Large-Scale Brain Network Dysfunction in Fibromyalgia

Large-Scale Brain Network Dysfunction in Fibromyalgia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Large-Scale Brain Network Dysfunction in Fibromyalgia Jiancheng Hou, Peitao Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9054674/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Fibromyalgia (FM) is a chronic pain disorder characterized by widespread pain and affective disturbances, yet the large-scale brain network mechanisms underlying these symptoms remain incompletely understood. The present study employed a data-driven and whole-brain functional network approach to investigate the neural network alterations in FM patients and their relationship with clinical symptoms. Methods Resting-state fMRI data from thirty-three female FM patients and thirty-three age-matched healthy controls (HC) were analyzed. Whole-brain network functional connectivity (FC) was compared between two groups, and correlations with clinical measures of pain (FIQ, MPQ), negative affect (HAMD, HAMA), and alexithymia (TAS) were examined. Permutation FDR correction p < .005 was applied for multiple comparisons. Results FM patients exhibited widespread hypoconnectivity compared to HC group, including less intra-network FC within the default mode network (DMN), frontoparietal network (FPN), and somatosensory network (SMN). Critically, FM patients showed significant thalamocortical decoupling between the subcortical network (thalamus) and the DMN, SMN, and FPN. Correlation analyses revealed distinct brain-behavior relationships in FM patients: pain impact (FIQ) and anxiety (HAMA) correlated positively with thalamus-FPN connectivity, while depression (HAMD) correlated negatively with DMN-dorsal attention network (DAN) FC. Alexithymia (TAS) uniquely correlated with FPN-ventral attention network (VAN) FC in FM patients — a pattern not observed in HC group. Conclusions Fibromyalgia is characterized by a broad disintegration of FC across multiple large-scale networks, extending beyond central sensitization to involve disrupted thalamocortical integration and impaired communication between affective, cognitive, and sensory systems. The distinct symptom-specific correlation patterns suggest that network-based biomarkers could inform personalized therapeutic approaches targeting specific network dysfunctions. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Neurology Biological sciences/Neuroscience fibromyalgia resting-state fMRI functional connectivity brain network clinical measures Figures Figure 1 Figure 2 Figure 3 1. Introduction Fibromyalgia (FM) is a complex and debilitating chronic pain disorder that predominantly affects women, with a global prevalence estimated to be significantly higher in females than in males. Clinically, FM is characterized by widespread, persistent pain, often accompanied by a constellation of other debilitating symptoms, including profound fatigue, unrefreshing sleep, cognitive disturbances (“fibro-fog”), and marked emotional distress (Kravitz & Katz, 2015 ). The high prevalence of psychiatric comorbidities, particularly depression and anxiety, underscores the intricate relationship between physical and emotional dysfunction in this condition (Galvez-Sanchez et al., 2019 ; Rahangdale & Ferraro, 2025 ). The lifetime prevalence of major depressive disorder in FM patients is estimated to be over 50%, with anxiety disorders affecting up to one-third of individuals (Oliveria Neto et al., 2025 ). This affective imbalance — characterized by diminished positive affect and heightened negative affect t— is not merely a secondary consequence of living with chronic pain; it is intrinsically linked to pain intensity, overall symptom severity, and reduced quality of life (Rost et al., 2021 ; Stroman et al., 2025 ; Zautra et al., 2005 ). The prevailing pathophysiological model for FM is one of central sensitization, a condition in which the central nervous system (CNS) amplifies sensory input, leading to hypersensitivity to painful (hyperalgesia) and non-painful (allodynia) stimuli (Jurado-Priego et al., 2024 ). This aberrant processing is thought to be driven by complex neurobiological alterations, including neuro-inflammation and imbalances in neurotransmitter systems (Sluka & Clauw, 2016 ). Neuroimaging has been instrumental in advancing our understanding of these CNS abnormalities, revealing that FM is not merely a disease of the peripheral tissues but one of altered brain structure, function, and connectivity (Jensen et al., 2013 ; Napadow & Harris, 2014 ). Functional magnetic resonance imaging (fMRI) studies have consistently demonstrated aberrant brain activity and functional connectivity (FC) in FM patients, particularly within the neural networks subserving pain modulation, salience detection, and emotion processing (Stroman et al., 2025 ). The so-called “pain matrix”, a distributed network of brain regions including the thalamus, insula, anterior cingulate cortex, and prefrontal cortices, shows heightened responses to both painful and non-painful stimuli in FM, reflecting the central amplification of sensory input (Ackermann et al., 2025 ; Staud, 2011 ). More recently, research has shifted focused from sensory-discriminative processing to the critical interplay between pain and emotion, given the profound affective disturbances in FM. A pivotal study by Balducci et al. ( 2024 ) provided crucial evidence that the neural circuits governing emotional experience are fundamentally altered in FM patients. Their fMRI findings revealed that patients, compared to healthy controls, exhibited hyperactivation of the left superior lateral occipital cortex during the processing of emotional stimuli, irrespective of valence. This hyperactivation was positively correlated with the severity of depression and anxiety, suggesting that visual attention and pain modulation networks may be recruited differently in FM during emotional experiences. Furthermore, Balducci et al. identified a valence-dependent disruption in the FC of the left pregenual anterior cingulate cortex (pACC) — a critical hub for the integration of affective and sensory information — with sensorimotor and opercular regions. Specifically, they observed higher pACC connectivity during positive emotion processing and lower connectivity during negative emotion processing in FM patients. These findings provide a compelling neural basis for the high prevalence of psychopathological symptoms and the strong affective component of pain in FM patients, demonstrating that emotional processing is not just altered, but is fundamentally dysregulated in a valence-specific manner. While studies like that of Balducci et al. have been instrumental in identifying key brain regions and their functional interactions, their analysis and results are often inherently limited by the specific regions. This approach may not capture the full extent of dysregulation across the brain’s interconnected systems. Understanding brain activity patterns in a data-driven, unbiased way can avoid the limitations and potential flaws inherent in a priori assumptions, which is particularly important when the precise mechanisms of conditions like FM are not fully known. To capture the full complexity of brain dysfunction, network science offers a powerful framework. It posits that the human brain is organized as a global network of interconnected regions, integrating long-range connections for global communication and local short-range connections for specialized processing, thereby supporting high-level cognitive and affective functions (Li et al., 2018 ; Park & Friston, 2013 ). This perspective allows us to move beyond isolated regional differences and examine how entire brain systems interact. As far as we know, relatively few studies have employed this data-driven, whole-brain network approach to examine the intrinsic functional architecture of FM. Therefore, the present study aimed to investigate the resting-state fMRI whole-brain network FC differences between FM patients and healthy controls through a data-driven approach, and to explore how these network-level alterations relate to the core clinical symptoms of pain, affect, and alexithymia. 2. Methods 2.1 Participants The participants’ information, and their anatomical T1-weighted imaging dataset, were obtained from a public dataset via OpenNeuro with accession number ds004144 ( https://openneuro.org/datasets/ds004144/versions/1.0.2 ). There were thirty-three female FM patients (mean age = 41.727 ± 6.094 years old, ranged from 30 to 50 years) and also thirty-three female healthy controls (HC; mean age = 41.515 ± 6.032 years old, ranged from 43 to 50 years) were selected. No significant age difference was found ( t (64) = 0.142, p = .887). Ethical approval was granted by the Research Ethics Committee of the National Institute of Psychiatry “Ramon de la Fuente Muñiz” in Mexico City. The inclusion and exclusion criteria followed those outlined by (Balducci et al., 2024 ). 2.2 Clinical measures Five clinical measures were used. To measure severity of depressive and anxiety, the 17-items Hamilton Depression Rating Scale (HAMD) (Ramos-Brieva & Cordero-Villafafila, 1988 ) and the Hamilton Anxiety Rating Scale (HAMA) (Lobo et al., 2002 ) were administered. Moreover, the alexithymia with the Toronto Alexithymia Scale (TAS) (Bagby et al., 1994 ) was used as a clinical variable to be measured using self-rating scales. Furthermore, the Fibromyalgia Impact Questionnaire (FIQ) (Burckhardt et al., 1991 ) and the McGill Pain Questionnaire (MPQ) (Masedo & Esteve, 2000 ; Melzack, 1975 ) were administrated in the fibromyalgia group to evaluate the severity of fibromyalgia and the characteristics of pain, respectively. 