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BioMed Research International
Volume 2016 (2016), Article ID 7296125, 9 pages
http://dx.doi.org/10.1155/2016/7296125
Review Article

Gray Matter Atrophy within the Default Mode Network of Fibromyalgia: A Meta-Analysis of Voxel-Based Morphometry Studies

Chemin Lin,1,2 Shwu-Hua Lee,2,3 and Hsu-Huei Weng4,5,6,7,8

1Department of Psychiatry, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
2College of Medicine, Chang Gung University, Taoyuan, Taiwan
3Department of Psychiatry, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
4Department of Radiology, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan
5Department of Diagnostic Radiology, Chiayi Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Chiayi, Taiwan
6Department of Respiratory Care, Chang Gung University of Science and Technology, Chiayi, Taiwan
7Department of Psychology, National Chung Cheng University, Chiayi, Taiwan
8Department of Imaging Physics, Division of Diagnostic Imaging, University of Texas, MD Anderson Cancer Center, Houston, TX, USA

Received 16 September 2016; Accepted 6 December 2016

Academic Editor: Sadiq Umar

Copyright © 2016 Chemin Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Wolfe, H. A. Smythe, M. B. Yunus et al., “The American College of Rheumatology 1990 criteria for the classification of fibromyalgia,” Arthritis & Rheumatism, vol. 33, no. 2, pp. 160–172, 1990. View at Google Scholar
  2. F. Wolfe, D. J. Clauw, M.-A. Fitzcharles et al., “The American College of Rheumatology preliminary diagnostic criteria for fibromyalgia and measurement of symptom severity,” Arthritis Care & Research, vol. 62, no. 5, pp. 600–610, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. J. A. Verbunt, D. H. F. M. Pernot, and R. J. E. M. Smeets, “Disability and quality of life in patients with fibromyalgia,” Health and Quality of Life Outcomes, vol. 6, no. 1, article 8, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Kivimäki, P. Leino-Arjas, L. Kaila-Kangas et al., “Increased absence due to sickness among employees with fibromyalgia,” Annals of the Rheumatic Diseases, vol. 66, no. 1, pp. 65–69, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. M.-A. Fitzcharles, P. A. Ste-Marie, and J. X. Pereira, “Fibromyalgia: evolving concepts over the past 2 decades,” Canadian Medical Association Journal, vol. 185, no. 13, pp. E645–E651, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. F. Wolfe, K. Ross, J. Anderson, I. J. Russell, and L. Hebert, “The prevalence and characteristics of fibromyalgia in the general population,” Arthritis and Rheumatism, vol. 38, no. 1, pp. 19–28, 1995. View at Publisher · View at Google Scholar · View at Scopus
  7. F. Wolfe, E. Brähler, A. Hinz, and W. Häuser, “Fibromyalgia prevalence, somatic symptom reporting, and the dimensionality of polysymptomatic distress: results from a survey of the general population,” Arthritis Care & Research, vol. 65, no. 5, pp. 777–785, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. P. Mease, “Fibromyalgia syndrome: review of clinical presentation, pathogenesis, outcome measures, and treatment,” The Journal of Rheumatology, vol. 75, pp. 6–21, 2005. View at Google Scholar · View at Scopus
  9. D. J. Clauw, “Fibromyalgia: an overview,” American Journal of Medicine, vol. 122, no. 12, pp. S3–S13, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. R. W. Simms, S. H. Roy, M. Hrovat et al., “Lack of association between fibromyalgia syndrome and abnormalities in muscle energy metabolism,” Arthritis & Rheumatism, vol. 37, no. 6, pp. 794–800, 1994. View at Publisher · View at Google Scholar · View at Scopus
