Altered central pain processing in fibromyalgia—A multimodal neuroimaging case-control study using arterial spin labelling

Monika Müller; Florian Wüthrich; Andrea Federspiel; Roland Wiest; Niklaus Egloff; Stephan Reichenbach; Aristomenis Exadaktylos; Peter Jüni; Michele Curatolo; Sebastian Walther

doi:10.1371/journal.pone.0235879

Abstract

Fibromyalgia is characterized by chronic pain and a striking discrepancy between objective signs of tissue damage and severity of pain. Function and structural alterations in brain areas involved in pain processing may explain this feature. Previous case-control studies in fibromyalgia focused on acute pain processing using experimentally-evoked pain paradigms. Yet, these studies do not allow conclusions about chronic, stimulus-independent pain. Resting-state cerebral blood flow (rsCBF) acquired by arterial spin labelling (ASL) may be a more accurate marker for chronic pain. The objective was to integrate four different functional and structural neuroimaging markers to evaluate the neural correlate of chronic, stimulus-independent pain using a resting-state paradigm. In line with the pathophysiological concept of enhanced central pain processing we hypothesized that rsCBF is increased in fibromyalgia in areas involved in processing of acute pain. We performed an age matched case-control study of 32 female fibromyalgia patients and 32 pain-free controls and calculated group differences in rsCBF, resting state functional connectivity, grey matter volume and cortical thickness using whole-brain and region of interest analyses. We adjusted all analyses for depression and anxiety. As centrally acting drugs are likely to interfere with neuroimaging markers, we performed a subgroup analysis limited to patients not taking such drugs. We found no differences between cases and controls in rsCBF of the thalamus, the basal ganglia, the insula, the somatosensory cortex, the prefrontal cortex, the anterior cingulum and supplementary motor area as brain areas previously identified to be involved in acute processing in fibromyalgia. The results remained robust across all neuroimaging markers and when limiting the study population to patients not taking centrally acting drugs and matched controls. In conclusion, we found no evidence for functional or structural alterations in brain areas involved in acute pain processing in fibromyalgia that could reflect neural correlates of chronic stimulus-independent pain.

Citation: Müller M, Wüthrich F, Federspiel A, Wiest R, Egloff N, Reichenbach S, et al. (2021) Altered central pain processing in fibromyalgia—A multimodal neuroimaging case-control study using arterial spin labelling. PLoS ONE 16(2): e0235879. https://doi.org/10.1371/journal.pone.0235879

Editor: Claudia Sommer, University of Würzburg, GERMANY

Received: June 21, 2020; Accepted: January 19, 2021; Published: February 2, 2021

Copyright: © 2021 Müller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: The research team with Michele Curatolo as main applicant were awarded a grant by the Bangerter-Rhyner Foundation to support the present study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Fibromyalgia is characterized by chronic and widespread pain with additional symptoms such as fatigue, sleep disturbance and cognitive dysfunctions [1–3]. The Impact on quality of life is comparable to other chronic diseases such as rheumatoid arthritis, diabetes mellitus and chronic obstructive lung disease [4, 5]. Therapeutic options remain limited with modest effects for most treatments and a high proportion of patients not responding to any treatment [6]. Despite the clinical significance of the disease, pathophysiological processes remain poorly understood which limits the development of diagnostic markers and novel treatments targeting pathophysiological mechanisms rather than disease symptoms.

The striking discrepancy between objective signs of tissue damage and magnitude of pain suggests a pathophysiological process involving the central nervous system with possible alterations in brain function and structure [7, 8]. A state of enhanced central pain response associated with increased neural activity in pain processing brain areas may lead to exaggerated pain response even to non-painful stimuli, high stimulus-independent pain and widespread pain [7, 9–14]. Brain areas identified to be involved in pain processing include subcortical regions such as thalamus and basal ganglia and the insula, somatosensory cortex, prefrontal cortex, anterior cingulum and supplementary motor area as cortical regions [8, 15, 16]. It remains however a subject of debate whether these brain areas are mainly processing acute pain signals or whether they also involved in chronic pain [8, 17]. Potential neuroimaging markers of chronic pain include resting-state cerebral blood flow (rsCBF) or functional connectivity on a functional level, as well as alterations of grey matter volume and cortical thickness on a structural level.

First neuroimaging studies investigating functional markers mainly focused on the comparison of acute pain processing mechanisms in fibromyalgia patients and pain-free controls using experimentally-evoked pain paradigms and blood oxygenation level dependent (BOLD) contrasts [7, 8]. In line with the pathophysiological concept of enhanced central pain response, these studies detected increased neural activity in the amygdala, the insula, the somatosensory cortex and the cingulate cortex of fibromyalgia patients after painful stimuli. However, for two reasons, these studies do not allow conclusions about processing of chronic, stimulus-independent pain that typically remains constant over time [18–20]. First, the studies used experimentally evoked acute pain paradigms rather than resting-state paradigms that are more likely to capture neural adaptation to chronic pain. Second, even if the researchers acquired resting-state data, they used BOLD contrasts and quantified resting-state connectivity as marker of chronic pain [21, 22] rather than specific regional resting-state neural activity. However, while BOLD provides a relative and indirect measure of brain activity, rCBF gives an absolute value that is directly related to local brain metabolism and thus appears perfectly suited to quantify neural activity in chronic pain.

Arterial spin labelling (ASL) as an advanced neuroimaging method quantifies resting-state cerebral flow (rsCBF) as a direct marker of neural activity at rest [17, 23]. First studies using ASL in different chronic pain conditions such as chronic low back pain [24, 25], postsurgical pain [26], trigeminal neuropathy [27], postherpetic neuralgia [28] and fibromyalgia [29] are emerging. Although these studies employed a resting-state paradigm, they only partly accounted for important confounders such as depression, age, and gender, while they failed to consider centrally acting drugs potentially biasing neuroimaging markers. This may explain why the results of the studies remain inconclusive. Additionally, previous studies did not integrate functional and structural neuroimaging by applying multimodal neuroimaging.

We conducted a multimodal neuroimaging study integrating functional and structural markers to explore the neural correlates of chronic pain in fibromyalgia. The primary objective was to compare rsCBF patterns of fibromyalgia patients and pain-free controls using ASL. In line with the paradigm of enhanced central pain response we hypothesized that rsCBF in areas involved in pain processing is increased in fibromyalgia. The secondary objective was to compare resting-state functional connectivity between pre-specified Regions of Interest, grey matter volume and cortical thickness between cases and controls assuming a reduction in these neuroimaging markers in fibromyalgia. We adjusted all our analyses for depression and anxiety and conducted a subgroup analysis limited to patients free of any centrally acting drugs.

Material and methods

Participants and study design

We performed a 1:1 frequency age-matched case-control study in fibromyalgia patients and pain-free controls using 10 years age bands. We centrally recruited right-handed [30] female participants at the University Hospital of Bern, Switzerland. We randomly sampled cases from a pool of 238 patients who were first diagnosed with fibromyalgia at either the Department of Rheumatology, the Department of Psychosomatic Medicine or the Pain Clinic between November 2013 and January 2015. We randomly sampled controls from a pool of 9253 women who presented during the same period at the Emergency Department. We stopped sampling when we reached the required sample size. We included controls if they did not suffer from any chronic pain disorder and were pain-free two weeks prior to neuroimaging. We included cases if they were confirmed with the diagnosis of fibromyalgia according to the diagnostic criteria of the American College of Rheumatology at repeat clinical examination at enrollment [2]. Their diagnostic criteria are based on the Widespread Pain Index, which characterizes spread of pain, and the Symptom Severity Scale, which assesses different core symptoms of fibromyalgia including depression that is frequently encountered in these patients. We therefore did not exclude patients with co-morbid depression from the study but adjusted our analyses for severity of depression measured by the Beck-Depression Inventory version 2 [31] as described in the statistical section.

Common exclusion criteria for cases and controls were conditions interfering with the MRI acquisition; neurologic co-morbidity or history of neurosurgical intervention; psychiatric co-morbidity other than unipolar depressive disorder; end-stage somatic co-morbidity defined as untreatable disease that represents a threaten for life in the next few months; intake of strong opioids or any psychopharmacological treatment other than anticonvulsants or antidepressants; inability to understand the consequences of study participation; and pregnancy. We performed the study according to a prospective protocol approved by the ethics committee of the Canton Bern, Switzerland (KEK 43/13), and in accordance with the Declaration of Helsinki [32]. All participants gave written informed consent.