2.3 MRI data acquisition The original neuroimaging dataset was acquired on a 3.0 T Philips Ingenia Magnetic Resonance Imaging scanner with a 32-channel phased array head coil. The parameters for RS-fMRI data were: TR = 3000 ms, TE = 40 ms, flip angle = 90°, feld of view = 256 × 256 mm 2 , voxel size = 2 × 2 mm, number of slices = 43, matrix = 128 × 128, slice thickness = 3 mm, gap = 0 mm. The parameters for anatomical T1-weighted data were: TR = 7.7 ms, TE = 3.2 ms, flip angle = 12°, feld of view = 256 × 256 mm 2 , matrix = 256 × 256, slice thickness = 1 mm, number of slices = 168, gap = 0 mm. 2.4 Data preprocessing Preprocessing for RS-fMRI data were performed using the Data Processing and Analysis of Brain Imaging (DPABI) toolbox version 6.0 ( http://rfmri.org/dpabi ). This toolbox involves one main function called Data Processing Assistant for Resting-state fMRI Advanced Edition toolbox (DPARSF V5.3) (Chang & Glover, 2010 ; Yan et al., 2016 ), which is a convenient plug-in software that works with Matlab and Statistical Parametric Mapping (SPM, version 12) ( https://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ). The original data was firstly arranged; the first five volumes were ignored so that participants could get used to scanner noise. The preprocessing steps, in order, includes slice timing, realignment, regressing out head motion parameters (scrubbing with Friston 24-parameter model regression) and normalization (spatial normalization to the MNI template, resampling voxel size of 3 × 3 × 3mm) (Chao-Gan & Yu-Feng, 2010 ; Kuhn et al., 2012 ). The symmetric correlation matrix (considered as spontaneous neural connectivity) for a 142 × 142 network, which include 142 regions/nodes with the Dosenbach atlas across whole brain (Dosenbach et al., 2010 ), was generated in each participant. Therefore, each participant had 20,164 unique pairwise RSFC in total, but only half of the pairwise RSFC (10,082) within the network was used for next network construction, because the top right half and bottom left half were the same in matrix. 2.5 Network functional connectivity construction Each participant’s network functional connectivity (FC) was constructed with the DPABINet V1.1 that is included in DPABI V6.0. The 142 nodes in the Dosenbach atlas were classified into seven subnetworks: visual network (VN), somatosensory network (SMN), ventral attention network (VAN), dorsal attention network (DAN), default mode network (DMN), subcortical network (SC) and frontoparietal network (FPN) (Yeo et al., 2011 ). Based on the 142 × 141 matrix generated at the preprocessing step, the network FC for any pair of two nodes was calculated as Pearson’s linear correlation coefficient, and the Fisher- z transformation was then used for the symmetric correlation matrix. 2.6 Statistical analysis The statistical analysis was performed in DPABINet. The group t -test was conducted to compare the network FC differences between FM patients and HC group. The age was considered as covariates. The multiple comparison correction was used the permutation false discovery rate (FDR) corrected p < .005 in DPABINet, and the results were visualized by DPABINet Viewer. Moreover, the correlation analysis between the clinical measures and the network FC was also performed, and the multiple comparison correction was also used the permutation FDR corrected p < .005, in DPABINet, in FM patients or HC group, respectively. 3. Results 3.1 Clinical measure tests The group t -test showed that compared to HC group, FM patients had significantly higher scores in HAMD and HAMA, but there was no significantly group differences in FIQ and MPQ (see Table 1 ). Table 1 . Clinical measure tests between FM patients and HC group. Tests FMs HCs t p FIQ 3.546 (2.137) 2.697 (2.186) 1.594 .116 MPQ 1.758 (1.091) 1.727 (0.911) 0.123 .903 HAMD 15.576 (6.374) 1.212 (1.932) 12.388 .000 HAMA 21.546 (6.833) 2.182 (2.430) 15.338 .000 TAS 42.788 (14.482) - - - 3.2 Network functional connectivity changes Table 2 and Figures 1 to 3 show the significant intra- and inter-network FC differences between FM patients and HC group. Compared to HC group, FM patients had significantly less intra-network FC between the right ventromedial prefrontal cortex and right superior temporal gyrus, between the left precuneus and right superior temporal gyrus, between the left posterior cingulate cortex and left inferior parietal lobule within the DMN; between the right ventrolateral prefrontal cortex and left inferior parietal lobule within the FPN; between the left precentral gyrus and left precentral gyrus, between the right precentral gyrus and right middle insula within the SMN. Compared to HC group, FM patients had significantly less inter-network FC between the SC and DMN, SMN or FPN, respectively; between the DMN and FPN pr DAN, respectively; between the FPN and SMN or VAN, respectively; between the SMN and DMN or VAN, respectively. Table 2 . Group t -test differences of network FC between FM patients and HC group. Nodes Networks t value p value Intra-network FC R ventromedial prefrontal cortex R superior temporal gyrus DMN DMN -3.058 .003 L precuneus R superior temporal gyrus DMN DMN -3.271 .001 L posterior cingulate cortex L inferior parietal lobule DMN DMN -3.250 .002 R ventrolateral prefrontal cortex L inferior parietal lobule FPN FPN -3.372 .001 L precentral gyrus L precentral gyrus SMN SMN -3.523 .001 R precentral gyrus R middle insula SMN SMN -3.499 .001 Inter-network FC L thalamus R superior temporal gyrus SC DMN -3.237 .001 L thalamus L precentral gyrus SC SMN -3.367 .002 L thalamus R precentral gyrus SC SMN -3.195 .001 R thalamus L precentral gyrus SC SMN -2.861 .004 L thalamus L ventral prefrontal cortex SC FPN -3.091 .003 L ventromedial prefrontal cortex L intraparietal sulcus DMN FPN -3.453 .001 L posterior cingulate cortex L inferior parietal lobule DMN FPN -3.162 .002 L posterior cingulate cortex R ventrolateral prefrontal cortex DMN FPN -3.118 .003 L ventromedial prefrontal cortex R intraparietal sulcus DMN DAN -3.251 .003 L inferior parietal lobule R intraparietal sulcus DMN DAN -3.324 .002 L ventral prefrontal cortex L middle insula FPN SMN -3.331 .001 L anterior cingulate cortex R posterior insula FPN SMN -3.039 .002 L inferior parietal lobule L temporal gyrus FPN SMN -3.068 .002 L dorsolateral prefrontal cortex L temporal gyrus FPN SMN -3.052 .003 L inferior parietal lobule R parietal lobule FPN VAN -3.700 .001 R inferior parietal lobule R parietal lobule FPN VAN -3.086 .003 R dorsolateral prefrontal cortex R parietal lobule FPN VAN -3.644 .001 L temporal gyrus L inferior parietal lobule SMN DMN -3.579 .001 L middle insula L posterior cingulate cortex SMN DMN -3.627 .001 L temporal gyrus L parietal lobule SMN VAN -3.040 .003 L temporal gyrus L parietal lobule SMN VAN -3.440 .001 Note: SMN; somatosensory network; VAN: ventral attention network; DAN: dorsal attention network; SC: salience network; FPN: frontoparietal network; DMN: default mode network; L: left; R: right. Negative t value means FMs had significantly less network FC than HCs. 3.3 Correlation analysis In FM patients, there were: jthe significantly positive correlations between FIQ and a intra-network FC between the right ventrolateral prefrontal cortex and left inferior parietal lobule, and a inter-network FC between the SC and FPN; k a significantly negative correlation between HAMD and a inter-network FC between the DMN and DAN; l the significantly positive correlations between HAMA and the inter-network FC between the SC and FPN, or DMN and DAN, respectively; m a significantly positive correlation between MPQ and a intra-network FC between the left posterior cingulate cortex and left inferior parietal lobule; n a significantly positive correlation between TAS and a inter-network FC between the FPN and VAN (see Table 3 ). For HC group, there were: j a significantly positive correlation between FIQ and a intra-network FC between the right precentral gyrus and right middle insula; k a significantly negative correlation between HAMD and a inter-network FC between the SC and FPN; l the significantly negative correlations between HAMA and the inter-network FC between the SC and SMN, or between the FPN and SMN, respectively; m a significantly positive correlation between MPQ and the inter-network FC between the DMN and FPN (see Table 3 ). Table 3 . Correlations between behavioral tasks and network FC. Tasks Nodes Networks r p FM patients FIQ R ventrolateral prefrontal cortex L inferior parietal lobule FPN FPN 3.022 .004 L thalamus L ventral prefrontal cortex SC FPN 3.022 .004 HAMD L ventromedial prefrontal cortex R intraparietal sulcus DMN DAN -3.770 .001 HAMA L thalamus L ventral prefrontal cortex SC FPN 3.011 .004 L ventromedial prefrontal cortex R intraparietal sulcus DMN DAN 3.053 .003 MPQ L posterior cingulate cortex L inferior parietal lobule DMN DMN 3.057 .004 TAS L inferior parietal lobule R parietal lobule FPN VAN 3.529 .001 HC group FIQ R precentral gyrus R middle insula SMN SMN 3.033 .004 HAMD L thalamus L ventral prefrontal cortex SC FPN -2.903 .004 HAMA R thalamus L precentral gyrus SC SMN -3.417 .002 L anterior cingulate cortex R posterior insula FPN SMN -3.530 .001 MPQ L posterior cingulate cortex L inferior parietal lobule DMN FPN 3.078 .003 4. Discussion The present study employed a data-driven, whole-brain network analysis to investigate resting-state functional connectivity (RSFC) differences between women with FM patients and HC group, and to explore how these network alterations relate to the core clinical symptoms of FM. Our findings reveal a pattern of widespread functional decoupling within and between several key large-scale brain networks in FM patients. Specifically, we observed significant hypoconnectivity within the DMN, FPN, and SMN, as well as between these networks and the SC and VAN. Crucially, these network disruptions were differentially correlated with clinical measures of pain impact, negative affect, and alexithymia in the FM patients, presenting a pattern distinct from that observed in HC group. These results provide new insights into the complex neurobiological architecture of FM, suggesting that the disorder is characterized not by hyperconnectivity in pain-related regions alone, but by a broader disintegration of the brain’s functional architecture. 