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Disruption of caudate working memory activation in chronic
blast-related traumatic brain injury
Mary R. Newsome
a,b
, Sally Durgerian
c
, Lyla Mourany
d
, Randall S. Scheibel
a,b
, Mark J. Lowe
e
, Erik B. Beall
e
Katherine A. Koenig
e
, Michael Parsons
d
, Maya Troyanskaya
a,b
, Christine Reece
d
, Elisabeth Wilde
a,b
Barbara L. Fischer
f
, Stephen E. Jones
e
, Rajan Agarwal
g,h
, Harvey S. Levin
a,b
, Stephen M. Rao
d,*
aResearch Service Line, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA bDepartment of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA cDepartment of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA dSchey Center for Cognitive Neuroimaging, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA eImaging Institute, Cleveland Clinic, Cleveland, OH, USA fGeriatric Research Education and Clinical Center (GRECC), Wm. S. Middleton Memorial Veterans Affairs Hospital, Madison, WI, USA gDiagnostic and Therapeutic Care, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA hDepartment of Radiology, Baylor College of Medicine, Houston, TX, USA
a r t i c l e i n f o a b s t r a c t
Article history: Received 29 March 2015 Received in revised form 29 April 2015 Accepted 30 April 2015 Available online 8 May 2015
Keywords: Traumatic brain injury fMRI Cortical plasticity Working memory Concussion Veteran
Mild to moderate traumatic brain injury (TBI) due to blast exposure is frequently diagnosed in veterans returning
from the wars in Iraq and Afghanistan. However, it is unclear whether neural damage resulting from blast TBI dif-
fers from that found in TBI due to blunt-force trauma (e.g., falls and motor vehicle crashes). Little is also known
about the effects of blast TBI on neural networks, particularly over the long term. Because impairment in working
memory has been linked to blunt-force TBI, the present functional magnetic resonance imaging (fMRI) study
sought to investigate whether brain activation in response to a working memory task would discriminate
blunt-force from blast TBI. Twenty-five veterans (mean age = 29.8 years, standard deviation = 6.01 years, 1 fe-
male) who incurred TBI due to blast an average of 4.2 years prior to enrollment and 25 civilians (mean age =
27.4 years, standard deviation = 6.68 years, 4 females) with TBI due to blunt-force trauma performed the Stern-
berg Item Recognition Task while undergoing fMRI. The task involved encoding 1, 3, or 5 items in working mem-
ory. A group of 25 veterans (mean age = 29.9 years, standard deviation = 5.53 years, 0 females) and a group of
25 civilians (mean age = 27.3 years, standard deviation = 5.81 years, 0 females) without history of TBI
underwent identical imaging procedures and served as controls. Results indicated that the civilian TBI group
and both control groups demonstrated a monotonic relationship between working memory set size and activa-
tion in the right caudate during encoding, whereas the blast TBI group did not (p b 0.05, corrected for multiple
comparisons using False Discovery Rate). Blast TBI was also associated with worse performance on the Sternberg
Item Recognition Task relative to the other groups, although no other group differences were found on neuropsy-
chological measures of episodic memory, inhibition, and general processing speed. These results could not be at-
tributed to caudate atrophy or the presence of PTSD symptoms. Our results point to a specific vulnerability of the
caudate to blast injury. Changes in activation during the Sternberg Item Recognition Task, and potentially other
tasks that recruit the caudate, may serve as biomarkers for blast TBI.