Assessment of socio-demographic, psychological and clinical characteristics

We assessed the following socio-demographic and psychological characteristics in all participants: age; education (higher education vs lower education); marital status (married vs not married); depression and anxiety. We considered participants with high school or university degree as having higher education. We used the Beck Depression Inventory version 2 (BDI-II) [31] and the State-Trait-Anxiety-Inventory (STAI) [33] to assess depression and anxiety in all participants. In cases we additionally assessed pain intensity, pain duration, spread of pain and disability. We used the Numerical Rating Scale to measure average pain intensity within the last 24 hours, pain intensity immediately before neuroimaging (NRS, 0 = no pain to 10 = worst pain) and the Fibromyalgia Impact Questionnaire to assess disability (FIQ, 0 = no disability, 100 = most severe disability) [34]. We used the Widespread Pain Index to characterize the spread of pain with (WPI, 0 = no body region with pain to 19 = generalized pain affecting all body regions) [2]. We recorded long-term daily intake of centrally acting drugs such as light opioids, antidepressants or anticonvulsants. The patients took these drugs also on the day of neuroimaging.

Neuroimaging

Image acquisition.

We performed multimodal neuroimaging at the Institute of Neuroradiology of the University Hospital of Bern to acquire four neuroimaging markers: resting-state cerebral blood flow (rsCBF); resting-state functional connectivity (rsFC); grey matter volume (GMV); and cortical thickness (CoTh). We performed the MRI with a 3-Tesla Trio whole-body scanner using a 12-channel radio-frequency head coil (Siemens Medical, Germany). All study participants were instructed to lie quietly with eyes closed not thinking of anything particular during functional scans. We obtained the sequences in the following order:

First, high resolution anatomical T1* weighted images: 176 sagittal slices with 256 × 224 matrix points with a non-cubic field of view (FOV) of 256 mm × 224 mm, yielding a nominal isotropic resolution of 1 mm³ (i.e. 1mm × 1mm × 1mm), repetition time (TR) = 7.92ms, echo time (TE) = 2.48ms, flip angle = 16°, inversion with symmetric timing (inversion time 910ms).

Second, a set of 80 functional T2* weighted images using a pseudo-continuous arterial spin labelling sequence (pCASL) which corresponds to 40 pairs of unlabeled and labeled images [35, 36]: eighteen axial slices at a distance of 1.0 mm; slice thickness = 6.0 mm; FOV = 230 x 230 mm²; matrix size = 128 x 128, yielding a voxel-size of 1.8mm × 1.8mm × 6mm; TR = 4000ms; TE = 18ms. The gap between the labeling slab and the proximal slice was 90 mm; gradient-echo; echo-planar readout; ascending order; acquisition time 45 ms per slice. Slice-selective gradient 6 mT/m, post-labeling delay w = 1250 ms, tagging duration τ = 1600 ms.

Third, BOLD functional T2* weighted images were acquired with an echo planar imaging (EPI) sequence: 32 axial slices, FOV = 192x192 mm², matrix size = 64x64, gap thickness = 0.75 mm, resulting in a voxel size of 3x3x3 mm³, TR/TE 1980ms/30ms, flip angle = 90°, bandwidth = 2232Hz/Px, echo spacing = 0.51ms, 460 volumes.

Selection of Region of Interests (ROIs).

Even though our main statistical approach was to perform voxel- and vertex-wise whole-brain analyses, we also investigated several pre-specified cortical Regions of Interests (ROIs) likely to be involved in pain processing in fibromyalgia based on previous evidence for functional neuroimaging markers [8] and on atlases for structural markers [37, 38]. We did not investigate software specific ROIs. For functional analyses of rsCBF and rsFC we defined the exact coordinates of 11 ROIs according to a recent meta-analysis by Dehghan et al [8]: Bilateral insula, left anterior and middle cingulate cortex (ACC, MCC), right Amygdala, bilateral superior temporal gyrus (STG), right lingual gyrus and left primary and secondary cortex (S I, S II). Notably, the right insula is represented in two ROIs in this selection while all other brain regions are represented by one ROI each. We created a box around the coordinates with size = 27 voxels (216 mm³). For structural analyses of GMV and CoTh we selected brain regions that represent the above specified regions from existing atlases. This resulted in 10 volumetric ROIs based on the Automatic Anatomical Labeling (AAL)-Atlas for GMV analyses [37] and 23 ROIs based on the Destrieux-Atlas which is based on gyral and sulcal surface for CoTh analyses [38]. We extracted rsCBF and grey matter values in for ROIs using MarsBaR (Version 0.44, http://marsbar.sourceforge.net/).

Image preprocessing and calculation of functional and structural neuroimaging markers.

We performed structural preprocessing for rsCBF, and GMV analyses in SPM12 (Revision 6225, Welcome Trust, London, U.K., https://www.fil.ion.ucl.ac.uk/spm/). T1* images were segmented into gray matter, white matter and cerebrospinal fluid, normalized to the Montreal Neurological Institute (MNI) space and smoothed using an 8 mm full-width at half maximum (FWHM) Gaussian kernel. To compute GMV, defined as relative proportion of grey matter in a region, accounting for brain size and shape, we applied voxel-based morphometry in SPM12 to the preprocessed T1* images applying an absolute threshold of 0.2 to the modulated grey matter segmentations. We calculated the total intracranial volume by adding the segmented grey matter, white matter and cerebrospinal fluid volumes. We carried out preprocessing for CoTh analyses using the standard FreeSurfer pipeline for cortical reconstruction 5.3.0 (Laboratory for Computational Neuroimaging at the Athinoula A. Martinos Center for Biomedical Imaging, Boston, U.S.A., http://surfer.nmr.mgh.harvard.edu/). This includedautomatic motion correction, segmentation, intensity normalization, inflation and registration to a spherical atlas.

We quantified resting-state cerebral blood flow (rsCBF, ml/100g/min) according to a previously applied, standardized protocol [39–41] using the following formula: where ΔM is the difference between labeled and control image; λ the blood/tissue water partition coefficient (assumed 0.9); α the tagging efficiency (assumed 0.85); M₀ the equilibrium brain tissue magnetization; τ the tagging duration; w the post-labeling delay; and T_1b the longitudinal relaxation time of blood (1650ms) [40]. This resulted in 40 unprocessed rsCBF maps per subject. They were realigned and co-registered to the corresponding raw T1* image and normalized using the deformation matrix of the corresponding T1* image. We smoothed the resulting images with a 8mm FWHM Gaussian kernel. We then calculated grey matter rsCBF applying grey matter masks based on the segmented grey matter T1* images which were thresholded at 0.3. We conducted subject-wise first-level generalized linear models with rsCBF of the grey matter as outcome variable and the rsCBF of the white matter, the rsCBF of the cerebrospinal fluid and the realignment parameters as explanatory variables to correct for residual motion and artifacts. We checked six motion parameters (x-, y-, z-translations, roll, pitch, and yaw) and set a limit of two voxels of motion for exclusion. This resulted in 40 preprocessed grey matter rsCBF maps per subject. There was no significant difference between groups in motion and we did not exclude any subject due to excessive motion. To increase the signal-to-noise ratio, we finally modelled a mean rsCBF map for each subject based on the 40 preprocessed maps. We used this mean rsCBF map for further analysis and computed mean global rsCBF within grey matter per subject based on this mean rsCBF map. Preprocessing of rsCBF maps was performed in SPM12 and Matlab (MATLAB 2015a; The MathWorks, Inc., Natick, MA, USA).

BOLD images were co-registered to the corresponding raw T1* image and then processed using the standard processing pipeline in CONN (Version 15, http://www.nitrc.org/projects/conn). This included realignment; slice-time correction; outlier-detection using ART-toolbox (global-signal z threshold 9, subject motion threshold 2 mm); normalization; smoothing with a 8 mm FWHM kernel; denoising by linear regression of white matter and cerebrospinal fluid signals, realigning and scrubbing parameters; and finally linear detrending as well as band-pass filtering between 0.008 and 0.09 Hz. Again, there was no significant difference between groups in motion and we did not exclude any subject due to excessive motion. We entered the 11 ROIs that were constructed based on Dehghan et al. [8] as described above and calculated rsFC between each pair of ROI for each subject by averaging and correlating the time-series of each ROI.

