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An electrocortical investigation of emotional face processing in military-related posttraumatic stress disorder
Accepted Manuscript An electrocortical investigation of emotional face processing in military-related posttraumatic stress disorder Julia A. DiGangi, Katie L. Burkhouse, Darrin M. Aase, Joseph M. Babione, Christopher Schroth, Amy E. Kennedy, Justin E. Greenstein, Eric Proescher, K. Luan Phan PII:
S0022-3956(17)30087-0
DOI:
10.1016/j.jpsychires.2017.03.013
Reference:
PIAT 3095
To appear in:
Journal of Psychiatric Research
Received Date: 18 January 2017 Revised Date:
9 March 2017
Accepted Date: 17 March 2017
Please cite this article as: DiGangi JA, Burkhouse KL, Aase DM, Babione JM, Schroth C, Kennedy AE, Greenstein JE, Proescher E, Phan KL, An electrocortical investigation of emotional face processing in military-related posttraumatic stress disorder, Journal of Psychiatric Research (2017), doi: 10.1016/ j.jpsychires.2017.03.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Running Head: LPP AND EMOTION FACE PROCESSING IN PTSD
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An electrocortical investigation of emotional face processing in military-related posttraumatic stress disorder.
Julia A. DiGangi1,2, Katie L. Burkhouse2, Darrin M. Aase1,2,3 , Joseph M. Babione1, Christopher
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Schroth1,2, Amy E. Kennedy1,2, Justin E. Greenstein1,2, Eric Proescher1,2, & K. Luan Phan1,2,4
1
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Mental Health Service Line, Jesse Brown VA Medical Center; 820 S. Damen Ave., Chicago, IL 60612 2 Department of Psychiatry, University of Illinois at Chicago; 1747 Roosevelt Road, Chicago, IL 60608 3 College of Health & Human Services, Governors State University; 1 University Parkway, University Park, IL 60484 4 Departments of Psychology, Anatomy and Cell Biology, University of Illinois at Chicago; 808 S. Wood St., Chicago, IL 60612
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Correspondence concerning this article should be addressed to: Julia DiGangi, Ph.D., Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA. E-mail address: [email protected]
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Abstract
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PTSD is a disorder of emotion dysregulation. Although much work has intended to elucidate the neural underpinnings of the disorder, much remains unknown about the neurobiological substrates of emotion dysregulation in PTSD. In order to assess the relationship between a neural measure of attention to emotion (i.e. the late positive potential; LPP) and PTSD symptoms, EEG was recorded and examined as a potential predictor of military-related PTSD symptoms in a sample of 73 OEF/OIF/OND veterans. Results revealed that higher PTSD symptoms were related to an attenuated LPP response to angry facial expressions. This finding was not observed for happy or fearful faces. The current study provides initial evidence that, in a relatively young, mostly male sample of OEF/OIF/OND veterans, hyporeactivity to angry faces at the neural level may provide phenotypic data to characterize individual differences in PTSD symptom severity. This work may assist in future studies that seek to examine useful psychophysiologic targets for treatment and early interventions.
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Keywords: PTSD, emotion processing, military, veteran, EEG, late positive potential
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Introduction Of the more than 2 million U.S. soldiers deployed to Afghanistan and Iraq, 14-
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16% have developed posttraumatic stress disorder (PTSD) (Moring, Blankenship, Williams, Molino, & Peterson, 2014) Consequently, PTSD represents one of the most
prevalent injuries incurred during Operations Enduring Freedom (OEF), Iraqi Freedom
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(OIF), and New Dawn (OND). Given the profound mental, physical, occupational and functional costs associated with PTSD ( Jackson et al., 2016; Tanielian et al., 2008)
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considerable emphasis has been placed on identifying the biological substrates that underlie the disorder (Michopoulos, Norrholm, & Jovanovic, 2015).
Against this backdrop, two primary types of neuroimaging have been used in a complementary fashion to elucidate the underlying pathophysiology of PTSD. Perhaps most well known is work from functional magnetic resonance imaging (fMRI) studies
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that have indicated disruptions in prefrontal, limbic and interactive brain function are most commonly associated with the disorder (Hayes, Hayes, & Mikedis, 2012). In
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addition to fMRI, electroencephalography (EEG) and, more specifically, event related potentials (ERPs) have been used to identify additional neural biomarkers of the disorder
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(Lobo et al., 2015). A complementary method of neural measurement, ERPs offer superior temporal resolution, thereby providing information that cannot be ascertained by fMRI alone. One ERP component that may be particularly relevant to examining PTSD is the late positive potential (LPP). The LPP is a centro-parietal, positive-going ERP component that appears approximately 400ms after stimulus onset and is larger for emotional (e.g., threatening) stimuli than neutral stimuli (Foti, Hajcak, & Dien, 2009; Schupp et al., 2000).
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Specifically, the LPP is considered to be a means of tracking motivated attention that is particularly sensitive to emotionally salient stimuli (Hajcak, MacNamara, Foti, Ferri, & Keil, 2013) Because of its relation to emotional processing and motivated attention, the
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LPP has been examined in disorders of affect dysregulation, such as depression (e.g.,
Foti, Olvet, Klein, & Hajcak, 2010) and anxiety disorders (e.g. MacNamara & Hajcak, 2010; MacNamara, Kotov, & Hajcak, 2016) For example, a study that examined LPP
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response in individuals with generalized anxiety disorder (GAD) as compared to healthy controls found that, for individuals with GAD, the LPP was enhanced to aversive targets
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relative to their healthy counterparts (MacNamara & Hajcak, 2010). Given the emotion and attentional deficits associated with PTSD, the LPP may provide important information as a putative neural measure of PTSD.
To date, however, it has been infrequently studied in relation to PTSD. In the
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limited studies evaluating the LPP in PTSD populations, findings are mixed such that some research indicates the disorder is associated with increased LPP reactivity to threat (Lobo et al., 2014) and other studies demonstrate an attenuated LPP response, suggesting
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emotional disengagement to threat (MacNamara, Post, Kennedy, Rabinak, & Phan, 2013). The reason for these discrepant findings is unclear. One plausible reason for the
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discrepancies may be attributable to individual differences in reactivity. Another plausible explanation is that the differences may be due to different subtypes of PTSD. For example, it is conceivable that dysregulation may be linked to either over- or underresponsiveness to threat in distinct subtypes of PTSD—and recent research has suggested there are likely divergent biological profiles of PTSD (Michopoulos, Norrholm, & Jovanovic, 2015). Finally, the heterogeneity of findings may be attributable to
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methodological differences; while both studies used emotionally provocative stimuli, the former used disturbing images of mutilated bodies and the latter used emotional faces (i.e., happy, fearful, angry).
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Notably, all prior studies examining the relation between PTSD and LPP response have used binary classifications (Fitzgerald et al., 2016, MacNamara, Post, Kennedy,
Rabinak, & Phan, 2013, Lobo et al., 2014) . For example, MacNamara and colleagues
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conducted their analyses based on 33 veterans, 19 who met full diagnostic criteria for PTSD and 14 who did not as determined by a clinical cutoff of 40 on the Clinician-
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Administered PTSD Scale (CAPS-IV). However, one challenge associated with traditional diagnostic categories is the exclusion of many veterans experiencing subthreshold PTSD symptoms, which thereby limits the generalizability to the larger veteran population. Of note, prior work that has examined trauma in conjunction with
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other neurobiological markers has found important differences in brain functioning associated with subclinical PTSD that are not seen in individuals without PTSD symptoms (Garrett et al., 2012; Peres et al., 2011). Such research suggests that a
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dimensional approach to PTSD has important consequences for understanding brainbehavior relationships in the context of trauma’s sequelae. Further pressing the need to
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take a dimensional approach to PTSD symptoms is recent work that has highlighted the profound clinical and functional implications (e.g., heightened suicide risk, greater health problems) associated with subclinical symptoms (Eekhout, Reijnen, Vermetten, & Geuze, 2016; Jakupcak et al., 2011; Pietrzak, Goldstein, Malley, Johnson, & Southwick, 2009). Instead of examining groups (i.e., PTSD patients vs. a non-PTSD group), this study sought to address these gaps by examining PTSD symptom severity as a continuous
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predictor of LPP in order to extend our understanding of the role of individual differences and psychophysiology in OEF/OIF/OND veterans. In the current study, we hypothesized that the extent of LPP reactivity in response to threatening (i.e., fear and/or angry faces
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but not happy) faces would be negatively related to military-related PTSD symptoms
al., 2013). Methods
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given prior evidence of blunted LPP reactivity to social signals of threat (MacNamara et
This study was approved by the Institutional Review Boards at Jesse Brown VA
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Medical Center, Chicago IL and its university affiliate, the University of Illinois at Chicago. Research was conducted in accordance with the Helsinki Declaration. Participants Seventy-three participants with LPP data from EEG were included from a larger sample of OEF/OIF/OND veterans recruited at the Jesse Brown VA
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Medical Center and the University of Illinois Chicago. After completing informed consent procedures, participants completed the ERP task, a clinical assessment, and selfreport measures. Exclusionary criteria for participants included: presence of a clinically
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significant medical or neurological condition, presence of an organic mental syndrome and/or psychotic disorder, intellectual disability or pervasive developmental disorder, and
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current substance abuse or suicidal ideation at a level that would interfere with the study protocol. Ages ranged from 21-49 years (mean: 32.78 SD: ±6.5); 80.8% of the sample was male (See Table 1 for demographic and clinical information). Military-related PTSD symptoms were assessed using the PTSD Checklist Military Version (PCL-M; Weathers, Litz, Herman, Huska, & Keane, 1993), a 17-item self-report instrument that asks respondents to rate the degree to which they have been bothered by symptoms of PTSD
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as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV; American Psychiatric Association, 1994) within the last month on a5-point scale from 1 (not at all) to 5(extremely). The instrument has demonstrated good
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psychometric properties (Weathers et al., 1993). Average PCL-M score was 41.82
(±19.3), and average HAM-D score was 8.63 (SD: ±5.7; see Measures and see Figure 1
for distribution of PTSD symptoms).1 Of the 73 participants, 5.5% (n = 4) had a primary
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diagnosis of an anxiety disorder that was not PTSD (e.g., Panic Disorder), 34.2% (n = 25) a mood disorder and 12.3% (n = 9) a current or past substance use disorder. At the time
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of enrollment, 42.5% of the sample was prescribed psychiatric medications. Measures All clinical measures were administered by a psychologist or a master’s level research assistant under the supervision of a licensed psychologist. Clinical Assessment. Psychiatric diagnoses were established using the Mini
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International Neuropsychiatric Interview 6.0 (M.I.N.I; Sheehan et al., 1998). The M.I.N.I. is a well-validated, semi-structured interview for the assessment of current and lifetime DSM-IV Axis I disorders. It was administered to every participant in order to establish
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other psychiatric diagnoses. Depression was assessed using the clinician-administered Hamilton Depression Rating Scale (HAM-D; Hamilton, 1960).
