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. Author manuscript; available in PMC: 2018 Feb 27.
Published in final edited form as: Psychiatry Res. 2015 Jul 2;233(2):194–200. doi: 10.1016/j.pscychresns.2015.06.012

Decreased Somatosensory Activity to Non-threatening Touch in Combat Veterans with Posttraumatic Stress Disorder

Amy S Badura-Brack a,*, Katherine M Becker b,c, Timothy J McDermott a, Tara J Ryan a,d, Madelyn M Becker a, Allison R Hearley a, Elizabeth Heinrichs-Graham c,e, Tony W Wilson b,c,e,f
PMCID: PMC5828504  NIHMSID: NIHMS943380  PMID: 26184460

Abstract

Posttraumatic stress disorder (PTSD) is a severe psychiatric disorder prevalent in combat veterans. Previous neuroimaging studies have demonstrated that patients with PTSD exhibit abnormal responses to non-threatening visual and auditory stimuli, but have not examined somatosensory processing. Thirty male combat veterans, 16 with PTSD and 14 without, completed a tactile stimulation task during a 306-sensor magnetoencephalography (MEG) recording. Significant oscillatory neural responses were imaged using a beamforming approach. Participants also completed clinical assessments of PTSD, combat exposure, and depression. We found that veterans with PTSD exhibited significantly reduced activity during early (0 to 125 ms) tactile processing compared with combat controls. Specifically, veterans with PTSD had weaker activity in the left postcentral gyrus, left superior parietal area, and right prefrontal cortex in response to nonthreatening tactile stimulation relative to veterans without PTSD. The magnitude of activity in these brain regions was inversely correlated with symptom severity, indicating that those with the most severe PTSD had the most abnormal neural responses. Our findings are consistent with a resource allocation view of perceptual processing in PTSD, which directs attention away from nonthreatening sensory information.

Keywords: PTSD, Somatosensory, Magnetoencephalography, MEG, Oscillation, Tactile, Military

1. Introduction

Posttraumatic stress disorder (APA, 2000; APA, 2013) is a significant psychiatric disorder, which may occur in the aftermath of combat exposure. The symptom picture in PTSD is complex, including re-experiencing, avoidance, mood, and hyperarousal symptoms (APA, 2000; APA, 2013). The lifetime incidence of PTSD is roughly 7–9% of the US population (Kessler et al., 1995; Kessler et al., 2005; APA, 2013), but PTSD is reported in 13–22% of recent veterans (Seal et al., 2007; Seal et al., 2009).

Neuroimaging studies in PTSD demonstrate the clear importance of brain structures implicated in fear processing including the amygdalae, hippocampi, anterior cingulate, and insula, (Rabinak et al., 2010; Morey et al., 2012; Pitman et al., 2012; Sripada et al., 2012; Badura-Brack et al., under review) as would be expected in a disorder rooted in traumatic, fear-provoking events. Imaging studies have also highlighted executive functioning deficits in patients with PTSD (Polak et al., 2012) and noted widespread alterations in parietal, frontal, and occipital areas (Eckart et al., 2011; Liu et al., 2012; Qi et al., 2013; Gong et al., 2014; Badura-Brack et al., under review) consistent with cognitive models of PTSD emphasizing disrupted attentional and perceptual processes (Ehlers and Clark, 2000).

Electrophysiological studies have examined auditory and visual sensory processing in patients with PTSD. Most of these studies have recorded event-related potentials (ERP) using trauma-eliciting stimuli and shown significant increases in the P300 response (for a review see Javanbakht et al., 2011), although Bae et al. (2011) found reduced P300 current-source density in patients with PTSD compared to healthy controls in response to non-threatening auditory stimuli.. These seemingly disparate results are consistent with a meta-analysis of ERP studies in PTSD, which found enhanced responses for trauma-related stimuli and reduced responses for neutral stimuli, particularly in the parietal cortex (Karl et al., 2006a). Since the P300 is thought to reflect top-down information processing, these findings may suggest reduced allocation of cognitive resources in response to stimuli evaluated as non-threatening (Karl et al., 2006a).

