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Topological alterations in white matter structural networks in fibromyalgia

  • Advanced Neuroimaging
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Abstract

Purpose

Neuroimaging studies employing analyses dependent on regional assumptions and specific neuronal circuits could miss characteristics of whole-brain structural connectivity critical to the pathophysiology of fibromyalgia (FM). This study applied the whole-brain graph-theoretical approach to identify whole-brain structural connectivity disturbances in FM.

Methods

This cross-sectional study used probabilistic diffusion tractography and graph theory analysis to evaluate the topological organization of brain white matter networks in 20 patients with FM and 20 healthy controls (HCs). The relationship between brain network metrics and clinical variables was evaluated.

Results

Compared with HCs, FM patients had lower clustering coefficient, local efficiency, hierarchy, synchronization, and higher normalized characteristic path length. Regionally, patients demonstrated a significant reduction in nodal efficiency and centrality; these regions were mainly located in the prefrontal, temporal cortex, and basal ganglia. The network-based statistical analysis (NBS) identified decreased structural connectivity in a subnetwork of prefrontal cortex, basal ganglia, and thalamus in FM. There was no correlation between network metrics and clinical variables (false discovery rate corrected).

Conclusions

The current research demonstrated disrupted topological architecture of white matter networks in FM. Our results suggested compromised neural integration and segregation and reduced structural connectivity in FM.

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Data availability

Data are available from a public dataset via OpenNeuro with accession number ds001928 (https://openneuro.org/datasets/ds001928/versions/1.1.0).

References

  1. Wolfe F, Clauw DJ, Fitzcharles MA, Goldenberg DL, Hauser W, Katz RS, Mease P, Russell AS, Russell IJ, Winfield JB (2011) Fibromyalgia criteria and severity scales for clinical and epidemiological studies: a modification of the ACR Preliminary Diagnostic Criteria for Fibromyalgia. J Rheumatol 38:1113–1122

    Article  PubMed  Google Scholar 

  2. Kim J, Loggia ML, Cahalan CM, Harris RE, Beissner FDPN, Garcia RG, Kim H, Wasan AD, Edwards RR, Napadow V (2015) The somatosensory link in fibromyalgia: functional connectivity of the primary somatosensory cortex is altered by sustained pain and is associated with clinical/autonomic dysfunction. Arthritis Rheumatol 67:1395–1405

    Article  PubMed  PubMed Central  Google Scholar 

  3. van Ettinger-Veenstra H, Boehme R, Ghafouri B, Olausson H, Wicksell RK, Gerdle B (2020) Exploration of functional connectivity changes previously reported in fibromyalgia and their relation to psychological distress and pain measures. J Clin Med 9:3560

    Article  PubMed  PubMed Central  Google Scholar 

  4. Cifre I, Sitges C, Fraiman D, Munoz MA, Balenzuela P, Gonzalez-Roldan A, Martinez-Jauand M, Birbaumer N, Chialvo DR, Montoya P (2012) Disrupted functional connectivity of the pain network in fibromyalgia. Psychosom Med 74:55–62

    Article  PubMed  Google Scholar 

  5. Tu Y, Wang J, Xiong F, Gao F (2022) Cortical abnormalities in patients with fibromyalgia: a pilot study of surface-based morphometry analysis. Pain Med 23(12):1939–1946

    Article  PubMed  Google Scholar 

  6. Basser PJ, Mattiello J, LeBihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophys J 66:259–267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ceko M, Bushnell MC, Fitzcharles MA, Schweinhardt P (2013) Fibromyalgia interacts with age to change the brain. Neuroimage Clin 3:249–260

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lutz J, Jager L, de Quervain D, Krauseneck T, Padberg F, Wichnalek M, Beyer A, Stahl R, Zirngibl B, Morhard D, Reiser M, Schelling G (2008) White and gray matter abnormalities in the brain of patients with fibromyalgia: a diffusion-tensor and volumetric imaging study. Arthritis Rheum 58:3960–3969

    Article  PubMed  Google Scholar 

  9. Kim DJ, Lim M, Kim JS, Son KM, Kim HA, Chung CK (2014) Altered white matter integrity in the corpus callosum in fibromyalgia patients identified by tract-based spatial statistical analysis. Arthritis Rheumatol 66:3190–3199

    Article  PubMed  Google Scholar 

  10. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008) Mapping the structural core of human cerebral cortex. PLoS Biol 6:e159

