by Harvey Levin, who’s the Director of the Neurons to Network TBI Injury Center of Excellence at the Michael E.  DeBakey VA Medical Center.  Harvey, are you set to go?

*Dr. Levin:*Yes.

*Moderator:*Harvey, just in the lower left-hand corner of your screen, you’ll see two arrows.  Just click the right-facing arrow and that’ll advance your slides.

*Dr. Levin*Yeah.  I don’t see the arrows yet.

*Moderator:*Okay.  It should be in the very lower left-hand corner.

*Dr. Levin*I’m looking.  I don’t see any arrows.

*Moderator:*Oh.  Hold on one second.  I think I see the problem.  I’m sorry.  What name did you type in?  I’m not seeing you in the list of presenters.

*Dr. Levin*Harvey.

*Moderator:*Okay.  There we go.  Okay, Harvey.  You should see those arrows pop up now.

*Dr. Levin*Now I do.

*Moderator:*Great.

*Dr. Levin*Yes, thank you.

*Moderator:*Thank you.

*Dr. Levin*     Okay.  Good afternoon.  This afternoon, I will provide an overview of brain imaging of veterans and service members with chronic mild traumatic brain injury.  I’d like to acknowledge the contributions of my co-investigators in the Center of Excellence who are engaged in our imaging research.  I’ll also mention these individuals as we proceed through the talk.  The goals of this presentation are to briefly cover some of the issues in diagnosing chronic mild TBI in veterans.  By chronic, all of the findings that I will present were obtained between two and five to six years after injury.  We’ll consider volumetric MRI, cortical thinning, diffusion tensor imaging, measuring functional connectivity using resting state fMRI data and task-related fMRI, specifically a recent study which showed dissociation of the effects of mild TBI from the effects of PTSD.  

The challenges in diagnosing chronic mild TBI include the reliance on self-report of these injuries, frequently without acute medical records.  As I mentioned, it’s not unusual for us to consent veterans who are a number of years since injury, so there is an issue about recalling details after such a long period.  Co-morbid PTSD and depression partially overlap in symptoms with the sequela of mild TBI.  Substance abuse is also a frequent problem, and this could affect the MRI findings independently of the effects of mild TBI.

Apart from the diagnostic problems inherent in the interview and self-report, there is an issue of the imaging data because there is a lack of reference data to identify subtle cortical atrophy or reduced brain region volumes in performing MRI.  There are specific studies of other populations—notably Alzheimer’s—but for appropriate comparison data that would be from veterans who did not sustain a TBI, reference data are not available.  

DTI metrics are potentially robust in terms of providing a biomarker, especially for white matter tracts that are injured by mild TBI.  However, there are center differences in the type of scanner and the software that is used, the specific imaging protocol, the quality assurance testing that’s done on the magnet and the method of analyzing the DTI data.  This makes it difficult to compare results across centers.

I’ll now present some preliminary data that we have collected on these different imaging modalities.  Here we see the gray and white matter volumes in veterans with chronic blast-related mild TBI.  For comparison, we have data from post-deployment veterans who did not sustain TBI and had no exposure to blast.  We could see that the notable findings here are in the cerebellar white matter and both the right side and the left side.  The findings are stronger for the right cerebellum.

Analysis of cortical thinning showed that there was also thinner cortex in the TBI group.  This was specifically in the anterior cingulate and in the parahippocampal gyrus as compared with veterans who had no TBI and no exposure to blast during deployment.  Diffusion tensor imaging measures the integrity of the white matter tracts.  In this slide, you see that the tracts are colored green, connect brain regions from an anterior to posterior direction.  The red represents fibers, such as the corpus callosum, that connects between the left and right hemispheres.  The blue represents the Z direction; that is, from top to bottom.  In this slide, we see a striking example.  You can appreciate the tract on your right has been compromised by the traumatic brain injury as compared to the one on the left.  This is a fairly dramatic example.  The findings with mild TBI are typically much more subtle and are brought out by quantitative analysis.

We have analyzed data from an ongoing merit review project of chronic mild TBI and used tract-based spatial statistics to compare the mild TBI group with a post-deployed group who had no TBI, no exposure to blast.  In the slide that I’ll show, we analyzed fractional anisotropy, a metric which reflects preferential diffusion of water parallel to the white matter tract.  In this slide, you’ll see that there’s areas of reduced integrity of the microstructure, including the corpus callosum, brain stem and cerebellum.