  11. J. A. Desmeules, C. Cedraschi, E. Rapiti et al., “Neurophysiologic evidence for a central sensitization in patients with fibromyalgia,” Arthritis & Rheumatism, vol. 48, no. 5, pp. 1420–1429, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Staud, “Evidence of involvement of central neural mechanisms in generating fibromyalgia pain,” Current rheumatology reports, vol. 4, no. 4, pp. 299–305, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. J. M. Mountz, L. A. Bradley, J. G. Modell et al., “Fibromyalgia in women. Abnormalities of regional cerebral blood flow in the thalamus and the caudate nucleus are associated with low pain threshold levels,” Arthritis and Rheumatism, vol. 38, no. 7, pp. 926–938, 1995. View at Publisher · View at Google Scholar · View at Scopus
  14. R. Kwiatek, L. Barnden, R. Tedman et al., “Regional cerebral blood flow in fibromyalgia: single-photon-emission computed tomography evidence of reduction in the pontine tegmentum and thalami,” Arthritis and Rheumatism, vol. 43, no. 12, pp. 2823–2833, 2000. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Wik, H. Fischer, B. Bragée, M. Kristianson, and M. Fredrikson, “Retrosplenial cortical activation in the fibromyalgia syndrome,” NeuroReport, vol. 14, no. 4, pp. 619–621, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. T. Giesecke, R. H. Gracely, M. A. B. Grant et al., “Evidence of augmented central pain processing in idiopathic chronic low back pain,” Arthritis & Rheumatism, vol. 50, no. 2, pp. 613–623, 2004. View at Publisher · View at Google Scholar · View at Scopus
  17. R. H. Gracely, F. Petzke, J. M. Wolf, and D. J. Clauw, “Functional magnetic resonance imaging evidence of augmented pain processing in fibromyalgia,” Arthritis & Rheumatism, vol. 46, no. 5, pp. 1333–1343, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. K. B. Jensen, E. Kosek, F. Petzke et al., “Evidence of dysfunctional pain inhibition in Fibromyalgia reflected in rACC during provoked pain,” Pain, vol. 144, no. 1-2, pp. 95–100, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Staud, J. G. Craggs, W. M. Perlstein, M. E. Robinson, and D. D. Price, “Brain activity associated with slow temporal summation of C-fiber evoked pain in fibromyalgia patients and healthy controls,” European Journal of Pain, vol. 12, no. 8, pp. 1078–1089, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Diersl, M. T. Schleyl, M. Rancel et al., “Differential central pain processing following repetitive intramuscular proton/prostaglandin E2 injections in female fibromyalgia patients and healthy controls,” European Journal of Pain, vol. 15, no. 7, pp. 716–723, 2011. View at Publisher · View at Google Scholar
  21. M. Burgmer, E. Pogatzki-Zahn, M. Gaubitz, E. Wessoleck, G. Heuft, and B. Pfleiderer, “Altered brain activity during pain processing in fibromyalgia,” NeuroImage, vol. 44, no. 2, pp. 502–508, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. I. Cifre, C. Sitges, D. Fraiman et al., “Disrupted functional connectivity of the pain network in fibromyalgia,” Psychosomatic Medicine, vol. 74, no. 1, pp. 55–62, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Ashburner and K. J. Friston, “Unified segmentation,” NeuroImage, vol. 26, no. 3, pp. 839–851, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. T. Schmidt-Wilcke, E. Leinisch, S. Gänbauer et al., “Affective components and intensity of pain correlate with structural differences in gray matter in chronic back pain patients,” Pain, vol. 125, no. 1-2, pp. 89–97, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. A. V. Apkarian, Y. Sosa, S. Sonty et al., “Chronic back pain is associated with decreased prefrontal and thalamic gray matter density,” Journal of Neuroscience, vol. 24, no. 46, pp. 10410–10415, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. T. Schmidt-Wilcke, E. Leinisch, A. Straube et al., “Gray matter decrease in patients with chronic tension type headache,” Neurology, vol. 65, no. 9, pp. 1483–1486, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. D. A. Seminowicz, J. S. Labus, J. A. Bueller et al., “Regional gray matter density changes in brains of patients with