Abstract
Chronic musculoskeletal pain (CMP) is a significant burden for Persian Gulf War Veterans (GWVs), yet the causes are poorly understood. Brain structure abnormalities are observed in GWVs, however relationships with modifiable lifestyle factors such as physical activity (PA) are unknown. We evaluated gray matter volumes and associations with symptoms, PA, and sedentary time in GWVs with and without CMP. Ninety-eight GWVs (10 females) with CMP and 56 GWVs (7 females) controls completed T1-weighted magnetic resonance imaging, pain and fatigue symptom questionnaires, and PA measurement via actigraphy. Regional gray matter volumes were analyzed using voxel-based morphometry and were compared across groups using analysis of covariance (ANCOVA). Separate multiple linear regression models were used to test associations between PA intensities, sedentary time, symptoms, and gray matter volumes. Familywise cluster error rates were used to control for multiple comparisons (α = 0.05). GWVs with CMP reported greater pain and fatigue symptoms, worse mood, and engaged in less moderate-to-vigorous PA and more sedentary time than healthy GWVs (all p values < 0.05). GWVs with CMP had smaller gray matter volumes in the bilateral insula and larger volumes in the frontal pole (p < 0.05adjusted). Gray matter volumes in the left insula were associated with pain symptoms (rpartial = 0.26, −0.29; p < 0.05adjusted). No significant associations were observed for either PA or sedentary time (p > 0.05adjusted). GWVs with CMP had smaller gray matter volumes within a critical brain region of the descending pain processing network and larger volumes within brain regions associated with pain sensation and affective processing, which may reflect pain chronification.
SIGNIFICANCE STATEMENT The pathophysiology of chronic pain in Gulf War veterans is understudied and not well understood. In a large sample of Gulf War veterans, we report veterans with chronic musculoskeletal pain have smaller gray matter volumes in brain regions associated with pain regulation and larger volumes in regions associated with pain sensitivity compared with otherwise healthy Gulf War veterans. Gray matter volumes in regions of pain regulation were significantly associated with pain symptoms and encompassed the observed group brain volume differences. These results are suggestive of deficient pain modulation that may contribute to pain chronification.
Introduction
Chronic musculoskeletal pain (CMP) is a significant and understudied condition in veterans of the Gulf War (GWVs; Thomas et al., 2006; Gwini et al., 2015). Epidemiological research has consistently identified CMP as a primary symptom of deployed GWVs and it is included as a core component of Gulf War illness case definitions (Fukuda et al., 1998). More recent meta-analytic data describe rates of ∼30% for back, joint, and muscular aches/pains, in deployed GWVs, roughly double that observed among nondeployed Gulf War–era veterans (Maule et al., 2018). These data indicate that upward of 250,000 GWVs experience CMP. The influence of this condition goes beyond pain symptomology; the resultant distress of living with CMP can further worsen mental and physical health (e.g., fatigue and mood) and increase the risk of becoming disabled (Turk et al., 2011; Lee et al., 2015).
The pathophysiology of CMP in GWVs is poorly understood; however, emerging evidence suggests the existence of both functional and structural brain abnormalities (Chao et al., 2010; Apfel et al., 2011; Christova et al., 2017; Zhang et al., 2020). Prior neuroimaging research involving disparate groups of GWVs has examined the consequences of neurotoxic chemical exposure, post-traumatic stress disorder, and Gulf War Illness, but to our knowledge has not determined associations with CMP (Heaton et al., 2007; Chao et al., 2010; 2011). Brain imaging studies of civilian CMP populations have consistently reported smaller regional brain volumes compared with healthy controls. Even so, the identification of critical brain pain regulatory regions has been elusive (Kuchinad et al., 2007; Schmidt-Wilcke et al., 2007; Wood et al., 2009; Jensen et al., 2013; Fallon et al., 2013). Further, clear patterns of results across studies have not emerged. Specific to GWVs with CMP, our lab has observed disrupted white matter structure along several pain regulation-relevant tracts, and these disruptions were significantly associated with CMP symptoms (Van Riper et al., 2017).