I’d like to acknowledge my colleague, Dr. Lisa Wilde for overseeing the DTI analysis and repairing the slides as well as the slides on the volumetrics.  Here you see—in the axial plane, you can see the involvement of the corpus callosum.  We also have preliminary functional connectivity findings.  These were analyzed by Dr. Mary Newsome at our center.  These data were collected during a resting state in which the veteran lied in a scanner with eyes closed but did not perform any task.  We have data for 17 veterans who had sustained mild TBI and 15 control veterans.  They were of similar demographic backgrounds.

Here, what you could see represented is the connectivity between the anterior cingulate cortex and the posterior cingulate cortex.  This connectivity—which is part of the default mode network—was reduced in veterans who had sustained a mild TBI.  This finding is consistent with a similar analysis in civilian mild TBI that has been reported in the last couple of years.  In contrast, on the right what we could see is a representation of increased connectivity between default mode brain regions and the left prefrontal cortex, which is an area that’s activated typically during demanding cognitive tasks.  

This area that you see represents a subtraction between the group that sustained a mild TBI and a control group, and so this connectivity was increased.  This type of finding has also been reported in the civilian literature following mild TBI.

The study of task-related brain activation was led by my colleague, Dr. Randy Scheibel.  In this study, we used a stimulus-response compatibility task.  At the beginning of a trial, there would be a blue arrow.  If the arrow pointed to the right, then the individual would push a button on the right because the instruction was to push the button on the same side that the arrow was pointing.  However, on our randomly occurring trials, the arrow was red instead of blue.  There was no warning.  On those trials, the instruction was that the veteran would push the button on the side opposite to which the arrow was pointing, so this creates a stimulus-response conflict.  

In this study, TBI was co-morbid with high levels of PTSD symptoms.  In comparison, the control group of veterans who had been deployed but had no TBI had much lower levels of PTSD symptoms.  We found that the TBI group had more activation in mesial prefrontal cortex, anterior cingulate gyrus and posterior regions after statistically controlling for group differences in PTSD, depression and reaction time.  What we see here—if we look at these three images in the middle, we see that these represent brain regions in which the veterans who had sustained TBI had more activation than the controls, but notice: the regions that were activated more in the veterans who had TBI did not involve the prefrontal region.  

Also, there was a lack of involvement of the temporal region, regions that are often activated on this task.  Typically, following brain injury, the literature indicates that there is greater activation of these regions in compensation for diminished cognitive resources.  However, after the analysis of covariance was performed, taking into account the co-morbid PTSD symptoms—the depression and the differences in reaction time—we could see that there are—now there’s greater activation in the TBI group in the temporal region, which we didn’t see without statistically adjusting for these co-morbidities.

Within the group that did not sustain TBI, we divided this group of veterans into those that had the PTSD symptoms above the median and those who had PTSD symptoms that were less severe than the median.  Recall, this was the group that did not have any history of TBI during their deployment.  You could see on the left that the group with low levels of PTSD symptoms had much more extensive activation on this stimulus-response conflict task than the group that had high levels of PTSD symptoms.  

Our interpretation is that high levels of PTSD symptoms have a dampening effect on brain activation associated with a stimulus-response conflict task.  In the PTSD literature on functional MRI, we’ve noted that there were other reports of the effect of PTSD tending to dampen activation on cognitive tasks.

In summary, we’ve seen that volumetric MRI, DPI, resting state functional MRI and task-related fMRI are sensitive to chronic effects of predominantly mild TBI, primarily due to blast.  We see that diffusion tensor imaging is sensitive to chronic mild TBI, and that functional connectivity in veterans with chronic mild TBI differs from controls, especially in the brain regions that are within the default mode network.  We saw that in the connectivity between anterior and posterior cingulate cortex.  Finally, we see that co-morbid PTSD reduces activation in task-related fMRI, its effect being opposite to the effects of chronic mild TBI.  Thank you.

*Moderator:*Thank you very much, Dr. Levin.  I’ll turn it off over to you now, Dr. Morey. 

*Dr. Morey*Hello.  Good afternoon.  I will be discussing just a few new developments in advanced imaging for mild TBI.  I will review two major findings from my labs.  In the first part, I’m actually going to circle back to findings that I reviewed in the last HSR&D seminar and look at injury to neural tissue from people with subconcussive injury.  In exposure to explosive forces from bombs and other improvised explosive devices, it’s common in the recent military conflicts.  The majority of mild traumatic brain injury is resulting from—in terms of blast injury—from improvised explosive devices and grenades, rocket-propelled grenades and mortar fire.  There is, in addition, non-blast type of injury as well, and I’m going to review damage to tissue from diffusion, DTI, in well-established cases with clinical symptoms of mild TBI.

