Abstract
Objective Abnormal functional connectivity (FC) and structure in the brain are found in patients with fibromyalgia (FM). This study investigated FC and structural alterations of the visual cortical system, the emerging contributor to pain processing, in patients with FM.
Methods Thirty pain-free participants and 26 patients with FM were enrolled. Clinical characteristics were evaluated using standardized scales. Structural and resting-state functional magnetic resonance imaging were conducted. Seed-based FC analyses, voxel-based morphometry, and surface-based morphometry were performed. The FC and cortical structure of the visual system were compared between the 2 groups. The correlation between functional and structural changes in the visual cortical system with clinical presentation in the FM group was analyzed.
Results The patients with FM showed increased FCs within visual networks, of which the FC between the visual medial network and the right lingual gyrus (LG) was positively correlated with the Fibromyalgia Impact Questionnaire (FIQ) score. However, the FM group showed decreased FCs from the visual occipital network (VON) to several regions, of which the FCs from the VON to the bilateral frontal orbital cortices were negatively correlated with the FIQ and Pittsburgh Sleep Quality Index scores. Cortical thickness of the lateral occipital cortex, LG, and pericalcarine in FM tended to increase.
Conclusion Altered FCs and structure in the visual cortical system might be involved in the pathomechanisms and clinical presentation in FM. These findings could potentially support further studies that seek to find diagnostic methods and mechanism-based therapies in patients with FM.
Fibromyalgia (FM) is a widespread pain disorder with fatigue and psychological distress.1 The brain is a main pathological target in FM.2 Previous studies reported that patients with FM showed altered resting-state functional connectivities (rs-FCs) in the salience network, default mode network, frontoparietal network, and somatosensory network.3-5 Changes in cortical structure were also observed in different brain regions in FM.4,6 Both functional and structural alterations were involved in the clinical presentation in FM.1,2
The visual cortical system, defined as the group of brain regions responsible for visual functions, has emerged as a pathological signature of pain and psychological distress.6-8 The visual cortical system might be divided into 3 networks: the visual medial network (VMN), the visual occipital network (VON), and the visual lateral network.9 The VMN comprising striate and parastriate regions (eg, the lingual gyrus [LG], cuneus, and calcarine cortex) is responsible for stimulus processing, memory consolidation, and cognitive responses.9-11 The VON, mainly located in the occipital pole, is proposed to play a critical role in the higher visual process and attention.9 Meanwhile, the visual lateral network, extending from the lateral occipital cortex (LOC) to the temporal and parietal cortices, takes responsibility for motion and multimodal processing.9,12 The abnormal alteration of the visual cortical system in chronic pain was repeatedly observed in patients with chronic low back pain,7 migraine,13 rheumatoid arthritis,14 and persistent somatoform pain disorder.15
A previous study demonstrated the linkage between visual functional networks and fatigue.16 Another study observed that the discomfort symptom in chronic pain has been shown to be correlated with the activation of the LG measured by functional magnetic resonance imaging (MRI).17 Changes in γ-aminobutyric acid (GABA) levels in the occipital cortex have been identified as a neurochemical abnormality that causes sleep disorders.18 It should be noted that fatigue, discomfort, and sleep disorders are common symptoms of FM.1 Moreover, previous studies reported that patients with FM have clinical visual disturbances and hypersensitivity that were not observed in other kinds of chronic pain disorders.19,20 Those findings imply that FM could affect the visual cortical system differently compared to other chronic pain disorders, and the alterations in visual cortical networks might be involved with several symptoms observed in FM, such as fatigue or sleep disturbances. However, to the best of our knowledge, no study has focused on visual cortical system alterations and their relationship with clinical presentation in FM. Our previous study using machine learning to build the model for identifying FM showed that some MRI features of visual cortical networks belong to the model.21 In this study,21 we used a feature selection method to select the group of features that interacted together to classify FM; thereby, the MRI features of visual networks observed in the machine learning model might not represent all specific alterations in visual cortical networks. In this regard, by using second-level seed-based FC analysis and voxel-based morphometry analysis, the current study aims to evaluate the abnormalities of rs-FC and structure in the visual cortical system, linking them with pain and FM presentation. We hypothesized that patients with FM might have altered FCs and structures in the visual cortical system, which are involved in the clinical effects of FM.