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198

    Article  CAS  PubMed  Google Scholar 

  12. Kim SH, Lee MW, Kang MJ, Lee SG, Lee JG, Mun CW (2020) Comparison analysis between the medication efficacy of the milnacipran and functional connectivity of neural networks in fibromyalgia patients. Brain Sci 10:295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kim H, Kim J, Loggia ML, Cahalan C, Garcia RG, Vangel MG, Wasan AD, Edwards RR, Napadow V (2015) Fibromyalgia is characterized by altered frontal and cerebellar structural covariance brain networks. Neuroimage Clin 7:667–677

    Article  PubMed  PubMed Central  Google Scholar 

  14. Pando-Naude V, Barrios FA, Alcauter S, Pasaye EH, Vase L, Brattico E, Vuust P, Garza-Villarreal EA (2019) Functional connectivity of music-induced analgesia in fibromyalgia. Sci Rep 9:15486

    Article  PubMed  PubMed Central  Google Scholar 

  15. Garcia Campayo J, Rodero B, Alda M, Sobradiel N, Montero J, Moreno S (2008) Validation of the Spanish version of the Pain Catastrophizing Scale in fibromyalgia. Med Clin (Barc) 131:487–492

    PubMed  Google Scholar 

  16. Kendall PC, Finch AJ Jr, Auerbach SM, Hooke JF, Mikulka PJ (1976) The State-Trait Anxiety Inventory: a systematic evaluation. J Consult Clin Psychol 44:406–412

    Article  CAS  PubMed  Google Scholar 

  17. Smarr KL, Keefer AL (2011) Measures of depression and depressive symptoms: Beck Depression Inventory-II (BDI-II), Center for Epidemiologic Studies Depression Scale (CES-D), Geriatric Depression Scale (GDS), Hospital Anxiety and Depression Scale (HADS), and Patient Health Questionnaire-9 (PHQ-9). Arthritis Care Res (Hoboken) 63(Suppl 11):S454–S466

    PubMed  Google Scholar 

  18. Cui Z, Zhong S, Xu P, He Y, Gong G (2013) PANDA: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci 7:42

    Article  PubMed  PubMed Central  Google Scholar 

  19. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34:144–155

    Article  CAS  PubMed  Google Scholar 

  20. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15:273–289

    Article  CAS  PubMed  Google Scholar 

  21. Pan Y, Liu Z, Xue Z, Sheng Y, Cai Y, Cheng Y, Chen X (2022) Abnormal network properties and fiber connections of DMN across major mental disorders: a probability tracing and graph theory study. Cereb Cortex 32:3127–3136

    Article  PubMed  Google Scholar 

  22. Li L, Lei D, Suo X, Li X, Yang C, Yang T, Ren J, Chen G, Zhou D, Kemp GJ, Gong Q (2020) Brain structural connectome in relation to PRRT2 mutations in paroxysmal kinesigenic dyskinesia. Hum Brain Mapp 41:3855–3866

    Article  PubMed  PubMed Central  Google Scholar 

  23. Suo X, Lei D, Chen F, Wu M, Li L, Sun L, Wei X, Zhu H, Li L, Kemp GJ, Gong Q (2017) Anatomic insights into disrupted small-world networks in pediatric posttraumatic stress disorder. Radiology 282:826–834

    Article  PubMed  Google Scholar 

  24. Xia M, Wang J, He Y (2013) BrainNet Viewer: a network visualization tool for human brain connectomics. PloS One 8:e68910

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Meng C, Brandl F, Tahmasian M, Shao J, Manoliu A, Scherr M, Schwerthoffer D, Bauml J, Forstl H, Zimmer C, Wohlschlager AM, Riedl V, Sorg C (2014) Aberrant topology of striatum's connectivity is associated with the number of episodes in depression. Brain 137:598–609

    Article  PubMed  Google Scholar 

  26. Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, Bullmore E (2010) Functional connectivity and brain networks in schizophrenia. J Neurosci 30:9477–9487

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Jung WH, Yucel M, Yun JY, Yoon YB, Cho KI, Parkes L, Kim SN, Kwon JS (2017) Altered functional network architecture in orbitofronto-striato-thalamic circuit of unmedicated patients with obsessive-compulsive disorder. Hum Brain Mapp 38:109–119

