Brain Imaging and Behavior DOI 10.1007/s11682-015-9376-6

MILITARY/VETERAN TBI

The suppression of brain activation in post-deployment military personnel with posttraumatic stress symptoms Randall S. Scheibel 1,2 & Nicholas J. Pastorek 1,2 & Maya Troyanskaya 1,2 & Jan E. Kennedy 3 & Joel L. Steinberg 4 & Mary R. Newsome 1,2 & Xiaodi Lin 1,2 & Harvey S. Levin 1,2

# Springer Science+Business Media New York (outside the USA) 2015

Abstract Previous research using cognitive paradigms has found task-related activation that includes prefrontal brain structures and that is attenuated in association with posttraumatic stress symptoms (PTSS). The present investigation used a cognitive control paradigm, the Arrows Task, to study subjects who had not sustained a traumatic brain injury during deployment and who had a wide range of scores on the Posttraumatic Stress Disorder Checklist (PCL). During the Arrows Task there was no significant activation within the full sample of 15 subjects, but deactivation was found within areas that are likely to be involved in cognitive control, including the dorsal anterior cingulate gyrus and parietal cortex. Exploratory analyses were also conducted to compare subjects with relatively high PTSS (HIGH PTSS, n=7) to those with lower severity or no symptoms (LOW PTSS, n=8). LOW PTSS subjects exhibited activation in nonfrontal brain areas and their activation was greater relative to the HIGH PTSS subjects. In contrast, the HIGH PTSS group had extensive deactivation and there was a negative relationship between activation and PCL

* Randall S. Scheibel [email protected] 1

Michael E. DeBakey Veterans Affairs Medical Center, Mail Code 153TBI, Room 2B-122, 2002 Holcombe Blvd., Houston, TX 77030, USA

2

Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA

3

General Dynamics Information Technology contractor for the Defense and Veterans Brain Injury Center, San Antonio Military Medical Center, Fort Sam Houston, TX, USA

4

Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA

scores within subcortical structures, the cerebellum, and higher-order cortical association areas. For the HIGH PTSS group there was also a positive relationship between PCL scores and activation within basic sensory and motor areas, as well as structures thought to have a role in emotion and the regulation of internal bodily states. These findings are consistent with widespread neural dysfunction in subjects with greater PTSS, including changes similar to those reported to occur with acute stress and elevated noradrenergic activity.

Keywords Posttraumatic stress disorder . Executive function . Functional magnetic resonance imaging (fMRI) . Cognitive control

Introduction Posttraumatic stress disorder (PTSD) may develop following exposure to intense fear-provoking or stressful events and is characterized by hyperarousal, avoidance, emotional numbing, and re-experiencing of the psychological trauma (American Psychiatric Association 2013). In contrast to acute stress responses, the diagnostic criteria require symptoms to be present for at least one month and PTSD often persists and can get worse over time (Marx et al. 2009). This disorder is common following combat deployment and an estimated 12 to 20 % of Operation Enduring Freedom/Operation Iraqi Freedom/ Operation New Dawn (OIF/OEF/OND) personnel eventually develop PTSD (Institute of Medicine 2012). The underlying pathology of PTSD is not well understood, but may involve dysfunction within neural systems that mediate fear conditioning, emotional control, and central regulation of the autonomic nervous system (Falconer et al. 2008; Newport and Nemeroff 2000). Studies of PTSD using symptom provocation paradigms, such as exposure to trauma-