© 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
- Introduction
Mild traumatic brain injury (TBI) is typically defined as a loss of con-
sciousness (LOC) up to 30 min, posttraumatic amnesia (PTA) not ex-
ceeding 24 h, or any period of confusion or disorientation associated
with a non-penetrating head injury (Kristman et al., 2014) in which a patient presents for health care with a Glasgow Coma Scale (GCS) (Teasdale and Jennett, 1974) score of 13–15. A moderate TBI is defined by PTA up to 7 days and loss of consciousness up to 24 h. Both mild and moderate TBI (TBI) can have long term consequences on cognition (Vanderploeg et al., 2005; Salmond et al., 2006; Ruttan et al., 2008; Silver et al., 2009). The most commonly studied type of TBI results from blunt-force trauma encountered in falls, vehicle accidents, contact sports, and assaults (Andriessen et al., 2011). Diffuse axonal injury, which occurs when the brain accelerates and decelerates within the
NeuroImage: Clinical 8 (2015) 543– 553
- Corresponding author at: Schey Center for Cognitive Neuroimaging, Neurological Institute, Cleveland Clinic, 9500 Euclid Avenue/U10 , Cleveland, OH 44195, USA. Tel.: + 216 444 1025; fax: +1 216 445 7013. E-mail address: [email protected] (S.M. Rao).
http://dx.doi.org/10.1016/j.nicl.2015.04. 2213-1582/© 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Contents lists available at ScienceDirect
NeuroImage: Clinical
j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i c l
skull (Adams et al., 1989), is considered to be the primary mechanism of
blunt-force TBI.
In contrast, the most common type of TBI in military personnel is the
result of exposure to improvised explosive devices and grenades. TBI,
largely due to blast exposure, has been estimated to occur in 15–30%
of service personnel (Hoge et al., 2008; Tanelian and Jaycox, 2008).
Blast explosions can result in several types of injury: primary blast
resulting from changes in pressure within the brain that lead to injury;
secondary blast caused by contact with external objects that are animat-
ed by the blast; and tertiary blast occurring when the õindividual is
thrown against an external surface, such as the ground or a wall. Any
other injury resulting from the explosion, e.g., burns, is referred to as a
quaternary blast. While the mechanisms behind secondary and tertiary
blast TBI are similar to those found in non-blast settings, less is known
about the effects of primary blast on the brain.
Blast explosions are associated with transient increases in air pres-
sure (overpressure) that produce a dose dependent increase in intracra-
nial pressure (Saljo et al., 2009), and have been linked to neuronal
injury, hemorrhage, and edema (Cernak et al., 2001; Saljo et al., 2011).
Blast has also been associated with acceleration of the brain (Courtney
and Courtney, 2011; Goldstein et al., 2012; Sosa et al., 2013). Animal
studies of primary blast TBI have revealed a variety of types of damage
to structures. Molecular changes have been reported in the thalamus,
hypothalamus, and hippocampus in mice (Woods et al., 2013), as well
as cell death in the nucleus accumbens in rats (Sajja et al., 2013). In
the brainstem, activated microglia, indicators of neuroinflammation,
have been found in the substantia nigra of rats exposed to blast
(Readnower et al., 2010), consistent with loss of dopaminergic neurons
in the substantia nigra of rats with non-blast TBI (Hutson et al., 2011).
However, less is known about pathological changes subsequent to
blast-related TBI in humans. For example, the brainstem may be equally
or even more vulnerable to the effects of blast (Taylor and Ford, 2009;
Yeh et al., 2014) than the frontal and temporal regions associated with
blunt-force TBI. These regional differences between blast and blunt-
force injuries may influence the pattern of neural and cognitive sequel-
ae of TBI.
Studies that have directly compared blast and blunt-force TBI on
symptom, neurocognitive, and psychiatric measures have typically re-
ported no differences between the groups (Kennedy et al., 2010;
Belanger et al., 2011; Luethcke et al., 2011; Cooper et al., 2012;
Mendez et al., 2013; Dretsch et al., 2014; Mac Donald et al., 2014). In
one study (Lippa et al., 2010), veterans with blast TBI endorsed elevated
cognitive symptoms on the Neurobehavioral Symptom Inventory (NSI)
(Cicerone and Kalmar, 1995), a measure of postconcussion symptoms,
approximately 3 years after injury, and the severity of symptoms was
similar to those reported by veterans with non-blast TBI; however, the
NSI queries general cognitive functioning and may not identify subtle
differences. Belanger et al. (2009) administered four standardized
tests measuring visual and verbal memories, interference resolution,
and IQ to veterans and reported no differences in performance between
the two types of TBI.