4.1 Disrupted DMN and FPN: a neural substrate for pain and affect integration A principal finding of the present study is the significant reduction in network FC within the DMN and FPN, and between these two networks, in the FM patients. The DMN, which includes core nodes like the ventromedial prefrontal cortex, posterior cingulate cortex, and precuneus, is typically deactivated during goal-directed tasks and is crucial for self-referential thought, memory consolidation, and emotional processing (Raichle, 2015 ). The hypoconnectivity we observed between the ventromedial prefrontal cortex, posterior cingulate cortex, precuneus, and temporal and parietal regions within the DMN suggests a fragmentation of this self-referential system. This aligns with a recent work, which identified a “persistent altered neural state” in FM, characterized by disrupted connectivity in regions involved in interoception and self-awareness (Stroman et al., 2025 ). The disintegration within the DMN may underlie the cognitive disturbances (“fibro-fog”) and altered body schema frequently reported by FM patients, as the integration of sensory information with a stable sense of self is compromised (Kravitz & Katz, 2015 ). Furthermore, the reduced FC between the DMN and the FPN — a network critical for cognitive control, attention, and emotion regulation (Marek & Dosenbach, 2018 ) — is particularly telling. The FPN, anchored by the dorsolateral prefrontal cortex and the inferior parietal lobule, exerts top-down regulatory control over emotional responses and pain perception. The observed hypoconnectivity between the DMN (ventromedial prefrontal cortex, posterior cingulate cortex) and FPN (inferior parietal lobule, ventrolateral prefrontal cortex) suggests a breakdown in the communication between the brain’s default mode and its executive control hubs. This finding is consistent with the valence-dependent disruptions in pregenual anterior cingulate cortex connectivity reported by Balducci et al. ( 2024 ). While Balducci et al. focused on task-based emotional processing, our resting-state data extend their work by showing that the functional architecture linking affective (DMN) and regulatory (FPN) networks is inherently unstable in FM, even in the absence of an external stimulus. This instability may explain the difficulty FM patients have in regulating negative affect and the heightened emotional reactivity to pain, as the neural systems needed for effective cognitive reappraisal are not adequately synchronized. 4.2 Thalamo-cortical decoupling: a core feature of central sensitization One of the most striking patterns in our data was the pervasive hypoconnectivity between the SC network (specifically the thalamus) and multiple cortical networks, including the DMN, SMN, and FPN. The thalamus acts as a critical relay station and gatekeeper for sensory and motor signals to the cortex. Aberrant thalamocortical connectivity is a well-established finding in FM and is considered a hallmark of central sensitization (Cifre et al., 2012 ; Napadow & Harris, 2014 ). Our results refine this understanding by demonstrating that this thalamic decoupling is not uniform but selectively involves networks central to sensory processing (SMN), cognitive control (FPN), and self-awareness (DMN). The reduced FC between the thalamus and the SMN (the precentral gyrus, insula) directly implicates a disruption in the sensory-discriminative aspect of pain. The insula, in particular, is a key hub for interoception — the sense of the physiological condition of the body — and its altered connectivity with the thalamus could contribute to the amplification of both painful and non-painful stimuli (allodynia and hyperalgesia) (Craig, 2009 ). The correlation we found between thalamus-FPN connectivity and clinical scores (FIQ, HAMA) in FM further underscores its importance. This suggests that the degree of thalamic disconnection from prefrontal regulatory regions is directly linked to the functional impact of the disease and the severity of anxiety, reinforcing the idea that FM is a disorder of thalamo-cortical integration (Ackermann et al., 2025 ). 4.3 Correlation analysis The correlation analysis revealed distinct patterns of brain-behavior relationships in FM patients compared to HC group, suggesting that the neurobiology of FM fundamentally alters how brain network FC relates to symptom experience. First, in FM patients, higher pain impact (FIQ) and anxiety (HAMA) were positively correlated with thalamus-FPN connectivity. This initially seems counterintuitive, as the current group comparison showed that FM patients have less thalamus-FPN connectivity than HC group. However, within the patient group, those with less severe disease (lower FIQ) or anxiety may have even further reduced connectivity, while those with more severe symptoms show connectivity levels that are closer to (but still below) the HC group’s range. This could represent a maladaptive compensatory mechanism, where a relative increase in thalamus-FPN connectivity (compared to other patients) is insufficient to restore normal function but reflects an attempt by the brain to engage cognitive control circuits in the face of persistent pain and anxiety. Second, a striking double dissociation was observed concerning the DMN-DAN connectivity. In FM patients, this inter-network connectivity was positively correlated with anxiety (HAMA) but negatively correlated with depression (HAMD). The DAN is involved in voluntary, goal-directed attention. Heightened anxiety in FM patients may be associated with increased coupling between the self-referential DMN and the externally focused DAN, reflecting a state of hypervigilance where internal worries (DMN) capture and direct external attention (DAN) towards potential threats (Saltafossi et al., 2025 ). Conversely, the negative correlation with depression suggests that as depressive symptoms worsen, this DMN-DAN integration breaks down, potentially leading to the passive rumination and disengagement from the external environment characteristic of major depressive episodes (Hamilton et al., 2015 ). This valence-specific relationship highlights the complex and opposing ways in which anxiety and depression, though highly comorbid in FM, may exert distinct influences on brain network dynamics (Rahangdale & Ferraro, 2025 ). Third, alexithymia (TAS), the difficulty in identifying and describing emotions, was uniquely correlated with FPN-VAN connectivity in the FM patients. The VAN, particularly the right temporoparietal junction, is crucial for detecting salient and unexpected stimuli, including emotional cues (Corbetta & Shulman, 2002 ). The positive correlation suggests that FM patients with greater difficulty processing emotions may have stronger connectivity between the cognitive control network (FPN) and the salience detection network (VAN). This could reflect a neural strategy where cognitive control mechanisms are co-opted to compensate for deficits in automatic emotional awareness, a finding that aligns with the high prevalence of alexithymia in FM and its link to poorer clinical outcomes (Di Tella & Castelli, 2016 ). 4.4 Limitations Several limitations of the present study should be acknowledged. First, the cross-sectional design precludes causal inferences; we cannot determine whether the observed network disruptions are a cause or a consequence of chronic pain and affective symptoms. Second, the sample was limited to female participants. While this reflects the higher prevalence of FM in women and increases sample homogeneity, it limits the generalizability of our findings to male patients. Third, we used a specific atlas (Dosenbach) for node definition. While this atlas is well-validated, different parcellation schemes can yield different connectivity patterns. Fourth, although the present study controlled for age (as a covariate), we did not control for other potential confounds such as medication use, which can significantly impact brain connectivity. Finally, while our data-driven approach avoids ROI bias, it is inherently exploratory, and our findings require replication in independent, larger cohorts. 4.5 Clinical implications and future directions Although there have some limitations, the current findings still have several important implications. They suggest that therapeutic interventions for FM should not only aim to reduce pain but also target the normalization of large-scale network interactions. For instance, cognitive-behavioral therapy (CBT) and mindfulness-based interventions have been shown to modulate connectivity within the DMN and FPN, potentially strengthening the regulatory circuits that are decoupled in our patients (Shpaner et al., 2014 ; Vago & Zeidan, 2016 ). Furthermore, non-invasive brain stimulation techniques, such as repetitive transcranial magnetic stimulation (rTMS) targeting the dorsolateral prefrontal cortex or insula, could aim to restore thalamo-cortical and DMN-FPN balance (Ackermann et al., 2025 ). The distinct correlation patterns also highlight the need for personalized medicine approaches; for example, a patient with high anxiety and low depression may have a different optimal neuromodulation target compared to a patient with high alexithymia. 5. Conclusion The present study demonstrates that fibromyalgia is characterized by a widespread disintegration of functional connectivity, particularly involving the thalamus and the brain’s default mode, frontoparietal, and somatosensory networks. The distinct and often opposing correlations of these network disruptions with anxiety, depression, and alexithymia in the patient group provide novel evidence for a complex neurobiological model where multiple, interacting symptom dimensions are underpinned by specific patterns of network dysfunction. These findings move beyond a simple model of central sensitization to one of central network dys-integration, offering new avenues for targeted, network-based therapeutic interventions. Declarations Competing Interests All authors have no conflicting interests. Funding This study was supported by Fujian Normal University Research Start-Up Funding (Y0720304K05). Author Contribution J.H. analyzed the dataset, wrote and revised the main manuscript text, prepared all tables and figures. P.X. analyzed some data, revised the main manuscript, and revised the figures. Data Availability The participants’ information, and their anatomical T1-weighted imaging dataset, were obtained from a public dataset via OpenNeuro with accession number ds004144 (https://openneuro.org/datasets/ds004144/versions/1.0.2). References Ackermann, L., Zeller, D., Odorfer, T., Homola, G. A., Kampf, T., Pham, M., Aster, H. C., & Sommer, C. (2025). Effect of Repetitive Transcranial Magnetic Stimulation on the Symptoms and Brain Imaging in Patients with Fibromyalgia Syndrome: A Randomized Controlled Pilot Trial. Pain Ther , 14 (5), 1547-1572. https://doi.org/10.1007/s40122-025-00770-2 Bagby, R. M., Parker, J. D., & Taylor, G. J. (1994). The twenty-item Toronto Alexithymia Scale--I. Item selection and cross-validation of the factor structure. 