There’s been a recent study showing compromised white matter in sports-related injury, even in the absence of clear concussive symptoms.  That’s symptoms such as loss of consciousness or altered sensorium or amnesia.  We call it “subconcussive exposure.”  Until recently, subconcussive exposure was not associated with injury.  This is just one important recent paper that was published in 2012 in *JAMA*showing that repeated heading of the ball by elite soccer players in Germany resulted in changes in DTI findings, even when the players did not have any objective symptoms of traumatic brain injuries such as concussions.  

We wanted to see: Is there injury in brain tissue in veterans with blast exposure?  This is one of the little poll questions, so you can answer. “Is there injury to brain tissue in veterans with blast exposure without clinical symptoms of TBI?”  

*Moderator:*Thank you, Dr. Morey.  It does look like the responses are streaming in, so we’ll give people a little bit more time to get their responses in.  It looks like we’ve had about a bit of 65 percent response rate, and 82 percent are responding, “Yes.”  About six percent are saying, “No” and about 11 percent are saying, “Not sure.”  Thank you to our respondents.

*Dr. Morey*Okay, thank you.  I’ll review some of the data that we’ve collected recently.  We used a very different approach to this than we have in the past.  In the past, we have used the tract-based spatial statistics, which is a whole-brain approach.  We believe that there’s two really important limitations to the tract-based spatial statistic approach to analyzing DTI data.  The reason is because we believe that there’s a huge amount of heterogeneity in the location of injury to tissue, so depending on the event surrounding the injury, some veterans may have injury in the frontal lobe, some in the occipital, some in the temporal, some in the inferior frontal and so on and so forth.  

Also, we believe that the injury is not only caused by that initial mechanical event, but also is caused by a lot of chronic neurochemical changes, neuroinflammatory changes that occur subsequent to the mechanical event, whether it’s the blast exposure or the blunt injury.  With a voxel-wise approach such as TBSS, the TBSS approach really requires that the location of the injury be in the identical location in the brain for all the subjects in order to detect a significant difference between the patient or the TBI group and the control group.  

What we decided, the approach that we used is we basically looked at the FA value at every one of the voxels in the skeleton, the white matter skeleton, and we looked at whether each of our subjects who had mild TBI, if the fMRI value was two standard deviations below the mean value of the control group here.  We have an illustration.  We’re looking to set one voxel here.  We have our reference group and we’re showing a mean as well as a plus and minus two standard deviations from the mean.  

In the test subjects for this particular voxel, obviously, this voxel here is below the two standard deviations.  This would be considered an “abnormal voxel,” if you will, in this particular subject.  Then we can actually do this for every subject in the TBI group.  With this approach, we can actually look at clusters of voxels that have low FA.  In this case, we looked at voxels that occurred in clusters of 25 voxels, 50 voxels, 75 voxels and greater than 75 voxels.  When we looked at clusters of voxels with low FA that were 25 to 50 voxels in size, we saw, actually, that the number of clusters was a lot greater in the blast-exposed group, which is in green, and also in the blast-mTBI group, which is in red.  They had a much higher number of clusters than the control group, the blast-unexposed group, which is in blue.  

Similarly, we saw a similar result for clusters with 50 voxels and clusters with 75 voxels—I’m sorry, with 50 voxels, not so with 75 or 100.  With this, we moved to implement or test this analysis approach to look at using this as a diagnostic approach, which I’ll get to at the next slide.  The slide is highlighting what I mentioned earlier, which is that the damage is very heterogeneous across subjects.  It would depend on the events that are surrounding the TBI, whether they—in which direction the blast exposure came or which direction the blunt impact came and also not just*[audio cuts out]* spatially heterogeneous across subjects, but also, it’s very widely dispersed.  

This slide is showing the low FA voxels.  In the top slide, it is showing—in the top panel, it’s showing in green, is subjects who had—sorry.  In green, is voxels that had low FA in one subject.  In purple, is voxels that had low FA in two subjects and blue is voxels that had low FA in three subjects.  You can see, by looking at the pattern of green, purple and blue that most of the voxels with low FA only occurred*[audio cuts out]* subject.  