Statistical analysis

To explore differences in rsCBF, GMV and CoTh between patients and controls we performed generalized linear models based on whole-brain voxel-wise (vertex-wise for CoTh) analyses and ROI-analyses. The whole-brain analyses of rsCBF was the main statistical analysis. We considered disease status (fibromyalgia patients vs pain-free controls) as primary explanatory variable. When modelling the unadjusted group difference of rsCBF we included mean global rsCBF as co-variable. Similarly, we included total intracranial volume as co-variable when modelling unadjusted group differences of GMV and CoTh. To account for the effects of depression and anxiety we also modelled adjusted group differences of all neuroimaging markers and additionally included BDI-II and STAI-Trait t-value as co-variables. We followed this approach for both, whole-brain and ROI analyses of rsCBF, GMV and CoTh. We explored group differences in rsFC and calculated adjusted group differences of rsFC for each pair of the 11 pre-specified ROIs and compared rsFC between patients and controls for all resulting 55 connections. Again, we included BDI-ll and STAI-Trait t-values as co-variables to account for the effects of depression and anxiety. Age was corrected for in the study design using frequency matching of cases and controls according to age. To correct for multiple comparisons, we used family wise error (FWE) in all analyses withcorrection at peak-level in whole-brain analyses.

We ran two sets of exploratory sensitivity analyses. First, we performed adjusted subgroup whole-brain analyses of the difference in rsCBF, GMV and CoTh between patients not taking any centrally acting drugs and their matched controls. Second, we explored the correlation of neuroimaging markers with clinical pain characteristics using Pearson correlation. We considered all neuroimaging markers with group differences at p≤0.10 in any of the analyses after correction for multiple comparisons. Due to the exploratory nature of these correlation analyses, multiple comparison correction was not employed. All reported p-values are two-sided and all confidence intervals refer to 95% boundaries. We performed statistical analyses for rsCBF and GMV in SPSS 23.0 (Version 23.0. Armonk, NY: IBM Corp., ROI-analyses, correlations)for CoTh in FreeSurfer (Version 5.3.0., http://surfer.nmr.mgh.harvard.edu/) and for rsFC in CONN (Version 15, http://www.nitrc.org/projects/conn).

Results

Study flow

We randomly selected and screened 151 fibromyalgia patients and 418 controls. The three most important reasons for excluding patients were neurologic or psychiatric co-morbidity other than unipolar depressive disorder (33 patients, 22%), inability to confirm fibromyalgia diagnosis at enrollment (23 patients, 15%) and inability to perform MRI (9 patients, 6%). The three most important reasons for excluding controls were neurologic or psychiatric co-morbidity (86 controls, 21%), chronic pain at enrollment (73 controls, 17%) and severe somatic co-morbidity (44 controls, 10%). We included 32 fibromyalgia patients and 32 age-matched pain-free controls. All participants were female and right-handed. S1 Fig presents the study flow diagram.

Socio-demographic, psychological and clinical characteristics

Table 1 presents the socio-demographic and psychological characteristics of the study population. Cases and controls were comparable in terms of age, education, and marital status. Patients had significantly higher scores for depression (p≤ 0.001) and anxiety (p≤ 0.001). Twenty-four patients (75%) had widespread body pain affecting more than 50% of their body and 20 patients (63%) reported average pain intensity of at least NRS 6. All patients took pain medications on a daily basis; 21 (66%) of them regularly took centrally acting drugs such as weak opioids, antidepressants or anticonvulsants (intake of strong opioids was an exclusion criterion).

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Table 1. Baseline characteristics of fibromyalgia patients and pain-free controls.

https://doi.org/10.1371/journal.pone.0235879.t001

Differences in resting-state cerebral blood flow and correlation with pain characteristics

We found no increase in rsCBF in fibromyalgia patients as compared to pain-free controls in whole-brain analyses with and without depression and anxiety as additional co-variables. We found significant lower rsCBF in patients in the right Dorsolateral Prefrontal Cortex (x = 44; y = 32; z = 26; cluster size = 13 voxels; T = -4.39; p_FWE = 0.03) in unadjusted analyses (Fig 1A) and in the left Inferior Middle Temporal Gyrus (x = -50; y = -50; z = -4; cluster size = 110 voxels; T = -6.01, p_FWE = 0.002) in adjusted analyses (Fig 2A). However, rsCBF in these two regions was not correlated with average pain intensity 24h before or pain intensity immediately before neuroimaging, spread of pain or disability in 32 fibromyalgia patients (Figs 1 and 2B).

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Fig 1. Unadjusted difference in resting-state cerebral blood flow (rsCBF) between cases (N = 32) and controls (N = 32) in the right Dorsolateral Prefrontal Cortex (x = 44; y = 32; z = 26) controlling for mean global rsCBF at cluster-level of 10 voxels (A) and correlation of pain characteristics with rsCBF of this region within patients (N = 32) (B).

Displayed are cluster-sizes of >10 voxels with colors indicating t-values. NRS: Numerical Rating Scale from 0 (no pain) to 10 (maximum pain). WPI: Widespread pain index from 0 (no body region with pain) to 19 (all body regions with pain). FIQ: Fibromyalgia Impact Questionnaire from 0 (no disability) to 100 (most severe disability). r: correlation coefficients.

https://doi.org/10.1371/journal.pone.0235879.g001

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Fig 2. Adjusted difference in resting-state cerebral blood flow (rsCBF) between cases (N = 32) and controls (N = 32) in the left Inferior Middle Temporal Gyrus (x = -50; y = -50; z = -4) controlling for mean global rsCBF, depression and anxiety at cluster-level of 10 voxels (A) and correlation of pain characteristics with rsCBF of this region within patients (N = 32) (B).

Displayed are cluster-sizes of >10 voxels with colors indicating t-values. NRS: Numerical Rating Scale from 0 (no pain) to 10 (maximum pain). WPI: Widespread pain index from 0 (no body region with pain) to 19 (all body regions with pain). FIQ: Fibromyalgia Impact Questionnaire from 0 (no disability) to 100 (most severe disability). r: correlation coefficients.

https://doi.org/10.1371/journal.pone.0235879.g002

Differences in local resting state cerebral blood flow of 11 pre-specified ROIs

Table 2 shows adjusted group differences in local rsCBF of 11 pre-specified ROIs after controlling for mean global rsCBF, depression and anxiety. Although in more than half of the areas (7 of 11 ROIs) model-based adjusted mean difference between patients and controls suggest an increased blood flow in patients, none of the increases was statistically significant without nor with FWE correction. S1 Table shows unadjusted differences of rsCBF of ROI-analyses only including mean global rsCBF as co-variable. Again, we were unable to detect relevant group differences.

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Table 2. Adjusted differences in resting state perfusion (rsCBF, ml/100g/min) between 32 fibromyalgia patients and 32 pain-free controls in 11 pre-specified Regions of Interest.

https://doi.org/10.1371/journal.pone.0235879.t002

Differences in resting-state functional connectivity, grey matter volume and cortical thickness

We found no differences in rsFC among areas associated with chronic pain in fibromyalgia in unadjusted or adjusted analyses after FWE correction. There was no evidence for differences in structural neuroimaging markers (GMV, CoTh) in patients as compared to controls in whole-brain analyses before or after FWE-correction. Tables 3 and 4 report group differences adjusted for total intracranial volume, depression and anxiety in GMV and CoTh of ROI-analyses. Patients showed non-significant decreases in structural neuroimaging markers in 8 of 10 ROIs for GMV and 12 of 23 ROIs for CoTh with and without FWE correction. S2 and S3 Tables showunadjusted differences of ROI-analyses of GMV and CoTh including only total intracranial volume as co-variable. As in adjusted analyses, we were unable to detect relevant group differences.

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Table 3. Adjusted differences in grey matter volume between (GMV) 32 fibromyalgia patients and 32 pain-free controls in 10 pre-specified Regions of Interest.

https://doi.org/10.1371/journal.pone.0235879.t003

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Table 4. Adjusted differences in cortical thickness (CoTh, mm) between 32 fibromyalgia patients and 32 pain-free controls in 23 pre-specified Regions of Interest.

https://doi.org/10.1371/journal.pone.0235879.t004

Subgroup analyses of patients not taking centrally acting drugs

Adjusted whole-brain subgroup-analyses, including depression, anxiety, and mean global rsCBF or total intracranial volume as co-variables, of 11 (34%) patients not taking centrally acting drugs and matched controls showed no group differences in rsCBF, GMV and CoTh.