METHODS
Participants. Twenty-six patients with FM at Taipei Medical University Hospital were recruited into the FM group. Thirty healthy people were recruited into the control group. According to previous evidence,22 the sample size used in this study could ensure statistical power in comparing MRI data between the 2 groups. Data for participants meeting eligibility criteria have been reported in the previous study, which applied machine learning with a feature selection method to classify patients with FM and did not focus on investigating the alterations of rs-FCs and structure of the visual cortical system in FM (different research questions, aims, and design).21 In brief, the included patients with FM were required to: (1) satisfy the American College of Rheumatology 2016 diagnosis criteria at the time of diagnosis; (2) have received stable medication dosages; (3) not have histories of brain injuries, major neurological disorders, substance abuse, or cancers; (4) not be pregnant; and (5) not have contraindications to MRI techniques.
Statement of ethics and consent. The institutional review board of Taipei Medical University approved the procedure of this study (N201812078). All participants were introduced to the details of this study, and then provided written informed consent before participating.
Clinical assessment. We re-evaluated clinical presentation of the patients before MRI acquisitions using standardized scales, including the Pittsburgh Sleep Quality Index (PSQI), Beck Anxiety Inventory (BAI), Beck Depression Inventory-II (BDI-II), visual analog scale (VAS), Widespread Pain Index, Symptom Severity Scale, and Fibromyalgia Impact Questionnaire (FIQ). The pressure pain threshold for patients with FM was measured by a handheld pressure gauge (Algometer, Paintest, Wagner Inc.; described in the Supplementary Data, available with the online version of this article).
MRI acquisitions and analysis. Functional and structural MRI were acquired and preprocessed (described in the Supplementary Data, available with the online version of this article). For functional MRI data, after preprocessing, the rs-FC was calculated by seed-to-voxel analysis using the CONN toolbox.23 The seeds included the VMN (Montreal Neurosciences Institute [MNI] coordinate: 2, −79, 12), the VON (MNI coordinate: 0, −93, −4), the left visual lateral network (MNI coordinate: −37, −79, 10), and the right lateral network (MNI coordinate: 38, −72, 13), defined by the built-in network of cortical regions of interest (ROIs) in the CONN toolbox, which were derived from the independent component analysis of the Human Connectome Project data.23 In the first-level analysis, the correlation analyses between the average blood oxygenation level-dependent time series of each seed and the whole-brain voxels were performed. The correlation coefficients were Fisher-transformed to z scores. In the second-level analyses, the independent sample t test was used to compare the z scores of FCs between the 2 groups. Given that age and sex together affect FCs in the participants, we used age and sex as covariates of no interest. The voxel threshold was selected at a 1-sided P < 0.001, and the cluster threshold was set at P < 0.05 with family-wise error (FWE) correction.24,25 The anatomical brain regions were labeled by the Harvard-Oxford cortical atlas (the built-in atlas in the CONN toolbox). Only the anatomical regions covered by at least 30 voxels of the significant voxel clusters were reported. Seed-to-ROI analyses were further performed to support the findings of seed-to-voxel analyses. The target ROIs were the anatomical brain regions covered by the significant clusters from seed-to-voxel analyses. The connection threshold was selected at a 1-sided P < 0.05 that was FWE-corrected (Holm-Bonferroni correction for multiple comparisons of each seed).