There were a few that occurred in two and three subjects.  That was true for the blast-exposed group as well as the blast-mTBI group.  We also looked at radial diffusivity, and the pattern was similar to the*[audio cuts out]* I showed two slides ago in FA.  Here, we see that the blast-exposed group and the blast-mTBI group have much higher number of low FA voxel clusters, and that is much higher than the blast-unexposed control group which we saw in the small clusters and also the medium clusters of voxels.

We then also looked at neuropsychological testing we found using the intra-extra dimensional shift, which basically is a test of learning by inferring rules and set-shifting.  We used hierarchical regression models, which included age, race, education, PTSD symptoms and depression symptoms and DTI measures.  These measures—including the DTI measures—predicted performance in the number stages completed on this task, the number of errors, also in a separate task with simple reaction time and also number of errors on a spatial working memory task.

Here, I’m going to shift to the main focus of this part of the talk, which is how we can use machine learning to hopefully diagnose individual subjects based on their DTI data.  Machine learning is a branch of artificial intelligence, and machine learning is a very broad area.  One area of machine learning is pattern recognition.  We used what’s called “multivariate pattern recognition.”  A very common or simple analogy or example of pattern recognition would be to teach a computer how to distinguish a male face and a female face.  Of course, humans learn this at a very early age, 12 months or 18 months or two years, but we can actually train a computer to recognize—to do simple recognition tasks or pattern classification tasks.  

In this example, I’m illustrating the basic principle behind a support vector machine, which is a type of pattern classifier.  We can actually look at our data.  In this case, we’re going to look at our DTI data and we can actually plot the data according to variables here.  We’re looking at a simple example where we look at two variables, one on the x-axis and one on the y-axis, so we can actually think of these as two variables.  Picking this very simple example, if we were to imagine x-axis was, say, height and y-axis was weight, we can plot our subjects according to height and weight.  

We can actually see that there is a line that separates this group of subjects with height and weight plotted here, and this group of subjects with height and weight plotted here.  The line that actually separates these two groups is called the “optimal separating plane” or “hyperplane.”  Those are all examples of linear separations.  Here we have an example of a nonlinear separation between this group and this group.  This is not relevant to our study, but this is just an example of how you can use a nonlinear plane.  We did this same analysis and we used, actually, three predictor variables.  We used the small potholes; that was 25 voxels with low FA.  We used medium voxels with 50 to 75 voxels in the cluster and large potholes.  

We actually plotted each subject in our group in this X, Y and Z on this three-dimensional space.  What we did is we used the subconcussive—well, let me backtrack.  We used the mild TBI group and the control group as a training group.  We trained our computer how to distinguish mild TBI from non-exposure to blast.  We actually then looked at each subject and we found that our—the computer learned this correctly and it was able to correctly identify and diagnose mild TBI in 98 percent of the time.  Then we actually introduced the subjects who had subconcussive blast exposure.  Now, the computer had never seen these subjects before, so we had not used the subconcussive exposure to train the computer.  We had only used the mild TBI group and the control group.  When we fed these subconcussive blast cases, we found that the computer reported that 96 percent of these subconcussive cases were actually—had injury that the computer classified as mild TBI.  We thought that was interesting.  This is obviously very preliminary and we will have to replicate that further.  

We used the similar approach that I described earlier in FA and radial diffusivity.  This time, we looked at T1-weighted MRI images and we looked at the gray matter intensity using voxel-based morphometry.  We actually looked at clusters of gray matter voxels that were below—two standard deviations below the mean intensity. *[Audio cuts out]* can see that the blast-exposed and the blast-mTDI had significantly more clusters of gray matter—of low gray matter intensity than we do with the blast-unexposed *[audio cuts out]* for the small potholes, the medium potholes and the large potholes.  

The reason we looked at gray matter intensity is we—as I mentioned before—we believe that the injury to brain tissue is not occurring just from the mechanical event that’s associated with the TBI, but it’s actually occurring from a whole host of neuroinflammatory—a cascade of neuroinflammatory and neurochemical events that cause damage to neural tissue.  White matter is being affected by these inflammatory processes, the injured tissue.  It’s certainly plausible that the gray matter would be affected as well.  This is actually a paper by Sylvain Bouix showing gray matter changes.  They used, actually, DTI data, whereas we used T1-weighted data to look at gray matter.  

