Brain Imaging of Veterans and Service 

Members with Chronic Mild TBI

Harvey Levin, PhD

Supported by Dept of Veterans Affairs Merit Review and Center of 

Excellence for Traumatic Brain Injury, and NINDS P01 NS056202
Acknowledgement: Imaging Core, Neurons to 

Networks Center of Excellence

• Rajan Agarwal, MD-neuroradiologist

• Shalini Mukhi, MD-neuroradiologist

• Mary Newsome, PhD-fMRI, resting state

• Randall Scheibel, PhD-task-related fMRI

• Brian Taylor, PhD-MR physicist

• Elisabeth Wilde, PhD-structural MRI, DTI

• Xiaodi Li, BA-Image data analyst

• Vanessa Abadia, M Ed-Image data analyst
Goals of Presentation

• Issues in diagnosing chronic mTBI in Veterans

• Volumetric MRI

• Cortical thinning

• Diffusion tensor imaging (DTI)

• Resting state fMRI- functional connectivity

• Task-related fMRI and dissociation of mTBI vs 

PTSD effects
Challenges in Diagnosing Chronic mTBI in 

OEF/OIF/OND Veterans

• Diagnosis often relies on self-report without 

acute medical records; co-morbid 

PTSD/depression partially overlap in Sx with 

mTBI. Substance abuse could affect MRI findings.

• Lack of reference data to identify subtle cortical 

atrophy and reduced brain region volumes on 

MRI. 

• DTI metrics are potentially robust imaging 

biomarkers for mTBI, but center differences in 

equipment, software, QA, and method of analysis
Gray and white matter volumes in Veterans with 

chronic blast-mTBI (n=30) and controls (n=12)
Cortical thinning in anterior cingulate and 

parahippocampal gyrus in Veterans imaged 5 

years post-blast related mTBI (n=12) as compared 

with Veterans who had no TBI during deployment 

(n=8)
Diffusion Tensor Imaging

DTI Data from Ongoing Merit Review 

Project of Chronic mTBI in Veterans

• Used tract based spatial statistics (TBSS) to 

compare mTBI (n=19) with post-deployed 

group without TBI (n=13) who were not 

exposed to blast.

• Fractional anisotropy (FA), a metric which 

reflects preferential diffusion of water parallel 

to tract, shows areas of reduced integrity of 

microstructure including corpus callosum, 

brain stem, and cerebellum.


Preliminary fcMRI Findings

• 17 Veterans were imaged 5 years post-mTBI

(mean age = 31.4 yrs, sd = 6.2) as were 15 

control Veterans unexposed to blast and without 

TBI (31.2 yrs, sd=5.9, sd = 2.2) 

• Groups did not differ in age or education

• In the scanner, subjects were instructed to close 

their eyes but not fall asleep. 
Functional connectivity (FC) of PCC and 

ACC was reduced in Veterans imaged 5 

years post-mTBI (n=17) as compared with 

controls (n=15); PCC connectivity with left 

prefrontal cortex was greater after mTBI. 
Altered brain activation in military personnel after ≥1 

TBI from blast. Scheibel et al (JINS 2012, 18:89-100) 

• Compared OEF/OIF groups with TBI (n=15) vs no

TBI on stimulus-response compatibility task.

• TBI was co-morbid with high PTSD symptoms

• Pressed button on side pointed to by blue arrows, 

but opposite of red arrow direction

• 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 RT .

• PTSD dampened frontal-temporal activation 

Figure 2 Modulation of cognitive control activation by PTSD symptom severity. Images on the left 

depict areas where veterans with lower scores on the PCL-C had greater Arrows task activation, 

relative to veterans with higher PCL-C scores. Figures on the right indicate brain areas where 

veterans with lower PCL-C scores had significant activation during the Arrows task. 

(Scheibel et al., 2012)
Summary

• Volumetric MRI, DTI, resting state fMRI and taskrelated fMRI are sensitive to chronic effects of 

predominantly mild TBI primarily due to blast

• DTI is sensitive to chronic mTBI. 

• Functional connectivity in Veterans with chronic 

mTBI differs from controls in DMN. 

• Co-morbid PTSD reduces activation in taskrelated fMRI, opposite to effects of chronic mTBI
Rajendra Morey, M.D.

Associate Professor

Duke University School of Medicine

Durham VA Medical Center

MIRECC

Mid-Atlantic

Mental Illness Research, Education & Clinical Center

Advanced Imaging and Analytic Methods for 

Mild TBI
•Part I

Inter-individual spatial heterogeneity of tissue damage 

from concussive injury

•Part II

Tissue damage from subconcussive exposure to blast
Background

• Exposure to explosive forces from bombs is common in Veterans of recent 

military conflicts

• Majority of traumatic brain injury (TBI) at forward deployed medical 

facilities involves exposure an improvised explosive device (IED), grenade, 

rocket-propelled grenade (RPG), or mortar fire.