Discussion

Main findings

To our knowledge, this is the first multimodal neuroimaging study integrating four different functional and structural markers of chronic pain in fibromyalgia. Our primary objective was to compare rsCBF patterns of fibromyalgia patients and pain-free controls using ASL as measure of neural activity at rest and thus likely to reproduce a neuroimaging marker of chronic, stimulus-independent pain. Based on the pathophysiological concept of enhanced central pain processes, we expected an increased neural activity at rest in pain processing areas, i.e., an increased rsCBF in these brain areas in cases as compared to controls. Contrary to our hypothesis, we found no evidence of increases in rsCBF in brain areas involved in pain processing in fibromyalgia neither in whole-brain nor in ROI analyses. Instead, we found decreased rsCBF in patients in the right Dorsolateral Prefrontal Cortex in whole-brain analysis after controlling for mean global rsCBF and in the left Inferior Middle Temporal Gyrus in whole-brain analyses adjusted for mean global rsCBF, depression and anxiety, even after correcting for multiple comparisons. However, rsCBF in these two areas did not correlate with pain characteristics such as pain intensity, spread of pain or disability in patients.

We believe that the decrease in rsCBF in the Dorsolateral Prefrontal Cortex is likely to be an effect of depression as the role of the Dorsolateral Prefrontal Cortex in depression is well established [42, 43] and the effect could not be observed anymore after controlling the analysis for depression. With regard to the decreased rsCBF in the Inferior Middle Temporal Gyrus we propose that this effect may either indicate a process secondary to the pain experience, such as the subjective evaluation of pain, or a false positive statistical finding rather than an effect attributable to pain. The rationale behind this is fourfold. First, there is no prior evidence for a pathophysiological involvement of the Inferior Middle Temporal Gyrus in fibromyalgia. Second, we detected a decreased rsCBF in this area in patients as compared to controls which is contrary to our main hypothesis of increased rsCBF in cases as result of central hypersensitivity. Third, we did not find any relevant correlations between rsCBF in this area and pain characteristics. Fourth, we were unable to see any group differences between cases and controls in this area for other neuroimaging marker. In view of the large number of analyses conducted, a false positive statistical finding is a possible explanation.”

Our secondary objective was to compare resting-state functional connectivity between pre-specified areas likely to be involved in pain processing, grey matter volume and cortical thickness between cases and controls assuming a reduction in these neuroimaging markers in fibromyalgia. Again contrary to our hypotheses, we neither found evidence of decreased rsFC, GMV or CoTh in brain areas involved in pain processing of fibromyalgia patients. The results remained robust in sensitivity analyses comparing fibromyalgia patients not taking centrally acting drugs with corresponding controls.

Scientific context of our findings

There is ongoing debate to what extent brain areas processing acute pain signals are also involved in the development and maintenance of chronic, stimulus-independent pain [8, 17]. We therefore defined the whole-brain analysis as the main statistical approach and performed secondary ROI analyses based on the recent meta-analysis by Dehghan and colleagues as most comprehensive evidence synthesis of functional and structural alterations in the central nervous system of fibromyalgia patients [8]. Previous neuroimaging studies typically focused on the comparison of acute pain processing mechanisms between fibromyalgia patients and pain-free controls using experimentally-evoked pain paradigms [7]. However, neuroimaging of chronic, stimulus-independent pain requires a different approach. Chronic pain typically remains constant during the course of an imaging session, rendering it invisible to traditional imaging techniques using pain paradigms. Task-free, resting-state parameters such as rsCBF are markers for brain activity at rest and thus more appropriate to measure chronic pain [18–20]. ASL is the sequence of choice to measure rsCBF [17]. Still, studies using rsCBF based on ASL are sparse in pain research with only one study up until now performed in fibromyalgia [24–29]. Although these studies employed a resting-state paradigm, none them integrated functional and structural neuroimaging by using multimodal scanning methods and none of them considered the effect of centrally acting drugs typically used by chronic pain patients on their results. Furthermore, only some of these studies addressed important confounders such as age, gender and concomitant depression. To our knowledge, Shokouhi and colleagues conducted the only study using ASL in fibromyalgia investigating resting state perfusion patterns [29]. They compared 23 patients with 16 pain-free controls and thus included a much smaller and unmatched study population as compared to our study. They reported hypoperfusion in the putamen in subjects with chronic pain that was correlated with degree in disability but not pain intensity thus suggesting that this hypoperfusion was due to adaptation processes. The correlation between rsCBF in the Putamen and disability was positive when using the Pain Disability Index to measure disability but negative when using the Fibromyalgia Impact Questionnaire as other measure of disability. The authors didn’t comment on this conflicting finding. However, they didn’t find group differences of rsCBF between fibromyalgia patients and controls what is in line with our findings. With regards to the literature on functional connectivity in fibromyalgia patients, our objective was to compare rsFC between pre-specified areas likely to be involved in pain processing as defined by Dehghan and colleagues [8]. Previous studies investigated rsFC of the Default Mode Network. These studies found an increased connectivity within the Default Mode Network and between the Insula and the Default Mode Network [21, 22]. The most recent meta-analysis investigating GMV showed grey matter decreases in the anterior and posterior cingulate cortex, the medial prefrontal cortex and the parahippocampal gyrus or fusiform cortex [44]. However, the original studies which were included in this meta-analysis [9–13] and the latest study investigating CoTh [14] typically did not correct for co-morbid depression or for multiple comparisons, and if they did, group differences in neuroimaging markers lost statistical significance [9–14]. Therefore, our inability to find relevant group differences in structural neuroimaging markers are in line with the findings of these previous studies [9–14]. Furthermore, evidence generation in the field of neuroimaging and fibromyalgia is still in its early stage. This phase of evidence generation is often characterized by conflicting findings as expression of the evidence accumulating process itself, previously referred to as proteus phenomenon [45, 46]. Therefore, our inability to find evidence to support the hypothesis of altered central pain processing might also be interpreted in the light of this phenomenon.

Strengths and limitations

Major strengths of the present study include integration of four different functional and structural neuroimaging markers using a multimodal scanning protocol, evaluation of rsCBF using ASL; adjustment for depression and anxiety; correction for multiple comparisons and performance of a sensitivity analysis in a subgroup of patients not taking centrally acting drugs. Although fibromyalgia is characterized by intensive, widespread pain not sufficiently explained by peripheral lesions [7, 12] making it a good model to study the pathophysiology of enhanced central pain mechanisms it also shows high co-morbidity with depression and anxiety. This may bias neuroimaging of chronic pain since brain areas involved in pain processing overlap with those involved in the pathophysiology of depression, e.g. prefrontal, cingulate, and supplementary motor cortex [39]. Pain processing is tightly linked to top-down emotional control processes involving prefrontal cortices and amygdala [47]. Hence, we adjusted all our analyses for depression and anxiety and again were unable to find any group differences. Another problem of studies in fibromyalgia is the long-term treatment with centrally acting drugs, which may also interfere with neuroimaging markers. For possible rebound-effects and ethical reasons we decided not stop current medication. To address this limitation, we performed a subgroup-analysis including only patients not taking any centrally acting drugs and matched controls. We again found no group differences in whole-brain analyses of rsCBF, GMV and CoTh. We recruited both cases and controls in the only tertiary care hospital in the capital of Switzerland, with the same referral pathways for fibromyalgia patients and pain-free controls. This allowed us sampling cases and controls from the same source population, which is important to avoid selection bias in case-control studies. Severity of fibromyalgia measures by the FIQ in our patients was similar to patients included in key randomized controlled trials evaluating non-pharmacological interventions [48, 49] and phase III effectiveness trials of different drugs [50, 51]. Our study included totally 64 participants, making it one of the largest in the field. Therefore, we do not think that our inability to observe statistically significant group differences was merely due to a lack of power. Our results were consistent across four imaging modalities and robust to the type of analysis (whole-brain and ROI-analyses, subgroup analysis in patients not taking centrally acting drugs). Additionally, we were unable to detect relevant correlation between neuroimaging markers with clinically important outcomes such as pain intensity, spread of pain and disability. Still, an argument for limited power to detect relevant group differences is the fact that patients showed numerically increased rsCBF, decreased GMV and decreased CoTh in most of the pre-specified ROIs even after adjusting for depression and anxiety what would be in line with our a priori hypotheses. Previous studies showing group-differences were both smaller and of less methodological rigor as compared to our study. They typically did not account for co-morbid depression and intake of pain-modulating, centrally acting drugs even though both features are particularly frequent in fibromyalgia and likely to influence neuroimaging results. Furthermore, by nature, case-control studies tend to inflate group-differences because severely sick cases are compared to healthy controls. This suggests that we would have detected moderate differences if they had been present. However, we did not acquire any fieldmaps and therefore could not correct our analyses for magnetic field inhomogeneity potentially leading to decreased sensitivity in detecting group differences. Yet, we believe that correcting our analyses for field inhomogeneity would not have changed the interpretation of our findings. Signal quality would have improved in both groups, the cases and controls alike and a more sensitive method would only have detected small group differences. The relevance of these small effect sizes, if at all present, would be equivocal.