irritable bowel syndrome,” Gastroenterology, vol. 139, no. 1, pp. 48.e2–57.e2, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. S. As-Sanie, R. E. Harris, V. Napadow et al., “Changes in regional gray matter volume in women with chronic pelvic pain: a voxel-based morphometry study,” Pain, vol. 153, no. 5, pp. 1006–1014, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. M. E. Robinson, J. G. Craggs, D. D. Price, W. M. Perlstein, and R. Staud, “Gray matter volumes of pain-related brain areas are decreased in fibromyalgia syndrome,” Journal of Pain, vol. 12, no. 4, pp. 436–443, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. M. Burgmer, M. Gaubitz, C. Konrad et al., “Decreased gray matter volumes in the cingulo-frontal cortex and the amygdala in patients with fibromyalgia,” Psychosomatic Medicine, vol. 71, no. 5, pp. 566–573, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. K. B. Jensen, P. Srinivasan, R. Spaeth et al., “Overlapping structural and functional brain changes in patients with long-term exposure to fibromyalgia pain,” Arthritis and Rheumatism, vol. 65, no. 12, pp. 3293–3303, 2013. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Lutz, L. Jäger, D. de Quervain et al., “White and gray matter abnormalities in the brain of patients with fibromyalgia: a diffusion-tensor and volumetric imaging study,” Arthritis & Rheumatism, vol. 58, no. 12, pp. 3960–3969, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. S. B. Eickhoff, A. R. Laird, C. Grefkes, L. E. Wang, K. Zilles, and P. T. Fox, “Coordinate-based activation likelihood estimation meta-analysis of neuroimaging data: a random-effects approach based on empirical estimates of spatial uncertainty,” Human Brain Mapping, vol. 30, no. 9, pp. 2907–2926, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. A. R. Laird, P. M. Fox, C. J. Price et al., “ALE meta-analysis: controlling the false discovery rate and performing statistical contrasts,” Human Brain Mapping, vol. 25, no. 1, pp. 155–164, 2005. View at Publisher · View at Google Scholar · View at Scopus
  35. R. C. K. Chan, X. Di, G. M. McAlonan, and Q.-Y. Gong, “Brain anatomical abnormalities in high-risk individuals, first-episode, and chronic schizophrenia: an activation likelihood estimation meta-analysis of illness progression,” Schizophrenia Bulletin, vol. 37, no. 1, pp. 177–188, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. E. Bora, A. Fornito, M. Yücel, and C. Pantelis, “Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder,” Biological Psychiatry, vol. 67, no. 11, pp. 1097–1105, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. S. Kühn and J. Gallinat, “Gray matter correlates of posttraumatic stress disorder: a quantitative meta-analysis,” Biological Psychiatry, vol. 73, no. 1, pp. 70–74, 2013. View at Publisher · View at Google Scholar · View at Scopus
  38. L. K. Ferreira, B. S. Diniz, O. V. Forlenza, G. F. Busatto, and M. V. Zanetti, “Neurostructural predictors of Alzheimer's disease: a meta-analysis of VBM studies,” Neurobiology of Aging, vol. 32, no. 10, pp. 1733–1741, 2011. View at Publisher · View at Google Scholar · View at Scopus
  39. H.-H. Weng, C.-F. Chen, Y.-H. Tsai et al., “Gray matter atrophy in narcolepsy: an activation likelihood estimation meta-analysis,” Neuroscience & Biobehavioral Reviews, vol. 59, pp. 53–63, 2015. View at Publisher · View at Google Scholar · View at Scopus
  40. R. F. Smallwood, A. R. Laird, A. E. Ramage et al., “Structural brain anomalies and chronic pain: a quantitative meta-analysis of gray matter volume,” Journal of Pain, vol. 14, no. 7, pp. 663–675, 2013. View at Publisher · View at Google Scholar · View at Scopus
  41. A. May, “Morphing voxels: the hype around structural imaging of headache patients,” Brain, vol. 132, no. 6, pp. 1419–1425, 2009. View at Publisher · View at Google Scholar · View at Scopus