For structural MRI data, voxel-based morphometry (VBM) and surface-based morphometry (SBM) analyses were performed by using the Computational Anatomy Toolbox (CAT12) in SPM12.26 We used independent sample t test design to compare brain morphology characteristics between the FM and control groups, focusing on visual cortical regions that included the cuneus, fusiform gyrus, inferior temporal cortex, LOC, LG, pericalcarine cortex, superior parietal cortex, inferior parietal cortex, and temporal pole.12,27 According to the guidance of the CAT12,26 total intracranial volume values (for VBM), as well as age, sex, and BMI (for both VBM and SBM), were selected as covariates of no interest. We first selected the P value (FWE-corrected) of < 0.05 as an initial significance level. However, because the sample size was relatively small and our a priori hypothesis focused on specific regions, if there was no difference observed at the initial significance level, we then selected the liberal, exploratory significance level of 0.001 uncorrected, which was commonly used in previous VBM studies (with a priori hypotheses) to guard against false-positive results.28,29 Additionally, the extent thresholds of 30 contiguous voxels were applied. The voxel clusters were labeled by the Harvard-Oxford cortical atlas (for VBM) and the Desikan–Killiany atlas (for SBM). The gray matter volume, white matter volume, and cortical thickness of the significantly altered regions in the FM group were extracted for further correlation analyses with clinical data.
Baseline characteristics comparisons and correlation analyses. The results were expressed as mean (SD), except the sex variable, which was expressed as the ratio of females to males. The normal distribution of variables was evaluated using the Shapiro-Wilk test. Differences between the 2 groups were examined using the independent sample t test (for continuous variables with normal distribution) or the Mann-Whitney U test (for continuous variables without normal distribution), and the chi-square test (for the sex variable). In addition, the Kendall rank correlation method (corrected by false discovery rate for multiple comparisons) was performed to evaluate the relationships between clinical assessment scores with the altered MRI features and the visual cortical structure. P < 0.05 was considered statistically significant. R (version 4.1.2) and Jeffrey’s Amazing Statistics Program (version 0.16.3)30 were used to perform statistical analysis.
RESULTS
Demographic and clinical presentation. The demographics and clinical assessment scores of the participants are shown in Table 1. The FM group and healthy control group did not show significant differences in age, sex, and BMI. The FM group showed significantly higher levels of sleep disorder (PSQI), anxiety (BAI), and depression (BDI-II).
Demographic and clinical participant characteristics.
Seed-to-voxel analysis for visual networks. Compared to the control group, the FM group showed increased FCs between the VMN and the voxel cluster encompassing the right LG and the right occipital fusiform gyrus (OFusG; P = 0.03, FWE-corrected; Table 2, Figure 1A). In contrast, compared to the control group, the FM group showed decreased FCs from the VON to the voxel cluster of the left frontal orbital cortex (FOrb) and left temporal pole (P < 0.001, FWE-corrected), as well as the voxel cluster of the right FOrb, subcallosal cortex, and right accumbens (P = 0.001, FWE-corrected; Table 2, Figure 1B). Differences in the FCs of the visual lateral network right and left between the 2 groups were not found.
The altered seed-to-voxel functional connectivity in patients with FM compared to control.
The seed-to-voxel FC analysis. The figure shows the differences in FCs of visual networks between the FM group and the control group. (A) In the VMN, the FCs in the FM group were increased in the voxel cluster 1 encompassing the right LG and the right occipital fusiform gyrus. (B) The decreased FCs between the VON with the voxel cluster 2 (encompassing the left frontal orbital cortex and the left temporal pole) as well as with the voxel cluster 3 (encompassing the right frontal orbital cortex, subcallosal cortex, and right accumbens) in the FM group compared to the control group. (A, B) The bar charts on the right side show differences between the 2 groups in FCs. The significance level was set at P < 0.001, uncorrected for voxel thresholds, and P < 0.05 FWE-corrected for cluster thresholds. The default atlas provided by the CONN toolbox was used for locating the anatomical regions. Note: x, y, and z are MNI coordinates, measured in mm. FC: functional connectivity; FM: fibromyalgia; FWE: family-wise error; LG: lingual gyrus; MNI: Montreal Neurosciences Institute; T: Kendall τ correlation coefficient; VMN: visual medial network; VON: visual occipital network.