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E., & Schlaggar, B. L. (2010). Prediction of individual brain maturity using fMRI. Science , 329 (5997), 1358-1361. https://doi.org/10.1126/science.1194144 Galvez-Sanchez, C. M., Duschek, S., & Reyes Del Paso, G. A. (2019). Psychological impact of fibromyalgia: current perspectives. Psychol Res Behav Manag , 12 , 117-127. https://doi.org/10.2147/PRBM.S178240 Hamilton, J. P., Farmer, M., Fogelman, P., & Gotlib, I. H. (2015). Depressive Rumination, the Default-Mode Network, and the Dark Matter of Clinical Neuroscience. Biol Psychiatry , 78 (4), 224-230. https://doi.org/10.1016/j.biopsych.2015.02.020 Jensen, K. B., Srinivasan, P., Spaeth, R., Tan, Y., Kosek, E., Petzke, F., Carville, S., Fransson, P., Marcus, H., Williams, S. C., Choy, E., Vitton, O., Gracely, R., Ingvar, M., & Kong, J. (2013). Overlapping structural and functional brain changes in patients with long-term exposure to fibromyalgia pain. Arthritis Rheum , 65 (12), 3293-3303. https://doi.org/10.1002/art.38170 Jurado-Priego, L. N., Cueto-Urena, C., Ramirez-Exposito, M. J., & Martinez-Martos, J. M. (2024). Fibromyalgia: A Review of the Pathophysiological Mechanisms and Multidisciplinary Treatment Strategies. Biomedicines , 12 (7). https://doi.org/10.3390/biomedicines12071543 Kravitz, H. M., & Katz, R. S. (2015). Fibrofog and fibromyalgia: a narrative review and implications for clinical practice. Rheumatol Int , 35 (7), 1115-1125. https://doi.org/10.1007/s00296-014-3208-7 Kuhn, S., Vanderhasselt, M. A., De Raedt, R., & Gallinat, J. (2012). Why ruminators won't stop: the structural and resting state correlates of rumination and its relation to depression. J Affect Disord , 141 (2-3), 352-360. https://doi.org/10.1016/j.jad.2012.03.024 Li, Q., Wang, X., Wang, S., Xie, Y., Li, X., Xie, Y., & Li, S. (2018). Musical training induces functional and structural auditory-motor network plasticity in young adults. Hum Brain Mapp , 39 (5), 2098-2110. https://doi.org/10.1002/hbm.23989 Lobo, A., Chamorro, L., Luque, A., Dal-Re, R., Badia, X., Baro, E., & Grupo de Validacion en Espanol de Escalas, P. (2002). [Validation of the Spanish versions of the Montgomery-Asberg depression and Hamilton anxiety rating scales]. Med Clin (Barc) , 118 (13), 493-499. https://doi.org/10.1016/s0025-7753(02)72429-9 (Validacion de las versiones en espanol de la Montgomery-Asberg Depression Rating Scale y la Hamilton Anxiety Rating Scale para la evaluacion de la depresion y de la ansiedad.) Marek, S., & Dosenbach, N. U. F. (2018). The frontoparietal network: function, electrophysiology, and importance of individual precision mapping. Dialogues Clin Neurosci , 20 (2), 133-140. https://doi.org/10.31887/DCNS.2018.20.2/smarek Masedo, A. I., & Esteve, R. (2000). Some empirical evidence regarding the validity of the Spanish version of the McGill Pain Questionnaire (MPQ-SV). Pain , 85 (3), 451-456. https://doi.org/10.1016/S0304-3959(99)00300-0 Melzack, R. (1975). The McGill Pain Questionnaire: major properties and scoring methods. Pain , 1 (3), 277-299. https://doi.org/10.1016/0304-3959(75)90044-5 Napadow, V., & Harris, R. E. (2014). What has functional connectivity and chemical neuroimaging in fibromyalgia taught us about the mechanisms and management of 'centralized' pain? Arthritis Res Ther , 16 (5), 425. https://doi.org/10.1186/s13075-014-0425-0 Oliveria Neto, P. G., Rego Ramos, L., & DosSantos, M. F. (2025). Behavioral Changes and Long-Term Cortical Thickness Alterations in Women with Fibromyalgia. J Manipulative Physiol Ther , 48 (1-5), 27-36. https://doi.org/10.1016/j.jmpt.2024.08.018 Park, H. J., & Friston, K. (2013). Structural and functional brain networks: from connections to cognition. Science , 342 (6158), 1238411. https://doi.org/10.1126/science.1238411 Rahangdale, A., & Ferraro, J. (2025). Assessing comorbid PTSD, depression, and anxiety in fibromyalgia patients: a retrospective observational study. BMC Psychiatry , 25 (1), 444. https://doi.org/10.1186/s12888-025-06708-4 Raichle, M. E. (2015). The brain's default mode network. Annu Rev Neurosci , 38 , 433-447. https://doi.org/10.1146/annurev-neuro-071013-014030 Ramos-Brieva, J. A., & Cordero-Villafafila, A. (1988). A new validation of the Hamilton Rating Scale for Depression. J Psychiatr Res , 22 (1), 21-28. https://doi.org/10.1016/0022-3956(88)90024-6 Rost, S., Crombez, G., Sutterlin, S., Vogele, C., Veirman, E., & Van Ryckeghem, D. M. L. (2021). Altered regulation of negative affect in patients with fibromyalgia: A diary study. Eur J Pain , 25 (3), 714-724. https://doi.org/10.1002/ejp.1706 Saltafossi, M., Heck, D., Kluger, D. S., & Varga, S. (2025). Common threads: Altered interoceptive processes across affective and anxiety disorders. J Affect Disord , 369 , 244-254. https://doi.org/10.1016/j.jad.2024.09.135 Shpaner, M., Kelly, C., Lieberman, G., Perelman, H., Davis, M., Keefe, F. J., & Naylor, M. R. (2014). Unlearning chronic pain: A randomized controlled trial to investigate changes in intrinsic brain connectivity following Cognitive Behavioral Therapy. Neuroimage Clin , 5 , 365-376. https://doi.org/10.1016/j.nicl.2014.07.008 Sluka, K. A., & Clauw, D. J. (2016). Neurobiology of fibromyalgia and chronic widespread pain. Neuroscience , 338 , 114-129. https://doi.org/10.1016/j.neuroscience.2016.06.006 Staud, R. (2011). Brain imaging in fibromyalgia syndrome. Clin Exp Rheumatol , 29 (6 Suppl 69), S109-117. https://www.ncbi.nlm.nih.gov/pubmed/22243558 Stroman, P. W., Staud, R., & Pukall, C. F. (2025). Evidence of a persistent altered neural state in people with fibromyalgia syndrome during functional MRI studies and its relationship with pain and anxiety. PLoS One , 20 (1), e0316672. https://doi.org/10.1371/journal.pone.0316672 Vago, D. R., & Zeidan, F. (2016). The brain on silent: mind wandering, mindful awareness, and states of mental tranquility. Ann N Y Acad Sci , 1373 (1), 96-113. https://doi.org/10.1111/nyas.13171 Yan, C. G., Wang, X. D., Zuo, X. N., & Zang, Y. F. (2016). DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics , 14 (3), 339-351. https://doi.org/10.1007/s12021-016-9299-4 Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zollei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol , 106 (3), 1125-1165. https://doi.org/10.1152/jn.00338.2011 Zautra, A. J., Fasman, R., Reich, J. W., Harakas, P., Johnson, L. M., Olmsted, M. E., & Davis, M. C. (2005). Fibromyalgia: evidence for deficits in positive affect regulation. Psychosom Med , 67 (1), 147-155. https://doi.org/10.1097/01.psy.0000146328.52009.23 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 11 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 18 Apr, 2026 Reviewers invited by journal 16 Apr, 2026 Editor invited by journal 12 Mar, 2026 Editor assigned by journal 10 Mar, 2026 Submission checks completed at journal 10 Mar, 2026 First submitted to journal 06 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9054674","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625790344,"identity":"81df8847-6aae-4c36-b263-e39c0541f9bc","order_by":0,"name":"Jiancheng Hou","email":"","orcid":"","institution":"Fujian Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jiancheng","middleName":"","lastName":"Hou","suffix":""},{"id":625790345,"identity":"c6b65ac3-b931-4d21-b644-af28a12a167a","order_by":1,"name":"Peitao Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACfvaGxMd/Kmx47I83EKlFsufAYwOeM2lyDGcOEKnFYIbjMwnetsPGDDcSiNUiwZxsIMGWltg48/HGGww1NtEEtZhLtyU+MOCxSWyWTiu2YDiWlttASIvlnDPJBgkSaYlt0jlmEowNhwlrMbiR/03igMHhxB7JM0RrSUiTbEg4bCwhwUOkFmAgJxszHEiTM+AB+iWBGL+Ao5Lxnw2PAfvhjTc+1NgQ1oLiSIkEUpRDtJCqYxSMglEwCkYGAACbTkKJ1yqrcgAAAABJRU5ErkJggg==","orcid":"","institution":"Fujian University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Peitao","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2026-03-07 01:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9054674/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9054674/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107696735,"identity":"e68a134c-5dde-4172-93bc-4d856da4a984","added_by":"auto","created_at":"2026-04-24 07:17:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33435,"visible":true,"origin":"","legend":"\u003cp\u003eThe matrix of network functional connectivity (FC) differences between FM patients and HC group. The color bar indicates the FC value. Negative value means FMs had significantly less network FC than HCs. VN: visual network; SMN; somatosensory network; VAN: ventral attention network; DAN: dorsal attention network; DMN: default mode network; SC: subcortical network; FPN: frontoparietal network.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9054674/v1/86cb2cbf4e1e75ddf4fbaf1a.png"},{"id":107707188,"identity":"ab6a12e9-a304-46c1-b74d-ad5b9cf6bdf4","added_by":"auto","created_at":"2026-04-24 09:19:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":323614,"visible":true,"origin":"","legend":"\u003cp\u003eThe circus representation of network functional connectivity (FC) differences between FM patients and HC group. Blue line means FM patients had significantly less network FC than HC group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9054674/v1/757477e8f01c7dbcae871247.png"},{"id":107696737,"identity":"21a08ed5-1298-4414-b449-f41695d2258b","added_by":"auto","created_at":"2026-04-24 07:17:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":747243,"visible":true,"origin":"","legend":"\u003cp\u003eThe brain network functional connectivity (FC) differences between FM patients and HC group. Blue line means FM patients had significantly less network FC than HC group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9054674/v1/2d45fab3bdcdb7d0927b68b7.png"},{"id":107709196,"identity":"7d4d6e83-4d5a-45c8-b02e-c43413ae1112","added_by":"auto","created_at":"2026-04-24 09:34:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1583868,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9054674/v1/cb69a9c7-ca45-430d-99a6-c93904c5f3bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Large-Scale Brain Network Dysfunction in Fibromyalgia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFibromyalgia (FM) is a complex and debilitating chronic pain disorder that predominantly affects women, with a global prevalence estimated to be significantly higher in females than in males. Clinically, FM is characterized by widespread, persistent pain, often accompanied by a constellation of other debilitating symptoms, including profound fatigue, unrefreshing sleep, cognitive disturbances (\u0026ldquo;fibro-fog\u0026rdquo;), and