  42. J. L. Lancaster, D. Tordesillas-Gutiérrez, M. Martinez et al., “Bias between MNI and talairach coordinates analyzed using the ICBM-152 brain template,” Human Brain Mapping, vol. 28, no. 11, pp. 1194–1205, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. A. R. Laird, J. L. Robinson, K. M. McMillan et al., “Comparison of the disparity between Talairach and MNI coordinates in functional neuroimaging data: validation of the Lancaster transform,” NeuroImage, vol. 51, no. 2, pp. 677–683, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. S. B. Eickhoff, D. Bzdok, A. R. Laird, F. Kurth, and P. T. Fox, “Activation likelihood estimation meta-analysis revisited,” NeuroImage, vol. 59, no. 3, pp. 2349–2361, 2012. View at Publisher · View at Google Scholar · View at Scopus
  45. P. E. Turkeltaub, G. F. Eden, K. M. Jones, and T. A. Zeffiro, “Meta-analysis of the functional neuroanatomy of single-word reading: method and validation,” NeuroImage, vol. 16, no. 3, part 1, pp. 765–780, 2002. View at Publisher · View at Google Scholar · View at Scopus
  46. S. B. Eickhoff, K. E. Stephan, H. Mohlberg et al., “A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data,” NeuroImage, vol. 25, no. 4, pp. 1325–1335, 2005. View at Publisher · View at Google Scholar · View at Scopus
  47. C. R. Genovese, N. A. Lazar, and T. Nichols, “Thresholding of statistical maps in functional neuroimaging using the false discovery rate,” NeuroImage, vol. 15, no. 4, pp. 870–878, 2002. View at Publisher · View at Google Scholar · View at Scopus
  48. C. J. Holmes, R. Hoge, L. Collins, R. Woods, A. W. Toga, and A. C. Evans, “Enhancement of MR images using registration for signal averaging,” Journal of Computer Assisted Tomography, vol. 22, no. 2, pp. 324–333, 1998. View at Publisher · View at Google Scholar · View at Scopus
  49. S. B. Eickhoff, S. Heim, K. Zilles, and K. Amunts, “Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps,” NeuroImage, vol. 32, no. 2, pp. 570–582, 2006. View at Publisher · View at Google Scholar · View at Scopus
  50. S. B. Eickhoff, T. Paus, S. Caspers et al., “Assignment of functional activations to probabilistic cytoarchitectonic areas revisited,” NeuroImage, vol. 36, no. 3, pp. 511–521, 2007. View at Publisher · View at Google Scholar · View at Scopus
  51. A. Kuchinad, P. Schweinhardt, D. A. Seminowicz, P. B. Wood, B. A. Chizh, and M. C. Bushnell, “Accelerated brain gray matter loss in fibromyalgia patients: premature aging of the brain?” The Journal of Neuroscience, vol. 27, no. 15, pp. 4004–4007, 2007. View at Publisher · View at Google Scholar · View at Scopus
  52. T. Schmidt-Wilcke, R. Luerding, T. Weigand et al., “Striatal grey matter increase in patients suffering from fibromyalgia—a voxel-based morphometry study,” Pain, vol. 132, no. 1, pp. S109–S116, 2007. View at Publisher · View at Google Scholar · View at Scopus
  53. M. C. Hsu, R. E. Harris, P. C. Sundgren et al., “No consistent difference in gray matter volume between individuals with fibromyalgia and age-matched healthy subjects when controlling for affective disorder,” Pain, vol. 143, no. 3, pp. 262–267, 2009. View at Publisher · View at Google Scholar · View at Scopus
  54. P. B. Wood, M. F. Glabus, R. Simpson, and J. C. Patterson II, “Changes in gray matter density in fibromyalgia: correlation with dopamine metabolism,” The Journal of Pain, vol. 10, no. 6, pp. 609–618, 2009. View at Publisher · View at Google Scholar · View at Scopus
  55. N. Fallon, J. Alghamdi, Y. Chiu, V. Sluming, T. Nurmikko, and A. Stancak, “Structural alterations in brainstem of fibromyalgia syndrome patients correlate with sensitivity to mechanical pressure,” NeuroImage: Clinical, vol. 3, pp. 163–170, 2013. View at Publisher · View at Google Scholar · View at Scopus
  56. M. Ceko, M. C. Bushnell, M.-A. Fitzcharles, and P. Schweinhardt, “Fibromyalgia interacts with age to change the brain,” NeuroImage: Clinical, vol. 3, pp. 249–260, 2013. View at Publisher · View at Google Scholar · View at Scopus