• Damage to brain tissue (white matter) using Diffusion Tensor Imaging 

(DTI) is well established in cases that clinical symptoms of mild TBI.

• Recent studies of sports participants show compromised white matter 

integrity even in the absence of clear concussive symptoms – 

subconcussive exposure.

• Until very recently, subconcussive exposure was not associated with 

“injury”

Question

Is the injury to brain tissue in Veterans with blast 

exposure without clinical symptoms of TBI ?

A) YES

B) NO

C) NOT SURE
reference group 

mean FA

test-subject FA

0

0.2

0.4

0.6

0.8

test-subject reference

FA

sample voxel

whole brain Z-map

reference subjects’ FA

+ 2 SD

- 2 SD

FA

Figure 1. Following preprocessing of DTI data, the analytic approach was based on registration of FA maps as implemented in the FSL tract based 

spatial statistics (TBSS). The primary statistical approach used the standard deviation of fractional anisotropy for each voxel in the reference group. Using 

the mean and standard deviation of FA for the reference group, the test-subject’s voxels were compared to the skeleton voxels (green highlight) that 

were generated for the reference group. For the test-subject, we computed a statistical map that reflects the number of standard deviations below the 

mean of the reference group, which is effectively a z- map. The whole brain z-map shows voxels (in red) where FA of the test-subject was found to be 

greater than two standard deviations below the mean FA of the reference group.
Figure 2. There was a significant main effect of group [F(2,42)=4.4, p=.02] on 

the number of small (25-50 voxel), medium (50-75 voxel), and large (75-100 

voxel) potholes, which were defined by low FA values (z < -2). The blastunexposed control group had significantly fewer potholes than the blast-mTBI 

group (p = .009) and the blast-exposed group (p = .036). Based on the number 

of potholes, the blast-exposed group resembled the blast-mTBI group when 

considered in relation to the blast-unexposed control group

Taber and Morey; accepted JHTR
Damage is spatially dispersed because of 

heterogeneous mechanisms of injury 

• Damage is spatially 

dispersed

• Damage is spatially 

heterogeneous 

across patients

Taber and Morey 2013; accepted JHTR
Figure 4. There was a significant main effect of group [F(2,42)=3.7, p=.034] 

on the number of small (25-50 voxel), medium (50-75 voxel), and large (75-

100 voxel) potholes, which were defined by low radial diffusivity (z < -2). The 

blast-unexposed control group had significantly fewer radial diffusivity potholes 

than the blast-mTBI group (p = .032) and the blast-exposed group (p = .025). 

Based on the number of potholes, the blast-exposed group resembled the 

blast-mTBI group when considered in relation to the blast-unexposed control 

group.

Taber and Morey 2013; accepted JHTR
Association with neuropsychological measures

• We found significant association with intra-extra 

dimensional shift (IED; Cambridge Cognition).

• Tests learning by inferring rules and set-shifting

• Hierarchical linear regression models including age, 

race, education, PTSD symptoms, MDD symptoms,and 

DTI measures

• These significantly predicted the 

– IED-number of stages completed [F(7,37)=2.4, p<.04] 

– IED-post-shift percent errors [F(7,37)=2.8, p<.02]; 

– Simple reaction time [F(7,37)=5.8, p<.001];

– Number of errors on the spatial working memory [F(7,37)=2.3, 

p<.05].
Support vector machine
Optimal separating hyperplane
Non-linear SVM solution
Machine learning (pattern classifier) can help diagnose 

mild TBI and subconcussive blast exposure

After Computer Training:

• 98% correct in diagnosing 

mild TBI

• 96% of subconcusssive 

blast cases were 

“diagnosed” with mild TBI

Morey et al, submitted PLoS ONE
Figure 5. There was a significant main effect of group 

[F(2,77)=33.8, p<.0001] on the number of small (25-50 voxel), 

medium (50-75 voxel), and large (75-100 voxel) potholes, which 

were defined by low grey matter volume (z < -2). The blastunexposed control group had significantly fewer potholes than the 

blast-mTBI group (p < .0001) and the blast-exposed group (p < 

.0001). Based on the number of potholes, the blast-exposed 

group resembled the blast-mTBI group when considered in 

relation to the blast-unexposed control group (p > .3).

Morey et al, submitted PLoS ONE

Potential Clinical and Policy Implications

• Blast-exposure may damage brain tissue (white matter) at comparable 

levels to mild TBI even in the absence of acute clinical symptoms 

(subconcussive blast). 