marked emotional distress (Kravitz \u0026amp; Katz, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The high prevalence of psychiatric comorbidities, particularly depression and anxiety, underscores the intricate relationship between physical and emotional dysfunction in this condition (Galvez-Sanchez et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rahangdale \u0026amp; Ferraro, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The lifetime prevalence of major depressive disorder in FM patients is estimated to be over 50%, with anxiety disorders affecting up to one-third of individuals (Oliveria Neto et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This affective imbalance \u0026mdash; characterized by diminished positive affect and heightened negative affect t\u0026mdash; is not merely a secondary consequence of living with chronic pain; it is intrinsically linked to pain intensity, overall symptom severity, and reduced quality of life (Rost et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Stroman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zautra et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe prevailing pathophysiological model for FM is one of central sensitization, a condition in which the central nervous system (CNS) amplifies sensory input, leading to hypersensitivity to painful (hyperalgesia) and non-painful (allodynia) stimuli (Jurado-Priego et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This aberrant processing is thought to be driven by complex neurobiological alterations, including neuro-inflammation and imbalances in neurotransmitter systems (Sluka \u0026amp; Clauw, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Neuroimaging has been instrumental in advancing our understanding of these CNS abnormalities, revealing that FM is not merely a disease of the peripheral tissues but one of altered brain structure, function, and connectivity (Jensen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Napadow \u0026amp; Harris, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Functional magnetic resonance imaging (fMRI) studies have consistently demonstrated aberrant brain activity and functional connectivity (FC) in FM patients, particularly within the neural networks subserving pain modulation, salience detection, and emotion processing (Stroman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The so-called \u0026ldquo;pain matrix\u0026rdquo;, a distributed network of brain regions including the thalamus, insula, anterior cingulate cortex, and prefrontal cortices, shows heightened responses to both painful and non-painful stimuli in FM, reflecting the central amplification of sensory input (Ackermann et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Staud, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore recently, research has shifted focused from sensory-discriminative processing to the critical interplay between pain and emotion, given the profound affective disturbances in FM. A pivotal study by Balducci et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) provided crucial evidence that the neural circuits governing emotional experience are fundamentally altered in FM patients. Their fMRI findings revealed that patients, compared to healthy controls, exhibited hyperactivation of the left superior lateral occipital cortex during the processing of emotional stimuli, irrespective of valence. This hyperactivation was positively correlated with the severity of depression and anxiety, suggesting that visual attention and pain modulation networks may be recruited differently in FM during emotional experiences. Furthermore, Balducci et al. identified a valence-dependent disruption in the FC of the left pregenual anterior cingulate cortex (pACC) \u0026mdash; a critical hub for the integration of affective and sensory information \u0026mdash; with sensorimotor and opercular regions. Specifically, they observed higher pACC connectivity during positive emotion processing and lower connectivity during negative emotion processing in FM patients. These findings provide a compelling neural basis for the high prevalence of psychopathological symptoms and the strong affective component of pain in FM patients, demonstrating that emotional processing is not just altered, but is fundamentally dysregulated in a valence-specific manner.\u003c/p\u003e \u003cp\u003eWhile studies like that of Balducci et al. have been instrumental in identifying key brain regions and their functional interactions, their analysis and results are often inherently limited by the specific regions. This approach may not capture the full extent of dysregulation across the brain\u0026rsquo;s interconnected systems. Understanding brain activity patterns in a data-driven, unbiased way can avoid the limitations and potential flaws inherent in a priori assumptions, which is particularly important when the precise mechanisms of conditions like FM are not fully known. To capture the full complexity of brain dysfunction, network science offers a powerful framework. It posits that the human brain is organized as a global network of interconnected regions, integrating long-range connections for global communication and local short-range connections for specialized processing, thereby supporting high-level cognitive and affective functions (Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Park \u0026amp; Friston, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This perspective allows us to move beyond isolated regional differences and examine how entire brain systems interact. As far as we know, relatively few studies have employed this data-driven, whole-brain network approach to examine the intrinsic functional architecture of FM.\u003c/p\u003e \u003cp\u003eTherefore, the present study aimed to investigate the resting-state fMRI whole-brain network FC differences between FM patients and healthy controls through a data-driven approach, and to explore how these network-level alterations relate to the core clinical symptoms of pain, affect, and alexithymia.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eThe participants\u0026rsquo; information, and their anatomical T1-weighted imaging dataset, were obtained from a public dataset via OpenNeuro with accession number ds004144 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openneuro.org/datasets/ds004144/versions/1.0.2\u003c/span\u003e\u003cspan address=\"https://openneuro.org/datasets/ds004144/versions/1.0.2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). There were thirty-three female FM patients (mean age\u0026thinsp;=\u0026thinsp;41.727\u0026thinsp;\u0026plusmn;\u0026thinsp;6.094 years old, ranged from 30 to 50 years) and also thirty-three female healthy controls (HC; mean age\u0026thinsp;=\u0026thinsp;41.515\u0026thinsp;\u0026plusmn;\u0026thinsp;6.032 years old, ranged from 43 to 50 years) were selected. No significant age difference was found (\u003cem\u003et\u003c/em\u003e\u003csub\u003e(64)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.142, \u003cem\u003ep\u003c/em\u003e = .887). Ethical approval was granted by the Research Ethics Committee of the National Institute of Psychiatry \u0026ldquo;Ramon de la Fuente Mu\u0026ntilde;iz\u0026rdquo; in Mexico City. The inclusion and exclusion criteria followed those outlined by (Balducci et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical measures\u003c/h2\u003e \u003cp\u003eFive clinical measures were used. To measure severity of depressive and anxiety, the 17-items Hamilton Depression Rating Scale (HAMD) (Ramos-Brieva \u0026amp; Cordero-Villafafila, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) and the Hamilton Anxiety Rating Scale (HAMA) (Lobo et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) were administered. Moreover, the alexithymia with the Toronto Alexithymia Scale (TAS) (Bagby et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) was used as a clinical variable to be measured using self-rating scales. Furthermore, the Fibromyalgia Impact Questionnaire (FIQ) (Burckhardt et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and the McGill Pain Questionnaire (MPQ) (Masedo \u0026amp; Esteve, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Melzack, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) were administrated in the fibromyalgia group to evaluate the severity of fibromyalgia and the characteristics of pain, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MRI data acquisition\u003c/h2\u003e \u003cp\u003eThe original neuroimaging dataset was acquired on a 3.0 T Philips Ingenia Magnetic Resonance Imaging scanner with a 32-channel phased array head coil. The parameters for RS-fMRI data were: TR\u0026thinsp;=\u0026thinsp;3000 ms, TE\u0026thinsp;=\u0026thinsp;40 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, feld of view\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm\u003csup\u003e2\u003c/sup\u003e, voxel size\u0026thinsp;=\u0026thinsp;2 \u0026times; 2 mm, number of slices\u0026thinsp;=\u0026thinsp;43, matrix\u0026thinsp;=\u0026thinsp;128 \u0026times; 128, slice thickness\u0026thinsp;=\u0026thinsp;3 mm, gap\u0026thinsp;=\u0026thinsp;0 mm. The parameters for anatomical T1-weighted data were: TR\u0026thinsp;=\u0026thinsp;7.7 ms, TE\u0026thinsp;=\u0026thinsp;3.2 ms, flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;, feld of view\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm\u003csup\u003e2\u003c/sup\u003e, matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256, slice thickness\u0026thinsp;=\u0026thinsp;1 mm, number of slices\u0026thinsp;=\u0026thinsp;168, gap\u0026thinsp;=\u0026thinsp;0 mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data preprocessing\u003c/h2\u003e \u003cp\u003ePreprocessing for RS-fMRI data were performed using the Data Processing and Analysis of Brain Imaging (DPABI) toolbox version 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rfmri.org/dpabi\u003c/span\u003e\u003cspan address=\"http://rfmri.org/dpabi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e This toolbox involves one main function called Data Processing Assistant for Resting-state fMRI Advanced Edition toolbox (DPARSF V5.3) (Chang \u0026amp; Glover, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which is a convenient plug-in software that works with Matlab and Statistical Parametric Mapping (SPM, version 12) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The original data was firstly arranged; the first five volumes were ignored so that participants could get used to scanner noise. The preprocessing steps, in order, includes slice timing, realignment, regressing out head motion parameters (scrubbing with Friston 24-parameter model regression) and normalization (spatial normalization to the MNI template, resampling voxel size of 3 \u0026times; 3 \u0026times; 3mm) (Chao-Gan \u0026amp; Yu-Feng, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kuhn et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The symmetric correlation matrix (considered as spontaneous neural connectivity) for a 142 \u0026times; 142 network, which include 142 regions/nodes with the Dosenbach atlas across whole brain (Dosenbach et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), was generated in each participant. Therefore, each participant had 20,164 unique pairwise RSFC in total, but only half of the pairwise RSFC (10,082) within the network was used for next network construction, because the top right half and bottom left half were the same in matrix.