Patients with somatic symptom disorder (SSD) persistently worry about body symptoms that resemble, and are misinterpreted as, known medical diseases (1). In addition to persistent distressing somatic symptoms, the psycho-behavioral criteria for SSD include disproportionate thoughts, feelings, and behaviors linked to somatic symptoms or health concerns (1). The prevalence of SSD in the general population is approximately 5% to 7% (1). de Vroege et al. (2) reported that patients with SSD exhibited increased frequency of comorbid depression and anxiety. In a study including 491 patients with SSD, Xiong et al. (3) reported that those with severe somatic symptoms were more likely to have depression and anxiety, an impaired quality of life, and a higher frequency of doctor's visits than the patients with SSD exhibiting mild to moderate somatic symptoms. For this reason, patients with SSD often visit a general doctor as well as a psychiatrist and have repeated health checkups that lead to large health care burdens (4). Although there has been progress in the study of patients with SSD, SSD biomarkers have not been established to identify patients with SSD, and the neurobiology underlying this condition is not yet clear.
Alterations in brain functional connectivity (FC) in SSD have been investigated using neuroimaging techniques such as functional magnetic resonance imaging. Thus far, an abnormality in the default mode network (DMN) has been considered to play a key role in the pathophysiology of SSD. In a previous study, patients with SSD showed increased regional neural activity in the left angular gyrus of the DMN (5). In another functional magnetic resonance imaging (fMRI) study, patients with SSD showed a dissociation pattern between the anterior and posterior DMN, including the bilateral superior medial prefrontal cortex and the left precuneus (6). Decreased interhemispheric FC in regions of the DMN, such as the insula and angular gyrus/supramarginal gyrus (SMG), has also been reported (6). Although there have been studies on DMN in SSD, FC within or between other brain networks, such as the sensorimotor network (SMN), salience network, and dorsal attention network (DAN) has not been studied in SSD.
However, there have been studies on intrinsic and extrinsic connectivity between the SMN, DMN, salience network, and DAN among patients with pain disorders (7,8) and chronic pain (9–12), to which we can refer. In the DSM-V (13), SSD with predominant pain replaces pain disorder, which was used in the DSM-IV-TR (14). In the DSM-IV-TR, pain disorders are diagnosed when pain is a major component of the clinical symptoms that cause severe distress or impairment, is not intentionally produced, and has an interaction with psychological influences in its course (14). An fMRI study investigating alterations in resting-state FC in persistent somatoform pain disorder reported alterations in co-activation within the SMN, DMN, and salience network and an increase in FC between the SMN and salience network (7). Alteration in activity within the SMN, including the somatosensory (postcentral gyrus) and motor areas (precentral gyrus), has been reported in previous studies of patients with failed back surgery syndrome (10). Several studies have suggested that increased FC within the DMN and between the DMN and other areas may be regarded as a biomarker in patients with chronic pain (11) or fibromyalgia (12). Loggia et al. (11) reported that patients with chronic pain had increased FC between the DMN and the pregenual anterior cingulate cortex (ACC), left inferior parietal lobule, and right insular cortex. In a study of patients with chronic pain, decreases in FC between the DMN and the anterior insular cortex were associated with decreased pain in fibromyalgia (12). Koenen et al. (9) reported increased FC between the frontoparietal attention network and the somatosensory cortex in women with somatic pain (9). Because chronic pain has been reported to be a strong disruptor of FC within all sensory, affective, and cognitive neural systems, FC in highly interactive high-order networks (SMN, DMN, salience network, and DAN) seemed to be altered.
Based on previous studies on SSD, pain disorders, and chronic pain, we aimed to investigate FC within and between brain networks including the DMN, SMN, salience network, and DAN in patients with SSD. We hypothesized that patients with SSD would have increased FC within the SMN, DMN, salience network, and DAN, as well as between the SMN, DMN, salience network, and DAN. In addition, anxiety and depressive symptoms would be associated with increased FC within and between brain networks. This study aimed to establish neurobiological mechanisms that may play a critical role in SSD.
Twenty-six patients with SSD, who visited the Department of Psychiatry at Chung-Ang University Medical Center, and 20 age- and sex-matched healthy control participants were recruited for the current study from March 2015 to August 2017. All participants were screened using the Structured Clinical Interview for DSM-V Disorders-Clinician Version (SCID-5-CV) (15). Based on the results, SSD was diagnosed independently by two psychiatrists. Furthermore, SSD was characterized by the presence of one or more physical symptoms associated with severe pain and disability in daily life, as well as excessive anxiety and concern for physical symptoms and related health concerns (15).