Just to highlight some of the potential clinical and policy implications of subconcussive blast exposure, blast exposure may damage brain tissue at comparable levels to mild TBI, even in the absence of acute clinical symptoms, subconcussive blasts.  The lack of clinical TBI symptoms following a blast exposure may lead us to the erroneous conclusion that there is little damage to brain tissue.  Consequently, this may go unnoticed, undiagnosed or underdiagnosed.  If subconcussive blast exposure leads later on to chronic symptoms, the patient may be misdiagnosed with clinical entities other than TBI, such as depression and PTSD.  If confirmed, our findings could support a diagnostic approach that includes a MRI-based approach using support vector machines or other pattern classifiers, other machine learning approaches.

I’m going to move on to quantitative susceptibility mapping.  We’ve talked about DTI.  This is just a visual representation of what we see in DTI, if you imagine each of these fish to be water molecules diffusing in the brain tissue.  This illustrates the path of diffusion of a water molecule, which is what we’re imaging in DTI.  If we have a restriction to the diffusion that’s sort of spherical, we could expect a diffusion pattern like you see here on the left.  

If you have a restriction to water diffusion that’s like these cylinders, you could *[audio cuts out]* diffusion pattern that you see here.  This is the next quiz question: “What is the principal structure that hinders water diffusion in white matter?”  I think, Molly, if you could hit the forward—well, actually I think I can—

*Moderator:*Do you want people to take a moment and answer that?

*Dr. Morey*Well, the A, B and C choices are not showing up there.

*Moderator:*Oh, I’m sorry.  I only saw it as an open-ended question.  Perhaps it was animated.

*Dr. Morey*Yeah, there’s an animation in there.

*Moderator:*Oh, shoot.  Let me see.  I can create it as a poll question real quick.

*Dr. Morey*Okay.

*Moderator:*Sorry about that.  Thanks, everybody, for your patience.  “What is the principal structure that hinders water diffusion in white matter?”  Take just a second.  What are your answer options?

*Dr. Morey*I think one of them was myelin.  The second one is the axon membrane and the third one is neural filaments.

*Moderator:*I’m sorry.  I’m going to spell a lot of this wrong, but people can do the best they can.

*Dr. Morey*A is myelin, B is axon membrane and C is neural filaments.

*Moderator:*Thank you all for working around my spelling ineptitude.  I’m not used to writing these ones out.

*Dr. Morey*It’s close enough.

*Moderator:*If only that had worked in grad school as well, “Close enough.”  All right.  It looks like the majority are going with myelin, about 85 percent.  About ten percent are saying, “Axon membrane” and about three and a half percent are saying, “Neural filaments.”  Thank you to our respondents.  I’ll get it back to your slides here.  There we go.

*Dr. Morey*Okay.  That was the full quiz question.  This is actually work that’s been done in several different animal models, but I’ll just review the very early work that was done and is reviewed in this paper in *NMR Medicine* in 2002.  Basically, they used the animal model, which is the garfish, and they found that water is significantly anisotropic in non-myelinated olfactory nerve.  The olfactory nerve of the garfish is non-myelinated.  Similarly, they found that the anisotropy was very similar in the trigeminal nerve—also in the garfish—which is myelinated with Schwann cells.  Looking at the optic nerve of the garfish that’s also myelinated, but with oligodendrocytes, that the anisotropy was very similar.  

The point is that even in the non-myelinated olfactory nerve, that though the anisotropy was very similar to these two other—the trigeminal and the optic nerve—and this is just a schematic showing that even in the absence of myelin that the axon membrane and these other membranes—which are basically lipid bilayers—like any cell membrane, provide a significant barrier to the free diffusion of water.  That’s an important point because that leads to susceptibility imaging.  Susceptibility weighted imaging has been discussed in terms of TBI as a potential application.  

Just as a review, susceptibility imaging takes advantage of the phase information.  Typically, in gradient—in GRE we just use the magnitude component and discard the phase.  Now, the susceptibility imaging is something that many TBI researchers acquire, but what we have—our group has been able to really enhance the contrast of the susceptibility image and we’ve been able to quantify the various tissue—to quantify the susceptibility contrast based on various tissues and actually, we are able to identify myelin using the susceptibility imaging.  