  57. C. Diaz-Piedra, M. A. Guzman, G. Buela-Casal, and A. Catena, “The impact of fibromyalgia symptoms on brain morphometry,” Brain Imaging and Behavior, pp. 1–14, 2015. View at Publisher · View at Google Scholar · View at Scopus
  58. C. Destrieux, B. Fischl, A. Dale, and E. Halgren, “Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature,” NeuroImage, vol. 53, no. 1, pp. 1–15, 2010. View at Publisher · View at Google Scholar · View at Scopus
  59. B. A. Vogt, “Pain and emotion interactions in subregions of the cingulate gyrus,” Nature Reviews Neuroscience, vol. 6, no. 7, pp. 533–544, 2005. View at Publisher · View at Google Scholar · View at Scopus
  60. B. A. Vogt, R. W. Sikes, and L. J. Vogt, “Anterior cingulate cortex and the medial pain system,” in Neurobiology of Cingulate Cortex and Limbic Thalamus, pp. 313–344, Springer, 1993. View at Google Scholar
  61. A. K. P. Jones, B. Kulkarni, and S. W. G. Derbyshire, “Pain mechanisms and their disorders: imaging in clinical neuroscience,” British Medical Bulletin, vol. 65, no. 1, pp. 83–93, 2003. View at Publisher · View at Google Scholar · View at Scopus
  62. V. Mylius, J. Reis, M. Kunz et al., “Modulation of electrically induced pain by paired pulse transcranial magnetic stimulation of the medial frontal cortex,” Clinical Neurophysiology, vol. 117, no. 8, pp. 1814–1820, 2006. View at Publisher · View at Google Scholar · View at Scopus
  63. W.-K. Yoo, Y.-H. Kim, W.-S. Doh et al., “Dissociable modulating effect of repetitive transcranial magnetic stimulation on sensory and pain perception,” NeuroReport, vol. 17, no. 2, pp. 141–144, 2006. View at Publisher · View at Google Scholar · View at Scopus
  64. Y. Tamura, S. Okabe, T. Ohnishi et al., “Effects of 1-Hz repetitive transcranial magnetic stimulation on acute pain induced by capsaicin,” Pain, vol. 107, no. 1-2, pp. 107–115, 2004. View at Publisher · View at Google Scholar · View at Scopus
  65. M. M. Heinricher, I. Tavares, J. L. Leith, and B. M. Lumb, “Descending control of nociception: specificity, recruitment and plasticity,” Brain Research Reviews, vol. 60, no. 1, pp. 214–225, 2009. View at Publisher · View at Google Scholar · View at Scopus
  66. I. Tracey and P. W. Mantyh, “The cerebral signature for pain perception and its modulation,” Neuron, vol. 55, no. 3, pp. 377–391, 2007. View at Publisher · View at Google Scholar · View at Scopus
  67. A. M. Abeles, M. H. Pillinger, B. M. Solitar, and M. Abeles, “Narrative review: the pathophysiology of fibromyalgia,” Annals of Internal Medicine, vol. 146, no. 10, pp. 726–734, 2007. View at Publisher · View at Google Scholar · View at Scopus
  68. T. Schmidt-Wilcke and D. J. Clauw, “Pharmacotherapy in fibromyalgia (FM)—implications for the underlying pathophysiology,” Pharmacology & Therapeutics, vol. 127, no. 3, pp. 283–294, 2010. View at Publisher · View at Google Scholar · View at Scopus
  69. L. Gormsen, R. Rosenberg, F. W. Bach, and T. S. Jensen, “Depression, anxiety, health-related quality of life and pain in patients with chronic fibromyalgia and neuropathic pain,” European Journal of Pain, vol. 14, no. 2, pp. 127.e1–127.e8, 2010. View at Publisher · View at Google Scholar · View at Scopus
  70. J. M. Glass, “Fibromyalgia and cognition,” The Journal of Clinical Psychiatry, vol. 69, no. 2, pp. 20–24, 2008. View at Publisher · View at Google Scholar · View at Scopus
  71. R. R. Edwards, C. O. Bingham III, J. Bathon, and J. A. Haythornthwaite, “Catastrophizing and pain in arthritis, fibromyalgia, and other rheumatic diseases,” Arthritis Care & Research, vol. 55, no. 2, pp. 325–332, 2006. View at Publisher · View at Google Scholar · View at Scopus
  72. R. H. Gracely, M. E. Geisser, T. Giesecke et al., “Pain catastrophizing and neural responses to pain among persons with fibromyalgia,” Brain, vol. 127, no. 4, pp. 835–843, 2004. View at Publisher · View at Google Scholar · View at Scopus