In addition, all patients were instructed to complete clinical questionnaires, such as the Symptom Checklist-90-Revised-Somatization (SCL-90-R-SOM) scale (16), Somato-Sensory Amplification Scale (SSAS) (17), and Beck Depressive Inventory (BDI) (18), and they underwent functional MRI scanning. The inclusion criteria were as follows: (a) diagnosis of SSD based on SCID-5-CV (15), (b) psychiatric drug-naivety, and (c) age ranging from 20 to 60 years. The exclusion criteria were as follows: (a) past or current episodes of any other psychiatric diagnosis based on the SCID-5-CV or severe medical illness such as liver or renal impairment, malignancy, hematologic disease, or cardiovascular disease; (b) BDI score of higher than 16, which indicates comorbid moderate to severe depression; (c) past or current substance use disorder; (d) history of head trauma; and (e) claustrophobia. We did not exclude patients with symptoms of anxiety because persistent high levels of anxiety about health or symptoms are one of the diagnostic criteria, as well as a key clinical feature, of SSD according to the DSM-V (15). Of the 26 patients with SSD, five were excluded because of higher BDI scores (BDI scores >16). Two patients exhibited other psychiatric comorbidities, including bipolar disorder and schizophrenia. One patient exhibited claustrophobia. Finally, 18 patients with SSD, and 20 healthy control participants completed the study protocol. Of the 18 patients with SSD, seven were diagnosed with SSD with predominant pain based on the DSM-V (previously pain disorder in DSM-IV-TR). This specifier is used for patients whose somatic symptoms mainly involve pain. The Institutional Review Board of Chung-Ang University Medical Center approved the protocol of the current study. All participants provided written informed consent.
The SCL-90R-SOM was used to evaluate the severity of distress arising from somatic symptoms (16). Among the 90 questions of the SCL-90R, which evaluate various symptoms of psychopathology (19), 12 items of somatization subscale (SCL-90R-SOM) reflect distress caused by perceptions of bodily dysfunction, including strong autonomic mediation and muscle pain (16). The SSAS was used to assess the level of amplification of visceral and somatic sensations (17,20). The SCL-90R-SOM achieved a reliability coefficient of .82 in a previous study (21). Regarding the SSAS, a previous study established the test-retest reliability for the 10-item SASS as .79 and internal consistency as .82 (Cronbach's α) (17). The BDI was used to assess depressive symptoms (18). Previous studies have established the high internal consistency as well as the validity of BDI (Cronbach's α = .916) (18,22).
MRI Acquisition and Preprocessing
Resting-state brain activity was assessed using 3 T blood oxygen level–dependent fMRI (Philips Achieva 3.0 Tesla TX MRI scanner, repetition time = 3 seconds, 12-minute scan, 240 volumes, 128 × 128 matrix, 40 slices, and 4.0-mm slice thickness). Preprocessing included despiking (AFNI: 3dDespike), motion correction (Statistical Parametric Mapping 12b [SPM 12b]), coregistration to Magnetization Prepared Rapid Acquisition Gradient Echo image (SPM 12b), normalization to Montreal Neurological Institute space (SPM 12b), temporal detrend (MATLAB: detrend.m), band-pass filtering (MATLAB: idealfilter.m), and voxel-wise regression of identically band-pass–filtered time series of six head motion parameters (realignment steps with six rigid-body parameters characterizing the estimated participant motion for each participant), degraded cerebrospinal fluid, degraded white matter, and facial soft tissues (MATLAB), as previously described (23–25). To address the possibility of microhead movements affecting the connectivity results (26,27), censoring of time points with head motions of greater than .2 mm was applied, but no regression of the global signal was performed (23,28,29).
A region-of-interest (ROI) analysis in 18 ROIs of the four networks was performed to increase the power to detect different connectivity strengths (30). For ROI-ROI correlations within or between brain networks, we extracted the following 18 regions: four regions in the DMN (middle prefrontal cortex [MPFC], right/left lateral parietal cortex, and posterior cingulate cortex [PCC]); seven regions in the salience network (right/left anterior insula, right/left SMG, right/left rostral prefrontal cortex [RPFC], and ACC); four regions in the DAN (right/left frontal eye field [FEF] and right/left inferior parietal sulcus), and three regions in the SMN (right lateral/left lateral/superior sensorimotor area [SMA]) from the automated anatomic labeling atlas of the brain (networks.nii/.txt/.info). These ROI regions were found based on previous studies involving brain network analysis; the DMN from the study by Loggia et al. (11), salience network and the DAN from the study by Fox et al. (31), and the SMN from the study by Kolesar et al. (10).