Another approach to identifying potential differences between blast
and blunt-force injuries involves structural brain imaging. Our group
found no differences when directly comparing blast and blunt-force
TBI groups on the presence of brain lesions and brain region volumes
(Fischer et al., 2014). Another study (Jorge et al., 2012) used diffusion
tensor imaging (DTI) to investigate changes in white matter in veterans
with blast TBI and civilians with blunt-force TBI by measuring fractional
anisotropy (FA) of whole white matter tracts and examining heteroge-
neity in FA, or “potholes”. The authors reported no significant group
differences when measurements were taken for an entire tract, but
civilians with acute blunt-force TBI had more potholes than veterans
with blast TBI. In a recent DTI study (Yeh et al., 2014), no white mat-
ter differences were found between blast and blunt-force TBI groups
in a whole brain diffusion measure; however, when hemispheric
asymmetries of FA were examined using tract-based spatial statistics
(Smith et al., 2006), the blast TBI group demonstrated more asymmetries than a blunt-force TBI group in tracts extending inferiorly to superiorly. In an autopsy study, identical neuropathology was found in the brains of veterans and mice exposed to blast and athletes with blunt-force TBI (Goldstein et al., 2012). The strongest evidence for identifying differences between blast and blunt-force TBI comes from functional imaging studies. Patients with blast TBI showed greater hypometabolism on positron emission tomog- raphy (PET) than patients with blunt-force TBI in the right superior parietal lobe (Mendez et al., 2013). Within the blast group, higher postconcussive symptom severity scores were related to decreased me- tabolism in the posterior cingulate cortex, while poorer performance on the Paced Auditory Serial Addition Test (Gronwall, 1977), a task involv- ing sustained attention, cognitive processing speed, and working mem- ory, was associated with hypometabolism in the medial frontal gyrus. In a functional magnetic resonance imaging (fMRI) study using the stop signal activation task, a measure of response inhibition, our group differentiated blast from blunt-force TBI by identifying alterations in an orbitofrontal–striatal inhibitory control circuit more than 4 years after blast exposure (Fischer et al., 2014). When correctly performing the inhibition task, veterans with blast TBI had alterations in activation similar to those in a civilian control group with TBI. However, when fail- ing to inhibit, the blast TBI group demonstrated increased activation in the caudate nucleus, consistent with other studies that link the striatum, particularly the caudate, to successful response inhibition (Li et al., 2008; Ghahremani et al., 2012; Ness and Beste, 2013). Moreover, in- creased activation was also found in cortical regions that enervate the striatum, the lateral orbitofrontal, anterior cingulate, and inferior tem- poral gyri (Alexander et al., 1986), suggesting that striatal pathways may be particularly vulnerable to blast injury. An additional frontostriatal circuit involving the dorsolateral pre- frontal cortex (DLPFC) has been closely linked to working memory (Levy et al., 1997), an executive function involved in maintaining and manipulating information in short term memory (Baddeley, 1986). The DLPFC–striatal working memory circuit extends from the DLPFC to the caudate, which in turn projects to other subcortical structures (globus pallidus, brainstem, and thalamus) and then back to the DLPFC. Given the vulnerability of the orbitofrontal–striatal inhibitory control circuit to blast as evidenced by the stop signal task in our previ- ous study (Fischer et al., 2014), we hypothesized that blast injury may also have a selective effect on the DLPFC–striatal working memory cir- cuit. To address this hypothesis, we compared veterans with blast TBI (military TBI; milTBI) and civilians with blunt-force (acceleration– deceleration) TBI (civTBI) performing a working memory task, the Sternberg Item Recognition Task (SIRT) (Sternberg, 1966), during fMRI. Veterans and non-veteran civilians without histories of blast exposure or TBI served as control groups. We also studied the pres- ence of long term neuropsychological sequelae in the TBI groups (Vanderploeg et al., 2005; Lippa et al., 2010). We predicted that the two TBI groups would demonstrate differing activation patterns in working memory circuits.