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related stimuli or narratives, have found heightened amygdalar responses and decreased activation within the medial prefrontal and anterior cingulate cortex (Shin et al. 2006). These findings have generally been interpreted to reflect the failure of medial prefrontal structures to exert adequate inhibitory control over the amygdala during emotional, traumarelated stimuli and thoughts (Francati et al. 2007; Shin et al. 2006). Investigations using cognitive paradigms have been less common and, unlike those using symptom provocation techniques, these have often failed to find greater amygdala activation with PTSD (Bremner et al. 2004; Morey et al. 2008; Simmons et al. 2011a; Shin et al. 2001). Activation changes within the prefrontal and anterior cingulate cortex also appear to differ depending upon whether the task design engages primarily cognitive or emotional processes (Brown and Morey 2012). For example, Morey et al. (2008) used a mixed functional magnetic resonance imaging (fMRI) paradigm which involved the presentation of combat-related and neutral scenes interleaved with a simple executive function task. During combat scenes they found activation within the ventromedial prefrontal cortex, inferior frontal cortex, and ventral anterior cingulate gyrus and this activation exhibited a positive relationship with the severity of posttraumatic stress symptoms (PTSS). In contrast, during the executive function task there was activation within a dorsal executive network that included the middle frontal gyrus, inferior parietal lobule, and dorsal anterior cingulate gyrus. Moreover, activation within this dorsal executive network had a negative relationship with symptom severity. Such findings are consistent with the operation of interrelated executive control and emotional processing networks that are differentially affected by stress-related pathology (Morey et al. 2008, 2009). Mild traumatic brain injury (mTBI) is also common in post-deployment populations, many post-concussive symptoms (PCSx) overlap with PTSS (Brenner et al. 2009), and mTBI is a risk factor for the development of PTSD (Yurgil et al. 2014). Differential diagnosis of these disorders can be difficult and returning service members frequently present with symptoms that could potentially be associated with either PTSD or mTBI, including poor concentration, memory impairment, fatigue, insomnia, irritability, hyperarousal, avoidance, depressed mood, and anxiety (Brenner et al. 2009; Stein and McAllister 2009). Studies using covariance approaches have found that PTSS, measured using self-report instruments such as the PTSD Checklist (PCL), may account for many of the complaints that separate those who had experienced deployment-related mTBI from those who had not (Hoge et al. 2008; Polusny et al. 2011). The failure to find significant differences after statistically adjusting for PCL scores raises questions about whether many post-deployment PCSx are secondary to PTSD, rather than the mTBI (Hoge et al. 2008; Polusny et al. 2011).

We recently used a cognitive control fMRI paradigm, the Arrows Task, to examine brain activation following military, blast-related mTBI while also considering the influence of PTSS (Scheibel et al. 2012). When compared with a group of subjects who had not experienced a brain injury during deployment, those with a history of blast-related mTBI exhibited greater task-related activation within the anterior cingulate gyrus, medial frontal cortex, and posterior cerebral areas involved in visual functions. Statistically adjusting for PCL scores increased these between-group activation differences. In addition, subjects without mTBI did not exhibit a significant level of brain activation and there was a negative relationship between activation and the severity of PTSS when examined within the entire sample. Although these overall findings are consistent with over-activation secondary to mTBI, the negative correlation with the PCL also suggests that activation was suppressed in the presence of PTSS (Scheibel et al. 2012). Our previous investigation had focused on mTBI and did not address either the negative relationship between PTSS and brain activation or the lack of significant task-related activation in subjects without traumatic brain injury (TBI). The current study builds upon that work by providing a more extensive analysis of Arrows Task data from those comparison subjects, all of whom had been screened to rule out any history of TBI or blast exposure. Prior studies of PTSS using other executive function paradigms have found activation within components of an executive control network that decreases in association with greater symptom severity (e.g., Falconer et al. 2008; Hayes et al. 2009; Jovanovic et al. 2013; Morey et al. 2008). Thus our primary hypothesis was that stressrelated pathology would attenuate Arrows Task brain activation within structures such as the middle frontal gyrus, inferior parietal lobule, and anterior cingulate gyrus. We also expected to find a significant positive correlation between measures of PTSS and PCSx because psychological trauma and mTBI may produce many of the same symptoms (Stein and McAllister 2009).

Methods Participants This research was conducted at the Michael E. DeBakey Veterans Affairs Medical Center in Houston, Texas, with Institutional Review Board approval, and all subjects provided written informed consent. Data from these subjects had been included in our previous publication (Scheibel et al. 2012) and all had been deployed to Afghanistan or Iraq, but for the current report the analyses were restricted to those without TBI or blast exposure. The screening for TBI and blast consisted of a self-report questionnaire and interview with a clinician

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experienced in the evaluation of individuals with combatrelated injury. These subjects included three active duty personnel recruited through advertisements at an army post, one veteran who was examined by the TBI clinic and found not to have experienced head trauma or blast, and 11 veterans who were identified through a database of consented research subjects. None reported current drug or alcohol abuse and none had a history of previous psychiatric disorder or learning disability. This full sample had a mean PCL score of 38.9 (SD= 19.45, range=17–77). Previous research assessing use of the PCL as a screening tool for PTSD provides some support for a cutoff score as small as the low to mid 30’s (McDonald and Calhoun 2010) and, within the distribution for our full sample, there was a gap between scores of 31 and 39 (see Fig. 1). For exploratory analyses this dividing point was used to split the sample into a group consisting of subjects with relatively high PTSS (HIGH PTSS group, PCL≥39, n=7) and a group with lower severity or no symptoms (LOW PTSS group, PCL≤31, n=8). Behavioral measures and demographic information All subjects completed the PTSD Checklist – Civilian (Dobie et al. 2002), the Brief Symptom Inventory (BSI) (Derogatis 1975), and the Neurobehavioral Symptom Inventory (NSI) (Cicerone and Kalmar 1995). The version of the PCL that was used is a brief, self-report instrument that does not require specification of a particular event and does not assume that all traumatic experiences are related to combat. The NSI is a measure of common PCSx (King et al. 2012). All subjects also provided demographic information needed for the calculation of an intellectual function estimate, the Barona IQ (Barona et al. 1984). Arrows task The event-related version of the Arrows Task (Scheibel et al. 2012) was administered as a rapid-presentation, stochastic fMRI paradigm. This type of design presents stimuli in