Fisher-transformed correlation coefficients were measured for each pair of ROIs for each participant. The FC between ROIs was calculated using the CONN-fMRI FC toolbox (Version 15; https://www.nitrc.org/projects/conn). Between-group effects were considered significant with a cluster-level false discovery rate q < .05 considering multiple comparison correction for 153 pairs of 18 regions.
The Mann-Whitney U test with a statistical significance level of p < .05 was used to compare the mean differences in age, years of education, and scores on the SCL-90R-SOM, SSAS, and BDI between patients with SSD and healthy control participants. Pearson's correlation analyses were performed to assess the correlations between clinical scales and brain connectivity in all participants and patients with SSD. For multiple comparisons between clinical scales and regions, the level of statistical significance was set to p < .0036 (.05/14, 11 brain regions and 3 clinical scales). For power analysis, G*Power 3 was used (32). The effect size of correlation analysis was |ρ| = .38 with a power (1-β error probability) = .8.
Demographic and Clinical Characteristics of the Study Sample
There were no significant differences in sex, age, or years of education between patients with SSD and healthy control participants. However, patients with SSD had higher scores in the SCL-90R-SOM, SSAS, and BDI than healthy control participants (all p < .001, Table 1).
Comparisons of Brain FC Between Patients With SSD and Healthy Comparison Participants
Patients with SSD had greater FC within the SMN from the right lateral SMA to superior SMA (t = 5.19, q = .037), DMN from the MPFC to PCC (t = 5.32, q = .031), and salience network from the right SMG to the right anterior insula (t = 5.54, q = .022) and the left SMG to left RPFC (t = 5.21, q = .039) than healthy control participants (Figure 1). In addition, patients with SSD had greater FC between the SMN (superior SMA) and DMN (MPFC) (t = 5.10, q = .041), SMN (superior SMA) and salience network (right anterior insula) (t = 5.94, q = .005), SMN and DAN (right lateral SMA-left FEF: t = 7.47, q < .001, right lateral SMA-right FEF: t = 5.67, q = .007, left lateral SMA-left FEF: t = 6.39, q < .001, left lateral SMA-right FEF: t = 5.78, q = .008), and salience network (ACC) and DAN (right FEF) (t = 5.80, q = .008).
Correlations Between Clinical Scales and Brain FC
In all patients, SSAS scores positively correlated with FC between the SMN (superior SMA) and the salience network (right anterior insula) (r = .66, p < .001), as well as between the SMN (right lateral SMA) and DAN (left FEF) (r = .61, p < .001, Figure 2). In patients with SSD, SSAS scores were positively correlated with the FC between the SMN (superior SMA) and the salience network (right anterior insula) (r = .82, p = .001), as well as between the SMN (right lateral SMA) and DAN (left FEF) (r = .66, p = .002). In patients with SSD, BDI scores positively correlated with the FC within the DMN from MPFC to PCC (r = .75, p = .002). In healthy comparison participants, there were no significant correlations between clinical scale scores and FC within the brain networks.
In the current study, patients with SSD exhibited greater FC within the SMN, DMN, and salience network than healthy control participants. Patients with SSD also exhibited greater FC between the SMN and DMN, SMN and salience network, SMN and DAN, and salience network and DAN. In addition, the SSAS scores were correlated with FC between the SMN and the salience network and with FC between the SMN and DAN in patients with SSD.
In the current study, patients with SSD exhibited greater FC within the SMN and between the SMN and all other functional brain networks (DMN, salience network, and DAN). Increased FC within the SMN in patients with somatoform pain disorder has been reported in previous studies (7,33). Zhao et al. (7) reported that patients with somatoform pain disorder had increased co-activation in the bilateral supplementary motor areas within the SMN. In other studies on somatoform pain disorders, patients had more activation in the SMN in response to painful stimuli than healthy controls (33). The SMN is primarily involved in sensorimotor integration, processing of motor-related information, and control of spontaneous movement (34). The sensory component of pain is thought to be processed via the primary and secondary somatosensory cortices in the SMN (35). The disruption of somatosensory cortices is associated with the inability to localize painful stimuli (36). Therefore, we suggest that the increased FC within the SMN seems to be associated with alterations of sensory-discriminative processing and the magnified labeling of pain intensity in patients with SSD. In addition, previous studies on somatoform pain disorder have shown that the SMN is strongly connected with high-order networks such as the DMN and salience network (7,8). These interactions suggest that sensory-discriminative processing of pain is closely related to cognition, self-referential thoughts, attention, and affective salience.