Implications

Our findings do not necessarily imply that altered central pain processing is not involved in fibromyalgia but may demonstrate that currently available functional and structural neuroimaging markers are not able to map stimulus-independent pain. Stimulus-independent, chronic pain may involve subtle functional and structural alterations which might be missed in medium-sized case-control studies like the present one and could be detected by larger multimodal studies also using resting-state designs. Because we analyzed brain areas involved in the processing of acute pain signals, our results suggest that these areas are unlikely to be involved in the development and maintenance of chronic, stimulus-independent pain.

Supporting information

S1 Fig. Flow diagram of fibromyalgia patients and controls clinically evaluated between 1^st July 2011 and 30^th June 2013 and recruited for the study between 1^st November 2013 and 31^st January 2015 at the University Hospital of Bern.

^$ according to criteria of American College of Rheumatology; *patients with light opioids, antidepressants, pregabalin or gabapentin included; °5 patients with mental retardation, 3 patients with pregnancy.

https://doi.org/10.1371/journal.pone.0235879.s001

(DOCX)

S1 Table. Unadjusted differences in resting state perfusion (rsCBF, ml/100g/min) between 32 fibromyalgia patients and 32 pain-free controls in 11 pre-specified Regions of Interest.

Results are data-based mean rsCBF with corresponding standard deviation (SD) and model-based unadjusted mean differences of rsCBF with corresponding 95% confidence intervals (CI), t-values and p-values from multivariable general linear models.

https://doi.org/10.1371/journal.pone.0235879.s002

(DOCX)

S2 Table. Unadjusted differences in grey matter volume between (GMV) 32 fibromyalgia patients and 32 pain-free controls in 10 pre-specified Regions of Interest.

Results are data-based mean GMV with corresponding standard deviation (SD) and model-based unadjusted mean differences of GMV with corresponding 95% confidence intervals (CI), t-values and p-values from multivariable general linear models.

https://doi.org/10.1371/journal.pone.0235879.s003

(DOCX)

S3 Table. Unadjusted differences in cortical thickness (CoTh, mm) between 32 fibromyalgia patients and 32 pain-free controls in 20 pre-specified Regions of Interest.

Results are data-based mean CoTh with corresponding standard deviation (SD) and model-based unadjusted mean differences of CoTh with corresponding 95% confidence intervals (CI), t-values and p-values from multivariable general linear models.

https://doi.org/10.1371/journal.pone.0235879.s004

(DOCX)

Acknowledgments

We would like to thank Katrin Ziegler, MSc, Clinical Trials Unit, University of Bern, Switzerland, for her support in data-management and Fabienne Treichel, study nurse, for her support in patient recruitment and data collection.