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Emotional Faces Task. Participants completed an EEG version of the Emotional
Face-Matching Task (EFMT, Hariri, Tessitore, Mattay, Fera, & Weinberger, 2002), which has proven useful in characterizing threat processing in anxious and non-anxious participants (Labuschagne et al., 2010) and facilitates comparison with prior ERP work in PTSD (MacNamara et al., 2013). On each trial, three faces or shapes were presented for 1
Of the 73 participants, only four did not report a Criterion A event. Of these four individuals, two recorded the lowest possible score on the PCL (i.e., endorsed no items). To ensure there was no effect on our results, we removed these 4 individuals from analyses and model results were unchanged.
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3000 ms, in a triangular arrangement – i.e., one image was centered in the top-half of the screen and the other two images were presented in the bottom-half of the screen (one to the left and one to the right). On each face-matching trial, the faces of three different
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actors were presented: two were always emotional and one always neutral. Participants were instructed to select one of the faces at the bottom of the screen that bore the same
emotional expression as the ‘target’ face centered in the top portion of the screen. Face-
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matching trials could be fearful, angry or happy. On shape-matching trials, participants
were instructed to choose the shape at the bottom of the screen that matched (i.e., had the
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same form as) the target shape at the top of the screen. In line with previous studies (e.g., Labuschagne et al., 2010; Phan et al., 2008), we used geometric shapes as control stimuli instead of neutral faces, because neutral faces may be more influenced by individual differences (Somerville, Kim, Johnstone, Alexander, & Whalen, 2004). The task was
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divided into two runs, with each block having 12 angry, 12 fearful, 12 happy and 12 shape-matching trials; trials were presented randomly within each run. The inter-trial interval varied between 1000 and 3000 ms, during which time a white fixation cross was
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centrally presented on a black background. Participants performed six practice trials prior to beginning the experiment. The task was administered on an Intel(R) Core(tm) i7 @
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1.60GHz computer with a 19-in. (48.3 cm) monitor, using Presentation software (Presentation, 2016).
EEG Data Recording. Continuous EEG was recorded using the ActiveTwo
BioSemi system (ActiveTwo, 2016). Thirty-four electrode sites (standard 32 channel setup, as well as FCz and Iz) were used, based on the 10/20 system; in addition, one electrode was placed on each of the left and right mastoids. The electrooculogram (EOG)
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generated from eye movements were recorded from four facial electrodes. The EEG signal was pre-amplified at the electrode to improve the signal-to-noise ratio. The data were digitized at 24- bit resolution with a Least Significant Bit (LSB) value of 31.25 nV
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and a sampling rate of 1024 Hz, using a low-pass fifth order sinc filter with a -3dB cutoff point at 204.8 Hz. The voltage from each active electrode was referenced online with respect to a common mode sense active electrode producing a monopolar (non-
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differential) channel. Off-line analyses were performed using Brain Vision Analyzer
software (Brain Vision Analyzer 2, 2006). Data were re-referenced to the average of the
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two mastoids, segmented 200 ms before stimulus onset and continuing for the 3000 ms stimulus duration, and band-pass filtered with high-pass and low-pass filters of 0.01 and 30 Hz, respectively. Eye blink and ocular corrections used the method developed by Miller, Gration, & Yee (1988). Semi-automated artifact rejection procedures were used to
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identify a voltage step of more than 50.0 µV between sample points, a voltage difference of 300.0 µV within a trial, and a maximum voltage difference of less than 0.50 µV within 100 ms intervals. Trials were also inspected visually for any remaining artifacts, and data
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from individual channels containing artifacts were rejected on a trial-to-trial basis. Data Analyses. The LPP was scored based on prior literature as well as prior
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studies from our lab and from visual depiction of the current data indicating the LPP is maximal at centro-parietal sites (e.g., MacNamara & Hajcak, 2010). Specifically, the LPP was scored at Cz, CP1, CP2, Pz, P3 and P4, and it was apparent in the overall sample beginning at approximately 500 ms and continuing throughout the stimulus duration of 3000 ms; thus, the LPP was scored across this entire window. In order to isolate the variance in the LPP related to emotional picture processing, our analyses focused on
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emotional face (i.e., angry, fearful, and happy separately) minus shapes difference scores; the decision to separate the emotional faces was supported by results from earlier work in which there was only an effect for angry faces (MacNamara et al., 2013). Behavioral
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data were analyzed using a repeated measures analysis of variance (ANOVA). Significant omnibus findings were followed up using independent t-tests for planned comparisons; post-hoc tests used the Bonferroni correction. Greenhouse Geisser corrections were
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applied as necessary for violations of sphericity. A mixed-measure analysis of
covariance (ANCOVA) was conducted to examine the main effects of emotion and
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symptomatology on LPP magnitude, as well as any interaction between emotion and symptoms. Statistical analyses were conducted in SPSS (IBM Corp., 2013). Results
Behavioral
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Participants performed well on the EFMT as average accuracy was 95% SD: ±3.7. There was an effect of condition on reaction time (F(2.38, 168.82) = 461.8, p <.001, η2 = .87). Post-hoc analyses indicated that trials with faces elicited slower reaction times than
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those with shapes. Specifically, angry faces elicited the slowest reaction time (i.e., angry >fear>happy>shapes; Bonferroni-corrected p <.001). Similarly, there was an effect of
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condition on accuracy (F(1.65, 117.08) = 111.05, p <.001, η2 = .61), such that trials with faces elicited worse performance that trials with shapes. Consistent with earlier research (MacNamara et al., 2013), angry faces elicited the worst performance of all conditions (i.e., shapes>happy>fear >angry; Bonferroni-corrected p < .001). Mixed-measure analysis of covariance (ANCOVA)
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As expected, PCL-M and HAM-D were strongly related (p < .001). Figure 2 depicts scalp distributions and grand-average waveforms for each condition at the centroparietal pooling. The within-subjects ANCOVA with a Greenhouse-Geisser correction
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revealed a significant main effect of emotion (F(1.98, 136.70) = 6.57, p <.005, η2 = .09. Post hoc Bonferroni comparisons revealed that there was a significant difference between angry and happy faces, such that the LPP was enhanced to happy faces relative to angry
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faces (p < .005). There was no significant difference between fearful faces and either angry or happy faces (p = ns). Additionally, there was a significant emotion x PTSD
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interaction (F(1.98, 136.70) = 4.05, p = .02, η2 = .11), when controlling for current depression symptoms. There was no interaction between emotion and depression symptoms. To further investigate the emotion x PTSD interaction, post hoc regression analyses were conducted. Results revealed the interaction was explained by reaction to
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angry faces such that higher reported military-related PTSD symptoms was related to greater blunting of the LPP to angry faces (F(2,69)=3.45, p = .04, R2 =.09; Fig 3). However, there was no relationship for PTSD symptoms and the LPP to fearful faces
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(F(2,69)=.46, p = ns), or happy faces (F(2,69)=.15, p = ns). Analyses also examined discrete PTSD clusters within the PCL-M. There was no unique effect for re-
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experiencing, avoidance or hyperarousal symptoms. Medication use, sex and other anxiety symptoms (i.e., non-PTSD symptoms) were tested but did not change model results.
Discussion The primary aim of the current study was to examine a neural measure of socioemotional processing (i.e., LPP to angry, fearful, and happy faces) as a potential predictor
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of military-related PTSD symptoms in a sample of OEF/OIF/OND veterans. Results revealed that higher PTSD symptoms were related to an attenuated LPP response to angry facial expressions. This finding was not observed for happy or fearful faces and
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was maintained when adjusting for current depression symptoms, suggesting that the
effects were partially independent from the veterans’ current depression. Although prior studies have consistently shown that affect dysregulation is a core feature of PTSD
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(Hayes, VanElzakker, & Shin, 2012), less is known about the underlying neurobiology
that contributes to the development and maintenance of the disorder. The present study
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extends the prior literature by offering insight into a potential biological correlate of PTSD symptoms.
PTSD is a highly complex disorder, characterized by high symptom heterogeneity and often presenting across different symptom domains. With the release of DSM-5,
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there are more than a half million ways in which an individual can be diagnosed with PTSD (Galatzer-Levy & Bryant, 2013). Currently, there is a lack of consistent evidence regarding the biological underpinnings of the disorder. Evidence across a variety of
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biological markers (e.g., ERPs, fMRI, cortisol, heart rate) indicates that multiple neurobiological systems and mechanisms likely underlie the etiology and maintenance of
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PTSD (Michopoulos, Norrholm, & Jovanovic, 2015). Contributing to the biological complexity, there is often conflicting results within a single biomarker. For example, earlier work with the LPP and PTSD has been associated with both increased (Lobo et al., 2014) and decreased reactivity (MacNamara et al., 2013) to threatening images. This heterogeneity across and within biological markers, such as the LPP, may be contributing
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to this phenotypic symptom variability. As such, recent work has begun to posit that there are different PTSD subtypes (Neylan, Schadt, & Yehuda, 2014). The current study provides initial evidence that, in a relatively young, mostly
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male sample of OEF/OIF/OND veterans, hyporeactivity to angry faces at the neural level may provide phenotypic data to characterize individual differences in PTSD symptom
severity. Of relevance, a meta-analysis found that while anger was a common feature of
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PTSD emerging from a diverse range of traumas, the strength of the relationship between PTSD and anger was strongest among military combat veterans (Orth and Wieland,
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2006). It is possible that greater PTSD symptoms in this cohort may be related to disengagement from these emotionally aversive stimuli (i.e., angry faces). Angry faces may be particularly distressing to these OEF/OIF/OND veterans and, subsequently, be associated with less motivated attention, or attentional avoidance,
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toward these particular stimuli as reflected by a blunted LPP. Although there were no unique effects for the avoidance cluster on the PCL-M, it remains possible that greater PTSD symptoms in this cohort may be related to disengagement from angry faces. This
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may be because the avoidance symptom cluster on the PCL is not a measure of attentional avoidance per se whereas the LPP is specifically considered to be a means to
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track motivated attention toward emotional stimuli (Hajcak, MacNamara, Foti, Ferri, & Keil, 2013). Moreover, while symptom clusters are important to understanding the phenomenology of PTSD, the disorder is best understood as a construct comprised of all three clusters and, indeed, our results suggest that it is the broader constellation of PTSD symptoms that best account for our findings and explain attentional disengagement from angry faces. Future work is certainly necessary to better understand the relationship the
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LPP shares with both subsyndromal PTSD as well as PTSD. Similarly, future research can also help elucidate how disparate symptom clusters are differentially related to the underlying biology and course of PTSD—both as a categorical and dimensional entity.
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Additionally, although we hypothesized that there would be effects for both angry and fearful faces, our data yielded results only for angry faces. This distinction between anger and fear may be partly attributable to the distinctions between the types of threat
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that anger versus fear embodies. Specifically, prior research has demonstrated that fearful faces represent ambiguous threat whereas angry faces represent non-ambiguous threat
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(Kim et al., 2011). Therefore, our results suggest that hyporeactivity to more overt threats are more predictive of military-related PTSD symptoms.