To understand the bottom-up somatosensory alterations associated with PTSD, several studies have focused on evoked responses earlier in the time-course. For example, neural responses from stimuli onset (0 ms) to roughly 150 ms are thought to reflect pre-attentive automatic functions such as stimulus registration and sensory filtering, Many studies of the preattentive time-course have assessed sensory gating by presenting paired stimuli so closely together in time that the normal response to the second stimulus is sharply reduced (i.e., gated). Gating studies in PTSD have consistently shown impaired early gating (Javanbakht et al., 2011) including reduced habituation to the second tone in PTSD patients compared to trauma and no-trauma controls (Karl et al., 2006b). This failure to suppress or habituate to repeating stimuli in both auditory (Neylan et al., 1999; Ghisolfi et al., 2004; Holstein et al., 2010; Gjini et al., 2013) and visual (Gjini et al., 2013) modalities suggests that PTSD patients have difficulty filtering out irrelevant sensory input.

Interestingly, studies focusing on evoked responses to neutral stimuli, have provided perhaps the most critical evidence for basic somatosensory alterations in PTSD. For example, a recent study showed that survivors of torture with PTSD have significantly smaller primary auditory and visual responses to neutral stimuli compared to controls, reflecting a decreased early response after simple stimuli presentation (Gjini et al., 2013). Likewise, a MEG study by Hunter et al. (2011) found attenuated source strength in response to neutral stimuli in the right auditory area of the PTSD group compared to healthy controls. Similar deficits in early visual processing of neutral pictures have been described, (Felmingham et al., 2011; Mueller-Pfeiffer et al., 2013) and a recent fMRI study identified diminished activity in the ventral visual stream, and dorsal and ventral attention systems in PTSD patients compared to controls (Mueller-Pfeiffer et al., 2013). Thus, processing deficits early in the time-course may be associated with impaired attentional processes consistent with automaticity of PTSD symptoms. From a clinical perspective, individuals with PTSD self-report greater sensory filtering disruption in perceptual modulation, including heightened stimulus sensitivity and sensory flooding, as well as general distractibility compared with trauma exposed and no trauma comparison groups (Stewart and White, 2008). Therefore, processing impairments are perceptible in the daily lives of PTSD patients.

The current study aimed to investigate oscillatory activity in response to non-threatening somatosensory stimuli (i.e., light air-puffs) in recent combat veterans with and without PTSD. Cortical oscillations, like evoked-potentials, are reflective of information processing in the brain and are a very sensitive measure of neuronal coding and communication both intra- and inter-regionally (Uhlhaas et al., 2009; Lisman & Jensen, 2013; Friston et al., 2015). Recent studies have connected information processing deficits in psychiatric and neurological conditions to aberrant cortical oscillatory activity, including Parkinson’s disease (Heinrichs-Graham et al., 2014), cognitive impairment (Wilson et al., 2013, 2015), autism (Wilson et al., 2007; Rojas et al., 2011, 2014), attention-deficit/hyperactivity disorder (Wilson et al., 2012, 2013; Franzen et al., 2013), and other disorders (Uhlhaas & Singer, 2010, 2012). However, oscillations have rarely been studied in PTSD and have yet to be examined in somatosensory processing, which involves strong oscillations in controls (Gaetz & Cheyne, 2006). Our primary aim was to examine potential differences in somatosensory tactile processing without attempting any manipulation of the stimuli to increase threat perception. Given the literature indicating reduced primary auditory and visual responses to neutral stimuli in PTSD, we hypothesized that combat veterans with PTSD would have reduced cortical activity early in the time course in the contralateral primary somatosensory cortex and parietal lobe compared to healthy, demographically matched combat veterans without PTSD.

2. Methods and Materials

2.1. Subject Selection

We evaluated a community sample of 30 Operation Iraqi Freedom and Operation Enduring Freedom (OIF/OEF) combat veterans. All participants were male and right-handed. Sixteen of the veterans were diagnosed with PTSD according to DSM-IV criteria (2) using the Clinical Administered PTSD Scale (CAPS) (Blake et al., 1995) and the F1/I2 rule (Weathers et al., 1999). The remaining 14 combat veterans were age-matched to the patient group and did not have PTSD or any other psychiatric diagnosis, as validated by the CAPS and the Mini International Neuropsychiatric Interview (M.I.N.I.; Sheehan et al., 1998). All participants also completed the Patient Health Questionnaire (PHQ-9; Kroenke et al., 2001) to assess depression, and a measure of combat exposure (Vogt et al., 2008) to assess trauma severity. Other exclusionary criteria included any known central nervous system disease, neoplasm, or lesion; history of significant head injury, or ferromagnetic implants/shrapnel. The Creighton University Institutional Review Board approved the study protocol, and all participants provided their written informed consent to participate in this study.