A critical gap in our understanding of the pathophysiological significance of brain imaging research in GWVs is the relative lack of studies that test associations between brain outcomes and modifiable lifestyle factors known to be related to brain structure and function (McLoughlin et al., 2011; Erickson et al., 2014; Dougherty et al., 2017). One such behavior, physical activity (PA), has been shown to improve pain and related symptoms (Ambrose and Golightly, 2015; Gwini et al., 2015), but the mechanisms of symptom improvement are unclear. Examination of neuroimaging-based brain outcomes with modifiable lifestyle factors, such as PA, is important for determining potential low-cost and scalable treatment options and adjuncts. Given the lack of effective alternatives for symptomatic GWVs, the need to identify modifiable behaviors that might offer some relief is critical. The purpose of the present investigation was to (1) compare regional and total gray matter brain volumes of GWVs with CMP to deployed, healthy GWV controls; (2) test associations between pain symptoms and brain volumes; and (3) test associations between PA and brain volumes. We hypothesized that (1) GWVs with CMP would have smaller brain volumes in regions associated with pain regulation (Robinson et al., 2011; Smallwood et al., 2013) and larger volumes in regions associated with pain sensation (Schmidt-Wilcke et al., 2007), (2) that symptoms would be negatively associated with brain volume within regions involved in pain regulation (e.g., anterior cingulate cortex, insular cortex; Ceko et al., 2013) and positively associated with regions involved in pain sensation (e.g., mid cingulate, somatosensory, and frontal cortices; Xu et al., 1997; Fulbright et al., 2001; Lutz et al., 2013), and (3) associations between PA and regional gray matter volumes would differ between GWVs with CMP and healthy controls.
Materials and Methods
Experimental design
Data for this project were sampled from three separate studies that aimed to determine the pathophysiology of Gulf War Illness. Specific to this project were the data for self-reported symptoms, accelerometer-based PA measures, and structural magnetic resonance imaging (MRI) data that were collected across the three projects. Each study was funded by the U.S. Department of Veterans Affairs (D.B.C., principal investigator) as part of the Clinical Science and Research Development, Merit Review Award Program (Grants I01 CX0011329, I01 CX000383, and 561-00436). One study was a multisite project conducted in collaboration with the War Related Illness and Injury Study Center and Kessler Foundation (Rocco Ortenzio Neuroimaging Center) in East Orange, New Jersey, whereas the other two were conducted in Madison, Wisconsin. Procedures for the New Jersey study site mirrored those executed at the Wisconsin site. All study procedures were approved by the appropriate committees and performed in accordance with both the U.S. Department of Veterans Affairs Federal Policy for the Protection of Human Subjects and the Declaration of Helsinki.
Inclusion/exclusion criteria
The sample for this project consisted of 170 GWVs (151 males, 19 females); 108 met criteria for chronic and widespread musculoskeletal pain (i.e., CMP). CMP status was assessed by a general physical examination, a medical record review, and a fibromyalgia assessment. To meet criteria for CMP, pain must have developed post-deployment, have persisted for longer than 3 months, and been present in three or more quadrants of the body. Deployment to the 1990–1991 Persian Gulf War was confirmed during a clinical interview. The Mini International Neuropsychiatric Interview (MINI; Sheehan et al., 1998) was used to screen for any psychological comorbidities (detailed below). The medical record review was further used to verify medication history, mental health diagnoses, and any conditions that could explain the veteran's CMP. The control group (n = 62) for this project consisted of deployed and otherwise healthy GWVs not reporting CMP and qualifying as healthy for each of the GWV projects that included a healthy control group.
Participants were excluded for any MRI contraindications, including claustrophobia, ferrous metal in their body, pregnancy or planning to become pregnant, or weighing more than the capacity of the scanner patient table (300 pounds). To reduce variability in brain structure and to control for potential confounds, individuals were also excluded if they reported any of the following: (1) current use of certain prescription pain (e.g., opiates, muscle relaxants) and/or anticonvulsant medications, (2) presence of any pain condition that could explain their CMP, (3) presence of mental health diagnoses that may affect brain morphology (i.e., major depressive disorder, post-traumatic stress disorder, substance abuse in the previous 2 years, schizophrenia, and bipolar disorder as assessed by the MINI), and (4) presence of any neurologic disorders (e.g., schizophrenia, Parkinson's disease). Additionally, participants were asked to abstain from caffeine and tobacco for 4 h before their study visits, and from alcohol, exercise, and pain medication (e.g., aspirin, nonsteroidal anti-inflammatory medications) for 24 h before their study visits.