Another contribution of our study is the dimensional approach to PTSD symptomatology in relation to the LPP. While prior studies of PTSD and the LPP have
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taken a categorical approach (i.e., compared PTSD group to a non-PTSD group), our study intentionally used a dimensional approach to PTSD symptoms. A dimensional approach has the theoretical advantage of being able to assess people on a continuum of
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illness—from healthy to sick. Lobo et al. (2015) highlight the limitations of a categorical system in which people can either have or not have PTSD by stating that a diagnosis
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“may be the tip of an iceberg—a late manifestation of a change that has been occurring in the brains of people” before they were formally diagnosed (p. 211). Therefore, a dimensional approach may be especially important to aid early PTSD detection and to guide better treatment options. However, only two prior studies have examined the LPP in relation to categorical diagnostic categories of PTSD in veterans (Fitzgerald et al., 2016; MacNamara et al., 2013). Moreover, no prior study has demonstrated that a
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dimensional approach to PTSD symptomatology is associated with a neural measure of threat reactivity (i.e., LPP to angry faces) in a diverse, heterogeneous sample of OEF/OIF/OND veterans. This dimensional approach to identifying biological correlates
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of PTSD symptomatology is particularly important in light of prior research that has shown subthreshold PTSD is associated with profound clinical and functional
implications (e.g., heightened suicide risk, greater health problem; (Eekhout, Reijnen,
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Vermetten, & Geuze, 2016; Jakupcak et al., 2011; Pietrzak, Goldstein, Malley, Johnson, & Southwick, 2009).
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These results should be interpreted in the context of important limitations. First, the experimental design is cross-sectional and, importantly, it is not clear if blunted neural reactivity to angry faces follows or precedes PTSD symptoms and/or traumatic experience. Additionally, future work that examines other types of emotionally
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provocative stimuli (e.g., non-social threat) would further develop understanding and generalizability of the relationship between PTSD and threat responding at the neural level. Moreover, there are important reasons to consider the effects of various types of
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stimuli on the LPP. Although the current study focused on emotion face processing, the literature on the relationship between PTSD and face processing is mixed (Felmingham,
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Bryant, & Gordon, 2003; Klimova, Bryant, Williams, & Felmingham, 2013; Poljac, Montagne, & de Haan, 2011). Therefore, future studies would offer benefit by also considering other forms of emotionally-evocative and trauma-related stimuli (e.g., war scenes). Likewise, future research that examines how additional variables, such as gender or trauma type, interface with neurobiological indices of reactivity would enhance our understanding of the LPP. Additionally, because we were explicitly interested in a
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dimensional analysis of PTSD symptoms, it is possible that the effect may have manifested differently if groups were categorically dichotomized by diagnosis. However, because prior literature indicates subclinical PTSD has important clinical consequences
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(e.g., Eekhout et al., 2016), we chose to include a range of PTSD severity. Finally, our sample did not solely contain PTSD symptoms, as individuals with a history of other
affective and anxiety disorders (e.g., panic disorder) were also included. Because our
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modest sample size precluded us from controlling for all possible confounding variables, the degree to which comorbid conditions contributed to the present findings remains
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unclear. However, given our heterogeneous sample, the findings are more likely to generalize to a diverse cohort of OEF/OIF/OND veterans.
In conclusion, the current study suggests that an attenuated neural response to angry facial expressions, as measured by the LPP, may provide endophenotypic
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information to help characterize PTSD severity in a sample of OEF/OIF/OND veterans. This finding provides compelling evidence that a dimensional approach to PTSD symptoms is important to consider in the context of PTSD’s pathophysiology. Such an
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interventions.
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approach may yield new and useful psychophysiologic targets for treatment and early
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References ActiveTwo. (2016). Bio Semi.
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Brain Vision Analyzer 2. (2006). Gilching, Germany: Brain Products GmbH.
Eekhout, I., Reijnen, A., Vermetten, E., & Geuze, E. (2016). Post-traumatic stress
symptoms 5 years after military deployment to Afghanistan: an observational
SC
cohort study. The Lancet Psychiatry, 3(1), 58–64. https://doi.org/10.1016/S22150366(15)00368-5
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Felmingham, K. L., Bryant, R. A., & Gordon, E. (2003). Processing angry and neutral faces in post-traumatic stress disorder: an event-related potentials study. Neuroreport, 14(5), 777–780.
https://doi.org/10.1097/01.wnr.0000065509.53896.e3
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Foti, D., Hajcak, G., & Dien, J. (2009). Differentiating neural responses to emotional pictures: Evidence from temporal-spatial PCA. Psychophysiology, 46(3), 521– 530. https://doi.org/10.1111/j.1469-8986.2009.00796.x
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Galatzer-Levy, I. R., & Bryant, R. A. (2013). 636,120 Ways to Have Posttraumatic Stress Disorder. Perspectives on Psychological Science: A Journal of the Association for
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Psychological Science, 8(6), 651–662.
https://doi.org/10.1177/1745691613504115
Garrett, A. S., Carrion, V., Kletter, H., Karchemskiy, A., Weems, C. F., & Reiss, A. (2012). Brain Activation to Facial Expressions in Youth with Ptsd Symptoms.
Depression and Anxiety, 29(5), 449–459. https://doi.org/10.1002/da.21892
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Hajcak, G., MacNamara, A., Foti, D., Ferri, J., & Keil, A. (2013). The dynamic allocation of attention to emotion: simultaneous and independent evidence from the late positive potential and steady state visual evoked potentials. Biological
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Psychology, 92(3), 447–455. https://doi.org/10.1016/j.biopsycho.2011.11.012 Hariri, A. R., Tessitore, A., Mattay, V. S., Fera, F., & Weinberger, D. R. (2002). The
Amygdala Response to Emotional Stimuli: A Comparison of Faces and Scenes.
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NeuroImage, 17(1), 317–323. https://doi.org/10.1006/nimg.2002.1179
Hayes, J. P., Hayes, S. M., & Mikedis, A. M. (2012). Quantitative meta-analysis of neural
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activity in posttraumatic stress disorder. Biology of Mood & Anxiety Disorders, 2, 9. https://doi.org/10.1186/2045-5380-2-9
Hayes, J. P., VanElzakker, M. B., & Shin, L. M. (2012). Emotion and cognition interactions in PTSD: a review of neurocognitive and neuroimaging studies.
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Frontiers in Integrative Neuroscience, 6. https://doi.org/10.3389/fnint.2012.00089 IBM Corp. (2013). IBM SPSS Statistics for Windows (Version 22.0). Armonk, NY: IBM Corp.
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Jackson, C. E., Green, J. D., Bovin, M. J., Vasterling, J. J., Holowka, D. W., Ranganathan, G., … Marx, B. P. (2016). Mild Traumatic Brain Injury, PTSD, and
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Psychosocial Functioning Among Male and Female U.S. OEF/OIF Veterans. Journal of Traumatic Stress, 29(4), 309–316. https://doi.org/10.1002/jts.22110
Jakupcak, M., Hoerster, K. D., Varra, A., Vannoy, S., Felker, B., & Hunt, S. (2011). Hopelessness and Suicidal Ideation in Iraq and Afghanistan War Veterans Reporting Subthreshold and Threshold Posttraumatic Stress Disorder: The
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Journal of Nervous and Mental Disease, 199(4), 272–275. https://doi.org/10.1097/NMD.0b013e3182124604 Kim, M. J., Loucks, R. A., Palmer, A. L., Brown, A. C., Solomon, K. M., Marchante, A.
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N., & Whalen, P. J. (2011). The structural and functional connectivity of the
amygdala: From normal emotion to pathological anxiety. Behavioural Brain Research, 223(2), 403–410. https://doi.org/10.1016/j.bbr.2011.04.025
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Klimova, A., Bryant, R. A., Williams, L. M., & Felmingham, K. L. (2013). Dysregulation in cortical reactivity to emotional faces in PTSD patients with high dissociation
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symptoms. European Journal of Psychotraumatology, 4. https://doi.org/10.3402/ejpt.v4i0.20430
Labuschagne, I., Phan, K. L., Wood, A., Angstadt, M., Chua, P., Heinrichs, M., … Nathan, P. J. (2010). Oxytocin Attenuates Amygdala Reactivity to Fear in
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Generalized Social Anxiety Disorder. Neuropsychopharmacology, 35(12), 2403– 2413. https://doi.org/10.1038/npp.2010.123 Lobo, I., David, I. A., Figueira, I., Campagnoli, R. R., Volchan, E., Pereira, M. G., & de
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Oliveira, L. (2014). Brain reactivity to unpleasant stimuli is associated with severity of posttraumatic stress symptoms. Biological Psychology, 103, 233–241.
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https://doi.org/10.1016/j.biopsycho.2014.09.002
Lobo, I., Portugal, L. C., Figueira, I., Volchan, E., David, I., Garcia Pereira, M., & de Oliveira, L. (2015). EEG correlates of the severity of posttraumatic stress symptoms: A systematic review of the dimensional PTSD literature. Journal of Affective Disorders, 183, 210–220. https://doi.org/10.1016/j.jad.2015.05.015
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MacNamara, A., & Hajcak, G. (2010). Distinct electrocortical and behavioral evidence for increased attention to threat in generalized anxiety disorder. Depression and Anxiety, 27(3), 234–243. https://doi.org/10.1002/da.20679
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MacNamara, A., Kotov, R., & Hajcak, G. (2016). Diagnostic and Symptom-Based
Predictors of Emotional Processing in Generalized Anxiety Disorder and Major
Depressive Disorder: An Event-Related Potential Study. Cognitive Therapy and
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Research, 40(3), 275–289. https://doi.org/10.1007/s10608-015-9717-1
MacNamara, A., Post, D., Kennedy, A. E., Rabinak, C. A., & Phan, K. L. (2013).
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Electrocortical processing of social signals of threat in combat-related posttraumatic stress disorder. Biological Psychology, 94(2), 441–449. https://doi.org/10.1016/j.biopsycho.2013.08.009
Michopoulos, V., Norrholm, S. D., & Jovanovic, T. (2015). Diagnostic Biomarkers for
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Posttraumatic Stress Disorder: Promising Horizons from Translational Neuroscience Research. Biological Psychiatry, 78(5), 344–353. https://doi.org/10.1016/j.biopsych.2015.01.005
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Miller, G. A., Gration, G., & Yee, C. M. (1988). Generalized Implementation of an Eye Movement Correction Procedure. Psychophysiology, 25(2), 241–243.