2.2. Experimental Paradigm

Participants were seated in a custom chair within the magnetically-shielded room with their head positioned inside the helmet-shaped MEG sensor array. They were instructed to remain still with both arms resting on a tray attached to the chair, while unilateral tactile stimulation was applied to the pad of the fifth digit of the right hand using a small airbladder (Figure 1). For each participant, more than 160 trials were collected using an inter-stimulus interval that varied randomly between 2.5 and 4.0 s.

Figure 1.

Figure 1

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Tactile Stimulation Device. Participants were seated in a custom MEG chair with both arms resting on a tray attached to the chair body. Mechanoreceptors within the pad of the fifth digit of the right hand were stimulated using a small airbladder that was encased in plastic housing and clipped onto the index finger. A plastic red hose connected the airbladder to a pneumatic delivery system that was located outside the magnetically-shielded room.

2.3 MEG Data Processing and Statistics

2.3.1 MEG Data Acquisition and sMRI Coregistration

All recordings were conducted in a one-layer magnetically-shielded room (MSR) with active shielding engaged. With an acquisition bandwidth of 0.1–330 Hz, neuromagnetic responses were sampled continuously at 1 kHz using an Elekta Neuromag system with 306 magnetic sensors, including 204 planar gradiometers and 102 magnetometers (Elekta, Helsinki, Finland). Using MaxFilter (v2.2; Elekta), MEG data from each participant were individually corrected for head motion and subjected to noise reduction using the signal space separation method with a temporal extension (tSSS; Taulu et al., 2005; Taulu and Simola, 2006). Prior to MEG measurement, four coils were attached to the participant’s head and the locations of these coils, together with the three fiducial points and scalp surface, were determined with a 3-D digitizer (Fastrak 3SF0002, Polhemus Navigator Sciences, Colchester, VT, USA). Once the participant was positioned for MEG recording, an electric current with a unique frequency label (e.g., 322 Hz) was fed to each of the coils. This induced a measurable field and allowed each coil to be localized in reference to the sensors throughout the recording session. Since coil locations were also known in head coordinates, all MEG measurements could be transformed into a common coordinate system. With this coordinate system (including the scalp surface points), each participant’s MEG data was coregistered with T1-weighted structural magnetic resonance imaging (sMRI) data for source space analyses. sMRI data were aligned parallel to the anterior and posterior commissures and were transformed into standard space after beamforming using BESA MRI (Version 2.0; BESA GmbH, Gräfelfing, Germany).

2.3.2. MEG Preprocessing, Time-Frequency Transformation, & Statistics

Cardio-artifacts were removed from the data using signal-space projection (SSP) and the projection operator was accounted for during source reconstruction (Uusitalo and Ilmoniemi, 1997). Artifact rejection was based on a fixed threshold method, supplemented with visual inspection. Epochs were of 1.1 s duration (−0.4 to 0.7 s), with 0.0 s defined as stimulation onset and the baseline being the −0.4 to 0.0 s window. For each participant, at least 115 artifact-free epochs remained for further analysis.

Artifact-free epochs were transformed into the time-frequency domain using complex demodulation (resolution: 2.0 Hz, 25 ms; Papp and Ktonas, 1977) and the resulting spectral power estimations per sensor were averaged over trials to generate time-frequency plots of mean spectral density. These data were normalized by dividing the power value of each predetermined time-frequency bin by the respective bin’s baseline power, which was calculated as the mean power during the −0.4 to 0.0 s time period. This normalization allowed task-related power changes to be readily visualized in sensor space.