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Network functional connectivity construction\u003c/h2\u003e \u003cp\u003eEach participant\u0026rsquo;s network functional connectivity (FC) was constructed with the DPABINet V1.1 that is included in DPABI V6.0. The 142 nodes in the Dosenbach atlas were classified into seven subnetworks: visual network (VN), somatosensory network (SMN), ventral attention network (VAN), dorsal attention network (DAN), default mode network (DMN), subcortical network (SC) and frontoparietal network (FPN) (Yeo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Based on the 142 \u0026times; 141 matrix generated at the preprocessing step, the network FC for any pair of two nodes was calculated as Pearson\u0026rsquo;s linear correlation coefficient, and the Fisher-\u003cem\u003ez\u003c/em\u003e transformation was then used for the symmetric correlation matrix.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was performed in DPABINet. The group \u003cem\u003et\u003c/em\u003e-test was conducted to compare the network FC differences between FM patients and HC group. The age was considered as covariates. The multiple comparison correction was used the permutation false discovery rate (FDR) corrected \u003cem\u003ep\u003c/em\u003e \u0026lt; .005 in DPABINet, and the results were visualized by DPABINet Viewer. Moreover, the correlation analysis between the clinical measures and the network FC was also performed, and the multiple comparison correction was also used the permutation FDR corrected \u003cem\u003ep\u003c/em\u003e \u0026lt; .005, in DPABINet, in FM patients or HC group, respectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Clinical measure tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe group \u003cem\u003et\u003c/em\u003e-test showed that compared to HC group, FM patients had significantly higher scores in HAMD and HAMA, but there was no significantly group differences in FIQ and MPQ (see \u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Clinical measure tests between FM patients and HC group.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFMs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHCs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eFIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e3.546 (2.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e2.697 (2.186)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e1.758 (1.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1.727 (0.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e.903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e15.576 (6.374)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e1.212 (1.932)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e12.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eHAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e21.546 (6.833)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e2.182 (2.430)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e15.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eTAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e42.788 (14.482)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 174px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Network functional connectivity changes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Table 2\u003c/strong\u003e and \u003cstrong\u003eFigures 1 to 3\u003c/strong\u003e show the significant intra- and inter-network FC differences between FM patients and HC group. Compared to HC group, FM patients had significantly less intra-network FC between the right ventromedial prefrontal cortex and right superior temporal gyrus, between the left precuneus and right superior temporal gyrus, between the left posterior cingulate cortex and left inferior parietal lobule within the DMN; between the right ventrolateral prefrontal cortex and left inferior parietal lobule within the FPN; between the left precentral gyrus and left precentral gyrus, between the right precentral gyrus and right middle insula within the SMN.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; Compared to HC group, FM patients had significantly less inter-network FC between the SC and DMN, SMN or FPN, respectively; between the DMN and FPN pr DAN, respectively; between the FPN and SMN or VAN, respectively; between the SMN and DMN or VAN, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Group \u003cem\u003et\u003c/em\u003e-test differences of network FC between FM patients and HC group.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"724\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 424px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNetworks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntra-network FC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eR ventromedial prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR superior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL precuneus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR superior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL posterior cingulate cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eR ventrolateral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL precentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL precentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eR precentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR middle insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInter-network FC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR superior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL precentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR precentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eR thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL precentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-2.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL ventral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL ventromedial prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL intraparietal sulcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL posterior cingulate cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL posterior cingulate cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR ventrolateral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL ventromedial prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR intraparietal sulcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eDAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR intraparietal sulcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eDAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL ventral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL middle insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL anterior cingulate cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR posterior insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL dorsolateral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eVAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eR inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eVAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eR dorsolateral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eR parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eVAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL middle insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL posterior cingulate cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eVAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eL temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eL parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eVAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e-3.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: SMN; somatosensory network; VAN: ventral attention network; DAN: dorsal attention network; SC: salience network; FPN: frontoparietal network; DMN: default mode network; L: left; R: right. Negative \u003cem\u003et\u003c/em\u003e value means FMs had significantly less network FC than HCs. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Correlation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;In FM patients, there were: jthe significantly positive correlations between FIQ and a intra-network FC between the right ventrolateral prefrontal cortex and left inferior parietal lobule, and a inter-network FC between the SC and FPN; k a significantly negative correlation between HAMD and a inter-network FC between the DMN and DAN; l the significantly positive correlations between HAMA and the inter-network FC between the SC and FPN, or DMN and DAN, respectively; m a significantly positive correlation between MPQ and a intra-network FC between the left posterior cingulate cortex and left inferior parietal lobule; n a significantly positive correlation between TAS and a inter-network FC between the FPN and VAN (see \u003cstrong\u003eTable 3\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; For HC group, there were: j a significantly positive correlation between FIQ and a intra-network FC between the right precentral gyrus and right middle insula; k a significantly negative correlation between HAMD and a inter-network FC between the SC and FPN; l the significantly negative correlations between HAMA and the inter-network FC between the SC and SMN, or between the FPN and SMN, respectively; m a significantly positive correlation between MPQ and the inter-network FC between the DMN and FPN (see \u003cstrong\u003eTable 3\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Correlations between behavioral tasks and network FC.