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https://doi.org/10.1111/j.1469-8986.1988.tb00999.x
Moring, J. C., Blankenship, A. E., Williams, J. M., Molino, A., & Peterson, A. L. (2014). PTSD and Mild Traumatic Brain Injury in Iraq and Afghanistan War. In C. R. Martin, V. R. Preedy, & V. B. Patel (Eds.), Comprehensive Guide to PostTraumatic Stress Disorder (pp. 1–12). Cham: Springer International Publishing. Retrieved from http://dx.doi.org/10.1007/978-3-319-08613-2_69-1
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Neylan, T. C., Schadt, E. E., & Yehuda, R. (2014). Biomarkers for combat-related PTSD: focus on molecular networks from high-dimensional data. European Journal of Psychotraumatology, 5. https://doi.org/10.3402/ejpt.v5.23938
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Peres, J. F. P., Foerster, B., Santana, L. G., Fereira, M. D., Nasello, A. G., Savoia, M., … Lederman, H. (2011). Police officers under attack: Resilience implications of an
https://doi.org/10.1016/j.jpsychires.2010.11.004
SC
fMRI study. Journal of Psychiatric Research, 45(6), 727–734.
Phan, K. L., Angstadt, M., Golden, J., Onyewuenyi, I., Popovska, A., & Wit, H. de.
M AN U
(2008). Cannabinoid Modulation of Amygdala Reactivity to Social Signals of Threat in Humans. The Journal of Neuroscience, 28(10), 2313–2319. https://doi.org/10.1523/JNEUROSCI.5603-07.2008
Pietrzak, R. H., Goldstein, M. B., Malley, J. C., Johnson, D. C., & Southwick, S. M.
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(2009). Subsyndromal posttraumatic stress disorder is associated with health and psychosocial difficulties in veterans of Operations Enduring Freedom and Iraqi Freedom. Depression and Anxiety, 26(8), 739–744.
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https://doi.org/10.1002/da.20574
Poljac, E., Montagne, B., & de Haan, E. H. F. (2011). Reduced recognition of fear and
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sadness in post-traumatic stress disorder. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 47(8), 974–980.
https://doi.org/10.1016/j.cortex.2010.10.002
Presentation. (2016). Albany, CA: Neurobehavioral Systems, Inc. Schupp, H. T., Cuthbert, B. N., Bradley, M. M., Cacioppo, J. T., Ito, T., & Lang, P. J. (2000). Affective picture processing: The late positive potential is modulated by
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motivational relevance. Psychophysiology, 37(2), 257–261. https://doi.org/10.1111/1469-8986.3720257 Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., …
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Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The Development and Validation of a Structured Diagnostic
Psychiatry, 59(suppl 20), 22–33.
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Psychiatric Interview for DSM-IV and ICD-10. The Journal of Clinical
Somerville, L. H., Kim, H., Johnstone, T., Alexander, A. L., & Whalen, P. J. (2004).
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Human amygdala responses during presentation of happy and neutral faces: correlations with state anxiety. Biological Psychiatry, 55(9), 897–903. https://doi.org/10.1016/j.biopsych.2004.01.007
Tanielian, T., Jaycox, L. H., Adamson, D. M., Burnam, A. A., Burns, R. M., Caldarone,
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L. B., … Yochelson, M. R. (2008). Invisible Wounds of War [Product Page]. Retrieved December 21, 2016, from
http://www.rand.org/pubs/monographs/MG720.html
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Kim, M. J., Loucks, R. A., Palmer, A. L., Brown, A. C., Solomon, K. M., Marchante, A. N., & Whalen, P. J. (2011). The structural and functional connectivity of the amygdala: From
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normal emotion to pathological anxiety. Behavioural Brain Research, 223(2), 403–410. http://doi.org/10.1016/j.bbr.2011.04.025
Labuschagne, I., Phan, K. L., Wood, A., Angstadt, M., Chua, P., Heinrichs, M., Nathan, P. J. (2010). Oxytocin attenuates amygdala reactivity to fear in generalized social anxiety disorder. Neuropsychopharmacology, 35(12), 2403–2413. http://doi.org/10.1038/npp.2010.123
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Lawrence, J. W., Fauerbach, J., & Munster, A. (1996). Early avoidance of traumatic stimuli predicts chronicity of intrusive thoughts following burn injury. Behaviour Research and Therapy, 34(8), 643–646.
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Lobo, I., David, I. A., Figueira, I., Campagnoli, R. R., Volchan, E., Pereira, M. G., & de Oliveira, L. (2014). Brain reactivity to unpleasant stimuli is associated with
http://doi.org/10.1016/j.biopsycho.2014.09.002
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severity of posttraumatic stress symptoms. Biological Psychology, 103, 233–241.
Lobo, I., Portugal, L. C., Figueira, I., Volchan, E., David, I., Garcia Pereira, M., & de
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Oliveira, L. (2015). EEG correlates of the severity of posttraumatic stress symptoms: A systematic review of the dimensional PTSD literature. Journal of Affective Disorders, 183, 210–220. http://doi.org/10.1016/j.jad.2015.05.015 MacNamara, A., & Hajcak, G. (2010). Distinct electrocortical and behavioral evidence
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for increased attention to threat in generalized anxiety disorder. Depression and Anxiety, 27(3), 234–243. http://doi.org/10.1002/da.20679 MacNamara, A., Post, D., Kennedy, A. E., Rabinak, C. A., & Phan, K. L. (2013).
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Electrocortical processing of social signals of threat in combat-related posttraumatic stress disorder. Biological Psychology, 94(2), 441–449.
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http://doi.org/10.1016/j.biopsycho.2013.08.009
Michopoulos, V., Norrholm, S. D., & Jovanovic, T. (2015). Diagnostic Biomarkers for Posttraumatic Stress Disorder: Promising Horizons from Translational Neuroscience Research. Biological Psychiatry, 78(5), 344–353.
https://doi.org/10.1016/j.biopsych.2015.01.005
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Miller, G. A., Gration, G., & Yee, C. M. (1988). Generalized Implementation of an Eye Movement Correction Procedure. Psychophysiology, 25(2), 241–243. http://doi.org/10.1111/j.1469-8986.1988.tb00999.x
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Moring, J. C., Blankenship, A. E., Williams, J. M., Molino, A., & Peterson, A. L. (2014). PTSD and Mild Traumatic Brain Injury in Iraq and Afghanistan War. In C. R. Martin, V. R. Preedy, & V. B. Patel (Eds.), Comprehensive Guide to Post-
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Traumatic Stress Disorder (pp. 1–12). Cham: Springer International Publishing. Retrieved from http://dx.doi.org/10.1007/978-3-319-08613-2_69-1
M AN U
Neylan, T. C., Schadt, E. E., & Yehuda, R. (2014). Biomarkers for combat-related PTSD: focus on molecular networks from high-dimensional data. European Journal of Psychotraumatology, 5. https://doi.org/10.3402/ejpt.v5.23938 Orth, U., & Wieland, E. (2006). Anger, hostility, and posttraumatic stress disorder in
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trauma-exposed adults: a meta-analysis. Journal of Consulting and Clinical Psychology, 74(4), 698–706. https://doi.org/10.1037/0022-006X.74.4.698 Perkonigg, A., Pfister, H., Stein, M. B., Höfler, M., Lieb, R., Maercker, A., & Wittchen,
EP
H.-U. (2005). Longitudinal course of posttraumatic stress disorder and posttraumatic stress disorder symptoms in a community sample of adolescents
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and young adults. The American Journal of Psychiatry, 162(7), 1320–1327.
https://doi.org/10.1176/appi.ajp.162.7.1320
Phan, K. L., Angstadt, M., Golden, J., Onyewuenyi, I., Popovska, A., & Wit, H. de. (2008). Cannabinoid modulation of amygdala reactivity to social signals of threat
in humans. The Journal of Neuroscience: The Official Journal of the Society for
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Neuroscience, 28(10), 2313–2319. http://doi.org/10.1523/JNEUROSCI.560307.2008 Pietrzak, R. H., Goldstein, M. B., Malley, J. C., Johnson, D. C., & Southwick, S. M.
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(2009). Subsyndromal posttraumatic stress disorder is associated with health and psychosocial difficulties in veterans of Operations Enduring Freedom and Iraqi
http://doi.org/10.1002/da.20574
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Freedom. Depression and Anxiety, 26(8), 739–744.
Presentation. (2016). Albany, CA: Neurobehavioral Systems, Inc.
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Rona, R. J., Jones, M., Iversen, A., Hull, L., Greenberg, N., Fear, N. T., Wessely, S. (2009). The impact of posttraumatic stress disorder on impairment in the UK military at the time of the Iraq war. Journal of Psychiatric Research, 43(6), 649– 655. https://doi.org/10.1016/j.jpsychires.2008.09.006
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Schupp, H. T., Cuthbert, B. N., Bradley, M. M., Cacioppo, J. T., Ito, T., & Lang, P. J. (2000). Affective picture processing: The late positive potential is modulated by motivational relevance. Psychophysiology, 37(2), 257–261.
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http://doi.org/10.1111/1469-8986.3720257 Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., &
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Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(suppl 20), 22–33.
Somerville, L. H., Kim, H., Johnstone, T., Alexander, A. L., & Whalen, P. J. (2004). Human amygdala responses during presentation of happy and neutral faces:
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correlations with state anxiety. Biological Psychiatry, 55(9), 897–903. http://doi.org/10.1016/j.biopsych.2004.01.007 Tanielian, T., Jaycox, L. H., Adamson, D. M., Burnam, A. A., Burns, R. M., Caldarone,
Retrieved December 21, 2016, from http://www.rand.org/pubs/monographs/MG720.html
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L. B., … Yochelson, M. R. (2008). Invisible Wounds of War [Product Page].
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Weathers, F., Litz, B., Herman, D., Huska, J., & Keane, T. (1993). The PTSD Checklist (PCL): Reliability, Validity, and Diagnostic Utility. San Antonio, TX: Annual
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Convention of the International Society for Traumatic Stress Studies.
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Acknowledgements
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This material is based on work supported by Veterans Affairs Merit Review Program Awards (I01BX007080 to KLP) from Clinical Sciences Research and Development, Office of Research and Development of the U.S. Department of Veterans Affairs.
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Financial Disclosures
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The authors report no financial interests or potential conflicts of interest.
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Table 1. Demographic and Clinical Characteristics PCL-M
HAM-D
HAM-A
FSIQ
32.78 (6.5)
8.63 (5.7)
11.53 (8.6)
101.95 (9.2)
Gender
Male
Primary Dx
Female Other Anxiety Disorder (not PTSD)
41.82 (19.3) n 59 14 4
Mood Disorder
25
Substance Use Disorder
9 31
% 80.8 19.2 5.5 34.2
12.3
42.5
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Current Psych Med Use
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Mean (+/- SD)
AGE
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n = 73
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Figure 1: Histogram illustrating distribution of PTSD symptoms as measured by the PCL-M.
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Figure 2: Scalp Distributions and waveforms for emotional faces condition (minus shapes) during 500-3000ms window and ERPs (negative up) at centro-parietal sites: A. Angry; B. Fear; C. Happy; D. All Emotions.
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Figure 3: Scalp distributions depicting responses to angry faces minus shapes among: A. participants high in PTSD symptoms (n =36), B. participants low in PTSD symptoms (n = 37). Note: Median splits were computed on PTSD symptoms for illustrative purposes only; analyses examined both variables as dimensional measures.