Seed-to-ROI analysis for visual networks. Compared to the control group, the FM group showed significant hyperconnectivity between the VMN with the right LG (β 0.17; T-value 1.96) and the right OFusG (β 0.17; T-value 2.24). In contrast, the FM group showed significant hypoconnectivity between the VON with the left FOrb (β −0.15; T-value −3.05), the right FOrb (β −0.14; T-value −2.95), and the right accumbens (β −0.14; T-value −3.51; Table 3).
The altered seed-to-ROI functional connectivity in patients with FM compared to control.
VBM and SBM analysis. At the initial significance level (P < 0.05, FWE-corrected), VBM and SBM analysis did not show a significant difference in visual cortical morphology between the 2 groups. However, at the exploratory significance level (P < 0.001; cluster-size > 30), the thickness of the left LOC (k 122), left LG (k 82), and left pericalcarine cortex (k 61) in the FM group were increased compared with the control group (P < 0.001 uncorrected; Figure 2).
Morphology differences in visual network between the 2 groups. The figure shows the differences in cortical thickness between the FM group and the control group, conducted by CAT12 in SPM12. The cortical thickness in the cluster of the left LOC, LG, and pericalcarine cortex in the FM group was higher than in the control group. The bar chart shows the contrast estimate and 90% CI for cortical thickness in the peak voxel of the cluster. The significance level was set at P < 0.001 uncorrected, with the extent thresholds being 30 voxels. CAT12: Computational Anatomy Toolbox; FM: fibromyalgia; LG: lingual gyrus; LOC: lateral occipital cortex.
Correlations between the FC of visual networks and clinical characteristics. The FC between the VMN and the right LG was positively correlated with the FIQ score (Kendall τ 0.35; P = 0.048, false discovery rate [FDR]–corrected; Figure 3A). The FC between the VON and the left FOrb was negatively correlated with the FIQ score (Kendall τ −0.36; P = 0.048, FDR-corrected; Figure 3B). In addition, the FC between the VON and the right FOrb was negatively correlated with the PSQI score (Kendall τ −0.38; P = 0.048, FDR-corrected; Figure 3C). There was no significant correlation between structural data and clinical data in this study.
The correlations between FC of visual networks and clinical assessment scores. (A) The FC of the VMN and the VMN–LGR was positively correlated to the FIQ score. (B) The FC from the VON to the FOrbL was negatively correlated with FIQ scores. (C) The FC from the VON to the FOrbR was negatively correlated with PSQI score. FC: functional connectivity; FIQ: Fibromyalgia Impact Questionnaire; FOrbL: left frontal occipital cortex; FOrbR: right frontal occipital cortex; LGR: right lingual gyrus; p-FDR: P value after false discovery rate correction; PSQI: Pittsburgh Sleep Quality Index; T: Kendall τ correlation coefficient; VMN: visual medial network; VON: visual occipital network.
DISCUSSION
This study investigates whether the visual cortical system is altered in patients with FM. We found that: (1) the FCs within visual networks were increased in FM, as evidenced by hyperconnectivity between the VMN with the visual-related brain regions (eg, the right LG and OFusG) in the FM group; (2) the increased FCs within visual networks were associated with clinical presentation in FM, as evidenced by the positive correlation between FCs of VMN to the right LG with FIQ score; (3) the internetwork FCs from visual networks to other cortical regions were impaired in FM, supported by the hypoconnectivity from the VON to several brain regions (eg, bilateral FOrb, temporal pole, accumbens, and subcallosal cortex) in the FM group; (4) the internetwork FCs from visual networks were related to the clinical presentation in FM, as evidenced by the correlations between FCs from the VON to bilateral FOrb with PSQI and FIQ scores; and (5) visual cortical structure tended to be altered in FM, supported by increased cortical thickness in several visual cortical regions (at a liberal threshold). Taken together, this study suggests that the visual cortical system might be involved in the pathomechanisms of FM, providing new insight into the understanding of FM etiology.