- Methods
2.1. Participants
All procedures and recruitment strategies were reviewed and ap- proved by the institutional review boards of the Cleveland Clinic, Baylor College of Medicine (BCM), Louis Stokes Veterans Affairs Medical Center (VAMC) (Cleveland), Michael E. DeBakey VAMC (Houston), and the U.S Department of Defense. Four groups of participants were enrolled: (1) veterans who had been deployed in the Afghanistan and Iraq wars (Operation Enduring Freedom and Operation Iraqi Freedom, OEF–OIF) who had experienced blast-related TBI (milTBI), (2) OEF–OIF veterans who had never experienced blast and/or head injury and who served as controls to the milTBI group (milCON), (3) civilians with TBI (civTBI)
with TBI. The battery included measures of processing speed, executive
function, and memory: written and oral forms of the Symbol Digit
Modalities Test (SDMT) (Smith, 1982), parts A and B of the Trail Making
Test (TMT) (Reitan, 1958), Controlled Oral Word Association Test
(COWAT) (Benton et al., 1983), and the California Verbal Learning
Test-II (CVLT-II) (Delis et al., 2000).
2.4. Sternberg Item Recognition Task
The Sternberg working memory task (Sternberg, 1966) consisted of
a total of 72 trials distributed over three imaging runs. A schematic of
the task is presented in Fig. 1. During the Encode phase, participants
were asked to commit to memory 1, 3, or 5 consonants (set size, SS)
over a 1800 ms interval. The number of encoded items constituted the
working memory load, or set size, with 24 trials for each set size; trials
were pseudo-randomized across set size. To maintain the same amount
of visual information across the set sizes, asterisks were used to replace
letters for set sizes 1 and 3 (see Fig. 1). Immediately following the
Encode phase, participants viewed a centrally fixated “+” for 4300 ms
(Maintenance phase). This was followed by the Response phase, in
which a single probe letter appeared on the screen for 2800 ms. On
50% of trials, the probe letter matched one of the items presented during
the Encode phase. Participants were instructed to respond with one of
two fingers if the probe letter matched a letter in the Encode stimulus
and with the other finger if the target did not match. The inter-trial in-
terval consisted of a centrally fixated “+” that varied in duration from
3830 to 14,330 ms to introduce jitter into the time series for the analysis
of this event-related fMRI task. The task was programmed using
E-Prime software (Psychology Software Tools, Inc., Sharpsburg, PA)
and displayed in the scanner using a back-projection video system
(Cleveland: Avotec Inc., Stuart, FL; BCM: Sharp USA, Mahwah, NJ). To
ensure that participants understood how to perform the task, individual
training sessions were provided prior to the scan, and all subjects
reached a criterion of 80% accuracy on SS1 during all runs.
2.5. MR image acquisition
Scanning was conducted at the Cleveland Clinic and Houston sites
using a Siemens TIM Trio 3 T MRI scanner (Erlangen, Germany)
equipped with a 12-channel receive-only head coil. Whole-brain
fMRI scans were acquired with a gradient-echo, echoplanar (EPI)
pulse sequence [31 4-mm thick contiguous axial slices, TE = 29 ms;
TR = 2800 ms, flip angle (FA) = 80°; FOV = 256 × 256 mm; matrix =
128 × 128; in-plane resolution = 2 × 2 mm]. The EPI sequence at the
Cleveland Clinic was modified to store the full 24-bit acquisition, but
otherwise scanning at the Houston sites and the Cleveland Clinic were
identical. The SIRT was performed over three imaging runs, each lasting
a total of 585 s (209 volumes per imaging run). High resolution struc- tural MRI scans [T1 with T1-weighted inversion recovery turboflash (MPRAGE), 120 axial slices, thickness 1–1.2 mm, FOV = 256 × 256, TI/TE/TR/FA 900 ms/1.71 ms/1900 ms/80, matrix 256 × 128, receiver band width (BW) 62 kHz] were acquired for registration with lower resolution EPI images. To facilitate combining data across sites, experi- enced MR physicists (MJL and EB) set up and tested identical MRI pro- tocols at both sites. Comparison of acquired phantom data indicated similar image quality and signal-to-noise ratio. Frequent quality assur- ance scans were performed at each institution to ensure that imaging data were free of scanner artifacts and comparable across sites.