Fig. 1 Scatter plot depicting the relationship between scores on the PCL and the Neurobehavioral Symptom Inventory (NSI) Total Score

random order at a rate that is much more rapid than the duration of the blood oxygen level dependent hemodynamic response, which is approximately 20 to 30 seconds (s) in duration, and with stimulus onset jittered to enable even sampling of the hemodynamic response curve over time. Evoked responses to the stimuli and their differences are modeled in terms of basis functions of the peri-stimulus time, which yields highly effective temporal resolution (Friston et al. 1998). The advantage of randomizing the order of trials in the event-related design reduces the likelihood that the response will be confounded by a subject’s cognitive set or systematically influenced by previous trials (Josephs and Henson 1999). Furthermore, rapid presentation event-related designs are more efficient than event-related designs with very long intervals between stimuli (Friston et al. 1999). During the Arrows Task the participants viewed arrows presented one at a time for 265 milliseconds (ms), with each followed by a blank screen for 200 ms and then a crosshair fixation point for a mean of 2235 ms, randomly jittered plus or minus 200 ms (see Fig. 2). Blue arrows were presented for 87.5 % of the trials and 12.5 % of the trials had red arrows. For each color, 50 % of the arrows pointed left and 50 % pointed right and these stimuli were randomly intermixed throughout each run of 80 arrows. When the arrows were blue the subjects were required to use their right or left index finger to press the response key on the same side that the arrow was pointing towards (i.e., compatible condition). When the arrows were red the subject pressed the response key opposite to the direction of the arrow (i.e., incompatible condition). Responses were collected between 200 and 1200 ms after stimulus onset and the first two trials in the run were always from the compatible condition. There was a 20 s fixation cross prior to the first trial to allow magnetization equilibrium to occur and additional fixation rest periods of 2000 ms occurred after 16, 32, 48, and 64 trials. Each of the three runs lasted 244 s, with a one minute rest period between runs, and the order of the runs was counterbalanced across subjects. Instructions and practice were provided within two hours prior to scanning until the

Fig. 2 Schematic diagram for the Arrows Task. Blue and red arrows are displayed on the screen, one at a time, for 265 ms and each is then followed by a blank screen for 200 ms and a crosshair fixation point for another 2235 ± 200 ms. Responses are collected between 200 and 1200 ms following stimulus onset. Subjects respond with a button press on the same side the arrows are pointing to when they are blue (87.5 % of the trials) and to the opposite side when they are red (12.5 % of trials)