Questionnaires and fibromyalgia assessment
All study participants completed both the Profile of Mood States (POMS; McNair et al., 1971) and the Medical Outcomes Short Form Health Survey (SF-36; Ware and Sherbourne, 1992) or a derivative of the SF-36, the Veterans Rand 36 Item Health Survey (VR-36; Kazis et al., 2004). The Bodily Pain scale of the SF-36 (or VR-36) served as the primary pain measure in our statistical analyses. Depending on study participation, GWVs also completed the Multidimensional Fatigue Inventory (MFI; Smets et al., 1995), McGill Pain Questionnaire (MPQ; Melzack, 1987), State-Trait Anxiety Inventory (STAI; Spielberger, 1984), and the Beck Depression Inventory (BDI; Beck and Steer, 1993). The MPQ, STAI, and BDI were completed by 102 GWVs [CMP, 71; healthy controls (CON), 31], and the MFI was completed by 116 GWVs (CMP, 85; CON, 31). Total mood disturbance, as calculated by the POMS, and total fatigue, as measured by the MFI, served as our primary measures of mood and fatigue, respectively. A diagnosis of fibromyalgia was not required for inclusion in any of the three studies; however, we examined the frequency of a positive fibromyalgia diagnosis for the present study. Both the 1990 (earliest study) and 2010 (later studies) American College of Rheumatology fibromyalgia criteria were used for this purpose (Wolfe et al., 1990, 2010).
Physical activity measurement
All participants wore a triaxial GT3X+ accelerometer (Actigraph) on their hip for 7 consecutive days. Participants received the monitor, and instructions on proper wear and usage were communicated in person. Participants were instructed to wear the device during all waking hours except for time spent swimming, bathing, or showering. Standard accelerometry inclusion criteria consisted of a minimum of 10 h of valid wear time per day for a minimum of 3 weekdays and 1 weekend day (Troiano et al., 2008). Wear time logs were provided to all participants and were used to verify activity detected by the accelerometer. Activity was analyzed in four intensity domains, sedentary (<100 counts/min), light (100–759 counts/min), moderate–vigorous (>760 counts/min), and vigorous (>1952 counts/min). Accelerometer data were further expressed as a percentage of total accelerometer wear time to account for individual differences.
Neuroimaging data acquisition parameters
High resolution anatomic magnetic resonance images (1 × 1 × 1 mm) were acquired with a GE Discovery MR750 3-T scanner (GE Medical Systems) for data acquired at the Wisconsin site and a Siemens MAGNETOM Skyra 3-T for data acquired at the New Jersey site. At the Wisconsin site, the head coil was upgraded from 8 to 32 channels following the earliest study (2012). At the New Jersey site, a 20-channel head/neck coil (with 16 channels in the head coil) was used. To account for potential confounding influences from the scanners used at the two sites and the head coil upgrade, statistical models included covariates for the scanner type and head coil upgrade. Foam cushions and MRI-compatible headphones were used during the scan to restrict head motion and allow for communication between the subject and experimenter, respectively. All studies, and both study sites, used a 3D IR-prepped fast-gradient echo-pulse sequence that consisted of 124 (1 mm thick) T1-weighted (TR = 9000 ms, TE = 93 ms, FOV = 24 cm, flip angle = 12°) axial images with a matrix of 256 × 256 × 64.