References

1. Robinson RL, Kroenke K, Mease P, Williams DA, Chen Y, D’Souza D, et al. Burden of Illness and Treatment Patterns for Patients with Fibromyalgia. Pain Med. 2012. pmid:22958298
- View Article
- PubMed/NCBI
- Google Scholar
2. Wolfe F, Clauw DJ, Fitzcharles MA, Goldenberg DL, Hauser W, Katz RS, et al. Fibromyalgia criteria and severity scales for clinical and epidemiological studies: a modification of the ACR Preliminary Diagnostic Criteria for Fibromyalgia. The Journal of rheumatology. 2011;38(6):1113–22. pmid:21285161
- View Article
- PubMed/NCBI
- Google Scholar
3. Robinson RL, Birnbaum HG, Morley MA, Sisitsky T, Greenberg PE, Claxton AJ. Economic cost and epidemiological characteristics of patients with fibromyalgia claims. The Journal of rheumatology. 2003;30(6):1318–25. pmid:12784409
- View Article
- PubMed/NCBI
- Google Scholar
4. Burckhardt CS, Clark SR, Bennett RM. Fibromyalgia and quality of life: a comparative analysis. The Journal of rheumatology. 1993;20(3):475–9. pmid:8478854
- View Article
- PubMed/NCBI
- Google Scholar
5. Hoffman DL, Dukes EM. The health status burden of people with fibromyalgia: a review of studies that assessed health status with the SF-36 or the SF-12. International journal of clinical practice. 2008;62(1):115–26. pmid:18039330
- View Article
- PubMed/NCBI
- Google Scholar
6. Macfarlane GJ, Kronisch C, Dean LE, Atzeni F, Hauser W, Fluss E, et al. EULAR revised recommendations for the management of fibromyalgia. Annals of the rheumatic diseases. 2017;76(2):318–28. pmid:27377815
- View Article
- PubMed/NCBI
- Google Scholar
7. Cagnie B, Coppieters I, Denecker S, Six J, Danneels L, Meeus M. Central sensitization in fibromyalgia? A systematic review on structural and functional brain MRI. Semin Arthritis Rheum. 2014;44(1):68–75. pmid:24508406
- View Article
- PubMed/NCBI
- Google Scholar
8. Dehghan M, Schmidt-Wilcke T, Pfleiderer B, Eickhoff SB, Petzke F, Harris RE, et al. Coordinate-based (ALE) meta-analysis of brain activation in patients with fibromyalgia. Human brain mapping. 2016;37(5):1749–58. pmid:26864780
- View Article
- PubMed/NCBI
- Google Scholar
9. Burgmer M, Gaubitz M, Konrad C, Wrenger M, Hilgart S, Heuft G, et al. Decreased gray matter volumes in the cingulo-frontal cortex and the amygdala in patients with fibromyalgia. Psychosomatic medicine. 2009;71(5):566–73. pmid:19414621
- View Article
- PubMed/NCBI
- Google Scholar
10. Robinson ME, Craggs JG, Price DD, Perlstein WM, Staud R. Gray matter volumes of pain-related brain areas are decreased in fibromyalgia syndrome. The journal of pain: official journal of the American Pain Society. 2011;12(4):436–43. pmid:21146463
- View Article
- PubMed/NCBI
- Google Scholar
11. Hsu MC, Harris RE, Sundgren PC, Welsh RC, Fernandes CR, Clauw DJ, et al. No consistent difference in gray matter volume between individuals with fibromyalgia and age-matched healthy subjects when controlling for affective disorder. Pain. 2009;143(3):262–7. pmid:19375224
- View Article
- PubMed/NCBI
- Google Scholar
12. Jensen KB, Srinivasan P, Spaeth R, Tan Y, Kosek E, Petzke F, et al. Overlapping structural and functional brain changes in patients with long-term exposure to fibromyalgia pain. Arthritis and rheumatism. 2013;65(12):3293–303. pmid:23982850
- View Article
- PubMed/NCBI
- Google Scholar
13. Ceko M, Bushnell MC, Fitzcharles M-A, Schweinhardt P. Fibromyalgia interacts with age to change the brain. NeuroImage: Clinical. 2013;3:249–60. pmid:24273710
- View Article
- PubMed/NCBI
- Google Scholar
14. Feraco P, Nigro S, Passamonti L, Grecucci A, Caligiuri ME, Gagliardo C, et al. Neurochemical Correlates of Brain Atrophy in Fibromyalgia Syndrome: A Magnetic Resonance Spectroscopy and Cortical Thickness Study. Brain Sci. 2020;10(6). pmid:32575715
- View Article
- PubMed/NCBI
- Google Scholar
15. Apkarian AV, Bushnell MC, Treede RD, Zubieta JK. Human brain mechanisms of pain perception and regulation in health and disease. Eur J Pain. 2005;9(4):463–84. pmid:15979027
- View Article
- PubMed/NCBI
- Google Scholar
16. Duerden EG, Albanese MC. Localization of pain-related brain activation: a meta-analysis of neuroimaging data. Hum Brain Mapp. 2013;34(1):109–49. pmid:22131304
- View Article
- PubMed/NCBI
- Google Scholar
17. Tracey I, Johns E. The pain matrix: reloaded or reborn as we image tonic pain using arterial spin labelling. Pain. 2010;148(3):359–60. pmid:20080346
- View Article
- PubMed/NCBI
- Google Scholar
18. Raichle ME. A paradigm shift in functional brain imaging. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2009;29(41):12729–34. pmid:19828783
- View Article
- PubMed/NCBI
- Google Scholar
19. Lee MH, Smyser CD, Shimony JS. Resting-State fMRI: A Review of Methods and Clinical Applications. AJNR American journal of neuroradiology. 2012. pmid:22936095
- View Article
- PubMed/NCBI
- Google Scholar
20. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Functional network organization of the human brain. Neuron. 2011;72(4):665–78. pmid:22099467
- View Article
- PubMed/NCBI
- Google Scholar
21. Čeko M, Frangos E, Gracely J, Richards E, Wang B, Schweinhardt P, et al. Default mode network changes in fibromyalgia patients are largely dependent on current clinical pain. NeuroImage. 2020;216:116877. pmid:32344063
- View Article
- PubMed/NCBI
- Google Scholar
22. Napadow V, LaCount L, Park K, As-Sanie S, Clauw DJ, Harris RE. Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity. Arthritis and rheumatism. 2010;62(8):2545–55. pmid:20506181
- View Article
- PubMed/NCBI
- Google Scholar
23. Loggia ML, Segerdahl AR, Howard MA, Tracey I. Imaging Clinically Relevant Pain States Using Arterial Spin Labeling. Pain Rep. 2019;4(4).
- View Article
- Google Scholar
24. Wasan AD, Loggia ML, Chen LQ, Napadow V, Kong J, Gollub RL. Neural correlates of chronic low back pain measured by arterial spin labeling. Anesthesiology. 2011;115(2):364–74. pmid:21720241
- View Article
- PubMed/NCBI
- Google Scholar
25. Loggia ML, Kim J, Gollub RL, Vangel MG, Kirsch I, Kong J, et al. Default mode network connectivity encodes clinical pain: an arterial spin labeling study. Pain. 2013;154(1):24–33. pmid:23111164
- View Article
- PubMed/NCBI
- Google Scholar
26. Howard MA, Krause K, Khawaja N, Massat N, Zelaya F, Schumann G, et al. Beyond patient reported pain: perfusion magnetic resonance imaging demonstrates reproducible cerebral representation of ongoing post-surgical pain. PloS one. 2011;6(2):e17096. pmid:21373203
- View Article
- PubMed/NCBI
- Google Scholar
27. Youssef AM, Gustin SM, Nash PG, Reeves JM, Petersen ET, Peck CC, et al. Differential brain activity in subjects with painful trigeminal neuropathy and painful temporomandibular disorder. Pain. 2014;155(3):467–75. pmid:24269492
- View Article
- PubMed/NCBI
- Google Scholar
28. Liu J, Hao Y, Du M, Wang X, Zhang J, Manor B, et al. Quantitative cerebral blood flow mapping and functional connectivity of postherpetic neuralgia pain: a perfusion fMRI study. Pain. 2013;154(1):110–8. pmid:23140909
- View Article
- PubMed/NCBI
- Google Scholar
29. Shokouhi M, Davis KD, Moulin DE, Morley-Forster P, Nielson WR, Bureau Y, et al. Basal Ganglia Perfusion in Fibromyalgia is Related to Pain Disability and Disease Impact: An Arterial Spin Labeling Study. The Clinical journal of pain. 2016;32(6):495–505. pmid:26340652
- View Article
- PubMed/NCBI
- Google Scholar
30. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9(1):97–113. pmid:5146491
- View Article
- PubMed/NCBI
- Google Scholar
31. Morley S, de CWA, Black S. A confirmatory factor analysis of the Beck Depression Inventory in chronic pain. Pain. 2002;99:289. pmid:12237207
- View Article
- PubMed/NCBI
- Google Scholar
32. Worlds. WMA Declaration of Helsinki—Ethical Principles for Medical Research Involving Human Subjects. Seoul: Wordls Medical Association. 2008.
33. Laux L, Glanzmann P, Schaffner P, Spielberger CD. State-Trait-Angstinventar (STAI). Göttingen: Hogrefe Verlag; 1981.
34. Offenbaecher M, Waltz M, Schoeps P. Validation of a German version of the Fibromyalgia Impact Questionnaire (FIQ-G). The Journal of rheumatology. 2000;27(8):1984–8. pmid:10955342
- View Article
- PubMed/NCBI
- Google Scholar
35. Wu WC, Fernandez-Seara M, Detre JA, Wehrli FW, Wang J. A theoretical and experimental investigation of the tagging efficiency of pseudocontinuous arterial spin labeling. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. 2007;58(5):1020–7. pmid:17969096
- View Article
- PubMed/NCBI
- Google Scholar
36. Dai W, Garcia D, de Bazelaire C, Alsop DC. Continuous flow-driven inversion for arterial spin labeling using pulsed radio frequency and gradient fields. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. 2008;60(6):1488–97. pmid:19025913
- View Article
- PubMed/NCBI
- Google Scholar
37. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15(1):273–89. pmid:11771995
- View Article
- PubMed/NCBI
- Google Scholar
38. Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage. 2010;53(1):1–15. pmid:20547229
- View Article
- PubMed/NCBI
- Google Scholar
39. Cantisani A, Stegmayer K, Bracht T, Federspiel A, Wiest R, Horn H, et al. Distinct resting-state perfusion patterns underlie psychomotor retardation in unipolar vs. bipolar depression. Acta Psychiatr Scand. 2016;134(4):329–38. pmid:27497085
- View Article
- PubMed/NCBI
- Google Scholar
40. Wang J, Aguirre GK, Kimberg DY, Roc AC, Li L, Detre JA. Arterial spin labeling perfusion fMRI with very low task frequency. Magn Reson Med. 2003;49(5):796–802. pmid:12704760
- View Article
- PubMed/NCBI
- Google Scholar
41. Walther S, Schappi L, Federspiel A, Bohlhalter S, Wiest R, Strik W, et al. Resting-State Hyperperfusion of the Supplementary Motor Area in Catatonia. Schizophr Bull. 2017;43(5):972–81. pmid:27729486
- View Article
- PubMed/NCBI
- Google Scholar
42. Ramirez-Mahaluf JP, Roxin A, Mayberg HS, Compte A. A Computational Model of Major Depression: the Role of Glutamate Dysfunction on Cingulo-Frontal Network Dynamics. Cereb Cortex. 2017;27(1):660–79. pmid:26514163
- View Article
- PubMed/NCBI
- Google Scholar
43. Mayberg HS, Liotti M, Brannan SK, McGinnis S, Mahurin RK, Jerabek PA, et al. Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am J Psychiatry. 1999;156(5):675–82. pmid:10327898
- View Article
- PubMed/NCBI
- Google Scholar
44. Shi H, Yuan C, Dai Z, Ma H, Sheng L. Gray matter abnormalities associated with fibromyalgia: A meta-analysis of voxel-based morphometric studies. Semin Arthritis Rheum. 2016;46(3):330–7. pmid:27989500
- View Article
- PubMed/NCBI
- Google Scholar
45. Ioannidis JP, Trikalinos TA. Early extreme contradictory estimates may appear in published research: the Proteus phenomenon in molecular genetics research and randomized trials. J Clin Epidemiol. 2005;58(6):543–9. pmid:15878467
- View Article
- PubMed/NCBI
- Google Scholar
46. Pfeiffer T, Bertram L, Ioannidis JP. Quantifying selective reporting and the Proteus phenomenon for multiple datasets with similar bias. PloS one. 2011;6(3):e18362. pmid:21479240
- View Article
- PubMed/NCBI
- Google Scholar
47. Johnstone T, van Reekum CM, Urry HL, Kalin NH, Davidson RJ. Failure to regulate: counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2007;27(33):8877–84. pmid:17699669
- View Article
- PubMed/NCBI
- Google Scholar
48. Cheng CA, Chiu YW, Wu D, Kuan YC, Chen SN, Tam KW. Effectiveness of Tai Chi on fibromyalgia patients: A meta-analysis of randomized controlled trials. Complement Ther Med. 2019;46:1–8. pmid:31519264
- View Article
- PubMed/NCBI
- Google Scholar
49. Chan CL, Wang CW, Ho RT, Ng SM, Ziea ET, Wong VT. Qigong exercise for the treatment of fibromyalgia: a systematic review of randomized controlled trials. J Altern Complement Med. 2012;18(7):641–6. pmid:22757663
- View Article
- PubMed/NCBI
- Google Scholar
50. Pauer L, Winkelmann A, Arsenault P, Jespersen A, Whelan L, Atkinson G, et al. An international, randomized, double-blind, placebo-controlled, phase III trial of pregabalin monotherapy in treatment of patients with fibromyalgia. The Journal of rheumatology. 2011;38(12):2643–52. pmid:21965636
- View Article
- PubMed/NCBI
- Google Scholar
51. Murakami M, Osada K, Mizuno H, Ochiai T, Alev L, Nishioka K. A randomized, double-blind, placebo-controlled phase III trial of duloxetine in Japanese fibromyalgia patients. Arthritis Res Ther. 2015;17:224. pmid:26296539
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Robinson RL, Kroenke K, Mease P, Williams DA, Chen Y, D’Souza D, et al. Burden of Illness and Treatment Patterns for Patients with Fibromyalgia. Pain Med. 2012. pmid:22958298
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Wolfe F, Clauw DJ, Fitzcharles MA, Goldenberg DL, Hauser W, Katz RS, et al. Fibromyalgia criteria and severity scales for clinical and epidemiological studies: a modification of the ACR Preliminary Diagnostic Criteria for Fibromyalgia. The Journal of rheumatology. 2011;38(6):1113–22. pmid:21285161
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Robinson RL, Birnbaum HG, Morley MA, Sisitsky T, Greenberg PE, Claxton AJ. Economic cost and epidemiological characteristics of patients with fibromyalgia claims. The Journal of rheumatology. 2003;30(6):1318–25. pmid:12784409
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Burckhardt CS, Clark SR, Bennett RM. Fibromyalgia and quality of life: a comparative analysis. The Journal of rheumatology. 1993;20(3):475–9. pmid:8478854
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Hoffman DL, Dukes EM. The health status burden of people with fibromyalgia: a review of studies that assessed health status with the SF-36 or the SF-12. International journal of clinical practice. 2008;62(1):115–26. pmid:18039330
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Macfarlane GJ, Kronisch C, Dean LE, Atzeni F, Hauser W, Fluss E, et al. EULAR revised recommendations for the management of fibromyalgia. Annals of the rheumatic diseases. 2017;76(2):318–28. pmid:27377815
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Cagnie B, Coppieters I, Denecker S, Six J, Danneels L, Meeus M. Central sensitization in fibromyalgia? A systematic review on structural and functional brain MRI. Semin Arthritis Rheum. 2014;44(1):68–75. pmid:24508406
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Dehghan M, Schmidt-Wilcke T, Pfleiderer B, Eickhoff SB, Petzke F, Harris RE, et al. Coordinate-based (ALE) meta-analysis of brain activation in patients with fibromyalgia. Human brain mapping. 2016;37(5):1749–58. pmid:26864780
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref9] 9. Burgmer M, Gaubitz M, Konrad C, Wrenger M, Hilgart S, Heuft G, et al. Decreased gray matter volumes in the cingulo-frontal cortex and the amygdala in patients with fibromyalgia. Psychosomatic medicine. 2009;71(5):566–73. pmid:19414621
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref10] 10. Robinson ME, Craggs JG, Price DD, Perlstein WM, Staud R. Gray matter volumes of pain-related brain areas are decreased in fibromyalgia syndrome. The journal of pain: official journal of the American Pain Society. 2011;12(4):436–43. pmid:21146463
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref11] 11. Hsu MC, Harris RE, Sundgren PC, Welsh RC, Fernandes CR, Clauw DJ, et al. No consistent difference in gray matter volume between individuals with fibromyalgia and age-matched healthy subjects when controlling for affective disorder. Pain. 2009;143(3):262–7. pmid:19375224
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref12] 12. Jensen KB, Srinivasan P, Spaeth R, Tan Y, Kosek E, Petzke F, et al. Overlapping structural and functional brain changes in patients with long-term exposure to fibromyalgia pain. Arthritis and rheumatism. 2013;65(12):3293–303. pmid:23982850
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref13] 13. Ceko M, Bushnell MC, Fitzcharles M-A, Schweinhardt P. Fibromyalgia interacts with age to change the brain. NeuroImage: Clinical. 2013;3:249–60. pmid:24273710
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref14] 14. Feraco P, Nigro S, Passamonti L, Grecucci A, Caligiuri ME, Gagliardo C, et al. Neurochemical Correlates of Brain Atrophy in Fibromyalgia Syndrome: A Magnetic Resonance Spectroscopy and Cortical Thickness Study. Brain Sci. 2020;10(6). pmid:32575715
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref15] 15. Apkarian AV, Bushnell MC, Treede RD, Zubieta JK. Human brain mechanisms of pain perception and regulation in health and disease. Eur J Pain. 2005;9(4):463–84. pmid:15979027
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref16] 16. Duerden EG, Albanese MC. Localization of pain-related brain activation: a meta-analysis of neuroimaging data. Hum Brain Mapp. 2013;34(1):109–49. pmid:22131304
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref17] 17. Tracey I, Johns E. The pain matrix: reloaded or reborn as we image tonic pain using arterial spin labelling. Pain. 2010;148(3):359–60. pmid:20080346
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref18] 18. Raichle ME. A paradigm shift in functional brain imaging. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2009;29(41):12729–34. pmid:19828783
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref19] 19. Lee MH, Smyser CD, Shimony JS. Resting-State fMRI: A Review of Methods and Clinical Applications. AJNR American journal of neuroradiology. 2012. pmid:22936095
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref20] 20. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Functional network organization of the human brain. Neuron. 2011;72(4):665–78. pmid:22099467
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref21] 21. Čeko M, Frangos E, Gracely J, Richards E, Wang B, Schweinhardt P, et al. Default mode network changes in fibromyalgia patients are largely dependent on current clinical pain. NeuroImage. 2020;216:116877. pmid:32344063
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref22] 22. Napadow V, LaCount L, Park K, As-Sanie S, Clauw DJ, Harris RE. Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity. Arthritis and rheumatism. 2010;62(8):2545–55. pmid:20506181
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref23] 23. Loggia ML, Segerdahl AR, Howard MA, Tracey I. Imaging Clinically Relevant Pain States Using Arterial Spin Labeling. Pain Rep. 2019;4(4).
View Article
Google Scholar