This study shows that intravisual network FCs were activated in the patients, which is compatible with a previous study observing hyperperfusion in several areas of the visual cortex, including the right superior occipital gyrus and the right cuneus in FM.31 Notably, FCs among visual cortical regions were also activated in other types of chronic pain disorders.32,33 For instance, a previous study performed heat stimulation in patients with different kinds of chronic pain disorders (including FM) and observed increased FCs among visual cortical regions, including the left cuneus, left LG, and left fusiform gyrus.32 Another study showed resting-state hyperconnectivity between the right occipital cortex and right cuneus cortex in chronic low back pain.33 In addition, the resting-state amplitude of low-frequency fluctuation levels in the bilateral middle occipital gyrus and right fusiform gyrus have been shown to augment in patients with migraine (without aura), which were considered as classifiers to discriminate patients with migraine from healthy controls.13 Those studies suggested that, in addition to other brain networks such as the default mode network and salience network, the changes within visual networks might also be involved in chronic pain disorders, including FM.
Our study demonstrated that the FC of the right LG within the VMN was increased correlative to FM effects measured by FIQ score. The VMN plays a primary role in processing stimuli, in which the LG is responsible for memorizing visual signals and emotional control, including the negative emotion in pain.10 Evidence suggests that the activation of visual networks might be a compensatory plasticity response of the central nervous system after long-term suffering from pain, distracting patients from pain to help them partially adapt to chronic pain,34 which might consequently increase cognitive load and mental fatigue in patients. Supporting this, a previous study showed that the hyperconnectivity among visual cortical regions (eg, cuneus, LG, occipital pole, and fusiform gyrus) was positively correlated with fatigue.16 Another study suggested that the LG might be involved in divergent thinking.35 In addition, the LG was activated during high cognitive demand, increasing mental fatigue in patients with multiple sclerosis.36 Altogether, the findings suggest that the VMN, including the LG, was activated to adapt to chronic pain, but this adaptive response further increased fatigue in patients with FM. Additionally, the current study did not find a significant correlation between FCs within the VMN and VAS score, implying that this adaptive functional alteration in visual networks might be time-dependent and not represent the current pain intensity of the patients.
We identified that patients with FM showed impaired FCs between visual networks and the other cortical regions of large-scale neural networks, including the executive control network (FOrb) and the default mode network (temporal pole and subcallosal cortex), which play a crucial role in pain processing, cognitive function, and emotional control.37,38 Supporting this, previous studies showed that the rs-FCs between superior bilateral occipital cortices and the posterior cingulate cortex were increased,39 whereas the rs-FCs between the visual cortex and the secondary somatosensory cortex were reduced in FM.40 Similarly, in patients with chronic low back pain, rs-FCs from visual networks were also abnormal, characterized by both hyperconnectivity and hypoconnectivity from visual networks to several cortical regions, including the primary sensory cortex (S1), motor cortex (M1), supplementary motor area, anterior cingulate cortex, inferior frontal gyrus, and precuneus.7 rs-FCs from visual networks to somatosensory networks, default mode networks, and audio networks were also reduced in persistent somatoform pain disorder.15 Those findings emphasized that the internetwork connectivity from visual networks was also impaired in chronic pain disorders, including FM. However, pathological and psychological differences among diseases could manifest in different patterns of FCs from visual networks.