Neuroimaging data processing
Preprocessing of the images included a collection of steps to clean, segment, register, and modulate data to prepare for voxel-based morphometry (VBM) analyses. Files were converted from DICOM (Digital Imaging and Communications in Medicine) format to NifTi (Neuroimaging Informatics Technology Initiative) format using the dcm2nii tool (Li et al., 2016). Following conversion, images were bias field corrected with an automated command from the Advanced Normalization Tools package (Tustison et al., 2010). Nonbrain tissue was then stripped away for all images using the Functional MRI of the Brain Software Library (Jenkinson et al., 2012) brain extraction tool (Smith, 2002). Images were then reviewed to ensure complete removal of all nonbrain tissue, no errant removal of any brain tissue, and a general data quality check (e.g., motion artifact). The following processing procedures were conducted in SPM 12 software using the Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) processing pipeline (Ashburner, 2007). First, images were segmented in native space to gray matter, white matter, and cerebrospinal fluid. The gray and white matter images were then brought into rough alignment between all subjects (DARTEL space) and averaged. The gray and white matter images of each subject were then registered to an average image of the entire sample with the flow fields preserved. The flow fields were then used to improve the registration of the gray matter images to Montreal Neurologic Institute (MNI) 152 space. Following registration to MNI space, all images were modulated with their Jacobian determinants. Images were smoothed with 8 mm isotropic Gaussian kernel. Analyses were constrained to a group average of all valid images created using the ImCalc tool as part of the SPM package. Results were overlaid on the group average image. The VBM toolbox for SPM was used to analyze the data, and the get_totals tool was used to calculate total and regional gray matter volumes. Total brain volume was calculated by totaling the gray matter, white matter, and cerebrospinal fluid volumes. Significant regional clusters identified in the SPM analyses were defined as regions of interest using the MarsBar tool (Brett, 2002). Volumes of each of those regions of interest were extracted from each individual's scan using the get_totals tool. The extracted volumes were then used in effect size and partial regression calculations.
Statistical analysis
Demographic, mood, and symptom data are reported as means and SDs. Missing demographic and symptom data were imputed with the mean of the group if <5% of the data were missing. Cohen's d effect sizes with 95% confidence intervals were used to measure the magnitude of the difference between groups (Cohen, 1988). Descriptive information about the data, effect sizes, and partial correlations were calculated in R 4.12 software (https://www.r-project.org/).
Aim 1: Group comparison of gray matter volumes
We applied a generalized linear model (ANCOVA) to compare GWVs with CMP and healthy GWV controls while controlling for age, body mass index (BMI), study site, head-channel upgrade, and total brain volume. The initial whole-brain, voxelwise search for all analyses was conducted with a p < 0.001 and extent threshold of 50 contiguous voxels. Clusters were then corrected at a familywise error p < 0.05. Cohen's d effect sizes were used to determine the magnitude of difference between extracted cluster volumes identified as significantly different between groups.
Aims 2 and 3: Examine associations between symptoms, physical activity, and brain gray matter volumes
Associations among pain, mood, and fatigue symptoms and gray matter volumes (Aim 2), as well as PA and gray matter volumes (Aim 3), were tested using separate multiple regression models of the whole brain while controlling for age, sex, BMI, study site, head-channel upgrade, and total brain volume. Both positive and negative relationships were tested for all measures. Regression estimates from Aim 3 were compared between groups using a multiple regression model with a Group by PA interaction term added. All models were tested with and without a dichotomous yes/no variable indicating fibromyalgia status. To determine global effects of pain and PA, associations with total gray matter volumes were tested using separate multiple regression models with PA or pain symptoms (depending on the analysis) as the predictors of interest while controlling for age, BMI, study site, head-channel upgrade, sex, and total brain volume. Partial correlations were calculated between the extracted volume of clusters identified in each regression model and the predictor of interest while controlling for all variables in the original analyses. To determine how much each symptom contributed to gray matter volume, we calculated the multiple linear regression between the extracted cluster volume and variables included in the voxelwise relations. We then removed the symptom variable from the model, recalculated the multiple linear regression, and compared total variance explained by the model with and without the symptom measurement.
Data availability
The data that support the findings of this study are available from the corresponding author on reasonable request. The data are not publicly available because of personal health information that could compromise the privacy of research participants.
Results
Group characteristics are presented in Table 1. Groups were similar with respect to age and height, but GWVs with CMP were heavier (d = 0.44, 95% CI = 0.13, 0.76) and had greater BMI (d = 0.4, 95% CI = 0.08, 0.72). GWVs with CMP reported greater symptoms of pain and fatigue, worse mood disturbance, and lower physical and mental function (i.e., SF-36 scores; Table 1). Of the veterans with CMP, 43 met criteria for fibromyalgia. When stratified by fibromyalgia status, GWVs with fibromyalgia were similar with respect to age, height, weight and BMI. GWVs with fibromyalgia reported greater symptoms of pain and fatigue, worse mood disturbance, and lower physical and mental function. A comparison of demographic and symptom data stratified by study location and CMP status are reported in Table 2. Reports of pain, mood disturbance, and physical and mental functioning did not differ between sites.