[90] View Article

[91] Google Scholar

[ref24] 24. Wasan AD, Loggia ML, Chen LQ, Napadow V, Kong J, Gollub RL. Neural correlates of chronic low back pain measured by arterial spin labeling. Anesthesiology. 2011;115(2):364–74. pmid:21720241
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref25] 25. Loggia ML, Kim J, Gollub RL, Vangel MG, Kirsch I, Kong J, et al. Default mode network connectivity encodes clinical pain: an arterial spin labeling study. Pain. 2013;154(1):24–33. pmid:23111164
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref26] 26. Howard MA, Krause K, Khawaja N, Massat N, Zelaya F, Schumann G, et al. Beyond patient reported pain: perfusion magnetic resonance imaging demonstrates reproducible cerebral representation of ongoing post-surgical pain. PloS one. 2011;6(2):e17096. pmid:21373203
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref27] 27. Youssef AM, Gustin SM, Nash PG, Reeves JM, Petersen ET, Peck CC, et al. Differential brain activity in subjects with painful trigeminal neuropathy and painful temporomandibular disorder. Pain. 2014;155(3):467–75. pmid:24269492
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref28] 28. Liu J, Hao Y, Du M, Wang X, Zhang J, Manor B, et al. Quantitative cerebral blood flow mapping and functional connectivity of postherpetic neuralgia pain: a perfusion fMRI study. Pain. 2013;154(1):110–8. pmid:23140909
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref29] 29. Shokouhi M, Davis KD, Moulin DE, Morley-Forster P, Nielson WR, Bureau Y, et al. Basal Ganglia Perfusion in Fibromyalgia is Related to Pain Disability and Disease Impact: An Arterial Spin Labeling Study. The Clinical journal of pain. 2016;32(6):495–505. pmid:26340652
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref30] 30. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9(1):97–113. pmid:5146491
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref31] 31. Morley S, de CWA, Black S. A confirmatory factor analysis of the Beck Depression Inventory in chronic pain. Pain. 2002;99:289. pmid:12237207
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref32] 32. Worlds. WMA Declaration of Helsinki—Ethical Principles for Medical Research Involving Human Subjects. Seoul: Wordls Medical Association. 2008.