Further, our study shows that interrupted connections between visual networks and the FOrb were negatively correlated with the effect of FM on the patients, as measured by the FIQ and PSQI scores. This means that the weaker connections between the FOrb and visual networks corresponded with the higher levels of fatigue and sleep disorder in patients. The FOrb is one of the executive control network nodes, which plays a crucial role in encoding the stimulus-reward relations for behavioral adaptation.3,41 In addition, the FOrb projects to sensory cortices to control precise perception, including the visual cortical system.42,43 FOrb activation could reduce the visual cortex responses for filtering the stimuli input.43 Moreover, the structure of FOrb was related to anxiety in FM.44 Another study reported that the activity of FOrb measured by positron emission tomography scans was related to fatigue in healthy participants.45 Therefore, we hypothesized that the hypoconnectivity between the visual networks and the FOrb, as observed in our study, might reflect the dysregulation from the FOrb to visual cortical systems. This could induce the impairment of filtering irrelevant stimuli encoded in visual networks, thereby augmenting fatigue in FM.
In our previous machine learning study, rs-FCs from the left visual lateral network to the dorsal attention network and the VMN to the cerebellar network were selected as features in the classification models, but those features did not correlate with clinical presentation in the patients.21 When we directly compared FCs of visual networks using second-level seed-based FC analysis in the current study, those FCs were also insignificantly different between the 2 groups. It should be noted that the machine learning approach focuses on building the model of interaction among several selected FC features to classify FM, whereas seed-based FC analysis focuses on comparing FCs from each visual network between the 2 groups, thereby leading to the different results observed in the 2 studies.23 Although the 2 studies suggested that the rs-FCs of visual networks appear to be altered in FM, whether visual networks are primarily changed or are mediators among other networks is still unclear. The mediation analysis study of FCs in FM needs to be further conducted in a larger sample to examine the internetwork effects, including visual cortical networks.
In addition to FCs, by applying a liberal threshold, the current study showed that the cortical thickness in several visual cortical regions, including the LOC, LG, and pericalcarine, tended to increase. Supporting this, a previous study noted that the gray matter volume of the cuneus, the other typical region of the visual cortical system, was increased in patients with FM, for which the patient’s attention to nociceptive and unpleasant stimuli increased.6 Moreover, the increased gray matter volume of the cuneus was related to the increased expression of GABA receptors, which were the molecular alterations in FM.6 However, previous evidence suggests that the structural changes of the brain in FM are varied, which might increase in the early stage to compensate for pain and then decrease in the late stage of FM.4 This suggests that the visual cortical system could be time-dependently altered along with FM progression, and thereby might not be linearly correlated with clinical presentation. It should be noted that the structure differences in the visual cortex in the current study are only at the exploratory level and thus require further investigation in studies with larger sample sizes.
Several limitations need to be considered in the current study. First, the study sample size was relatively small. In addition, as mentioned previously, the visual cortical system might be time-dependently changed depending on disease duration, which makes the results varied among cross-sectional studies. Therefore, further longitudinal studies with a large sample size need to be conducted to confirm the alterations in the visual cortical system in FM. Second, our study showed that visual networks were altered in FM but could not clarify whether those alterations were the primary pathological characteristics or the secondary consequences of other mechanisms in FM. It should be noted that actual neuronal activity in the visual cortex might not change correlatively with FCs. Thus, further studies should address the underlying mechanisms of neural alterations, including the visual cortex in FM. Finally, the study population is not treatment-naïve for FM. Therefore, the potential effects of treatments (eg, medications) cannot be excluded from the present study.
In conclusion, our study suggests that the FCs and structure of the visual cortical system were altered in FM. Those alterations might reflect the effects of the disease on the patients, which might be the partial pathomechanisms for their psychological distress, especially fatigue and sleep disturbances. The study findings might help extend the understanding of FM pathology and support the associations between visual networks and chronic pain disorders. The findings might also support further studies addressing effective therapeutic approaches in FM, such as whether we can attenuate pain and psychological symptoms in patients with FM by modulating FCs of neural networks, including visual cortical networks.
Footnotes
This study was supported by a grant from Ministry of Science and Technology, Taiwan (108-2314-B-038-101-MY2).
N.T. Nhu and D.Y.T. Chen contributed equally to this work.
The authors declare no conflicts of interest relevant to this article.
- Accepted for publication March 21, 2023.
- Copyright © 2023 by the Journal of Rheumatology