Group comparisons of mood, function, demographic data
Comparisons of mood, function, demographic data across study location
Physical activity data are presented in Table 3. Valid accelerometer data were obtained from 136 GWVs (GWVs with CMP, n = 85; healthy GWVs, n = 51). Average wear time was not different between GWVs with CMP and healthy GWVs. Veterans with CMP spent a significantly larger percentage of time being sedentary (d = 0.39, 95% CI = 0.038, 0.74) and significantly less time engaged in moderate to vigorous intensity activities expressed both in terms of percentage of wear time (d = −0.49, 95% CI = −0.85, −0.14) and total minutes (d = 0.53, 95% CI = 0.17, 0.89) compared with healthy GWVs.
Group comparisons of accelerometer data
Aim 1: Group comparisons of brain volumes
One hundred fifty-eight GWVs completed their neuroimaging session. Four images were deemed poor quality and were excluded from the analyses; thus 98 GWVs with CMP and 56 healthy GWVs were compared. Groups did not differ in total gray matter volume (d = −0.11, 95% CI = −0.44, 0.21). Regional gray matter volume group comparisons are illustrated in Figures 1 and 2 and reported in Table 3. GWVs with CMP had significantly smaller volumes compared with healthy GWVs in two clusters. One cluster included the medial aspect of the left insula extending into the left basal ganglia (d = 0.53, 95% CI = 0.2, 0.87). The other cluster included the midposterior right insular cortex extending into lateral aspects of the right putamen (d = 0.41, 95% CI = 0.08, 0.74). GWVs with CMP had larger volumes than healthy GWVs in a large cluster of the bilateral frontal pole that encompassed the left inferior frontal and right frontal orbital cortex/middle frontal gyri (d = 0.36, 95% CI = 0.03, 0.69). When pain intensity, as measured by the SF-36 Bodily Pain scale, was included as a covariate, no regions were significantly different between groups. When fibromyalgia status was included as a covariate, results were largely unchanged; aspects of both right and left insula were significantly smaller in GWVs with CMP, and regions within the frontal pole were significantly larger in GWVs with CMP.
Group differences in gray matter volume: GWVs with CMP lower than healthy GWVs. Gulf War veterans with CMP had lower regional gray matter volume in the left insula. Cluster results were thresholded at a familywise error level <0.05. Cluster is superimposed on the group average image. Blue indicates gray matter volume regions smaller in GWVs with CMP compared with healthy GWVs. Violin plots were created in R using ggplot2 and are unadjusted for all covariates. Removal of the individual with the highest gray matter volume in the CMP group did not affect the results. CMP, GWVs with CMP; CON, healthy GWVs.
Group differences in gray matter volume, GWVs with CMP larger than healthy GWVs. Gulf War veterans with CMP had larger regional gray matter volume in the bilateral frontal pole. Cluster results were thresholded at a familywise error level <0.05. Cluster is superimposed on the group average image. Red indicates gray matter volume regions that were larger in GWVs with CMP compared with healthy GWVs. Violin plots were created in R using ggplot2 and are unadjusted for all covariates. Removal of the individual with the highest gray matter volume in the CMP group did not affect the results. CMP, GWVs with CMP; CON, healthy GWVs.
Aims 2 and 3: Symptom and brain volume associations
Across the entire sample (n = 154) Bodily Pain scores were not significantly associated with regional gray matter volumes. When controlled for fibromyalgia status, Bodily Pain scores were significantly and positively related to gray matter volume in two clusters, one including the medial aspects of the left insula (rpartial = 0.26) and one encompassing the right insula extending to the postcentral gyrus (rpartial = 0.23). These clusters are illustrated in Figure 3 and reported in Table 4. Bodily pain was not significantly related to total gray matter volumes (Fmodel = 29.76, df = 146, p > 0.05). There was considerable overlap between the results of the group comparison ANCOVA and bodily pain symptom regression, which is illustrated in Figure 4.