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subject performed with 65 % accuracy or better in both conditions. During fMRI data acquisition the response accuracy, onset time, and reaction time (RT) were recorded for each stimulus and only correct responses were included in analyses for RT. Image data acquisition Whole brain imaging was performed using a multi-channel sensitivity encoding (SENSE) head coil on a Philips Achieva 3 T system (Philips Healthcare, Best, The Netherlands). Blood oxygen level dependent T2* weighted single-shot gradientecho echoplanar images were acquired as 160 volumes with 32 axial slices of 3.75 mm (mm) thickness with a 0.5 mm gap, using a 240 mm field of view (FOV), 64×64 matrix, repetition time (TR) of 1700 ms, echo time (TE) of 30 ms, a 73° flip angle, and a SENSE factor of 2.0. The first 20 s of each run were discarded to allow for signal magnetization equilibrium to be achieved. A set of high-resolution T1-weighted 3Dturbo field echo anatomical images were also acquired in 132 axial slices of 1.0 mm thickness (no gap) with 240 mm FOV, 256×256 matrix, TR of 9.9 ms, TE of 4.6 ms, a 8.0° flip angle, and a SENSE factor of 1.2. Additional anatomical series were performed to assess neuropathology and these included T2-weighted gradient echo (25 axial slices, slice thickness=5.0 mm, TR=2500, TE=32, FOV=224 mm, flip angle=30 to 40°), T2-weighted fluid attenuated inversion recovery (25 axial slices, slice thickness=5.0 mm with 1.0 mm gap, TR=11000, TE=105, FOV=240 mm, flip angle=90°), and T2-weighted spin echo imaging (25 axial slices, slice thickness=5.0 mm, TR=2141, TE=80, FOV=230 mm, flip angle=90). A board certified neuroradiologist examined the anatomical imaging and all of the subjects had normal findings. Image post-processing and analysis The fMRI data were subjected to voxel by voxel analyses using Statistical Parametric Mapping 2 (SPM2) software (Wellcome Department of Cognitive Neuroscience, London, UK) implemented in Matlab (Mathworks Inc. Sherborn MA, USA). Procedures for image processing and analysis, including the statistical thresholds, were the same as for the earlier study of brain activation following blast-related mTBI (Scheibel et al. 2012). After slice-timing correction, the fMRI time series were realigned and unwarped to correct for head motion and susceptibility-by-movement interactions. Series with motion greater than 2.0 mm translational or 3.0° rotational were eliminated from analysis. The time series were coregistered to the high-resolution anatomical scan, normalized to the Montreal Neurological Institute (MNI) template using the normalization parameters from the anatomical scan, resliced to 2×2×2 mm, and spatially smoothed using a 6 mm isotropic full width at half maximum Gaussian filter. A high-

pass temporal filter with a cutoff period of 128 s was used to reduce low-frequency noise. First-level analyses were then conducted using the general linear model at each voxel for each subject, using only correct response trial events and with incorrect trials modeled as a nuisance regressor, to contrast the amplitude of the hemodynamic response (HRF) associated with the onset of red arrows (i.e., incompatible condition) with the HRF associated with the onset of blue arrows (i.e., compatible condition). For each subject the incompatible minus compatible contrast image was carried forward into second-level SPM2 random effects analyses. These included t tests to examine activation and deactivation within the full sample and separately within each group, t tests to perform between-group comparisons, and an Analysis of Covariance (ANCOVA) model to examine between-group contrasts while controlling for scores on the BSI Depression Scale. A preliminary slopes interaction analysis was first completed and this indicated that the parallel slopes assumption of ANCOVA was met (Kleinbaum et al. 1998). Simple regression analyses were also performed to examine the relationship between activation and accuracy during the incompatible condition, scores on the BSI Depression Scale, and the PCL Total Score. The cluster-defining (height) threshold for all second-level analyses was set at voxel-level t=2.50. Reported clusters were statistically significant (corrected p HIGH PTSS 0.000 33145 0 −20 44 Amygdala (L), Angular Gyrus (B), Anterior Cingulate Gyrus (B), Caudate Body (R), Cerebellum (B), Cingulate Gyrus (L), Cingulate Gyrus (R), Claustrum (B), Cuneus (B), Fusiform Gyrus (B), Globus Pallidus (B), Inferior Frontal Gyrus (B), Inferior Occipital Gyrus (B), Inferior Parietal Lobule (B), Inferior Temporal Gyrus (B), Insula (B), Lingual Gyrus (B), Medial Frontal Gyrus (B), Midbrain (B), Middle Frontal Gyrus (B), Middle Occipital Gyrus (B), Middle Temporal Gyrus (B), Paracentral Lobule (B), Parahippocampal Gyrus (B), Postcentral Gyrus (B), Posterior Cingulate Gyrus (B), Precentral Gyrus (B), Precuneus (B), Putamen (B), Superior Frontal Gyrus (B), Superior Occipital Gyrus (B), Superior Parietal Lobule (B), Superior Temporal Gyrus (B), Supramarginal Gyrus (B), Thalamus (B), Transverse Temporal Gyrus (B), Uncus (L) 0.046 312 −24 2 8 Caudate Body (L), Claustrum (L), Insula (L), Globus Pallidus (L), Putamen (L), Thalamus (L) b. ANCOVA: LOW PTSS > HIGH PTSS (Covariate: BSI Depression Scale) 0.000 36883 −18 −76 4 Amygdala (B), Angular Gyrus (R), Anterior Cingulate Gyrus (B), Caudate Body (L), Caudate Tail (L), Cerebellum (B), Cingulate Gyrus (B), Claustrum (R), Cuneus (L), Cuneus (R), Fusiform Gyrus (B), Globus Pallidus (B), Hippocampus (B), Inferior Frontal Gyrus (B), Inferior Occipital Gyrus (B), Inferior Parietal Lobule (B), Inferior Temporal Gyrus (B), Insula (B), Lingual Gyrus (B), Medial Frontal Gyrus (B), Midbrain (B), Middle Frontal Gyrus(B), Middle Occipital Gyrus (B), Middle Temporal Gyrus (B), Paracentral Lobule (B), Parahippocampal Gyrus (B), Postcentral Gyrus (B), Posterior Cingulate Gyrus (B), Precentral Gyrus (B), Precuneus (B), Putamen (B), Superior Frontal Gyrus (B), Superior Occipital Gyrus (B), Superior Parietal Lobule (B), Superior Temporal Gyrus (B), Supramarginal Gyrus (L), Thalamus (B), Transverse Temporal Gyrus (B), Uncus (B) MNI Montreal Neurological Institute; R Right Side; L Left Side; B Both Sides or Bilateral; PTSS Posttraumatic Stress Symptoms a