I’m actually speeding up a little bit because of time, but we have—this is an example of the information, the quantitative information that we can get from susceptibility imaging.  We call it “Quantitative Susceptibility Mapping” or QSM.  We see here that the red nucleus is an example of an area where you see a lot of iron-rich—it’s one of the iron-rich nuclei.  These iron-rich nuclei show positive QSM or quantitative susceptibility values and the white matter, which is actually imaging myelin because myelin is a macromolecule that the quantitative—the QSM values are negative for corpus callosum and in our internal capsule, et cetera.

We thought it would be DTI—well, that QSM would be a valuable tool to look at—for looking at TBI.  We know DTI is valuable in assessing the axon membrane integrity but not the myelin integrity, or at least this is the commonly held belief.  The commonly held belief also is that radial diffusivity is indicative of myelin damage.  We actually assessed this in 65 subjects, recent veterans from Iran and Afghanistan.  We had 43 with mild TBI and 21 non-TBI controls.  What we found, actually—when we looked at the traditional tract-based spatial statistics approach, we found that fractional anisotropy was actually different, but it was very localized to a few voxels.  

Again, as I mentioned earlier, there’s limitations to using a whole-brain voxel-wise approach that I mentioned earlier in the talk.  We used the same approach to look at quantitative susceptibility mapping and we see actually significantly greater differences between the TBI groups.  They’re highlighted in blue here in the lower panel, in this lower panel showing QSM.  Then we wanted to actually compare QSM to radial diffusivity.  As you may know, that radial diffusivity is often interpreted as showing damage to—radial diffusivity is often interpreted as showing damage to white matter, often called “perpendicular diffusion.”  

Here in red, we show the radial diffusivity, differences between the TBI and control, and in blue, we show the QSM, which is directly measuring myelin.  We see that there is voxels that have radial diffusivity differences but not QSM differences, and vice versa.  We, again, because of the reasons I’ve talked about, we looked at the QSM approach using potholes.  We looked at low QSM control group versus the TBI group and we found that the TBI group actually has a lot more clusters of low QSM voxels or abnormal QSM voxels for the medium and the large potholes.

To summarize, the FA and radial diffusivity actually may not be ideal for measuring myelin integrity.  QSM shows dramatically greater differences in mild TBI than FA, confirming that damage has occurred to myelin and not just to axon membranes.  While radial diffusivity has been purported to show myelin diffusivity, further research comparing radial diffusivity to QSM is needed, given that QSM directly measures myelin.  Myelin damage shows interindividual and spatial heterogeneity we saw in FA and in radial diffusivity.  I’d like to just acknowledge folks in my lab. *[Audio cuts out]* in my lab as well as my collaborators.  Thank you.

*Moderator:*Great.  Thank you both for your presentations.  We do have a couple questions pending.  For those of you that joined after the top of the hour, if you’d like to submit a question or comment, just use the Q&A box located in the upper right-hand corner.  I believe this one came in when you were speaking, Dr. Morey.  “What is your estimate of the sensitivity/specificity for diagnosis of mTBI/concussion?”

*Dr. Morey:*Sorry.  I was getting a lot of static.  Can you just say the question—? 

*Moderator:*Yeah, there is a lot of static.  I’m not sure whose line that is on.  “What is your estimate of sensitivity/specificity for diagnosis of mTBI /concussion?”

*Dr. Morey:*What is my estimate of sensitivity/specificity?  Well, I think if I were to venture a guess, it would be pretty speculative.  I think all this work is in the very early stages, so we would really need to do this in a large scale to be able to answer that question.

*Moderator:*Okay.  Thank you for that response.  The next question, I believe, was coming up during those last images.  “Are these images showing QSM values mapped onto the TBSS skeletons?”

*Dr. Morey:*Yes, good question.  We did map the QSM values onto the TBSS skeleton, that’s right.  That was a somewhat complicated procedure in terms of the registration, but yes, we did map the QSM images onto the TBSS skeleton.

*Moderator:*Thank you for that reply.  “Should there be more spatial overlap between the changes seen in radial diffusivity and QSM?”

*Dr. Morey:*Yes.  We were perplexed about that finding, that there was very little overlap between the QSM and the radial diffusivity.  We’re just beginning to unpack that.  We think that there may be—so, for example, in places where the radial diffusivity is showing differences between the TBI and the control group but the QSM is not, perhaps the myelin is intact but the axon membrane is damaged or compromised.  The question is then: Why isn’t the myelin restricting the diffusion of water, because it should to some extent restrict the diffusion of water?  We don’t really know fully what the answer is to that.  On the other hand, there’s areas where the QSM is damaged and the axon membrane is—where the QSM is showing differences, but the radial diffusivity is not.  We think there, obviously, the myelin could be damaged, but the axon membrane is intact and it’s hindering the diffusion of water.  That seems like a more straightforward interpretation.