[ref33] 33. Laux L, Glanzmann P, Schaffner P, Spielberger CD. State-Trait-Angstinventar (STAI). Göttingen: Hogrefe Verlag; 1981.

[ref34] 34. Offenbaecher M, Waltz M, Schoeps P. Validation of a German version of the Fibromyalgia Impact Questionnaire (FIQ-G). The Journal of rheumatology. 2000;27(8):1984–8. pmid:10955342
View Article
PubMed/NCBI
Google Scholar

[127] View Article

[128] PubMed/NCBI

[129] Google Scholar

[ref35] 35. Wu WC, Fernandez-Seara M, Detre JA, Wehrli FW, Wang J. A theoretical and experimental investigation of the tagging efficiency of pseudocontinuous arterial spin labeling. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. 2007;58(5):1020–7. pmid:17969096
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref36] 36. Dai W, Garcia D, de Bazelaire C, Alsop DC. Continuous flow-driven inversion for arterial spin labeling using pulsed radio frequency and gradient fields. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine. 2008;60(6):1488–97. pmid:19025913
View Article
PubMed/NCBI
Google Scholar

[135] View Article

[136] PubMed/NCBI

[137] Google Scholar

[ref37] 37. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15(1):273–89. pmid:11771995
View Article
PubMed/NCBI
Google Scholar

[139] View Article

[140] PubMed/NCBI

[141] Google Scholar

[ref38] 38. Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage. 2010;53(1):1–15. pmid:20547229
View Article
PubMed/NCBI
Google Scholar

[143] View Article

[144] PubMed/NCBI

[145] Google Scholar

[ref39] 39. Cantisani A, Stegmayer K, Bracht T, Federspiel A, Wiest R, Horn H, et al. Distinct resting-state perfusion patterns underlie psychomotor retardation in unipolar vs. bipolar depression. Acta Psychiatr Scand. 2016;134(4):329–38. pmid:27497085
View Article
PubMed/NCBI
Google Scholar

[147] View Article

[148] PubMed/NCBI

[149] Google Scholar

[ref40] 40. Wang J, Aguirre GK, Kimberg DY, Roc AC, Li L, Detre JA. Arterial spin labeling perfusion fMRI with very low task frequency. Magn Reson Med. 2003;49(5):796–802. pmid:12704760
View Article
PubMed/NCBI
Google Scholar

[151] View Article

[152] PubMed/NCBI

[153] Google Scholar

[ref41] 41. Walther S, Schappi L, Federspiel A, Bohlhalter S, Wiest R, Strik W, et al. Resting-State Hyperperfusion of the Supplementary Motor Area in Catatonia. Schizophr Bull. 2017;43(5):972–81. pmid:27729486
View Article
PubMed/NCBI
Google Scholar

[155] View Article

[156] PubMed/NCBI

[157] Google Scholar

[ref42] 42. Ramirez-Mahaluf JP, Roxin A, Mayberg HS, Compte A. A Computational Model of Major Depression: the Role of Glutamate Dysfunction on Cingulo-Frontal Network Dynamics. Cereb Cortex. 2017;27(1):660–79. pmid:26514163
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref43] 43. Mayberg HS, Liotti M, Brannan SK, McGinnis S, Mahurin RK, Jerabek PA, et al. Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am J Psychiatry. 1999;156(5):675–82. pmid:10327898
View Article
PubMed/NCBI
Google Scholar

[163] View Article

[164] PubMed/NCBI

[165] Google Scholar

[ref44] 44. Shi H, Yuan C, Dai Z, Ma H, Sheng L. Gray matter abnormalities associated with fibromyalgia: A meta-analysis of voxel-based morphometric studies. Semin Arthritis Rheum. 2016;46(3):330–7. pmid:27989500
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref45] 45. Ioannidis JP, Trikalinos TA. Early extreme contradictory estimates may appear in published research: the Proteus phenomenon in molecular genetics research and randomized trials. J Clin Epidemiol. 2005;58(6):543–9. pmid:15878467
View Article
PubMed/NCBI
Google Scholar

[171] View Article

[172] PubMed/NCBI

[173] Google Scholar

[ref46] 46. Pfeiffer T, Bertram L, Ioannidis JP. Quantifying selective reporting and the Proteus phenomenon for multiple datasets with similar bias. PloS one. 2011;6(3):e18362. pmid:21479240
View Article
PubMed/NCBI
Google Scholar

[175] View Article

[176] PubMed/NCBI

[177] Google Scholar

[ref47] 47. Johnstone T, van Reekum CM, Urry HL, Kalin NH, Davidson RJ. Failure to regulate: counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2007;27(33):8877–84. pmid:17699669
View Article
PubMed/NCBI
Google Scholar

[179] View Article

[180] PubMed/NCBI

[181] Google Scholar

[ref48] 48. Cheng CA, Chiu YW, Wu D, Kuan YC, Chen SN, Tam KW. Effectiveness of Tai Chi on fibromyalgia patients: A meta-analysis of randomized controlled trials. Complement Ther Med. 2019;46:1–8. pmid:31519264
View Article
PubMed/NCBI
Google Scholar

[183] View Article

[184] PubMed/NCBI

[185] Google Scholar

[ref49] 49. Chan CL, Wang CW, Ho RT, Ng SM, Ziea ET, Wong VT. Qigong exercise for the treatment of fibromyalgia: a systematic review of randomized controlled trials. J Altern Complement Med. 2012;18(7):641–6. pmid:22757663
View Article
PubMed/NCBI
Google Scholar

[187] View Article

[188] PubMed/NCBI

[189] Google Scholar

[ref50] 50. Pauer L, Winkelmann A, Arsenault P, Jespersen A, Whelan L, Atkinson G, et al. An international, randomized, double-blind, placebo-controlled, phase III trial of pregabalin monotherapy in treatment of patients with fibromyalgia. The Journal of rheumatology. 2011;38(12):2643–52. pmid:21965636
View Article
PubMed/NCBI
Google Scholar

[191] View Article

[192] PubMed/NCBI

[193] Google Scholar

[ref51] 51. Murakami M, Osada K, Mizuno H, Ochiai T, Alev L, Nishioka K. A randomized, double-blind, placebo-controlled phase III trial of duloxetine in Japanese fibromyalgia patients. Arthritis Res Ther. 2015;17:224. pmid:26296539
View Article
PubMed/NCBI
Google Scholar

[195] View Article

[196] PubMed/NCBI

[197] Google Scholar

Figures

Abstract

Introduction

Material and methods

Participants and study design

Assessment of socio-demographic, psychological and clinical characteristics

Neuroimaging

Image acquisition.

Selection of Region of Interests (ROIs).

Image preprocessing and calculation of functional and structural neuroimaging markers.

Statistical analysis

Results

Study flow

Socio-demographic, psychological and clinical characteristics

Differences in resting-state cerebral blood flow and correlation with pain characteristics

Differences in local resting state cerebral blood flow of 11 pre-specified ROIs

Differences in resting-state functional connectivity, grey matter volume and cortical thickness

Subgroup analyses of patients not taking centrally acting drugs

Discussion

Main findings

Scientific context of our findings

Strengths and limitations

Implications

Supporting information

S1 Fig. Flow diagram of fibromyalgia patients and controls clinically evaluated between 1st July 2011 and 30th June 2013 and recruited for the study between 1st November 2013 and 31st January 2015 at the University Hospital of Bern.

S1 Table. Unadjusted differences in resting state perfusion (rsCBF, ml/100g/min) between 32 fibromyalgia patients and 32 pain-free controls in 11 pre-specified Regions of Interest.

S2 Table. Unadjusted differences in grey matter volume between (GMV) 32 fibromyalgia patients and 32 pain-free controls in 10 pre-specified Regions of Interest.

S3 Table. Unadjusted differences in cortical thickness (CoTh, mm) between 32 fibromyalgia patients and 32 pain-free controls in 20 pre-specified Regions of Interest.

Acknowledgments

References

S1 Fig. Flow diagram of fibromyalgia patients and controls clinically evaluated between 1^st July 2011 and 30^th June 2013 and recruited for the study between 1^st November 2013 and 31^st January 2015 at the University Hospital of Bern.