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"755\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTasks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 419px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNetworks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003er\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFM patients\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eFIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eR ventrolateral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eL thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eL ventral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eL ventromedial prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eR intraparietal sulcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eDAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-3.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eL thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eL ventral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eL ventromedial prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eR intraparietal sulcus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eDAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eL posterior cingulate cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eTAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eR parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eVAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 212px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eFIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eR precentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eR middle insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eL thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eL ventral prefrontal cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-2.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eHAMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eR thalamus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eL precentral gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-3.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eL anterior cingulate cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eR posterior insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-3.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003eMPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eL posterior cingulate cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 212px;\"\u003e\n \u003cp\u003eL inferior parietal lobule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eDMN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eFPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study employed a data-driven, whole-brain network analysis to investigate resting-state functional connectivity (RSFC) differences between women with FM patients and HC group, and to explore how these network alterations relate to the core clinical symptoms of FM. Our findings reveal a pattern of widespread functional decoupling within and between several key large-scale brain networks in FM patients. Specifically, we observed significant hypoconnectivity within the DMN, FPN, and SMN, as well as between these networks and the SC and VAN. Crucially, these network disruptions were differentially correlated with clinical measures of pain impact, negative affect, and alexithymia in the FM patients, presenting a pattern distinct from that observed in HC group. These results provide new insights into the complex neurobiological architecture of FM, suggesting that the disorder is characterized not by hyperconnectivity in pain-related regions alone, but by a broader disintegration of the brain\u0026rsquo;s functional architecture.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Disrupted DMN and FPN: a neural substrate for pain and affect integration\u003c/h2\u003e \u003cp\u003eA principal finding of the present study is the significant reduction in network FC within the DMN and FPN, and between these two networks, in the FM patients. The DMN, which includes core nodes like the ventromedial prefrontal cortex, posterior cingulate cortex, and precuneus, is typically deactivated during goal-directed tasks and is crucial for self-referential thought, memory consolidation, and emotional processing (Raichle, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The hypoconnectivity we observed between the ventromedial prefrontal cortex, posterior cingulate cortex, precuneus, and temporal and parietal regions within the DMN suggests a fragmentation of this self-referential system. This aligns with a recent work, which identified a \u0026ldquo;persistent altered neural state\u0026rdquo; in FM, characterized by disrupted connectivity in regions involved in interoception and self-awareness (Stroman et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The disintegration within the DMN may underlie the cognitive disturbances (\u0026ldquo;fibro-fog\u0026rdquo;) and altered body schema frequently reported by FM patients, as the integration of sensory information with a stable sense of self is compromised (Kravitz \u0026amp; Katz, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, the reduced FC between the DMN and the FPN \u0026mdash; a network critical for cognitive control, attention, and emotion regulation (Marek \u0026amp; Dosenbach, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) \u0026mdash; is particularly telling. The FPN, anchored by the dorsolateral prefrontal cortex and the inferior parietal lobule, exerts top-down regulatory control over emotional responses and pain perception. The observed hypoconnectivity between the DMN (ventromedial prefrontal cortex, posterior cingulate cortex) and FPN (inferior parietal lobule, ventrolateral prefrontal cortex) suggests a breakdown in the communication between the brain\u0026rsquo;s default mode and its executive control hubs. This finding is consistent with the valence-dependent disruptions in pregenual anterior cingulate cortex connectivity reported by Balducci et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While Balducci et al. focused on task-based emotional processing, our resting-state data extend their work by showing that the functional architecture linking affective (DMN) and regulatory (FPN) networks is inherently unstable in FM, even in the absence of an external stimulus. This instability may explain the difficulty FM patients have in regulating negative affect and the heightened emotional reactivity to pain, as the neural systems needed for effective cognitive reappraisal are not adequately synchronized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Thalamo-cortical decoupling: a core feature of central sensitization\u003c/h2\u003e \u003cp\u003eOne of the most striking patterns in our data was the pervasive hypoconnectivity between the SC network (specifically the thalamus) and multiple cortical networks, including the DMN, SMN, and FPN. The thalamus acts as a critical relay station and gatekeeper for sensory and motor signals to the cortex. Aberrant thalamocortical connectivity is a well-established finding in FM and is considered a hallmark of central sensitization (Cifre et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Napadow \u0026amp; Harris, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Our results refine this understanding by demonstrating that this thalamic decoupling is not uniform but selectively involves networks central to sensory processing (SMN), cognitive control (FPN), and self-awareness (DMN).\u003c/p\u003e \u003cp\u003eThe reduced FC between the thalamus and the SMN (the precentral gyrus, insula) directly implicates a disruption in the sensory-discriminative aspect of pain. The insula, in particular, is a key hub for interoception \u0026mdash; the sense of the physiological condition of the body \u0026mdash; and its altered connectivity with the thalamus could contribute to the amplification of both painful and non-painful stimuli (allodynia and hyperalgesia) (Craig, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The correlation we found between thalamus-FPN connectivity and clinical scores (FIQ, HAMA) in FM further underscores its importance. This suggests that the degree of thalamic disconnection from prefrontal regulatory regions is directly linked to the functional impact of the disease and the severity of anxiety, reinforcing the idea that FM is a disorder of thalamo-cortical integration (Ackermann et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Correlation analysis\u003c/h2\u003e \u003cp\u003eThe correlation analysis revealed distinct patterns of brain-behavior relationships in FM patients compared to HC group, suggesting that the neurobiology of FM fundamentally alters how brain network FC relates to symptom experience.\u003c/p\u003e \u003cp\u003eFirst, in FM patients, higher pain impact (FIQ) and anxiety (HAMA) were positively correlated with thalamus-FPN connectivity. This initially seems counterintuitive, as the current group comparison showed that FM patients have less thalamus-FPN connectivity than HC group. However, within the patient group, those with less severe disease (lower FIQ) or anxiety may have even further reduced connectivity, while those with more severe symptoms show connectivity levels that are closer to (but still below) the HC group\u0026rsquo;s range. This could represent a maladaptive compensatory mechanism, where a relative increase in thalamus-FPN connectivity (compared to other patients) is insufficient to restore normal function but reflects an attempt by the brain to engage cognitive control circuits in the face of persistent pain and anxiety.