2.6. Image analysis (structural MRI)
All structural MRI scans were reviewed for TBI-related and inciden- tal pathology by board-certified neuroradiologists (RA and SEJ). Quanti- tative regional brain volumes were obtained using the parcellation method incorporated in Freesurfer 5.1 software (http://freesurfer.net/ fswiki) using the Desikan atlas (Desikan et al., 2006). Results for each participant were visually inspected by a single rater to ensure accuracy of the cortical surface reconstruction. Manual editing, where necessary, was performed to optimize accuracy. The surface inaccuracies involving skull stripping or frank exclusion of brain parenchyma were edited ei- ther by (1) adding control points to aid FreeSurfer in the identification of white matter (since it uses the WM/GM boundary as a starting place for reconstructing the pial surface), (2) by fixing the skull strip by removing remaining dura, or (3) by adding back in the sections of brain that were inadvertently automatically removed. Correction for in- tracranial volume (ICV) was achieved by dividing the volume of interest by ICV and multiplying by 100.
2.7. Image analysis (fMRI)
The first 4 pre-steady-state volumes of the EPI time series were re- moved. The remaining images were time-shifted, motion corrected, and spatially filtered using a 2D 4 mm full width at half maximum (FWHM) Gaussian filter in the Fourier domain. A deconvolution analysis was used to extract the hemodynamic response function (HRF) to the task for each of the set sizes (SS1, SS3, and SS5). For data reduction pur- poses, the analysis for this study focused on the Encode phase, which was characterized by the sum of the HRF points 2.8 and 5.6 s post stim- ulus onset. Individual subject Encode t-maps for SS1, SS3, and SS5 trial types were converted to z-maps and transformed to Talairach stereo- taxic space (Talairach and Tournoux, 1988). Because brain activity dur- ing errors can be different than that during correct responses, to report brain activation that corresponds with working memory processing, events for which responses were errors were omitted from analysis.
Fig. 1. Schematic of the encoding, maintenance, and response events in the Sternberg Item Recognition Task. ITI = inter-trial interval; SS = set size.
Results in the functional regions of interest (fROI) were defined by
brain regions that demonstrated working memory load effects during
the Encode phase. T-maps of load effect were generated for each of
the four groups (milTBI, milCON, civTBI, and civCON). A significant clus-
ter was defined by an individual voxel probability (p b 0.005) and a min-
imum cluster size (0.684 ml), for an overall family-wise error of p b 0.05.
A disjunction mask was created by combining the suprathreshold
voxels from the group t-maps. Large fROIs were divided along local min-
ima in the averaged t-maps. Within each fROI and set size, z-statistics
were averaged for each subject.
For each fROI, a 3 (SS1, SS3, and SS5) × 2 (TBI/CON) × 2 (mil/civ)
ANOVA was conducted. The within-subject factor was set size and be-
tween subject factors were TBI/CON and mil/civ. False discovery rate
(FDR) was used to correct for multiple comparisons. For those two-
way (TBI/CON × Load; mil/civ × Load) and three-way (TBI/CON × mil/
civ × Load) interactions surviving the FDR correction, Tukey B post
hoc analyses were used to identify which groups contributed to the sig-
nificant interaction. Finally, to examine the specificity of the role of any
regions distinguishing military from civilian TBI groups, exploratory
analyses were conducted that related regions that were significant in
the three-way ANOVA to SIRT performance and neuropsychological
variables. To understand the role of potential neurodegeneration, signif-
icant regions were also related to time since injury.