Number of contiguous 2×2×2 mm voxels that exceed threshold

b

The structure that is closest to the voxel with the maximum t value is indicated with bold print

magnitude of this correlation was underestimated and the extent of the clusters was reduced (Pedhazur 1997). Despite its limitations, the present investigation allowed the comparison of brain activation between groups whose symptom report differed and whose level of task performance did not. The pattern of the activation and deactivation findings within the two groups was highly dissimilar and, while the LOW PTSS exhibited no deactivation, the HIGH PTSS group had extensive deactivation and completely lacked significant task-related activation. Results of the between-group analyses were consistent with these within-group findings in indicating greater activation within the LOW PTSS group. Overall, these within- and between-group results suggest that activation during cognitive control is suppressed when PTSS are elevated, but they provide few clues about the nature of the mechanism underlying such changes. Within the HIGH PTSS group the relationship between symptom intensity and Arrows Task activation was examined further using image regression analyses. For these subjects increasing PTSS were associated with heightened activation within structures with basic sensory and motor functions

(e.g., precentral and postcentral gyri, basal ganglia), as well as some that are thought to have a role in emotion and the regulation of internal bodily states (e.g., insula, right orbitofrontal cortex) (Porges 2009; Schore 2000). However, with greater PTSS there was also a general pattern of decreasing activation (i.e., negative correlation) within other brain areas that included higher-order association cortex of the frontal, temporal, and parietal lobes. These particular cortical areas undergo late myelination and are characterized by having long association fibers, high connectivity with other association areas, a lack of strong coupling with primary sensory or motor cortex, and a cytoarchitectural structure that facilitates signal integration (Buckner and Krienen 2013). Frontal areas with such characteristics are involved in executive functions and the control of attention (Fuster 2001; Burgess et al. 2007), while posterior supramodal association areas are thought to mediate complex cognitive operations for functions such as abstraction and language (Heilman et al. 2003; Luria 1980). A significant negative correlation with PTSS was also found for activation within various subcortical structures, including the thalamus, as well as parts of the cerebellum which have strong connections with

Brain Imaging and Behavior Table 6

Coordinates and anatomical region definitions for results of the LOW PTSS and HIGH PTSS within-group regression analyses

Cluster-level P value Cluster Maximum (corrected) size (k)α t Value MNI coordinates (x, y, z; mm)