The specific time-frequency windows used for imaging were determined by statistical analysis of the spectrograms corresponding to each of the 204 gradiometer-type sensors. Each data point in the spectrogram was initially evaluated using a mass univariate approach based on the general linear model. To reduce the risk of false positive results while maintaining reasonable sensitivity, a two-stage procedure was followed to control for Type 1 error. In the first stage, one-sample t-tests were conducted on each data point and the output spectrogram of t-values was thresholded at p < 0.05 to define time-frequency bins containing potentially significant oscillatory deviations across participants in each group. In stage two, time-frequency bins that survived the threshold were clustered with temporally and/or spectrally neighboring bins that were also above the (p < 0.05) threshold, and a cluster value was derived by summing all of the t-values of all data points in the cluster. Nonparametric permutation testing was then used to derive a distribution of cluster-values and the significance level of the observed clusters (from stage one) was tested directly using this distribution (Ernst, 2004; Maris and Oostenveld, 2007). For each comparison, at least 10,000 permutations were computed to build a distribution of cluster values. Based on these analyses, the time-frequency window(s) that contained significant oscillatory events were derived and these window(s) were used for the beamforming analysis. We defined the precise time-frequency parameters using the single sensor with the highest t-value, but the results would have been identical had we used any of the gradiometers surrounding the peak sensor.

2.3.4. MEG Source Imaging & Statistics

Using the time-frequency windows determined by the analysis described above, cortical networks were imaged through an extension of the linearly constrained minimum variance vector beamformer (Van Veen et al., 1997; Liljeström et al., 2005), which employs spatial filters in the frequency domain to calculate source power for the entire brain volume. We used a beamformer in this study because this approach is ideal for imaging cortical osciallatory activity, which was our primary focus (Hillebrand et al., 2005). In this method the single images are derived from the cross spectral densities of all combinations of MEG sensors averaged over the time-frequency range of interest, and the solution of the forward problem for each location on a grid specified by input voxel space. Following convention, the source power in these images was normalized per participant using a separately averaged pre-stimulus noise period of equal duration and bandwidth (Van Veen et al., 1997). In principle, the beamformer operator generates a spatial filter for each grid point, which passes signals without attenuation from the given neural region while minimizing interference from activity in all other brain areas. The properties of these filters are determined from the MEG covariance matrix and the forward solution for each grid point in the image space, which are used to allocate sensitivity weights to each sensor in the array for each voxel in the brain (for a review, see Hillebrand et al., 2005). MEG preprocessing and imaging used the Brain Electrical Source Analysis software (BESA version 6.0; BESA GmbH, Gräfelfing, Germany).

Normalized source power was computed for the selected frequency band over the entire brain volume per participant at 4.0 × 4.0 × 4.0 mm resolution. Prior to statistical analysis each participant’s functional images, which were coregistered to neuroanatomical images prior to beamforming, were transformed into standardized space using the transformation previously applied to the sMRI volume and spatially resampled. The resulting 3D maps of brain activity were statistically evaluated using a mass univariate approach based on the general linear model. Briefly, the effect of group was examined using a random effects analysis for the time-frequency bin of interest, whereas one-sample t-tests were conducted to probe activation patterns in each group. All output statistical maps were displayed as a function of alpha level.

3. Results

3.1. Participant Demographics & Clinical Measures

The male combat veterans included in this study were all right handed and did not differ significantly on age (p = 0.38) or educational level (p = 0.91). Mean age was 33.7 years-old (range: 25–44) in the PTSD group and 31.6 years-old (range: 23–45) in the control group. Mean educational level was 13.8 years in the PTSD group and 14.0 years in the control group. Consistent with their diagnosis, combat veterans with PTSD scored significantly higher on the CAPS and its subscales than combat veterans without PTSD (all p’s < 0.001). Notably, the PTSD and combat control groups did not differ significantly on combat exposure (p = 0.17). Finally, consistent with DSM-5 criteria, the PTSD group reported significantly more depressive symptoms than the control group (p < 0.001). Regarding comorbidity, 11 of 16 veterans with PTSD met criteria for a diagnosis of depression, two for alcohol abuse, four for another anxiety disorder. Four veterans with PTSD had no comorbid diagnoses, and no one in the control group had any psychiatric diagnosis. See Table 1 for descriptive and summary statistics on clinical measures.

Table 1.