S0022-3956(17)30087-0
DOI:
10.1016/j.jpsychires.2017.03.013
Reference:
PIAT 3095
To appear in:
Journal of Psychiatric Research
Received Date: 18 January 2017 Revised Date:
9 March 2017
Accepted Date: 17 March 2017
Please cite this article as: DiGangi JA, Burkhouse KL, Aase DM, Babione JM, Schroth C, Kennedy AE, Greenstein JE, Proescher E, Phan KL, An electrocortical investigation of emotional face processing in military-related posttraumatic stress disorder, Journal of Psychiatric Research (2017), doi: 10.1016/ j.jpsychires.2017.03.013. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Running Head: LPP AND EMOTION FACE PROCESSING IN PTSD
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An electrocortical investigation of emotional face processing in military-related posttraumatic stress disorder.
Julia A. DiGangi1,2, Katie L. Burkhouse2, Darrin M. Aase1,2,3 , Joseph M. Babione1, Christopher
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Schroth1,2, Amy E. Kennedy1,2, Justin E. Greenstein1,2, Eric Proescher1,2, & K. Luan Phan1,2,4
1
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Mental Health Service Line, Jesse Brown VA Medical Center; 820 S. Damen Ave., Chicago, IL 60612 2 Department of Psychiatry, University of Illinois at Chicago; 1747 Roosevelt Road, Chicago, IL 60608 3 College of Health & Human Services, Governors State University; 1 University Parkway, University Park, IL 60484 4 Departments of Psychology, Anatomy and Cell Biology, University of Illinois at Chicago; 808 S. Wood St., Chicago, IL 60612
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Correspondence concerning this article should be addressed to: Julia DiGangi, Ph.D., Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA. E-mail address: [email protected]
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Abstract
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PTSD is a disorder of emotion dysregulation. Although much work has intended to elucidate the neural underpinnings of the disorder, much remains unknown about the neurobiological substrates of emotion dysregulation in PTSD. In order to assess the relationship between a neural measure of attention to emotion (i.e. the late positive potential; LPP) and PTSD symptoms, EEG was recorded and examined as a potential predictor of military-related PTSD symptoms in a sample of 73 OEF/OIF/OND veterans. Results revealed that higher PTSD symptoms were related to an attenuated LPP response to angry facial expressions. This finding was not observed for happy or fearful faces. The current study provides initial evidence that, in a relatively young, mostly male sample of OEF/OIF/OND veterans, hyporeactivity to angry faces at the neural level may provide phenotypic data to characterize individual differences in PTSD symptom severity. This work may assist in future studies that seek to examine useful psychophysiologic targets for treatment and early interventions.
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Keywords: PTSD, emotion processing, military, veteran, EEG, late positive potential
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Introduction Of the more than 2 million U.S. soldiers deployed to Afghanistan and Iraq, 14-
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16% have developed posttraumatic stress disorder (PTSD) (Moring, Blankenship, Williams, Molino, & Peterson, 2014) Consequently, PTSD represents one of the most
prevalent injuries incurred during Operations Enduring Freedom (OEF), Iraqi Freedom
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(OIF), and New Dawn (OND). Given the profound mental, physical, occupational and functional costs associated with PTSD ( Jackson et al., 2016; Tanielian et al., 2008)
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considerable emphasis has been placed on identifying the biological substrates that underlie the disorder (Michopoulos, Norrholm, & Jovanovic, 2015).
Against this backdrop, two primary types of neuroimaging have been used in a complementary fashion to elucidate the underlying pathophysiology of PTSD. Perhaps most well known is work from functional magnetic resonance imaging (fMRI) studies
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that have indicated disruptions in prefrontal, limbic and interactive brain function are most commonly associated with the disorder (Hayes, Hayes, & Mikedis, 2012). In
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addition to fMRI, electroencephalography (EEG) and, more specifically, event related potentials (ERPs) have been used to identify additional neural biomarkers of the disorder
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(Lobo et al., 2015). A complementary method of neural measurement, ERPs offer superior temporal resolution, thereby providing information that cannot be ascertained by fMRI alone. One ERP component that may be particularly relevant to examining PTSD is the late positive potential (LPP). The LPP is a centro-parietal, positive-going ERP component that appears approximately 400ms after stimulus onset and is larger for emotional (e.g., threatening) stimuli than neutral stimuli (Foti, Hajcak, & Dien, 2009; Schupp et al., 2000).
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Specifically, the LPP is considered to be a means of tracking motivated attention that is particularly sensitive to emotionally salient stimuli (Hajcak, MacNamara, Foti, Ferri, & Keil, 2013) Because of its relation to emotional processing and motivated attention, the
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LPP has been examined in disorders of affect dysregulation, such as depression (e.g.,
Foti, Olvet, Klein, & Hajcak, 2010) and anxiety disorders (e.g. MacNamara & Hajcak, 2010; MacNamara, Kotov, & Hajcak, 2016) For example, a study that examined LPP
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response in individuals with generalized anxiety disorder (GAD) as compared to healthy controls found that, for individuals with GAD, the LPP was enhanced to aversive targets
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relative to their healthy counterparts (MacNamara & Hajcak, 2010). Given the emotion and attentional deficits associated with PTSD, the LPP may provide important information as a putative neural measure of PTSD.
To date, however, it has been infrequently studied in relation to PTSD. In the
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limited studies evaluating the LPP in PTSD populations, findings are mixed such that some research indicates the disorder is associated with increased LPP reactivity to threat (Lobo et al., 2014) and other studies demonstrate an attenuated LPP response, suggesting
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emotional disengagement to threat (MacNamara, Post, Kennedy, Rabinak, & Phan, 2013). The reason for these discrepant findings is unclear. One plausible reason for the
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discrepancies may be attributable to individual differences in reactivity. Another plausible explanation is that the differences may be due to different subtypes of PTSD. For example, it is conceivable that dysregulation may be linked to either over- or underresponsiveness to threat in distinct subtypes of PTSD—and recent research has suggested there are likely divergent biological profiles of PTSD (Michopoulos, Norrholm, & Jovanovic, 2015). Finally, the heterogeneity of findings may be attributable to
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methodological differences; while both studies used emotionally provocative stimuli, the former used disturbing images of mutilated bodies and the latter used emotional faces (i.e., happy, fearful, angry).
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Notably, all prior studies examining the relation between PTSD and LPP response have used binary classifications (Fitzgerald et al., 2016, MacNamara, Post, Kennedy,
Rabinak, & Phan, 2013, Lobo et al., 2014) . For example, MacNamara and colleagues
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conducted their analyses based on 33 veterans, 19 who met full diagnostic criteria for PTSD and 14 who did not as determined by a clinical cutoff of 40 on the Clinician-
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Administered PTSD Scale (CAPS-IV). However, one challenge associated with traditional diagnostic categories is the exclusion of many veterans experiencing subthreshold PTSD symptoms, which thereby limits the generalizability to the larger veteran population. Of note, prior work that has examined trauma in conjunction with
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other neurobiological markers has found important differences in brain functioning associated with subclinical PTSD that are not seen in individuals without PTSD symptoms (Garrett et al., 2012; Peres et al., 2011). Such research suggests that a
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dimensional approach to PTSD has important consequences for understanding brainbehavior relationships in the context of trauma’s sequelae. Further pressing the need to
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take a dimensional approach to PTSD symptoms is recent work that has highlighted the profound clinical and functional implications (e.g., heightened suicide risk, greater health problems) associated with subclinical symptoms (Eekhout, Reijnen, Vermetten, & Geuze, 2016; Jakupcak et al., 2011; Pietrzak, Goldstein, Malley, Johnson, & Southwick, 2009). Instead of examining groups (i.e., PTSD patients vs. a non-PTSD group), this study sought to address these gaps by examining PTSD symptom severity as a continuous
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predictor of LPP in order to extend our understanding of the role of individual differences and psychophysiology in OEF/OIF/OND veterans. In the current study, we hypothesized that the extent of LPP reactivity in response to threatening (i.e., fear and/or angry faces
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but not happy) faces would be negatively related to military-related PTSD symptoms
al., 2013). Methods
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given prior evidence of blunted LPP reactivity to social signals of threat (MacNamara et
This study was approved by the Institutional Review Boards at Jesse Brown VA
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Medical Center, Chicago IL and its university affiliate, the University of Illinois at Chicago. Research was conducted in accordance with the Helsinki Declaration. Participants Seventy-three participants with LPP data from EEG were included from a larger sample of OEF/OIF/OND veterans recruited at the Jesse Brown VA
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Medical Center and the University of Illinois Chicago. After completing informed consent procedures, participants completed the ERP task, a clinical assessment, and selfreport measures. Exclusionary criteria for participants included: presence of a clinically
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significant medical or neurological condition, presence of an organic mental syndrome and/or psychotic disorder, intellectual disability or pervasive developmental disorder, and
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current substance abuse or suicidal ideation at a level that would interfere with the study protocol. Ages ranged from 21-49 years (mean: 32.78 SD: ±6.5); 80.8% of the sample was male (See Table 1 for demographic and clinical information). Military-related PTSD symptoms were assessed using the PTSD Checklist Military Version (PCL-M; Weathers, Litz, Herman, Huska, & Keane, 1993), a 17-item self-report instrument that asks respondents to rate the degree to which they have been bothered by symptoms of PTSD
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as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV; American Psychiatric Association, 1994) within the last month on a5-point scale from 1 (not at all) to 5(extremely). The instrument has demonstrated good
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psychometric properties (Weathers et al., 1993). Average PCL-M score was 41.82
(±19.3), and average HAM-D score was 8.63 (SD: ±5.7; see Measures and see Figure 1
for distribution of PTSD symptoms).1 Of the 73 participants, 5.5% (n = 4) had a primary
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diagnosis of an anxiety disorder that was not PTSD (e.g., Panic Disorder), 34.2% (n = 25) a mood disorder and 12.3% (n = 9) a current or past substance use disorder. At the time
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of enrollment, 42.5% of the sample was prescribed psychiatric medications. Measures All clinical measures were administered by a psychologist or a master’s level research assistant under the supervision of a licensed psychologist. Clinical Assessment. Psychiatric diagnoses were established using the Mini
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International Neuropsychiatric Interview 6.0 (M.I.N.I; Sheehan et al., 1998). The M.I.N.I. is a well-validated, semi-structured interview for the assessment of current and lifetime DSM-IV Axis I disorders. It was administered to every participant in order to establish
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other psychiatric diagnoses. Depression was assessed using the clinician-administered Hamilton Depression Rating Scale (HAM-D; Hamilton, 1960).
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Emotional Faces Task. Participants completed an EEG version of the Emotional
Face-Matching Task (EFMT, Hariri, Tessitore, Mattay, Fera, & Weinberger, 2002), which has proven useful in characterizing threat processing in anxious and non-anxious participants (Labuschagne et al., 2010) and facilitates comparison with prior ERP work in PTSD (MacNamara et al., 2013). On each trial, three faces or shapes were presented for 1
Of the 73 participants, only four did not report a Criterion A event. Of these four individuals, two recorded the lowest possible score on the PCL (i.e., endorsed no items). To ensure there was no effect on our results, we removed these 4 individuals from analyses and model results were unchanged.