Anatomical labels within each clusterb

a. Within-Group Positive Regression (PCL): HIGH PTSSc 0.000 571 −20 32 6 Anterior Cingulate Gyrus (L), Anterior Cingulate Gyrus (R), Caudate Body (R), Inferior Frontal Gyrus (R), Middle Frontal Gyrus (R), Orbital Gyrus (R) 0.000 388 −14 16 24 Anterior Cingulate Gyrus (L), Caudate Body (L), Cingulate Gyrus (L), Claustrum (L), Inferior Frontal Gyrus (L), Insula (L), Precentral Gyrus (L) 0.001 379 36 −24 30 Caudate Body (R), Globus Pallidus (R), Putamen (R), Thalamus (R) 0.039 217 28 0 12 Claustrum (R), Insula (R), Putamen (R) 0.045 212 −30 −32 46 Cingulate Gyrus (L), Postcentral Gyrus (L), Precentral Gyrus (L) b. Within-Group Negative Regression (PCL): HIGH PTSSc 0.000 2991 4 -68 36 Cerebellum (L), Cingulate Gyrus (B), Cuneus (B), Parahippocampal Gyrus (B), Postcentral Gyrus (L), Posterior Cingulate Gyrus (B), Precuneus (L), Precuneus (R), Superior Parietal Lobule (L) 0.000 1836 -18 −78 −46 Cerebellum (L), Cerebellum (R), Fusiform Gyrus (L), Inferior Occipital Gyrus (L) 0.000 1241 12 −2 −4 Amygdala (R), Caudate Body (R), Caudate Head (R), Claustrum (R), Fusiform Gyrus (R), Hippocampus (R), Hypothalamus (R), Inferior Frontal Gyrus (R), Globus Pallidus (R), Midbrain (R), Parahippocampal Gyrus (R), Putamen (R), Superior Temporal Gyrus (R), Thalamus (R), Uncus (R) 0.000 811 0 64 −12 Anterior Cingulate Gyrus (R), Inferior Frontal Gyrus (R), Medial Frontal Gyrus (L), Medial Frontal Gyrus (R), Middle Frontal Gyrus (R), Superior Frontal Gyrus (B) 0.000 696 −14 30 46 Anterior Cingulate Gyrus (L), Cingulate Gyrus (R), Medial Frontal Gyrus (L), Medial Frontal Gyrus (R), Middle Frontal Gyrus (L), Superior Frontal Gyrus (L), Precentral Gyrus (L) 0.000 456 22 −70 −40 Cerebellum (R) 0.003 314 −48 −12 −18 Fusiform Gyrus (L), Inferior Temporal Gyrus (L), Middle Temporal Gyrus (L) 0.003 306 −30 58 6 Medial Frontal Gyrus (L), Middle Frontal Gyrus (L), Superior Frontal Gyrus (L) 0.008 274 −48 −42 6 Middle Temporal Gyrus (L), Superior Temporal Gyrus (L) 0.009 271 34 8 −42 Inferior Temporal Gyrus (R), Middle Temporal Gyrus (R), Superior Temporal Gyrus (R) c. Within-Group Positive Regression (Red-Accuracy): LOW PTSSd 0.000 2010 8 −54 −24 Cerebellum (L), Cerebellum (R), Cuneus (R), Fusiform Gyrus (B), Inferior Occipital Gyrus (B), Lingual Gyrus (B), Middle Occipital Gyrus (R), Parahippocampal Gyrus (R) 0.000 1081 0 −64 10 Cuneus (B), Lingual Gyrus (B), Middle Occipital Gyrus (R), Parahippocampal Gyrus (L), Posterior Cingulate Gyrus (L), Posterior Cingulate Gyrus (R), Precuneus (B) 0.000 556 14 −6 70 Medial Frontal Gyrus (B), Middle Frontal Gyrus (B), Superior Frontal Gyrus (L), Superior Frontal Gyrus (R) 0.000 448 −24 −16 −4 Caudate Tail (L), Claustrum (L), Globus Pallidus (L), Hippocampus (L), Hypothalamus (L), Insula (L), Precentral Gyrus (L), Putamen (L), Thalamus (L), Transverse Temporal Gyrus (L), Superior Temporal Gyrus (L) 0.008 306 48 2 −34 Fusiform Gyrus (R), Inferior Temporal Gyrus (R), Middle Temporal Gyrus (R), Superior Temporal Gyrus (R) 0.009 301 10 −48 70 Inferior Parietal Lobule (R), Medial Frontal Gyrus (R), Paracentral Lobule (R), Postcentral Gyrus (R), Precentral Gyrus (R), Precuneus (R), Superior Parietal Lobule (R) 0.017 276 52 −28 10 Insula (R), Postcentral Gyrus (R), Precentral Gyrus (R), Superior Temporal Gyrus (R), Transverse Temporal Gyrus (R) 0.032 250 16 34 30 Anterior Cingulate Gyrus (B), Cingulate Gyrus (R), Medial Frontal Gyrus (L), Medial Frontal Gyrus (R) MNI Montreal Neurological Institute; R Right Side; L Left Side; B Both Sides or Bilateral; PCL Posttraumatic Stress Disorder Checklist; PTSS Posttraumatic Stress Symptoms a

Number of contiguous 2×2×2 mm voxels that exceed threshold

b

The structure that is closest to the voxel with the maximum t value is indicated with bold print

c

Results of the simple regression of brain activation with the PCL Total Score for only those subjects within the HIGH PTSS group

d

Results of the simple regression of brain activation with accuracy (percent correct) during the red arrows task condition for only those subjects within the LOW PTSS group