Clinical Measures

Measure Group N Mean SD t df p
Combat Exposure PTSD 15 16.87 5.87 1.40 27 0.173
Control 14 13.64 6.54
PTSD (CAPS total) PTSD 16 73.63 18.15 9.34 28 <0.001
Control 14 21.57 10.92
Reexperiencing (CAPS B) PTSD 16 18.56 5.92 6.66 28 <0.001
Control 14 5.57 4.55
Avoidance (CAPS C) PTSD 16 29.25 10.91 6.86 28 <0.001
Control 14 6.43 6.38
Arousal (CAPS D) PTSD 16 25.81 6.51 6.71 28 <0.001
Control 14 9.57 6.72
Depression (PHQ-9) PTSD 16 13.00 7.40 5.53 28 <0.001
Control 14 1.79 1.72
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3.2. MEG Sensor-Based Time-Frequency Analyses

Sensor level spectrograms were statistically examined using nonparametric permutation testing to derive the precise time-frequency bins for follow up beamforming analyses. The results showed significant (p < 0.05; corrected) oscillatory responses in a subset of gradiometers near the left sensorimotor cortex of each group, which stretched from 8–14 Hz during the 0 to 125 ms time window (0.0 s = stimulation onset; Figure 2). This time-frequency window, and a window of equal bandwidth and duration from the baseline period, was imaged using beamforming to derive the spatial location of significant oscillatory responses associated with tactile stimulation.

Figure 2.

Figure 2

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Average Time-Frequency Spectra in Veterans with PTSD and Combat-Exposed Control Veterans. Time (in ms) is denoted on the x-axis, with 0 ms defined as the onset of tactile stimulation and the baseline being the −400 to 0 ms time period. Frequency (in Hz) is shown on the y-axis. The average spectra for a MEG gradiometer near the left sensorimotor area, expressed as percent difference from baseline (scale bar appears on far right side), are shown for veterans with PTSD on the left and combat-exposed control veterans on the right. The early reduction in 8–14 Hz activity can be clearly discerned in veterans with PTSD, whereas controls exhibited a sharp increase in 8–14 Hz activity during this same time period (0 – 125 ms). An increase in low-frequency (< 7 Hz) activity slightly after stimulation onset, likely reflecting the evoked response, can also be seen in both groups.

3.3. MEG Beamforming Analyses

Stimulation upon the pad of the fifth-digit of the right hand in veterans with PTSD produced a decrease in 8–14 Hz neural activity (relative to the baseline period), which was centered in the left central sulcus and extended onto the left precentral and postcentral gyri (p < 0.001, corrected; Figure 3). In contrast, veterans without PTSD exhibited a sharp increase in 8–14 Hz in the left postcentral gyrus and left superior parietal cortices (Figure 3). Group comparisons indicated that veterans with PTSD had significantly weaker 8–14 Hz activity in the left postcentral gyrus, directly posterior to the hand knob feature of the precentral gyrus (Yousry et al., 1997), as well as reduced activity in the left superior parietal and right prefrontal cortices (p < 0.001, corrected; Figure 4).

Figure 3.

Figure 3

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Significant Oscillatory (8–14 Hz) Neuronal Activity Following Tactile Stimulation to the Right Hand. Veterans with PTSD (left) exhibited reduced alpha activity in the left postcentral gyrus in response to mechanoreceptor stimulation(Peak Coordinates: −40, −31, 52; Cohen’s d = 0.78), whereas combat-exposed veterans without PTSD showed robust increases in 8–14 Hz neural oscillatory activity in a similar area of the left postcentral gyrus (Peak Coordinates: −46, −23, 48; Cohen’s d = 1.62) and in a region of the left superior parietal cortices (Peak Coordinates: −24, −60, 48; Cohen’s d = 1.4). All images are shown in radiological convention and as a function of alpha level.

Figure 4.

Figure 4

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Group Differences between Veterans with PTSD and Combat-Exposed Control Veterans. Veterans with PTSD had significantly reduced 8–14 Hz activity in the right prefrontal cortices (Peak Coordinates: 23, 44, 37; Cohen’s d = 1.38), left superior parietal area (Peak Coordinates: −24, −73, 43, Cohen’s d = 1.81), and the left postcentral gyrus (Peak Coordinates: −45, −29, 49; Cohen’s d = 1.93) during the first 125 ms following tactile stimulation. The 2D image is in radiological orientation (L = R) and all images are shown as a function of alpha level. Legend for both images appears below the 2D image.