The salience network is thought to represent a system that detects significant salient events using a variety of somatic and visceral sensory modalities (37,38). Several parts of the salience network, including the anterior cingulate and anterior insular cortex, have been suggested to be involved in the affective or unpleasant component of physical pain (39). Selective increase in the unpleasantness of noxious stimuli (affective component) without altering the intensity (sensory component) may be associated with increased activity of the ACC within the salience network without alteration of the activity of the somatosensory cortex within the SMN (39). Interestingly, in this study, the FC between the SMN and salience network was increased in patients with SSD, and the SSAS scores were correlated with the FC between the SMN and the salience network. Previous studies have shown that self-reports of pain and unpleasantness correlated with increased activity of the ACC (40) and bilateral anterior insular cortex (41) within the salience network. Taking the previously mentioned observations together, we cautiously speculate that SSD may be associated with dysfunctional sensory-discriminative processing of pain and other somatic symptoms, which is influenced by affective salience.
In the current study, patients with SSD exhibited greater FC within the DMN and between the SMN and DMN than did the healthy control participants. The DMN involves self-referential cognition, which is used to prepare the body for internal physiological activity (42). Alterations in the DMN in patients with SSD have been reported in several studies. In a systematic review study on neuroimaging of SSD, it was suggested that the DMN may play an important role in the pathophysiology of SSD (43). In serial studies of first-episode patients with SSD, Su et al. (6) and Su et al. (44) reported that patients with SSD had increased FC strength within the DMN, as well as dissociation patterns in the anterior and posterior DMN. The MPFC is known to be the core region of the DMN and plays an important role in emotional processing. DMN dysfunction causes damage to top-down regulation, which is considered the pathophysiology of cognitive, emotional, and behavioral alterations in SSD (6). DMN alterations have been reported in patients with somatoform pain disorder as well (45), and it seems to be correlated to pain catastrophizing in patients with fibromyalgia (46). In addition, in our results, BDI scores positively correlated with FC within the DMN in patients with SSD. DMN activity is known to be increased in patients with major depressive disorder (47), and depressive symptoms are highly correlated with somatic symptoms in patients with major depressive disorder and those with SSD (36,48).
In this study, patients with SSD also exhibited greater FC between the SMN and DAN as well as the salience network and DAN, when compared with that of control participants. In contrast to the DMN, the DAN is typically activated in response to attention-demanding tasks and regulates bodily functions aimed at interactions with external stimuli (49). The resting-state activity of the brain can be divided into two modes, including a task-negative (i.e., DMN) and a task-positive (i.e., DAN) network, which are temporally anticorrelated (49). Increased FC within the DMN and DAN has been suggested in studies of patients with pain disorder (9,11,12). We cautiously suggest that patients with SSD have a deficit in attention, which leads to misperception of external stimuli, as well as failure to regulate bodily functions aimed at interactions with external stimuli.
There are some limitations to the current study. First, the small number of participants was insufficient to generalize the obtained results because of high risk for type 1 and type 2 errors. Second, the study sample was heterogeneous, because it included both male and female participants. Because of the small number of participants, we were unable to examine different populations separately and/or control for sex. However, because there may be sex differences in the occurrence of somatic complaints (50), future studies should investigate sex differences in brain FC in patients with SSD. Third, the cross-sectional design of the current study did not differentiate the causes from the results of the changes in brain connectivity. Fourth, because there was no control group of patients with objectively established physical disease with comparable levels and types of complaints who did not meet SSD criteria, we cannot conclude whether the established FC patterns and other findings are specific for SSD or also present in other patients with somatic complaints. Future studies should recruit sufficiently larger numbers of participants to investigate sex differences and use a cohort-based study design with a control group of patients with established physical disease with similar levels and types of complaints who do not meet the criteria for SSD.
Based on the results of the FC analysis between the SMN and salience network, we suggest that SSD may be associated with alterations of sensory-discriminative processing of pain and other somatic symptoms, which is influenced by affective processing. Based on the results of the FC analysis of the SMN and DAN, we suggest that patients with SSD have a deficit in attention, which leads to misperception of external stimuli and failure to regulate bodily functions aimed at interactions with external stimuli.
Source of Funding and Conflicts of Interest: This study was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number HC17C0117). The authors report no conflicts of interest.
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