*Moderator:*Thank you.  We just have two more questions.  “Do you have a feeling about which version of QSM is most useful: STI Suite, MEDI, others?”

*Dr. Morey:*Can you repeat it again?  I was getting some static again.

*Moderator:*Yeah.  “Thank you for the response regarding TBSS.  Do you have a feeling about which version of QSM is most useful: STI Suite, MEDI, others?”

*Dr. Morey:*Well, the QSM is—I’m not sure I completely understood the question—but the QSM is basically the acquisition; the data was acquired.

*Moderator:*I guess they’re referencing which inversion method.

*Dr. Morey:*Which inversion method?  I’m actually not familiar with the inversion methods that the person who asked the question is referencing.  The method we use for the QSM acquisition and also getting the quantitative values is published in Li, et al.—that’s L I, et al.—in *Neuroimage 2011*.  I’m sorry.  I’m not familiar with the ones that were referenced.

*Moderator:*Okay.  “Do you have a timeline estimate of when we can use imaging for diagnosis?”

*Dr. Morey:*Harvey, did you want to answer that?  I can take a shot at it.  I think it’s going to be still several years.  I think the answer to that is going to hinge on developing—on accumulating a large library, a large repository of normative data, of reference data that we can use to create templates and reference images.  Then, I think that’s the first step.  Once we get that, it still will be a while before we get to that because to use it in a clinical setting we would have to have a very high level of both sensitivity and specificity.  We certainly wouldn’t want to be missing diagnoses, nor would we want to misdiagnose people.

*Moderator:*Harvey, did you want to add to that at all?

*Dr. Levin*Well, certainly the normative or reference data are essential.  I think we have to distinguish between data that may be useful locally, that were collected from healthy persons on the scanner that we’re using to study those with brain injury, but as far as extrapolating to other centers, we need representative reference data that can be used in various geographic regions.  We don’t have that at this time.  I think the answer to the question also depends on the context and the time since injury.  There have been advances even using MRI in recent years.  Certain patterns seen on MRI appear to be promising, such as having a certain number of abnormal intensities present.  

Again, this has to be interpreted in relation to the other clinical features of the patient, but I do agree that we’re not close to having the specificity and sensitivity worked out.  This is especially the case for chronic mild TBI.

*Moderator:*Thank you for those responses.  To the people using the hand-raising function, I can’t call on you, so please type your question or comment into the Q&A box in the upper right-hand corner.  The next question, “Of all the TBI patients in your study, how long is the time between the MRI and injury?  Is there any time sensitivity to show the positive findings?”

*Dr. Morey*In our studies, it’s certainly chronic, as I think Dr. Levin mentioned in his talk.  The time from the TBI or multiple TBIs and the time of the scan could be months, even, in some cases, years.  We have looked at the—at that as a predictor variable.  In other words, we’ve looked to see if the time between the scan and the event, the TBI, somehow predicts our measures, our imaging measures, whether it’s QSM or DTI.  We have not actually—we have not found an association there.  We’re not sure if that means we just don’t have the power to detect that and we don’t have the number of subjects, or maybe there’s really no association.

*Moderator:*Great.  Thank you.  We have just one more question.  “Do you find any other aspects are important for choosing QSM controls, like age, handedness, medications?”

*Dr. Morey*Sure.  I think the number one—the number one criteria for USM controls would be age.  There’s a clear progression in terms of the QSM values with increasing age.  That’s one.  The other is certainly—with QSM especially—the positive QSM values, we would expect to see more of those in higher—we would expect to see differences in people who, say, have chronic hypertension or have arteriosclerosis.  They may have these really small microbleeds throughout the brain related to hypertension and cardiovascular disease.

*Moderator:*Thank you for that reply.  That was our final question.  I’m going to ask our participants to hang on for just a second.  I’m about to put up your feedback survey.  It’s your opinions that help guide which sessions we have presented.  I definitely want to thank Doctors Levin and Morey for presenting for us today.  We appreciate you lending your expertise and to Dr. Ralph DePalma for setting this session up.  This does conclude today’s HSR&D cyber seminar.  I’m going to go ahead and put up the feedback survey now.  We appreciate you taking just a moment to answer it.  Thanks, everybody.

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