3.4. Correlations: MEG Indices and Clinical Scales

To examine possible relationships between MEG measures of oscillatory neural activity and PTSD symptomatology, we conducted a series of correlation analyses using the full sample. We used the peak amplitude value in each brain region where significant group differences were found between veterans with and without PTSD, and the severity of PTSD as defined by the CAPS total score to determine the presence of an overarching relationship. We then conducted follow-up correlation analyses using peak activity in each brain region-of-interest and scores on each of the major DSM-IV PTSD diagnostic criteria, as well as depression. Data distributions for CAPS scores and peak amplitudes were not significantly different from normality; however depression scores did not meet normality criteria, so we opted for Spearman correlations throughout. Significant negative correlations (all p’s < .01; corrected) with PTSD severity were found in the right prefrontal cortex (rs(30) = −0.70), left superior parietal region (rs(30) = −0.63), and the left postcentral gyrus (rs(30) = −0.46). Many of the follow-up symptom cluster and peak amplitude correlations were significant, but no correlation was found between combat exposure and brain activity; these Bonferroni corrected results are reported in Table 2.

Table 2.

Correlations Among Clinical Measures and Peak Voxel Values for 0–125ms, 8–14 Hz

Measure rPFC postcentral gyrus superior parietal
Combat Exposure −0.326 −0.292 −0.423
Reexperiencing −0.687* −0.559* −0.733*
Avoidance −0.682* −0.479 −0.535*
Arousal −0.576* −0.339 −0.588*
Depression −0.714* −0.427 −0.647*
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*

significant at Bonferroni corrected alpha = .05/15 = .003

4. Discussion

In the time window from 0–125 ms (8–14 Hz), we found group differences in the left postcentral gyrus (somatosensory cortex), left superior parietal area, and the right prefrontal cortex (rPFC) in response to non-threatening tactile stimulation on the pad of the right fifth digit. In all cases, the PTSD patients exhibited a decrease in 8–14 Hz oscillatory activity whereas the controls exhibited a sharp increase in oscillatory activity as compared to baseline. The response profile in healthy combat controls was expected, as increased oscillatory activity in this time-frequency band within the left postcentral gyrus and left superior parietal region is the normal response to such tactile stimulation (Gaetz and Cheyne, 2006; Kurz, et al., 2014). Notably, patients with PTSD did not display typical reactions to touch; instead, this group exhibited a slight decrease in neural oscillatory activity and one could argue that PTSD patients essentially showed no somatosensory response to the stimulation. Additionally, the rPFC decrease noted in the PTSD patients, lacking any analogous activity in the combat control sample, suggests a particular lack of attention/engagement with the nonthreatening touch among the patients with PTSD early in the time course. The somatosensory and attentional deficits noted in this study are consistent with broad impairments in executive, sensory and motor processing in PTSD.

One hypothesis about information processing deficits in PTSD suggests that, context dependent information processing reduces cognitive resources in the presence of neutral stimuli, and increases information processing resources in response to threatening stimuli to enhance alertness toward subsequent stimuli (Karl, et al., 2006a). Consistent with this view, a study utilizing self-reported sensory perception thresholds for warm, cold, and touch stimuli in PTSD indicated significantly higher sensory thresholds for all three non-threatening stimuli in PTSD patients compared with controls (Defrin et al., 2008). However, PTSD patients rated overtly painful (threatening) stimuli as much more intense than controls (Defrin et al., 2008). Another study also found no thermal detection differences between groups; however, when they assessed thermal pain thresholds using a ramped stimulus approach, PTSD patients had significantly lower pain detection thresholds than combat and no-combat controls (Kraus et al., 2009). Perhaps the difference between these findings lies in the gradual versus sudden exposure to painful stimuli, suggesting a cortical attention patterned geared toward novel, threatening stimuli. Such a processing system is broadly consistent with our current observations; however, a limitation of the current study is not having a measure of physiological arousal during MEG recordings to assess the perceptual salience of the tactile stimulation.