- Results
The final sample consisted of 100 participants, with 25 in each group
(see Table 1). No significant group differences in gender were observed,
with the majority of the participants being male. The military groups
were older by 2 years (p = 0.039) and reported a year less education
(p = 0.021) than the civilian groups. Time since the most severe injury
was significantly longer for milTBI than civTBI participants (p = 0.001).
For the milTBI group, 11 participants (44% of the sample) had been ex-
posed to a single blast event, 5 (20%) reported 2 blast exposures, and the
remaining 9 (36%) reported multiple blast exposures (range 3–20).
3.1. Self-report measures
Participants in the milTBI group endorsed significantly more
concussion-related symptoms (NSI), posttraumatic stress disorder
(PTSD) (PCL-C), depression (CES-D), and pain compared to the par-
ticipants in the three other groups (Table 1). The milTBI group also
reported significantly more fatigue relative to the civTBI group.
3.2. Neuropsychological and Sternberg performance
Two-way (TBI/CON vs. mil/civ) ANOVAs were conducted on each neuropsychological test (Table 2). No differences in performance were identified between the groups for the neuropsychological measures. Behavioral performance on the SIRT was measured in terms of accu- racy and reaction time. Responses that were errors were excluded from analyses. Overall accuracy, collapsed across set sizes, had a significant group effect, with the milTBI group showing the lowest accuracy rates. At the most difficult working memory condition (SS5), the milTBI group was less accurate than the other three groups (milCON, civTBI, civCON) (see Fig. 2). Despite these group differences, all four groups performed well above chance (50%). Analysis of reaction time was conducted in three different ways. First, the average reaction time was calculated across all set sizes. There were no significant differences between the means of overall reaction time between the groups. Second, the reaction times were an- alyzed for each individual set size (see Table 2 and right panel of Fig. 2). At SS1, the TBI groups (civTBI and milTBI) were significantly slower than the control groups (civCON and milCON). Finally, a linear regres- sion was fit for each subject, with reaction time as the dependent variable and set size as the independent variable. From this regression, the slope and intercept were extracted and compared using a 2 × 2 ANOVA. The average intercept of the TBI groups was significantly great- er than the average intercept of the control groups (Table 2). No signif- icant group differences were observed for slope.
3.3. Structural MRI
None of the participants had lesions consistent with TBI on conven- tional MRI. Groups did not differ on gray matter, white matter, or cere- brospinal fluid whole brain volumes (Table 2). No significant group differences were observed following False Discovery Rate correction for individual cortical and subcortical volumes and cortical thickness measures (Supplementary Tables 2–4).
3.4. fMRI
The disjunction analysis identified 25 Encode fROIs that demonstrated differential activation based on set size (Fig. 3 and Supplementary Table 1). As noted above, all fROIs were analyzed for set size by group ef- fects using a 3 × 2 × 2 ANOVA. False discovery rate was used to control for multiple comparisons. None of the regions had a significant interaction
Fig. 2. Accuracy and reaction time as a function of memory set size, or load, for each group. For load 3, the military TBI subjects were significantly less accurate than the civilian TBI subjects and civilian control subjects. For load 5, the military TBI subjects were significantly less accurate than all other three groups, which did not differ from each other.
at SS3; in contrast, the TBI groups did not demonstrate deactivation at
SS3 (Fig. 4).
Two regions demonstrated 3-way interactions: right tail of the cau-
date and right head/body of the caudate (see row 3, middle and right-
most graphs in Fig. 4). In both of these ROIs, activation in the milTBI
group did not change with working memory load, whereas the three
other groups demonstrated a monotonic increase associated with
working memory load. Effects were still significant after covarying for
age, education, PTSD, depression, pain, and fatigue (Supplementary Table 5) and after removing subjects with moderate TBI.