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3000 ms, in a triangular arrangement – i.e., one image was centered in the top-half of the screen and the other two images were presented in the bottom-half of the screen (one to the left and one to the right). On each face-matching trial, the faces of three different
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actors were presented: two were always emotional and one always neutral. Participants were instructed to select one of the faces at the bottom of the screen that bore the same
emotional expression as the ‘target’ face centered in the top portion of the screen. Face-
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matching trials could be fearful, angry or happy. On shape-matching trials, participants
were instructed to choose the shape at the bottom of the screen that matched (i.e., had the
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same form as) the target shape at the top of the screen. In line with previous studies (e.g., Labuschagne et al., 2010; Phan et al., 2008), we used geometric shapes as control stimuli instead of neutral faces, because neutral faces may be more influenced by individual differences (Somerville, Kim, Johnstone, Alexander, & Whalen, 2004). The task was
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divided into two runs, with each block having 12 angry, 12 fearful, 12 happy and 12 shape-matching trials; trials were presented randomly within each run. The inter-trial interval varied between 1000 and 3000 ms, during which time a white fixation cross was
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centrally presented on a black background. Participants performed six practice trials prior to beginning the experiment. The task was administered on an Intel(R) Core(tm) i7 @
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1.60GHz computer with a 19-in. (48.3 cm) monitor, using Presentation software (Presentation, 2016).
EEG Data Recording. Continuous EEG was recorded using the ActiveTwo
BioSemi system (ActiveTwo, 2016). Thirty-four electrode sites (standard 32 channel setup, as well as FCz and Iz) were used, based on the 10/20 system; in addition, one electrode was placed on each of the left and right mastoids. The electrooculogram (EOG)
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generated from eye movements were recorded from four facial electrodes. The EEG signal was pre-amplified at the electrode to improve the signal-to-noise ratio. The data were digitized at 24- bit resolution with a Least Significant Bit (LSB) value of 31.25 nV
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and a sampling rate of 1024 Hz, using a low-pass fifth order sinc filter with a -3dB cutoff point at 204.8 Hz. The voltage from each active electrode was referenced online with respect to a common mode sense active electrode producing a monopolar (non-
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differential) channel. Off-line analyses were performed using Brain Vision Analyzer
software (Brain Vision Analyzer 2, 2006). Data were re-referenced to the average of the
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two mastoids, segmented 200 ms before stimulus onset and continuing for the 3000 ms stimulus duration, and band-pass filtered with high-pass and low-pass filters of 0.01 and 30 Hz, respectively. Eye blink and ocular corrections used the method developed by Miller, Gration, & Yee (1988). Semi-automated artifact rejection procedures were used to
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identify a voltage step of more than 50.0 µV between sample points, a voltage difference of 300.0 µV within a trial, and a maximum voltage difference of less than 0.50 µV within 100 ms intervals. Trials were also inspected visually for any remaining artifacts, and data
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from individual channels containing artifacts were rejected on a trial-to-trial basis. Data Analyses. The LPP was scored based on prior literature as well as prior
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studies from our lab and from visual depiction of the current data indicating the LPP is maximal at centro-parietal sites (e.g., MacNamara & Hajcak, 2010). Specifically, the LPP was scored at Cz, CP1, CP2, Pz, P3 and P4, and it was apparent in the overall sample beginning at approximately 500 ms and continuing throughout the stimulus duration of 3000 ms; thus, the LPP was scored across this entire window. In order to isolate the variance in the LPP related to emotional picture processing, our analyses focused on
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emotional face (i.e., angry, fearful, and happy separately) minus shapes difference scores; the decision to separate the emotional faces was supported by results from earlier work in which there was only an effect for angry faces (MacNamara et al., 2013). Behavioral
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data were analyzed using a repeated measures analysis of variance (ANOVA). Significant omnibus findings were followed up using independent t-tests for planned comparisons; post-hoc tests used the Bonferroni correction. Greenhouse Geisser corrections were
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applied as necessary for violations of sphericity. A mixed-measure analysis of
covariance (ANCOVA) was conducted to examine the main effects of emotion and
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symptomatology on LPP magnitude, as well as any interaction between emotion and symptoms. Statistical analyses were conducted in SPSS (IBM Corp., 2013). Results
Behavioral
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Participants performed well on the EFMT as average accuracy was 95% SD: ±3.7. There was an effect of condition on reaction time (F(2.38, 168.82) = 461.8, p <.001, η2 = .87). Post-hoc analyses indicated that trials with faces elicited slower reaction times than
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those with shapes. Specifically, angry faces elicited the slowest reaction time (i.e., angry >fear>happy>shapes; Bonferroni-corrected p <.001). Similarly, there was an effect of
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condition on accuracy (F(1.65, 117.08) = 111.05, p <.001, η2 = .61), such that trials with faces elicited worse performance that trials with shapes. Consistent with earlier research (MacNamara et al., 2013), angry faces elicited the worst performance of all conditions (i.e., shapes>happy>fear >angry; Bonferroni-corrected p < .001). Mixed-measure analysis of covariance (ANCOVA)
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As expected, PCL-M and HAM-D were strongly related (p < .001). Figure 2 depicts scalp distributions and grand-average waveforms for each condition at the centroparietal pooling. The within-subjects ANCOVA with a Greenhouse-Geisser correction
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revealed a significant main effect of emotion (F(1.98, 136.70) = 6.57, p <.005, η2 = .09. Post hoc Bonferroni comparisons revealed that there was a significant difference between angry and happy faces, such that the LPP was enhanced to happy faces relative to angry
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faces (p < .005). There was no significant difference between fearful faces and either angry or happy faces (p = ns). Additionally, there was a significant emotion x PTSD
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interaction (F(1.98, 136.70) = 4.05, p = .02, η2 = .11), when controlling for current depression symptoms. There was no interaction between emotion and depression symptoms. To further investigate the emotion x PTSD interaction, post hoc regression analyses were conducted. Results revealed the interaction was explained by reaction to
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angry faces such that higher reported military-related PTSD symptoms was related to greater blunting of the LPP to angry faces (F(2,69)=3.45, p = .04, R2 =.09; Fig 3). However, there was no relationship for PTSD symptoms and the LPP to fearful faces
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(F(2,69)=.46, p = ns), or happy faces (F(2,69)=.15, p = ns). Analyses also examined discrete PTSD clusters within the PCL-M. There was no unique effect for re-
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experiencing, avoidance or hyperarousal symptoms. Medication use, sex and other anxiety symptoms (i.e., non-PTSD symptoms) were tested but did not change model results.
Discussion The primary aim of the current study was to examine a neural measure of socioemotional processing (i.e., LPP to angry, fearful, and happy faces) as a potential predictor
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of military-related PTSD symptoms in a sample of OEF/OIF/OND veterans. Results revealed that higher PTSD symptoms were related to an attenuated LPP response to angry facial expressions. This finding was not observed for happy or fearful faces and
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was maintained when adjusting for current depression symptoms, suggesting that the
effects were partially independent from the veterans’ current depression. Although prior studies have consistently shown that affect dysregulation is a core feature of PTSD
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(Hayes, VanElzakker, & Shin, 2012), less is known about the underlying neurobiology
that contributes to the development and maintenance of the disorder. The present study
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extends the prior literature by offering insight into a potential biological correlate of PTSD symptoms.
PTSD is a highly complex disorder, characterized by high symptom heterogeneity and often presenting across different symptom domains. With the release of DSM-5,
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there are more than a half million ways in which an individual can be diagnosed with PTSD (Galatzer-Levy & Bryant, 2013). Currently, there is a lack of consistent evidence regarding the biological underpinnings of the disorder. Evidence across a variety of
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biological markers (e.g., ERPs, fMRI, cortisol, heart rate) indicates that multiple neurobiological systems and mechanisms likely underlie the etiology and maintenance of
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PTSD (Michopoulos, Norrholm, & Jovanovic, 2015). Contributing to the biological complexity, there is often conflicting results within a single biomarker. For example, earlier work with the LPP and PTSD has been associated with both increased (Lobo et al., 2014) and decreased reactivity (MacNamara et al., 2013) to threatening images. This heterogeneity across and within biological markers, such as the LPP, may be contributing
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to this phenotypic symptom variability. As such, recent work has begun to posit that there are different PTSD subtypes (Neylan, Schadt, & Yehuda, 2014). The current study provides initial evidence that, in a relatively young, mostly
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male sample of OEF/OIF/OND veterans, hyporeactivity to angry faces at the neural level may provide phenotypic data to characterize individual differences in PTSD symptom
severity. Of relevance, a meta-analysis found that while anger was a common feature of
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PTSD emerging from a diverse range of traumas, the strength of the relationship between PTSD and anger was strongest among military combat veterans (Orth and Wieland,
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2006). It is possible that greater PTSD symptoms in this cohort may be related to disengagement from these emotionally aversive stimuli (i.e., angry faces). Angry faces may be particularly distressing to these OEF/OIF/OND veterans and, subsequently, be associated with less motivated attention, or attentional avoidance,
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toward these particular stimuli as reflected by a blunted LPP. Although there were no unique effects for the avoidance cluster on the PCL-M, it remains possible that greater PTSD symptoms in this cohort may be related to disengagement from angry faces. This
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may be because the avoidance symptom cluster on the PCL is not a measure of attentional avoidance per se whereas the LPP is specifically considered to be a means to
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track motivated attention toward emotional stimuli (Hajcak, MacNamara, Foti, Ferri, & Keil, 2013). Moreover, while symptom clusters are important to understanding the phenomenology of PTSD, the disorder is best understood as a construct comprised of all three clusters and, indeed, our results suggest that it is the broader constellation of PTSD symptoms that best account for our findings and explain attentional disengagement from angry faces. Future work is certainly necessary to better understand the relationship the
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LPP shares with both subsyndromal PTSD as well as PTSD. Similarly, future research can also help elucidate how disparate symptom clusters are differentially related to the underlying biology and course of PTSD—both as a categorical and dimensional entity.
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Additionally, although we hypothesized that there would be effects for both angry and fearful faces, our data yielded results only for angry faces. This distinction between anger and fear may be partly attributable to the distinctions between the types of threat
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that anger versus fear embodies. Specifically, prior research has demonstrated that fearful faces represent ambiguous threat whereas angry faces represent non-ambiguous threat
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(Kim et al., 2011). Therefore, our results suggest that hyporeactivity to more overt threats are more predictive of military-related PTSD symptoms.