Interpretation of our somatosensory findings is aided by previous studies showing attenuated evoked responses to non-threatening stimuli in the auditory (Hunter et al., 2011; Gjini et al., 2013) and visual (Felmingham et al., 2011; Mueller-Pfeiffer et al., 2013) modalities.. These diminished responses may reflect an information processing style in PTSD, which directs attention away from nonthreatening information, thereby sparing cognitive resources to attend to future, potentially threatening information. Two recent MEG studies that examined evoked responses to threatening pictures in PTSD patients compared to controls are especially relevant here. The first study found hyperactivations in the superior parietal cortex selectively to threatening pictures, which correlated with trauma severity (Catani et al., 2009). This study revealed increased activity in the superior parietal cortex of PTSD patients in response to threatening visual stimuli, whereas our current findings indicate reduced responsiveness to benign stimulation in the superior parietal cortex of PTSD patients. The second MEG study found hyperactivations early in the time-course (at 130 – 160 ms) in the rPFC, consistent with an early vigilant cortical response in PTSD to threatening pictures (Adenauer et al., 2010). This early response may suggest early direct prefrontal processing, not requiring later executive control, to categorize the material as threatening, consistent with bottom up processing (see Adenauer et al., 2010). This response to threatening stimuli opposes our findings of decreased rPFC activity in response to benign touch; thereby, supporting a the model of early stimulus categorization in PTSD resulting in hyperactivity to threatening stimuli and hypoactivity to benign stimuli.

Finally, our results suggest that veterans with PTSD do not perceive nonthreatening somatosensory information at a level equivalent to that of controls, and this lack of perception may be reflective of early categorization of the repetitive, neutral stimulation as non-important. Severity of combat exposure did not differ between groups, nor was severity of combat exposure significantly associated with deficits in neural activity in any of the identified brain regions of interest. This suggests that the presence of PTSD itself, not severity of trauma, is critically connected to our observed neural activation differences. Directly to this point, neural activity was significantly correlated with overall severity of PTSD across our entire sample, indicating that participants with the most abnormal neuronal activity also had the most severe PTSD. Reexperiencing symptoms were consistently correlated with neural activity in all regions where group differences were observed, and importantly, reexperiencing was the only symptom cluster to significantly correlate with the primary somatosensory cortical activity elicited by the tactile stimulation task. Such a focus on reexperiencing symptoms is consistent with a theory of a cognitive preoccupation with threatening information; however, given the inherent interrelationships among symptom clusters in PTSD, and the presence of other significant subscale correlations, this finding should be interpreted with caution. For example, although PHQ-9 scores may serve as a proxy for DSM-5 negative alterations in mood and cognition, and PHQ-9 scores did not correlate with activations in the somatosensory cortex, depression did correlate with other neural activity and most veterans with PTSD had comorbid depression. Future research should clarify the relationships between neural hypoactivity in PTSD and depression, by investigating somatosensory processing in a purely depressed sample.

We believe this is the first neuroimaging study directly assessing somatosensory stimulation conducted in PTSD, and our findings offer critical insight into perceptual processing deficits associated with the severity of this debilitating disorder. Our current MEG findings indicate a lack of cortical responsiveness early in the time-course specifically in the left somatosensory cortex, left superior parietal cortex and rPFC of patients with PTSD in response to benign right-sided tactile stimulation. These results are consistent with a recent meta-analysis (Hayes et al., 2012a) and review (Hayes et al., 2012b) article of neuroimaging findings, which concluded that PTSD is associated with a processing allocation model geared toward threat detection at the expense of attentional processes and other cognitive operations. Across our full sample of combat veterans, higher levels of PTSD were associated with more abnormal levels of neural activity in all three brain regions where significant group differences were observed between veterans with and without PTSD. We propose that these findings support a resource allocation style in PTSD, which directs neural processing away from nonthreatening sensory information.

Acknowledgments

This research was supported by a grant from the nonprofit organization At Ease, USA to ABB, by a Creighton University College of Arts and Science Summer Undergraduate Research Fellowship (TJM), and grant R01-MH103220 from the National Institutes of Health to TWW. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Footnotes

Financial Disclosures

Dr. Badura-Brack, Ms. Becker, Mr. McDermott, Ms. Ryan, Ms. Becker, Ms. Hearley, Ms. Heinrichs-Graham, and Dr. Wilson report no competing financial or other interests.

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