• The lack of clinical TBI symptoms following blast exposure may lead to the 

erroneous conclusion that there is little damage to brain tissue and 

consequently may go unnoticed. 

• If subconcussive blast exposure leads to later chronic symptoms, the 

patient may be misdiagnosed with clinical entities such as PTSD and 

depression. 

• If confirmed, our findings would support a diagnostic approach that 

includes novel MRI-based findings that "look below the surface for 

pathology". 
• Part II

Quantitative susceptibility mapping (QSM) to 

achieve myelin-specific imaging in TBI
Principle of diffusion tensor imaging
isotropic vs. anisotropic diffusion
Quiz Question

What is the principle structure that hinders water diffusion 

in white matter?

(a) myelin

(b) axonal membrane

(c) microtubules / neurofilaments
White matter structure - restricted diffusion

• Water is significantly 

anisotropic in nonmyelinated olfactory nerve 

of the garfish

• Anisotropy was similar to 

the trigeminal nerve of 

garfish myelinated with 

Schwann Cells 

• And similar to the optic 

nerve of the garfish that is 

myelinated with 

oligodendrocytes. 

Beaulieu 2002 – NMR Medicine
magnitude, phase, susceptibility images

Li et al 2011 NeuroImage
How can we image myelin?

• Frequency shift of gradient-echo MRI provides valuable information 

for assessing brain tissues.

• Frequency and susceptibility contrast depend on white matter fiber 

orientation

• Source of susceptibility anisotropy in white matter is myelin - loss of 

susceptibility anisotropy in the dysmyelinating shiverer mouse brain

Li et al 2012 NeuroImage
Quantitative Susceptibility Mapping

(QSM) values 

• Iron rich nuclei have positive QSM whereas myelin rich 

areas have highly negative QSM values.

Li et al 2011 NeuroImage
QSM for investigating TBI

• DTI (FA) is valuable for assessing axon 

membrane integrity but not myelin integrity.

• While altered FA and Radial diffusivity have been 

reported in TBI it is unclear if these findings 

demonstrate myelin damage or damage to other 

components of white matter.

• QSM provides the ability to isolate the specific 

components of white matter that are damaged.
QSM assessment in Mild TBI

Veterans (n=65) from Iraq and Afghanistan

Table 1. Demographic and Clinical Characteristics of Participants by Group *

 Characteristic Mild TBI (n=43) Non-TBI

(n=21) Group Comparison 

Age (years), [SD] 40.4 [8.8] 42.0 [9.4] t(30) = .67, p > .5

Gender, No. (%) of females 4 [19] 6 [29] Χ2 (1)=4.0, p = .05

Handedness, No. (%) right-handed 34 [79] 20 [95] Χ2 (2)=4.1, p > .2

Race, No. (%) of Caucasian 5 [24] 25[58] Χ2 (3)=8.7, p = .03

Ethnicity (%) Hispanic 3 (21) 3 (7) Χ2 (2)=3.5, p = .19

Clinician Administered PTSD Scale [SD] 33.6 (31.9) 13.7 [25.6] t(12) = 2.5, p = .016*

Loss of Consciousness (any) 16 [37] 0 [0] Χ2 (1)=10.4, p = .001

Total knocked out [SD] 0.93 (1.87) 0 [0] t(12) = 3.3, p = .002*
QSM reveals more TBI associated

differences than FA

z = 50 z = 40 z = 30 z = 20 z = 10 z = 0 z = -10 

Fractional Anisotropy (FA)

Quantitative Susceptibility Mapping (QSM)
QSM vs. Radial Diffusivity

Red = radial diffusivity Blue = QSM (filled)
QSM potholes in mild TBI
Summary & Conclusions

• FA and radial diffusivity are not 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 the axon membrane.

• While radial diffusivity has been purported to show myelin 

diffusivity, further research comparing radial diffusivity to 

QSM is needed.

• Myelin damage shows interindividual and spatial 

heterogeneity we saw in FA and radial diffusivity.
MIRECC

Mid-Atlantic

Mental Illness Research, Education & Clinical Center

Lab members and Collaborators

• Shannon Beall

• Vanessa Brown 

• Courtney Haswell

• Andrea Gold

• Jasmeet Hayes

• Jeff Hoerle

• Jessica Nasser

• Chris Petty 

• Elizabeth Selgrade

• Avani Vora

• Madeline Jacobs

• Gregory McCarthy

• Kevin LaBar

• Chunlei Liu

• Nankuei Chen

• Ryan Wagner

• Ahmad Hariri

• Jonathan Weiner

• Mike Hauser

• Christine Marx