Group comparison of gray matter volumes
Gray matter volume relationships to pain symptoms (p < 0.05, cluster FWE corrected)
Associations with SF-36 Bodily Pain score and gray matter volume in GWVs regardless of pain status (GWVs with CMP, n = 98; healthy GWVs, n = 56). Significant positive associations were observed for the SF-36 Bodily Pain and gray matter volume in the medial aspect of the left insular cortex and the midposterior right insular cortex. Cluster results were thresholded at a familywise error level <0.05. Cluster is superimposed on the group average image. Red indicates gray matter volume regions significantly positively associated with the SF-36 Bodily Pain. Scatter plots were created in R using ggplot2 and are unadjusted for all covariates. CMP, GWVs with CMP; CON, healthy GWVs. Regression line is adjusted for all covariates in the voxelwise analysis.
Overlapping regions identified by the separate whole-brain analyses of the group comparison ANCOVA and bodily pain score symptom regression. Regions labeled in red were identified as both significantly smaller volumes in GWVs with CMP and significantly positively related to bodily pain (more pain, lower volume) scores regardless of CMP status.
Ninety-four GWVs completed the MPQ. MPQ [Visual Analog Scale (VAS)] ratings were significantly and negatively associated with brain volumes within one cluster that included the medial aspects of the left anterior/middle insula (rpartial = −0.29). These results are illustrated in Figure 5 and reported in Table 5. When fibromyalgia status was included as a covariate, the cluster identified in the original regression was larger (1136 voxels vs 2264 voxels, rpartial = −0.27). This cluster is illustrated in Figure 6 and reported in Table 4. VAS scores were not significantly related to total gray matter volumes (Fmodel = 37.83, df = 87, p > 0.05).
Associations with the McGill Pain Questionnaire Visual Analog Scale score and gray matter volume in GWVs regardless of pain status (GWVs with CMP, n = 66 healthy GWVs, n = 28). Significant negative associations were observed for the MPQ-VAS and gray matter volume in the left insular cortex. Cluster results were thresholded at a familywise error level <0.05. Cluster is superimposed on the group average image. Blue indicates gray matter volume regions significantly negatively associated with the MPQ-VAS. Scatter plots were created in R using ggplot2 and are unadjusted for all covariates. CMP, GWVs with CMP; CON, healthy GWVs. Regression line is adjusted for all covariates in the voxelwise analysis.
Associations with the McGill Pain Questionnaire Visual Analog Scale score and gray matter volume in GWVs regardless of pain status while controlling for fibromyalgia Status (GWVs with CMP, n = 66; healthy GWVs, n = 28). Significant negative associations were observed for the MPQ-VAS and gray matter volume in the left insular cortex. Cluster results were thresholded at a familywise error level <0.05. Cluster is superimposed on the group average image. Blue indicates gray matter volume regions significantly negatively associated with the MPQ-VAS. Scatter plots were created in R using ggplot2 and are unadjusted for all covariates. CMP, GWVs with CMP; CON, healthy GWVs. Regression line is adjusted for all covariates in the voxelwise analysis.
All 154 GWVs completed the POMS. Total mood disturbance scores were not significantly (p > 0.05) related to brain volumes. These results were unchanged with the addition of the fibromyalgia score as a covariate. Total mood disturbance scores were not significantly related to total gray matter volumes (Fmodel = 30.03, df = 146, p > 0.05).
One hundred and three GWVs completed the MFI. MFI scores were not significantly (p > 0.05) related to brain volumes. These results were unchanged with the addition of fibromyalgia status as a covariate. Fatigue scores were not significantly related to total gray matter volumes (Fmodel = 7.1, df = 98, p > 0.05).
Physical activity and brain volume associations
No significant (p > 0.05) associations were found for sedentary time, light, or moderate to vigorous PA for either regional or total gray matter volume regressions. Results did not differ by group and were unchanged when covarying for fibromyalgia status.
Discussion
The associations between brain structure and CMP in GWVs are poorly understood. Further, the interrelationships of modifiable lifestyle factors such as PA and sedentary behaviors are currently unexplored. Extending previous research in our labs of white matter microstructural abnormalities (Van Riper et al., 2017), we show that compared with deployed GWV controls, GWVs with CMP have smaller regional gray matter volumes in critical pain regulatory regions and larger volumes in regions associated with pain sensitivity/chronicity. Group differences in brain volumes were further supported with significant associations between pain symptoms and brain volumes in overlapping regions. We also show that PA is not related to gray matter volumes, either regional or total, with no difference in the associations between groups. This study adds to the growing body of literature demonstrating that CMP is negatively associated with brain structures involved in both perception and regulation, which may contribute to pain chronification.