    Article  PubMed  Google Scholar 

  28. Wang Y, Deng F, Jia Y, Wang J, Zhong S, Huang H, Chen L, Chen G, Hu H, Huang L, Huang R (2019) Disrupted rich club organization and structural brain connectome in unmedicated bipolar disorder. Psychol Med 49:510–518

    Article  PubMed  Google Scholar 

  29. Zalesky A, Fornito A, Bullmore ET (2010) Network-based statistic: identifying differences in brain networks. Neuroimage 53:1197–1207

    Article  PubMed  Google Scholar 

  30. Watts DJ, Strogatz SH (1998) Collective dynamics of 'small-world' networks. Nature 393:440–442

    Article  CAS  PubMed  Google Scholar 

  31. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069

    Article  PubMed  Google Scholar 

  32. Zhang Y, Liu J, Li L, Du M, Fang W, Wang D, Jiang X, Hu X, Zhang J, Wang X, Fang J (2014) A study on small-world brain functional networks altered by postherpetic neuralgia. Magn Reson Imaging 32:359–365

    Article  PubMed  Google Scholar 

  33. Huang X, Chen J, Liu S, Gong Q, Liu T, Lu C, Qin Z, Cui H, Chen Y, Zhu Y (2021) Impaired frontal-parietal control network in chronic prostatitis/chronic pelvic pain syndrome revealed by graph theoretical analysis: A DTI study. Eur J Neurosci 53:1060–1071

    Article  CAS  PubMed  Google Scholar 

  34. Supekar K, Musen M, Menon V (2009) Development of large-scale functional brain networks in children. PLoS Biol 7:e1000157

    Article  PubMed  PubMed Central  Google Scholar 

  35. Lee SJ, Song HJ, Decety J, Seo J, Kim SH, Kim SH, Nam EJ, Kim SK, Han SW, Lee HJ, Do Y, Chang Y (2013) Do patients with fibromyalgia show abnormal neural responses to the observation of pain in others? Neurosci Res 75:305–315

    Article  PubMed  Google Scholar 

  36. Burgmer M, Pogatzki-Zahn E, Gaubitz M, Wessoleck E, Heuft G, Pfleiderer B (2009) Altered brain activity during pain processing in fibromyalgia. Neuroimage 44:502–508

    Article  PubMed  Google Scholar 

  37. Mouraux A, Diukova A, Lee MC, Wise RG, Iannetti GD (2011) A multisensory investigation of the functional significance of the "pain matrix". Neuroimage 54:2237–2249

    Article  PubMed  Google Scholar 

  38. Tracey I (2005) Nociceptive processing in the human brain. Curr Opin Neurobiol 15:478–487

    Article  CAS  PubMed  Google Scholar 

  39. Nachev P, Wydell H, O'Neill K, Husain M, Kennard C (2007) The role of the pre-supplementary motor area in the control of action. Neuroimage 36(Suppl 2):T155–T163

    Article  PubMed  Google Scholar 

  40. Sumner P, Nachev P, Morris P, Peters AM, Jackson SR, Kennard C, Husain M (2007) Human medial frontal cortex mediates unconscious inhibition of voluntary action. Neuron 54:697–711

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Friebel U, Eickhoff SB, Lotze M (2011) Coordinate-based meta-analysis of experimentally induced and chronic persistent neuropathic pain. Neuroimage 58:1070–1080

    Article  PubMed  Google Scholar 

  42. Loggia ML, Berna C, Kim J, Cahalan CM, Gollub RL, Wasan AD, Harris RE, Edwards RR, Napadow V (2014) Disrupted brain circuitry for pain-related reward/punishment in fibromyalgia. Arthritis Rheumatol 66:203–212

    Article  PubMed  PubMed Central  Google Scholar 

  43. Glass JM, Williams DA, Fernandez-Sanchez ML, Kairys A, Barjola P, Heitzeg MM, Clauw DJ, Schmidt-Wilcke T (2011) Executive function in chronic pain patients and healthy controls: different cortical activation during response inhibition in fibromyalgia. J Pain 12:1219–1229

    Article  PubMed  PubMed Central  Google Scholar 

  44. Borsook D, Upadhyay J, Chudler EH, Becerra L (2010) A key role of the basal ganglia in pain and analgesia--insights gained through human functional imaging. Mol Pain 6:27

    Article  PubMed  PubMed Central  Google Scholar 

  45. Starr CJ, Sawaki L, Wittenberg GF, Burdette JH, Oshiro Y, Quevedo AS, McHaffie JG, Coghill RC (2011) The contribution of the putamen to sensory aspects of pain: insights from structural connectivity and brain lesions. Brain 134:1987–2004