Brain Imaging and Behavior Fig. 4 Brain surface images displaying cortical areas with significant image regression results for the HIGH PTSS group. Positive correlations are displayed using a red-yellow color scale and negative correlations in bluegreen

association cortex (Buckner et al. 2011). When considered in combination, the results of these whole brain image analyses are consistent with altered neural function within multiple distributed networks, including enhanced activation within sensorimotor cortex and structures engaged in autonomic regulation and the suppression of activation within others involved in higher levels of human cognition. Many behavioral and physiological symptoms of PTSD, such as enhanced startle and elevated norepinephrine, suggest the presence of heightened sympathetic tone and increased arousal has been found to alter the pattern of brain activation and functional connectivity during fMRI (Qin et al. 2009; Hermans et al. 2011). High levels of noradrenergic activity, such as those that occur under stressful conditions and with PTSD, have been said to redirect neural resources away from the executive control network and towards others involved in reorienting, stimulus processing, vigilance, motor responding, and autonomic-neuroendocrine control (Ashton-Jones et al. 1999; Falconer et al. 2008; Hermans et al. 2011; Qin et al. 2009). These dynamic functional alterations can result in a bias favoring Bbottom up^ over Btop down^ processing that may be adaptive in situations demanding rapid threat detection and responding, but that are also likely to interfere with more complex mental activity such as divergent thinking, deliberation, and self-reflection (Heilman et al. 2003; Hermans et al. 2011; Qin et al. 2009). The present results are generally consistent with a similar redirection of neural resources in individuals who develop PTSS following deployment. A view of PTSD that includes a global resource allocation shift is consistent with the high overlap between PTSS and symptoms of mTBI, another condition with mild diffuse brain dysfunction and subtle impairment involving multiple domains (Stein and McAllister 2009). We found that correlations between measures of PTSS and PCSx were high and, within the HIGH PTSS group, six subjects had scores on the NSI that exceeded the mean of a large sample with mTBI (Belanger

et al. 2009). However, all of our subjects were screened to rule out a history of TBI or blast exposure. Consequently, the current findings raise concerns about the potential for misdiagnosis in post-deployment populations and about the common research practice of using self-report measures of PTSS, such as the PCL, to statistically control for co-morbid PTSD since this approach may also remove variance that overlaps with genuine PCSx (Pedhazur 1997). For research with postdeployment mTBI populations it seems advisable to carefully document information about the possible TBI event, to assess whether the Diagnostic Statistical Manual criteria for PTSD have been met using methods that include a clinician-based interview, and to take such information into consideration within the research design (e.g., group composition) and statistical models. Indeed, there is concern that utilizing symptom-based measures as proxies for diagnostic entities, rather than as somewhat non-specific rating scales of current health outcomes, can lead to confusion about how diagnostic entities are related to each other and overall outcome (Vanderploeg et al. 2012). Although mTBI and PTSD exhibit considerable symptom overlap, the current results and previously published findings suggest these disorders have different neural correlates and are dissociable. During the Arrows Task those subjects with a history of blast-related mTBI exhibit over-activation (Scheibel et al. 2012), a finding that is generally consistent with the results of civilian TBI studies that have used cognitive control fMRI paradigms (e.g., Olsen et al. 2014; Scheibel et al. 2007, 2009). A number of explanations have been offered for this type of neuropathology-related activation increase, including compensatory mechanisms and inefficiencies secondary to diffuse axonal injury (Olsen et al. 2014; Price and Friston 2002; Scheibel et al. 2012). However, the pattern observed in our post-deployment subjects with elevated PTSS and no history of TBI was highly dissimilar, including attenuated activation within multiple cortical and subcortical brain