Gray matter differences and associations with pain
As hypothesized, GWVs with CMP had smaller regional gray matter volumes within the insular cortex, a brain region that is critical for pain regulation (Gracely et al., 2002). These results are consistent with multiple studies of civilians with CMP (Hsu et al., 2009; Robinson et al., 2011; Smallwood et al., 2013; Pomares et al., 2017) and provide further evidence that CMP is in part maintained by neurobiological abnormalities of sensory integration (Napadow et al., 2012; Sluka and Clauw, 2016). Moreover, our results for the group comparisons were essentially unchanged when controlling for fibromyalgia status, suggesting that smaller insula gray matter volumes are a general characteristic of chronic widespread pain and not specific to a diagnosis of fibromyalgia.
The insula plays a central role in multiple aspects of pain processing (Craig, 2003; Segerdahl et al., 2015) and has been proposed as a region where connectivity (Santana et al., 2019) and activity (López-Solà et al., 2017) can accurately classify chronic pain. As part of the salience network, the insula is thought to aid in the integration of sensory (including pain), emotional, and other intrinsic stimuli before behavioral decision-making (Seeley et al., 2007; Orenius et al., 2017). Functional neuroimaging research has demonstrated the role of the anterior insula in the evaluation and anticipation of pain (Brown et al., 2008; Wiech et al., 2010; Lutz et al., 2013). In CMP conditions such as fibromyalgia, the insula exhibits augmented stimulus-driven activity (Gracely et al., 2002; Cook et al., 2004; Staud et al., 2008; Brown et al., 2014; Boehme et al., 2020). Further, and consistent with our findings, resting-state connectivity is altered and negatively correlated with pain symptoms in fibromyalgia (Hsiao et al., 2017). As an area involved in descending pain modulation (Lu et al., 2016), smaller insular volumes may reflect dysregulated descending pain modulation or deficits resulting from long-term CMP. However, this cross-sectional comparison is not able to discern the mechanism of smaller brain volume among GWVs with CMP.
An important element for interpreting brain structural outcomes in CMP is the degree to which they relate to behavioral aspects of the disease. In the present study and consistent with our hypotheses, we identified significant associations between self-reported pain and gray matter volume within the insula and overlapping the group differences. Surprisingly, few studies have tested voxelwise associations between pain symptoms and gray matter volume in CMP populations; of those, none has found significant associations in the insula (Schmidt-Wilcke et al., 2007; Jensen et al., 2013; Diaz-Piedra et al., 2016). By using independent, whole-brain analyses corrected for multiple comparisons, we provide converging evidence that this region is important for chronic pain symptom severity. The associations that we identified between symptoms and gray matter volume were only significant for pain, as indicated by nonsignificant associations between MFI (i.e., fatigue) and total mood disturbance scores, and the elimination of group differences within this region when controlling for body pain symptoms. However, because of the multicollinearity of pain, fatigue, and mood measures, we were unable to test the specificity of pain symptoms in a single model. Moreover, because our groups were chosen based on chronic pain symptoms, relationships between pain symptoms and gray matter volumes are not surprising, although still important to characterize. Future research that includes more independent measures of pain, fatigue, and mood is needed to better test the relative contributions of these symptoms.