    Article  PubMed  PubMed Central  Google Scholar 

  46. Leung A, Shirvalkar P, Chen R, Kuluva J, Vaninetti M, Bermudes R, Poree L, Wassermann EM, Kopell B, Levy R, the Expert Consensus P (2020) Transcranial magnetic stimulation for pain, headache, and comorbid depression: INS-NANS Expert Consensus Panel Review and Recommendation. Neuromodulation 23:267–290

    Article  PubMed  Google Scholar 

  47. Argaman Y, Granovsky Y, Sprecher E, Sinai A, Yarnitsky D, Weissman-Fogel I (2021) Clinical effects of repetitive transcranial magnetic stimulation of the motor cortex are associated with changes in resting-state functional connectivity in patients with fibromyalgia syndrome. J Pain 23(4):595–615

    Article  PubMed  Google Scholar 

  48. Khedr EM, Omran EAH, Ismail NM, El-Hammady DH, Goma SH, Kotb H, Galal H, Osman AM, Farghaly HSM, Karim AA, Ahmed GA (2017) Effects of transcranial direct current stimulation on pain, mood and serum endorphin level in the treatment of fibromyalgia: A double blinded, randomized clinical trial. Brain Stimul 10:893–901

    Article  PubMed  Google Scholar 

  49. Cummiford CM, Nascimento TD, Foerster BR, Clauw DJ, Zubieta JK, Harris RE, DaSilva AF (2016) Changes in resting state functional connectivity after repetitive transcranial direct current stimulation applied to motor cortex in fibromyalgia patients. Arthritis Res Ther 18:40

    Article  PubMed  PubMed Central  Google Scholar 

  50. Fagerlund AJ, Hansen OA, Aslaksen PM (2015) Transcranial direct current stimulation as a treatment for patients with fibromyalgia: a randomized controlled trial. Pain 156:62–71

    Article  PubMed  Google Scholar 

  51. Lorenz J, Minoshima S, Casey KL (2003) Keeping pain out of mind: the role of the dorsolateral prefrontal cortex in pain modulation. Brain 126:1079–1091

    Article  CAS  PubMed  Google Scholar 

  52. Brown CA, El-Deredy W, Jones AK (2014) When the brain expects pain: common neural responses to pain anticipation are related to clinical pain and distress in fibromyalgia and osteoarthritis. Eur J Neurosci 39:663–672

    Article  PubMed  Google Scholar 

  53. Caumo W, Alves RL, Vicuna P, Alves C, Ramalho L, Sanches PRS, Silva DP, da Silva Torres IL, Fregni F (2021) Impact of bifrontal home-based transcranial direct current stimulation in pain catastrophizing and disability due to pain in fibromyalgia: a randomized, double-blind sham-controlled study. J Pain 23(4):641–656

    Article  PubMed  Google Scholar 

  54. Flodin P, Martinsen S, Lofgren M, Bileviciute-Ljungar I, Kosek E, Fransson P (2014) Fibromyalgia is associated with decreased connectivity between pain- and sensorimotor brain areas. Brain Connect 4:587–594

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

We thank all the participants in this study.

Funding

None.

Author information

Authors and Affiliations

  1. Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Ye Tu, Jihong Wang, Zheng Li & Feng Gao

  2. Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Ye Tu, Jihong Wang, Zheng Li & Feng Gao

  3. Wuhan Clinical Research Center for Geriatric Anesthesia, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Ye Tu, Jihong Wang, Zheng Li & Feng Gao

  4. Department of Radiology, PLA Central Theater General Hospital, Wuhan, China

    Fei Xiong

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  1. Ye Tu
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Correspondence to Fei Xiong or Feng Gao.

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This research was approved by the Ethics Committee of the Bioethics Committee of the Institute of Neurobiology, UNAM Juriquilla, Queretaro, Mexico.

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ESM 1

Supplementary material The symmetric weighted matrix of representative subject of each group. A = representative subject of fibromyalgia group, B = representative subject of healthy control group. (PNG 418 kb)

High resolution image (TIF 1282 kb)

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Tu, Y., Wang, J., Li, Z. et al. Topological alterations in white matter structural networks in fibromyalgia. Neuroradiology 65, 1737–1747 (2023). https://doi.org/10.1007/s00234-023-03225-7

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