Another contribution of our study is the dimensional approach to PTSD symptomatology in relation to the LPP. While prior studies of PTSD and the LPP have
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taken a categorical approach (i.e., compared PTSD group to a non-PTSD group), our study intentionally used a dimensional approach to PTSD symptoms. A dimensional approach has the theoretical advantage of being able to assess people on a continuum of
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illness—from healthy to sick. Lobo et al. (2015) highlight the limitations of a categorical system in which people can either have or not have PTSD by stating that a diagnosis
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“may be the tip of an iceberg—a late manifestation of a change that has been occurring in the brains of people” before they were formally diagnosed (p. 211). Therefore, a dimensional approach may be especially important to aid early PTSD detection and to guide better treatment options. However, only two prior studies have examined the LPP in relation to categorical diagnostic categories of PTSD in veterans (Fitzgerald et al., 2016; MacNamara et al., 2013). Moreover, no prior study has demonstrated that a
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dimensional approach to PTSD symptomatology is associated with a neural measure of threat reactivity (i.e., LPP to angry faces) in a diverse, heterogeneous sample of OEF/OIF/OND veterans. This dimensional approach to identifying biological correlates
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of PTSD symptomatology is particularly important in light of prior research that has shown subthreshold PTSD is associated with profound clinical and functional
implications (e.g., heightened suicide risk, greater health problem; (Eekhout, Reijnen,
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Vermetten, & Geuze, 2016; Jakupcak et al., 2011; Pietrzak, Goldstein, Malley, Johnson, & Southwick, 2009).
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These results should be interpreted in the context of important limitations. First, the experimental design is cross-sectional and, importantly, it is not clear if blunted neural reactivity to angry faces follows or precedes PTSD symptoms and/or traumatic experience. Additionally, future work that examines other types of emotionally
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provocative stimuli (e.g., non-social threat) would further develop understanding and generalizability of the relationship between PTSD and threat responding at the neural level. Moreover, there are important reasons to consider the effects of various types of
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stimuli on the LPP. Although the current study focused on emotion face processing, the literature on the relationship between PTSD and face processing is mixed (Felmingham,
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Bryant, & Gordon, 2003; Klimova, Bryant, Williams, & Felmingham, 2013; Poljac, Montagne, & de Haan, 2011). Therefore, future studies would offer benefit by also considering other forms of emotionally-evocative and trauma-related stimuli (e.g., war scenes). Likewise, future research that examines how additional variables, such as gender or trauma type, interface with neurobiological indices of reactivity would enhance our understanding of the LPP. Additionally, because we were explicitly interested in a
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dimensional analysis of PTSD symptoms, it is possible that the effect may have manifested differently if groups were categorically dichotomized by diagnosis. However, because prior literature indicates subclinical PTSD has important clinical consequences
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(e.g., Eekhout et al., 2016), we chose to include a range of PTSD severity. Finally, our sample did not solely contain PTSD symptoms, as individuals with a history of other
affective and anxiety disorders (e.g., panic disorder) were also included. Because our
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modest sample size precluded us from controlling for all possible confounding variables, the degree to which comorbid conditions contributed to the present findings remains
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unclear. However, given our heterogeneous sample, the findings are more likely to generalize to a diverse cohort of OEF/OIF/OND veterans.
In conclusion, the current study suggests that an attenuated neural response to angry facial expressions, as measured by the LPP, may provide endophenotypic
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information to help characterize PTSD severity in a sample of OEF/OIF/OND veterans. This finding provides compelling evidence that a dimensional approach to PTSD symptoms is important to consider in the context of PTSD’s pathophysiology. Such an
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interventions.
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approach may yield new and useful psychophysiologic targets for treatment and early
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References ActiveTwo. (2016). Bio Semi.
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Brain Vision Analyzer 2. (2006). Gilching, Germany: Brain Products GmbH.
Eekhout, I., Reijnen, A., Vermetten, E., & Geuze, E. (2016). Post-traumatic stress
symptoms 5 years after military deployment to Afghanistan: an observational
SC
cohort study. The Lancet Psychiatry, 3(1), 58–64. https://doi.org/10.1016/S22150366(15)00368-5
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Felmingham, K. L., Bryant, R. A., & Gordon, E. (2003). Processing angry and neutral faces in post-traumatic stress disorder: an event-related potentials study. Neuroreport, 14(5), 777–780.
https://doi.org/10.1097/01.wnr.0000065509.53896.e3
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Foti, D., Hajcak, G., & Dien, J. (2009). Differentiating neural responses to emotional pictures: Evidence from temporal-spatial PCA. Psychophysiology, 46(3), 521– 530. https://doi.org/10.1111/j.1469-8986.2009.00796.x
EP
Galatzer-Levy, I. R., & Bryant, R. A. (2013). 636,120 Ways to Have Posttraumatic Stress Disorder. Perspectives on Psychological Science: A Journal of the Association for
AC C
Psychological Science, 8(6), 651–662.
https://doi.org/10.1177/1745691613504115
Garrett, A. S., Carrion, V., Kletter, H., Karchemskiy, A., Weems, C. F., & Reiss, A. (2012). Brain Activation to Facial Expressions in Youth with Ptsd Symptoms.
Depression and Anxiety, 29(5), 449–459. https://doi.org/10.1002/da.21892
ACCEPTED MANUSCRIPT
Hajcak, G., MacNamara, A., Foti, D., Ferri, J., & Keil, A. (2013). The dynamic allocation of attention to emotion: simultaneous and independent evidence from the late positive potential and steady state visual evoked potentials. Biological
RI PT
Psychology, 92(3), 447–455. https://doi.org/10.1016/j.biopsycho.2011.11.012 Hariri, A. R., Tessitore, A., Mattay, V. S., Fera, F., & Weinberger, D. R. (2002). The
Amygdala Response to Emotional Stimuli: A Comparison of Faces and Scenes.
SC
NeuroImage, 17(1), 317–323. https://doi.org/10.1006/nimg.2002.1179
Hayes, J. P., Hayes, S. M., & Mikedis, A. M. (2012). Quantitative meta-analysis of neural
M AN U
activity in posttraumatic stress disorder. Biology of Mood & Anxiety Disorders, 2, 9. https://doi.org/10.1186/2045-5380-2-9
Hayes, J. P., VanElzakker, M. B., & Shin, L. M. (2012). Emotion and cognition interactions in PTSD: a review of neurocognitive and neuroimaging studies.
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Frontiers in Integrative Neuroscience, 6. https://doi.org/10.3389/fnint.2012.00089 IBM Corp. (2013). IBM SPSS Statistics for Windows (Version 22.0). Armonk, NY: IBM Corp.
EP
Jackson, C. E., Green, J. D., Bovin, M. J., Vasterling, J. J., Holowka, D. W., Ranganathan, G., … Marx, B. P. (2016). Mild Traumatic Brain Injury, PTSD, and
AC C
Psychosocial Functioning Among Male and Female U.S. OEF/OIF Veterans. Journal of Traumatic Stress, 29(4), 309–316. https://doi.org/10.1002/jts.22110
Jakupcak, M., Hoerster, K. D., Varra, A., Vannoy, S., Felker, B., & Hunt, S. (2011). Hopelessness and Suicidal Ideation in Iraq and Afghanistan War Veterans Reporting Subthreshold and Threshold Posttraumatic Stress Disorder: The
ACCEPTED MANUSCRIPT
Journal of Nervous and Mental Disease, 199(4), 272–275. https://doi.org/10.1097/NMD.0b013e3182124604 Kim, M. J., Loucks, R. A., Palmer, A. L., Brown, A. C., Solomon, K. M., Marchante, A.
RI PT
N., & Whalen, P. J. (2011). The structural and functional connectivity of the
amygdala: From normal emotion to pathological anxiety. Behavioural Brain Research, 223(2), 403–410. https://doi.org/10.1016/j.bbr.2011.04.025
SC
Klimova, A., Bryant, R. A., Williams, L. M., & Felmingham, K. L. (2013). Dysregulation in cortical reactivity to emotional faces in PTSD patients with high dissociation
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symptoms. European Journal of Psychotraumatology, 4. https://doi.org/10.3402/ejpt.v4i0.20430
Labuschagne, I., Phan, K. L., Wood, A., Angstadt, M., Chua, P., Heinrichs, M., … Nathan, P. J. (2010). Oxytocin Attenuates Amygdala Reactivity to Fear in
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Generalized Social Anxiety Disorder. Neuropsychopharmacology, 35(12), 2403– 2413. https://doi.org/10.1038/npp.2010.123 Lobo, I., David, I. A., Figueira, I., Campagnoli, R. R., Volchan, E., Pereira, M. G., & de
EP
Oliveira, L. (2014). Brain reactivity to unpleasant stimuli is associated with severity of posttraumatic stress symptoms. Biological Psychology, 103, 233–241.
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https://doi.org/10.1016/j.biopsycho.2014.09.002
Lobo, I., Portugal, L. C., Figueira, I., Volchan, E., David, I., Garcia Pereira, M., & de Oliveira, L. (2015). EEG correlates of the severity of posttraumatic stress symptoms: A systematic review of the dimensional PTSD literature. Journal of Affective Disorders, 183, 210–220. https://doi.org/10.1016/j.jad.2015.05.015
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MacNamara, A., & Hajcak, G. (2010). Distinct electrocortical and behavioral evidence for increased attention to threat in generalized anxiety disorder. Depression and Anxiety, 27(3), 234–243. https://doi.org/10.1002/da.20679
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MacNamara, A., Kotov, R., & Hajcak, G. (2016). Diagnostic and Symptom-Based
Predictors of Emotional Processing in Generalized Anxiety Disorder and Major
Depressive Disorder: An Event-Related Potential Study. Cognitive Therapy and
SC
Research, 40(3), 275–289. https://doi.org/10.1007/s10608-015-9717-1
MacNamara, A., Post, D., Kennedy, A. E., Rabinak, C. A., & Phan, K. L. (2013).
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Electrocortical processing of social signals of threat in combat-related posttraumatic stress disorder. Biological Psychology, 94(2), 441–449. https://doi.org/10.1016/j.biopsycho.2013.08.009
Michopoulos, V., Norrholm, S. D., & Jovanovic, T. (2015). Diagnostic Biomarkers for
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Posttraumatic Stress Disorder: Promising Horizons from Translational Neuroscience Research. Biological Psychiatry, 78(5), 344–353. https://doi.org/10.1016/j.biopsych.2015.01.005
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Miller, G. A., Gration, G., & Yee, C. M. (1988). Generalized Implementation of an Eye Movement Correction Procedure. Psychophysiology, 25(2), 241–243.
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https://doi.org/10.1111/j.1469-8986.1988.tb00999.x
Moring, J. C., Blankenship, A. E., Williams, J. M., Molino, A., & Peterson, A. L. (2014). PTSD and Mild Traumatic Brain Injury in Iraq and Afghanistan War. In C. R. Martin, V. R. Preedy, & V. B. Patel (Eds.), Comprehensive Guide to PostTraumatic Stress Disorder (pp. 1–12). Cham: Springer International Publishing. Retrieved from http://dx.doi.org/10.1007/978-3-319-08613-2_69-1
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Neylan, T. C., Schadt, E. E., & Yehuda, R. (2014). Biomarkers for combat-related PTSD: focus on molecular networks from high-dimensional data. European Journal of Psychotraumatology, 5. https://doi.org/10.3402/ejpt.v5.23938
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Peres, J. F. P., Foerster, B., Santana, L. G., Fereira, M. D., Nasello, A. G., Savoia, M., … Lederman, H. (2011). Police officers under attack: Resilience implications of an
https://doi.org/10.1016/j.jpsychires.2010.11.004
SC
fMRI study. Journal of Psychiatric Research, 45(6), 727–734.