Veterans with CMP had larger gray matter volumes in a cluster of the frontal lobe that spanned multiple regions including the frontal orbital inferior, middle, and superior frontal gyri, and the ventromedial, dorsomedial, and orbitofrontal cortices. Collectively, the prefrontal cortex is involved in a diverse array of integrative functions. These include critical aspects of emotion, cognition, and sensory integration (Groenewegen and Uylings, 2000); appraisal of external stimuli and generation of affective meaning (Roy et al., 2012; Dixon et al., 2017); and anticipation of painful sensations (Hsieh et al., 1999; Ploghaus et al., 2003). Depending on the context of the situation, each of these regions can contribute to pain processing. For instance, increased ventromedial prefrontal cortex activity is associated with spontaneous, sustained flares in chronic back pain (Baliki et al., 2006). Impending, unpredictable pain has also been shown to increase blood flow to the prefrontal cortex, whereas predictable pain stimuli resulted in decreased blood flow (Hsieh et al., 1999). The orbitofrontal prefrontal cortex is activated by painful stimulation (Rolls et al., 2003) and is involved in guiding behavior based on nociceptive information (Winston et al., 2014). Increased connectivity between the medial prefrontal cortex and the nucleus accumbens has been reported in chronic pain with pain-induced activity in the nucleus accumbens distinguishing between chronic pain and healthy groups (Baliki et al., 2010). This novel finding, although not entirely consistent with the civilian CMP literature, suggests that that CMP in GWVs is positively associated with gray matter volume in regions of pain amplification. Replication of this finding will be critical toward understanding the unique aspects of CMP in veterans of the Persian Gul War.
Physical activity
Consistent with previous research conducted in civilians with CMP, GWVs with CMP engaged in less moderate to vigorous PA and more sedentary behaviors than deployed, healthy GWV controls (Segura-Jiménez et al., 2015). There has been surprisingly little research into the PA behaviors of veterans in general, with even less focusing on those with CMP. Population-based research has indicated that veterans are more likely to report a lack of PA compared with active military members and reservists (Hoerster et al., 2012) and that a higher proportion of veterans meet national PA recommendations in comparison to civilians (Littman et al., 2009). However, similar rates of veterans and civilians report no moderate or vigorous PA (Widome et al., 2011). The current study adds to this limited database by using device-based PA monitoring to more accurately measure the intensity of PA and sedentary time.
Contrary to our hypothesis, we found no differential associations between PA or sedentary time and regional gray matter volumes. We are not aware of previous research that has examined associations between PA and brain volume in CMP; however, there is a large body of literature describing positive associations between PA and brain volume in healthy adults (Killgore et al., 2013), healthy older adults (Erickson et al., 2014), and older adults with cognitive impairment (Halloway et al., 2019). In general, this literature suggests that those who are more physically active have larger brain gray matter volumes (Erickson et al., 2014; Batouli and Saba, 2017). However, the evidence is mixed; several studies have reported nonsignificant relationships when including appropriate covariates (e.g., BMI; Rosano et al., 2010; Ho et al., 2011; Smith et al., 2011; Boraxbekk et al., 2016). The few studies that have examined PA outcomes and gray matter volumes in middle-aged adults have found conflicting results (Killgore et al., 2013; Spartano et al., 2019). The narrow age, PA, and sedentary time ranges of the GWV sample may have precluded finding significant relationships. Alternatively, measures such as cardiopulmonary fitness may be more relevant for GWV populations than current PA levels (Dougherty et al., 2020). Because GWV illnesses have been hypothesized as diseases of accelerated aging (Zundel et al., 2019), future research examining the relative contributions of fitness and PA to GWV brain health is warranted.
Limitations
We are not able to generalize to all GWVs with Gulf War Illness as our sample was CMP specific; however, by reducing disease heterogeneity we were able to focus more specifically on a critical component of the illness. This cross-sectional design cannot determine causality. Prospective studies and trials that manipulate PA behaviors are necessary to determine the temporal associations among chronic pain, brain gray matter volume, and the efficacy of PA interventions. We encountered a fair amount of missing data, with nearly 40 GWVs not having either an MRI scan or valid accelerometer data. Despite this, the current study is one of the largest neuroimaging investigations in CMP to date.
Conclusions
The experience of pain is fundamentally related to brain structure and function, but the associations with important and modifiable lifestyle factors are understudied. We demonstrate meaningful differences in pain-relevant brain structures that are related to pain perception suggestive of reduced pain regulation and chronic pain maintenance. Longitudinal research that prospectively examines the changes in brain structure and CMP symptoms is needed to determine the causes of CMP and whether modifiable lifestyle behaviors affect both symptoms and potential mechanisms of disease.
Footnotes
This work was supported by the U.S. Department of Veterans Affairs (Grants 561-00436, IO1 CX000383, IO1 CX001329, and IK2 CX001679). We thank the veterans for volunteering their time and effort for this project.
The authors declare no competing financial interests.
- Correspondence should be addressed to Dane B. Cook at dane.cook{at}wisc.edu
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