Brain Imaging and Behavior

structures. These alterations may reflect a global neural resource shift similar to that which has been said to occur during survival situations associated with threat and hyperarousal (Ashton-Jones et al. 1999; Hermans et al. 2011), but which is likely to be maladaptive if it becomes chronic or is invoked within the wrong context (Arnsten 2009; Southwick et al. 1999). Such an incongruity between environmental context and physiological state is often reported by patients with PTSD, including heightened vigilance and arousal in safe social situations (Hoge 2010), and these symptoms and the associated alterations in neural networks may be related to a deficit in autonomic adaptation (Porges 2001; Williamson et al. 2013). The cognitive fMRI paradigm used for the current study was not designed to be a symptom provocation technique and it did not include emotional stimuli, yet elevated PTSS were associated with widespread changes in brain activation. Therefore there is the possibility that, within postdeployment populations with PTSS, chronic hyperarousal or increased sensitivity to general environmental stressors may contribute to a maladaptive functional reorganization. The present investigation also addressed the relationship between brain activation and depression, which is a common condition in post-deployment populations and in those with mTBI (Haagsma et al. 2014; Vanderploeg et al. 2012). Between-group differences for the BSI Depression Scale were not statistically significant, there was no correlation between this measure and activation during cognitive control, and the overall pattern of the between-group activation findings was not altered when depression was included within the analysis as a covariate. These findings suggest that stress-related pathology, rather than depression, was primarily responsible for the alterations in brain activation that we observed in our subjects with elevated PCL scores. However, depression is an important post-deployment issue and a relationship with depression may have been missed due to low statistical power or sample biases produced by the exclusion of subjects with substance abuse or pre-deployment psychiatric disorder. Limitations The original study was not designed to be an investigation of PTSD, specifically, but PCL data did allow for a broader examination of PTSS and research with similar post-deployment samples has also included individuals who did not meet full diagnostic criteria for PTSD (e.g., Morey et al. 2008). Regardless, the lack of a separate group of subjects with a formal PTSD diagnosis based upon a clinician interview should be considered a limitation of the current study. A group which clearly lacked PTSS would have been helpful, as well, since the inclusion of subjects with minor PCL elevations may have altered the activation pattern within the LOW PTSS group. Another significant limitation is the small sample size

since this raises the possibility that some statistical relationships may have been missed due to low power, as well as the risk of false positive results that may disappear if the sample size is increased (Simmons et al. 2011b). Thus the findings of the current study should be considered preliminary due to the small sample, the lack of a normal control group, inefficiencies associated with an fMRI paradigm that was not optimized to study normal cognition, and the inability to use PTSD as a variable within the research design. Conclusions The attenuation of activation within the executive control network and related brain areas is consistent with a number of symptoms of PTSD, including difficulties with attention and concentration, memory, and executive functions (Brenner et al. 2009; Stein and McAllister 2009). These and other PTSS overlap with those of mTBI (Stein and McAllister 2009), but prior findings with cognitive control paradigms are most consistent with a pattern of heightened activation following TBI (Olsen et al. 2014; Scheibel et al. 2007, 2009, 2012). Even though many symptoms of mTBI and PTSD are similar, functional neuroimaging reveals different neural correlates for these conditions and further development of such techniques may eventually lead to better methods to inform differential diagnosis. Additionally, the pattern of cognitive controlrelated activation found with elevated PTSS may reflect a global neural resource shift similar to that which is thought to occur in the presence of acute stressors (Ashton-Jones et al. 1999; Hermans et al. 2011; Qin et al. 2009). The proposed relationship between these functional alterations and catecholamines is speculative, but others have also invoked mechanisms involving autonomic dysregulation and catecholamines to explain chronic, stress-related changes in brain function and behavior (e.g., Arnsten 2009; Falconer et al. 2008; van Wingen et al. 2012; Williamson et al. 2013). Use of physiological measures of arousal, as sometimes employed in studies of acute stress (e.g., Qin et al. 2009), might permit a more direct examination of potential mechanisms and provide information to guide the development of new treatments. Pharmacological and behavioral interventions targeting elevations in sympathetic tone may be effective for improving symptoms and normalizing the pattern of brain activation in individuals with PTSS. Acknowledgments This work was supported by a Department of Veterans Affairs Center of Excellence grant (Levin, grant number: B6812C), Neurorehabilitation: Neurons to Networks Center for Rehabilitation Research; and by Department of Veterans Affairs Merit Review grants (Levin and Scheibel, grant number: B1320I; Scheibel, grant number: O1062-I). The Michael E. DeBakey

Brain Imaging and Behavior Veterans Affairs Medical Center in Houston, TX, and the South Central Mental Illness Research, Education, and Clinical Center (MIRECC) provided access to equipment and facilities used for the analysis of the image data. We thank Xiaoqi Li for assisting with statistical analysis, Drs. Helene K. Henson and David P. Graham for assisting with the screening and recruitment of subjects, and Dr. Majdi Radaideh for review of the structural imaging. Conflicts of interest/disclosures Randall S. Scheibel, Nicholas J. Pastorek, Maya Troyanskaya, Jan E. Kennedy, Joel L. Steinberg, Mary R. Newsome, Xiaodi Lin, and Harvey S. Levin report that they have no conflicts of interest.

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