\u003c/p\u003e \u003cp\u003eSecond, a striking double dissociation was observed concerning the DMN-DAN connectivity. In FM patients, this inter-network connectivity was positively correlated with anxiety (HAMA) but negatively correlated with depression (HAMD). The DAN is involved in voluntary, goal-directed attention. Heightened anxiety in FM patients may be associated with increased coupling between the self-referential DMN and the externally focused DAN, reflecting a state of hypervigilance where internal worries (DMN) capture and direct external attention (DAN) towards potential threats (Saltafossi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Conversely, the negative correlation with depression suggests that as depressive symptoms worsen, this DMN-DAN integration breaks down, potentially leading to the passive rumination and disengagement from the external environment characteristic of major depressive episodes (Hamilton et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This valence-specific relationship highlights the complex and opposing ways in which anxiety and depression, though highly comorbid in FM, may exert distinct influences on brain network dynamics (Rahangdale \u0026amp; Ferraro, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThird, alexithymia (TAS), the difficulty in identifying and describing emotions, was uniquely correlated with FPN-VAN connectivity in the FM patients. The VAN, particularly the right temporoparietal junction, is crucial for detecting salient and unexpected stimuli, including emotional cues (Corbetta \u0026amp; Shulman, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The positive correlation suggests that FM patients with greater difficulty processing emotions may have stronger connectivity between the cognitive control network (FPN) and the salience detection network (VAN). This could reflect a neural strategy where cognitive control mechanisms are co-opted to compensate for deficits in automatic emotional awareness, a finding that aligns with the high prevalence of alexithymia in FM and its link to poorer clinical outcomes (Di Tella \u0026amp; Castelli, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations of the present study should be acknowledged. First, the cross-sectional design precludes causal inferences; we cannot determine whether the observed network disruptions are a cause or a consequence of chronic pain and affective symptoms. Second, the sample was limited to female participants. While this reflects the higher prevalence of FM in women and increases sample homogeneity, it limits the generalizability of our findings to male patients. Third, we used a specific atlas (Dosenbach) for node definition. While this atlas is well-validated, different parcellation schemes can yield different connectivity patterns. Fourth, although the present study controlled for age (as a covariate), we did not control for other potential confounds such as medication use, which can significantly impact brain connectivity. Finally, while our data-driven approach avoids ROI bias, it is inherently exploratory, and our findings require replication in independent, larger cohorts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Clinical implications and future directions\u003c/h2\u003e \u003cp\u003eAlthough there have some limitations, the current findings still have several important implications. They suggest that therapeutic interventions for FM should not only aim to reduce pain but also target the normalization of large-scale network interactions. For instance, cognitive-behavioral therapy (CBT) and mindfulness-based interventions have been shown to modulate connectivity within the DMN and FPN, potentially strengthening the regulatory circuits that are decoupled in our patients (Shpaner et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Vago \u0026amp; Zeidan, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, non-invasive brain stimulation techniques, such as repetitive transcranial magnetic stimulation (rTMS) targeting the dorsolateral prefrontal cortex or insula, could aim to restore thalamo-cortical and DMN-FPN balance (Ackermann et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The distinct correlation patterns also highlight the need for personalized medicine approaches; for example, a patient with high anxiety and low depression may have a different optimal neuromodulation target compared to a patient with high alexithymia.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe present study demonstrates that fibromyalgia is characterized by a widespread disintegration of functional connectivity, particularly involving the thalamus and the brain\u0026rsquo;s default mode, frontoparietal, and somatosensory networks. The distinct and often opposing correlations of these network disruptions with anxiety, depression, and alexithymia in the patient group provide novel evidence for a complex neurobiological model where multiple, interacting symptom dimensions are underpinned by specific patterns of network dysfunction. These findings move beyond a simple model of central sensitization to one of central network dys-integration, offering new avenues for targeted, network-based therapeutic interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eAll authors have no conflicting interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by Fujian Normal University Research Start-Up Funding (Y0720304K05).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.H. analyzed the dataset, wrote and revised the main manuscript text, prepared all tables and figures. P.X. analyzed some data, revised the main manuscript, and revised the figures.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe participants\u0026rsquo; information, and their anatomical T1-weighted imaging dataset, were obtained from a public dataset via OpenNeuro with accession number ds004144 (https://openneuro.org/datasets/ds004144/versions/1.0.2).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAckermann, L., Zeller, D., Odorfer, T., Homola, G. A., Kampf, T., Pham, M., Aster, H. C., \u0026amp; Sommer, C. (2025). Effect of Repetitive Transcranial Magnetic Stimulation on the Symptoms and Brain Imaging in Patients with Fibromyalgia Syndrome: A Randomized Controlled Pilot Trial. \u003cem\u003ePain Ther\u003c/em\u003e,\u003cem\u003e\u0026nbsp;14\u003c/em\u003e(5), 1547-1572. https://doi.org/10.1007/s40122-025-00770-2\u003c/li\u003e\n \u003cli\u003eBagby, R. M., Parker, J. D., \u0026amp; Taylor, G. J. (1994). 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Fibromyalgia: evidence for deficits in positive affect regulation. \u003cem\u003ePsychosom Med\u003c/em\u003e,\u003cem\u003e\u0026nbsp;67\u003c/em\u003e(1), 147-155. https://doi.org/10.1097/01.psy.0000146328.52009.23\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"fibromyalgia, resting-state fMRI, functional connectivity, brain network, clinical measures","lastPublishedDoi":"10.21203/rs.3.rs-9054674/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9054674/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFibromyalgia (FM) is a chronic pain disorder characterized by widespread pain and affective disturbances, yet the large-scale brain network mechanisms underlying these symptoms remain incompletely understood. The present study employed a data-driven and whole-brain functional network approach to investigate the neural network alterations in FM patients and their relationship with clinical symptoms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eResting-state fMRI data from thirty-three female FM patients and thirty-three age-matched healthy controls (HC) were analyzed. Whole-brain network functional connectivity (FC) was compared between two groups, and correlations with clinical measures of pain (FIQ, MPQ), negative affect (HAMD, HAMA), and alexithymia (TAS) were examined. Permutation FDR correction \u003cem\u003ep\u003c/em\u003e \u0026lt; .005 was applied for multiple comparisons.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFM patients exhibited widespread hypoconnectivity compared to HC group, including less intra-network FC within the default mode network (DMN), frontoparietal network (FPN), and somatosensory network (SMN). Critically, FM patients showed significant thalamocortical decoupling between the subcortical network (thalamus) and the DMN, SMN, and FPN. Correlation analyses revealed distinct brain-behavior relationships in FM patients: pain impact (FIQ) and anxiety (HAMA) correlated positively with thalamus-FPN connectivity, while depression (HAMD) correlated negatively with DMN-dorsal attention network (DAN) FC. Alexithymia (TAS) uniquely correlated with FPN-ventral attention network (VAN) FC in FM patients \u0026mdash; a pattern not observed in HC group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFibromyalgia is characterized by a broad disintegration of FC across multiple large-scale networks, extending beyond central sensitization to involve disrupted thalamocortical integration and impaired communication between affective, cognitive, and sensory systems. The distinct symptom-specific correlation patterns suggest that network-based biomarkers could inform personalized therapeutic approaches targeting specific network dysfunctions.\u003c/p\u003e","manuscriptTitle":"Large-Scale Brain Network Dysfunction in Fibromyalgia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-24 07:17:46","doi":"10.21203/rs.3.rs-9054674/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-11T09:46:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69315229896149969585642085423623402048","date":"2026-05-08T06:28:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140334935055789081484480360349997578995","date":"2026-04-18T17:27:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T10:22:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-12T14:27:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-10T09:52:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-10T09:51:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-07T01:44:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6df2e3af-ec87-4fc6-b812-20cb482b9290","owner":[],"postedDate":"April 24th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-11T09:46:15+00:00","index":61,"fulltext":""},{"type":"reviewerAgreed","content":"69315229896149969585642085423623402048","date":"2026-05-08T06:28:53+00:00","index":60,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66603986,"name":"Health sciences/Biomarkers"},{"id":66603987,"name":"Health sciences/Diseases"},{"id":66603988,"name":"Health sciences/Neurology"},{"id":66603989,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-24T07:17:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-24 07:17:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9054674","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9054674","identity":"rs-9054674","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}