3.5. Relation of SIRT performance, neuropsychological, and post-injury interval to caudate activation
Exploratory analyses revealed that activation of the right caudate head/body was significantly related to reaction time slope in the civilian
Table 3 Regions demonstrating significant two- and three-way interactions with WM load.
Tailarach coordinates
Side Region BA x y z Vol (ml). TBI/CON ∗ Load mil/civ ∗ Load TBI/CON ∗ mil/civ ∗ Load
1 L Insula, inf. frontal gyrus 45 − 32 22 4 4.0 * − − 2 B Mid. orbital gyrus 12 0 42 − 10 4.9 * − − 3 R Angular gyrus 39 50 − 74 29 1.7 * − − 4 R Caudate (head, body) − 16 9 18 12.3 * − * 5 R Caudate (tail) − 23 − 22 28 2.9 * − * 6 L Caudate (head), putamen, pallidum − − 18 − 2 14 9.0 * − − 7 B Cerebellar vermis − 2 − 63 − 24 7.2 * − −
*p b 0.05.
Fig. 4. Significant two-way interactions (1–7) between group (control vs. TBI) and set size (1, 3, 5) and significant three-way interactions (8–9) between group (control vs. TBI), set size (1, 3, 5) and military status (military vs. control).
TBI group (p b 0.029), while no region was related to reaction time slope
or any other task performance measure in the military TBI group (Fig. 5).
Conversely, the military TBI group showed significant or marginally sig-
nificant negative relations between caudate activation and two DSM-IV
clusters of PTSD, re-experiencing (p = 0.047) and avoidance (p =
0.054), in addition to a pain measure (p = 0.068), that were not observed
in the civilian TBI group. Regarding post-injury interval, there were no
significant relations with caudate in either TBI group; however, the mili-
tary TBI group showed a negative relation between post-injury interval
and slope of activation in bilateral middle orbital gyri, a region that was
significant in the two-way (Group × Set Size) interaction.
3.6. Number of blasts
Fourteen of 25 military TBI subjects (56%) were exposed to two or
more blasts. Subjects with one blast were compared to those who had
experienced two or more blasts on all outcome measures and encoding
activation. Except for pain, which was significantly greater in subjects
with two or more blasts, all results were NS (Supplementary Tables 6
and 7).
- Discussion
Our fMRI study provides support for the hypothesis that the basal
ganglia, in particular the caudate nucleus, may be specifically vulnerable
to the effects of blast injury. The blast TBI group (milTBI), in contrast to
the blunt-force TBI group (civTBI) and both control groups, failed to
demonstrate a monotonic relation between set size and activation in
the caudate nucleus (head, body, tail). The lack of a monotonic effect
in the blast TBI group could not be attributed to working memory errors,
since these trials were removed from the image analyses. Notably, re-
duced activation in the caudate in the blast TBI group was significant
even when age, education, PTSD, depression, fatigue, and pain symp-
toms were taken into account.
The caudate has long been implicated in verbal working memory.
Set size effects have previously been found in the caudate of healthy
subjects (Braver et al., 1997; Cairo et al., 2004; Chang et al., 2007). The
caudate is active during working memory encoding (Chein and Fiez,
2001; Chang et al., 2007), maintenance (Chein and Fiez, 2001; Chang
et al., 2007), manipulation (Lewis et al., 2004), retrieval (Chang et al.,
2007), and in preparation of a motor response to a working memory
stimulus (Postle and D 3 Esposito, 1999). In addition, functional connec-
tivity of the caudate has been reported to have a positive relation with
performance in an n-back working memory task (Gordon et al., 2015).