Phan, K. L., Angstadt, M., Golden, J., Onyewuenyi, I., Popovska, A., & Wit, H. de.
M AN U
(2008). Cannabinoid Modulation of Amygdala Reactivity to Social Signals of Threat in Humans. The Journal of Neuroscience, 28(10), 2313–2319. https://doi.org/10.1523/JNEUROSCI.5603-07.2008
Pietrzak, R. H., Goldstein, M. B., Malley, J. C., Johnson, D. C., & Southwick, S. M.
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(2009). Subsyndromal posttraumatic stress disorder is associated with health and psychosocial difficulties in veterans of Operations Enduring Freedom and Iraqi Freedom. Depression and Anxiety, 26(8), 739–744.
EP
https://doi.org/10.1002/da.20574
Poljac, E., Montagne, B., & de Haan, E. H. F. (2011). Reduced recognition of fear and
AC C
sadness in post-traumatic stress disorder. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 47(8), 974–980.
https://doi.org/10.1016/j.cortex.2010.10.002
Presentation. (2016). Albany, CA: Neurobehavioral Systems, Inc. Schupp, H. T., Cuthbert, B. N., Bradley, M. M., Cacioppo, J. T., Ito, T., & Lang, P. J. (2000). Affective picture processing: The late positive potential is modulated by
ACCEPTED MANUSCRIPT
motivational relevance. Psychophysiology, 37(2), 257–261. https://doi.org/10.1111/1469-8986.3720257 Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., …
RI PT
Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The Development and Validation of a Structured Diagnostic
Psychiatry, 59(suppl 20), 22–33.
SC
Psychiatric Interview for DSM-IV and ICD-10. The Journal of Clinical
Somerville, L. H., Kim, H., Johnstone, T., Alexander, A. L., & Whalen, P. J. (2004).
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Human amygdala responses during presentation of happy and neutral faces: correlations with state anxiety. Biological Psychiatry, 55(9), 897–903. https://doi.org/10.1016/j.biopsych.2004.01.007
Tanielian, T., Jaycox, L. H., Adamson, D. M., Burnam, A. A., Burns, R. M., Caldarone,
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L. B., … Yochelson, M. R. (2008). Invisible Wounds of War [Product Page]. Retrieved December 21, 2016, from
http://www.rand.org/pubs/monographs/MG720.html
EP
Kim, M. J., Loucks, R. A., Palmer, A. L., Brown, A. C., Solomon, K. M., Marchante, A. N., & Whalen, P. J. (2011). The structural and functional connectivity of the amygdala: From
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normal emotion to pathological anxiety. Behavioural Brain Research, 223(2), 403–410. http://doi.org/10.1016/j.bbr.2011.04.025
Labuschagne, I., Phan, K. L., Wood, A., Angstadt, M., Chua, P., Heinrichs, M., Nathan, P. J. (2010). Oxytocin attenuates amygdala reactivity to fear in generalized social anxiety disorder. Neuropsychopharmacology, 35(12), 2403–2413. http://doi.org/10.1038/npp.2010.123
ACCEPTED MANUSCRIPT
Lawrence, J. W., Fauerbach, J., & Munster, A. (1996). Early avoidance of traumatic stimuli predicts chronicity of intrusive thoughts following burn injury. Behaviour Research and Therapy, 34(8), 643–646.
RI PT
Lobo, I., David, I. A., Figueira, I., Campagnoli, R. R., Volchan, E., Pereira, M. G., & de Oliveira, L. (2014). Brain reactivity to unpleasant stimuli is associated with
http://doi.org/10.1016/j.biopsycho.2014.09.002
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severity of posttraumatic stress symptoms. Biological Psychology, 103, 233–241.
Lobo, I., Portugal, L. C., Figueira, I., Volchan, E., David, I., Garcia Pereira, M., & de
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Oliveira, L. (2015). EEG correlates of the severity of posttraumatic stress symptoms: A systematic review of the dimensional PTSD literature. Journal of Affective Disorders, 183, 210–220. http://doi.org/10.1016/j.jad.2015.05.015 MacNamara, A., & Hajcak, G. (2010). Distinct electrocortical and behavioral evidence
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for increased attention to threat in generalized anxiety disorder. Depression and Anxiety, 27(3), 234–243. http://doi.org/10.1002/da.20679 MacNamara, A., Post, D., Kennedy, A. E., Rabinak, C. A., & Phan, K. L. (2013).
EP
Electrocortical processing of social signals of threat in combat-related posttraumatic stress disorder. Biological Psychology, 94(2), 441–449.
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http://doi.org/10.1016/j.biopsycho.2013.08.009
Michopoulos, V., Norrholm, S. D., & Jovanovic, T. (2015). Diagnostic Biomarkers for Posttraumatic Stress Disorder: Promising Horizons from Translational Neuroscience Research. Biological Psychiatry, 78(5), 344–353.
https://doi.org/10.1016/j.biopsych.2015.01.005
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Miller, G. A., Gration, G., & Yee, C. M. (1988). Generalized Implementation of an Eye Movement Correction Procedure. Psychophysiology, 25(2), 241–243. http://doi.org/10.1111/j.1469-8986.1988.tb00999.x
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Moring, J. C., Blankenship, A. E., Williams, J. M., Molino, A., & Peterson, A. L. (2014). PTSD and Mild Traumatic Brain Injury in Iraq and Afghanistan War. In C. R. Martin, V. R. Preedy, & V. B. Patel (Eds.), Comprehensive Guide to Post-
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Traumatic Stress Disorder (pp. 1–12). Cham: Springer International Publishing. Retrieved from http://dx.doi.org/10.1007/978-3-319-08613-2_69-1
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Neylan, T. C., Schadt, E. E., & Yehuda, R. (2014). Biomarkers for combat-related PTSD: focus on molecular networks from high-dimensional data. European Journal of Psychotraumatology, 5. https://doi.org/10.3402/ejpt.v5.23938 Orth, U., & Wieland, E. (2006). Anger, hostility, and posttraumatic stress disorder in
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trauma-exposed adults: a meta-analysis. Journal of Consulting and Clinical Psychology, 74(4), 698–706. https://doi.org/10.1037/0022-006X.74.4.698 Perkonigg, A., Pfister, H., Stein, M. B., Höfler, M., Lieb, R., Maercker, A., & Wittchen,
EP
H.-U. (2005). Longitudinal course of posttraumatic stress disorder and posttraumatic stress disorder symptoms in a community sample of adolescents
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and young adults. The American Journal of Psychiatry, 162(7), 1320–1327.
https://doi.org/10.1176/appi.ajp.162.7.1320
Phan, K. L., Angstadt, M., Golden, J., Onyewuenyi, I., Popovska, A., & Wit, H. de. (2008). Cannabinoid modulation of amygdala reactivity to social signals of threat
in humans. The Journal of Neuroscience: The Official Journal of the Society for
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Neuroscience, 28(10), 2313–2319. http://doi.org/10.1523/JNEUROSCI.560307.2008 Pietrzak, R. H., Goldstein, M. B., Malley, J. C., Johnson, D. C., & Southwick, S. M.
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(2009). Subsyndromal posttraumatic stress disorder is associated with health and psychosocial difficulties in veterans of Operations Enduring Freedom and Iraqi
http://doi.org/10.1002/da.20574
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Freedom. Depression and Anxiety, 26(8), 739–744.
Presentation. (2016). Albany, CA: Neurobehavioral Systems, Inc.
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Rona, R. J., Jones, M., Iversen, A., Hull, L., Greenberg, N., Fear, N. T., Wessely, S. (2009). The impact of posttraumatic stress disorder on impairment in the UK military at the time of the Iraq war. Journal of Psychiatric Research, 43(6), 649– 655. https://doi.org/10.1016/j.jpsychires.2008.09.006
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Schupp, H. T., Cuthbert, B. N., Bradley, M. M., Cacioppo, J. T., Ito, T., & Lang, P. J. (2000). Affective picture processing: The late positive potential is modulated by motivational relevance. Psychophysiology, 37(2), 257–261.
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http://doi.org/10.1111/1469-8986.3720257 Sheehan, D. V., Lecrubier, Y., Sheehan, K. H., Amorim, P., Janavs, J., Weiller, E., &
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Dunbar, G. C. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of Clinical Psychiatry, 59(suppl 20), 22–33.
Somerville, L. H., Kim, H., Johnstone, T., Alexander, A. L., & Whalen, P. J. (2004). Human amygdala responses during presentation of happy and neutral faces:
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correlations with state anxiety. Biological Psychiatry, 55(9), 897–903. http://doi.org/10.1016/j.biopsych.2004.01.007 Tanielian, T., Jaycox, L. H., Adamson, D. M., Burnam, A. A., Burns, R. M., Caldarone,
Retrieved December 21, 2016, from http://www.rand.org/pubs/monographs/MG720.html
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L. B., … Yochelson, M. R. (2008). Invisible Wounds of War [Product Page].
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Weathers, F., Litz, B., Herman, D., Huska, J., & Keane, T. (1993). The PTSD Checklist (PCL): Reliability, Validity, and Diagnostic Utility. San Antonio, TX: Annual
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Convention of the International Society for Traumatic Stress Studies.
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Acknowledgements
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This material is based on work supported by Veterans Affairs Merit Review Program Awards (I01BX007080 to KLP) from Clinical Sciences Research and Development, Office of Research and Development of the U.S. Department of Veterans Affairs.
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Financial Disclosures
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The authors report no financial interests or potential conflicts of interest.
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Table 1. Demographic and Clinical Characteristics PCL-M
HAM-D
HAM-A
FSIQ
32.78 (6.5)
8.63 (5.7)
11.53 (8.6)
101.95 (9.2)
Gender
Male
Primary Dx
Female Other Anxiety Disorder (not PTSD)
41.82 (19.3) n 59 14 4
Mood Disorder
25
Substance Use Disorder
9 31
% 80.8 19.2 5.5 34.2
12.3
42.5
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Current Psych Med Use
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Mean (+/- SD)
AGE
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n = 73
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Figure 1: Histogram illustrating distribution of PTSD symptoms as measured by the PCL-M.
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Figure 2: Scalp Distributions and waveforms for emotional faces condition (minus shapes) during 500-3000ms window and ERPs (negative up) at centro-parietal sites: A. Angry; B. Fear; C. Happy; D. All Emotions.
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Figure 3: Scalp distributions depicting responses to angry faces minus shapes among: A. participants high in PTSD symptoms (n =36), B. participants low in PTSD symptoms (n = 37). Note: Median splits were computed on PTSD symptoms for illustrative purposes only; analyses examined both variables as dimensional measures.