The caudate has been suggested to facilitate updating contents in
working memory by receiving dopamine from the brainstem for trans-
mission to the dorsolateral prefrontal cortex (Murty et al., 2011) and by
disinhibiting the mediodorsal nucleus of the thalamus (Ashby et al., 2005). It has also been shown to be functionally and structurally con- nected to both prefrontal cortex and thalamus (Robinson et al., 2012). Further, altered activation during working memory tasks in patients with civilian TBI has been suggested to be related to a disrupted dopa- mine system (McAllister et al., 2004; Wishart et al., 2011) and modulat- ed by dopaminergic genes (McAllister, 2009). Recently, Yeh et al. (2014) identified white matter disruptions in the fronto-striatal circuit and brainstem in active duty military personnel with blast TBI, suggesting that connections from brainstem to prefrontal cortex via the thalamus and caudate may be particularly vulnerable to blast injury. What characteristics of the caudate may have contributed to deficits in working memory activation after blast exposure? The lack of signifi- cant differences in brain volume or cortical thickness suggests that al- tered activation was not driven by atrophy of the caudate. However, a report of blast-related white matter hemispheric asymmetries in the in- ternal capsule (Yeh et al., 2014), adjacent to the caudate, could have im- plications for impaired neural transmission. Moreover, the striatum (caudate and putamen) is a highly plastic area linked to changes in both learning and disease (Kreitzer and Malenka, 2008), thereby affect- ing the caudate 3 s role in working memory. As well, neuroplasticity of the caudate has been found after intensive training on a task involving attention and working memory (Nikolaidis et al., 2014), and changes in caudate activation during that task further predicted individual dif- ferences during performance of a second, unpracticed, task — the SIRT (Nikolaidis et al., 2014). In a post-mortem study of brains of athletes and veterans who had a history of repetitive TBI, chronic traumatic encephalopathy (CTE), a type of progressive neurodegeneration linked to increased tau patholo- gy, was found in 80% of the brains (McKee et al., 2013), suggesting that veterans from the Iraq and Afghanistan wars may be at risk for CTE. Macroscopic changes in CTE are preceded by subtle changes in memory and attention (McKee et al., 2009; McKee et al., 2013), and it is plausible that the performance deficits observed during the SIRT could be related to early CTE symptoms. Veterans from older wars who experienced TBI were found to be more likely to develop dementia than veterans who did not experience TBI (Barnes et al., 2014). However, the majority of the veterans in the Barnes et al. (2014) report had more severe TBI than the veterans in this paper, and a recent meta-analysis of civilian mild TBI suggests there is insufficient evidence for an association of sin- gle or repetitive mild TBI with dementia (Godbolt et al., 2014). Addi- tionally, increased endorsement of PTSD symptoms in the military TBI group may suggest alterations in cognition and pathology as a result of PTSD (Tian et al., 2014). TBI and PTSD may share neurocircuitry susceptible to neurodegeneration, possibly similar to how CTE has been linked to Parkinson 3 s disease, Alzheimer 3 s disease, motor neuron disease, and frontotemporal dementia (McKee et al., 2013), and by
Fig. 5. Slopes for activation in the right caudate head/body and reaction time were positively correlated in the civilian TBI group, but not in the military TBI group, suggesting a dissociation between the two groups.
and potentially other tasks that recruit the caudate, may serve as bio-
markers for blast TBI.
Acknowledgements
This study was supported by grants from the U.S Department of
Defense (W81XWH-08-2-0124, W81XWH-10-1-0609) and was sup-
ported in part by the Department of Veterans Affairs, Veterans Health
Administration, Office of Rehabilitation Research and Development,
Project #B6812C Traumatic Brain Injury Center of Excellence. The con-
tent is solely the responsibility of the authors and does not necessarily
represent the official views of the Department of Veterans Affairs.
There are no potential conflicts of interest, including financial, between
any of the authors and the work presented in this manuscript. We are
grateful to the veterans taking part in the research. Thanks to laboratory
members who provided helpful contributions: Gregory Vogt, Xiaodi Lin,
Tricia Merkley, Rebecca Marsh, Brian Taylor, and Mark Walker.
Appendix A. Supplementary data
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.nicl.2015.04.024.
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