  73. A. Ploghaus, I. Tracey, J. S. Gati et al., “Dissociating pain from its anticipation in the human brain,” Science, vol. 284, no. 5422, pp. 1979–1981, 1999. View at Publisher · View at Google Scholar · View at Scopus
  74. T. V. Sewards and M. A. Sewards, “Fear and power-dominance drive motivation: neural representations and pathways mediating sensory and mnemonic inputs, and outputs to premotor structures,” Neuroscience & Biobehavioral Reviews, vol. 26, no. 5, pp. 553–579, 2002. View at Publisher · View at Google Scholar · View at Scopus
  75. R. Luerding, T. Weigand, U. Bogdahn, and T. Schmidt-Wilcke, “Working memory performance is correlated with local brain morphology in the medial frontal and anterior cingulate cortex in fibromyalgia patients: structural correlates of pain–cognition interaction,” Brain, vol. 131, no. 12, pp. 3222–3231, 2008. View at Publisher · View at Google Scholar · View at Scopus
  76. D. M. Amodio and C. D. Frith, “Meeting of minds: the medial frontal cortex and social cognition,” Nature Reviews Neuroscience, vol. 7, no. 4, pp. 268–277, 2006. View at Publisher · View at Google Scholar · View at Scopus
  77. F. Å. Nielsen, D. Balslev, and L. K. Hansen, “Mining the posterior cingulate: segregation between memory and pain components,” NeuroImage, vol. 27, no. 3, pp. 520–532, 2005. View at Publisher · View at Google Scholar · View at Scopus
  78. B. A. Vogt, G. R. Berger, and S. W. G. Derbyshire, “Structural and functional dichotomy of human midcingulate cortex,” European Journal of Neuroscience, vol. 18, no. 11, pp. 3134–3144, 2003. View at Publisher · View at Google Scholar · View at Scopus
  79. A. Etkin, T. Egner, and R. Kalisch, “Emotional processing in anterior cingulate and medial prefrontal cortex,” Trends in Cognitive Sciences, vol. 15, no. 2, pp. 85–93, 2011. View at Publisher · View at Google Scholar · View at Scopus
  80. B. A. Vogt, “Submodalities of emotion in the context of cingulate subregions,” Cortex, vol. 59, pp. 197–202, 2014. View at Publisher · View at Google Scholar · View at Scopus
  81. B. Vogt, Cingulate Neurobiology and Disease, Oxford University Press, 2009.
  82. S. S. Can, A. Gencay-Can, and Z. Gunendi, “Validity and reliability of the clock drawing test as a screening tool for cognitive impairment in patients with fibromyalgia,” Comprehensive Psychiatry, vol. 53, no. 1, pp. 81–86, 2012. View at Publisher · View at Google Scholar · View at Scopus
  83. R. Cánovas, I. León, M. D. Roldán, R. Astur, and J. M. Cimadevilla, “Virtual reality tasks disclose spatial memory alterations in fibromyalgia,” Rheumatology, vol. 48, no. 10, pp. 1273–1278, 2009. View at Publisher · View at Google Scholar · View at Scopus
  84. S.-H. Kim, S.-H. Kim, S.-K. Kim, E. J. Nam, S. W. Han, and S. J. Lee, “Spatial versus verbal memory impairments in patients with fibromyalgia,” Rheumatology International, vol. 32, no. 5, pp. 1135–1142, 2012. View at Publisher · View at Google Scholar · View at Scopus
  85. D. A. Gusnard, E. Akbudak, G. L. Shulman, and M. E. Raichle, “Medial prefrontal cortex and self-referential mental activity: relation to a default mode of brain function,” Proceedings of the National Academy of Sciences of the United States of America, vol. 98, no. 7, pp. 4259–4264, 2001. View at Publisher · View at Google Scholar · View at Scopus
  86. P. Fransson and G. Marrelec, “The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation network analysis,” NeuroImage, vol. 42, no. 3, pp. 1178–1184, 2008. View at Publisher · View at Google Scholar · View at Scopus
  87. S. Minoshima, B. Giordani, S. Berent, K. A. Frey, N. L. Foster, and D. E. Kuhl, “Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease,” Annals of Neurology, vol. 42, no. 1, pp. 85–94, 1997. View at Publisher · View at Google Scholar · View at Scopus
  88. R. Leech and D. J. Sharp, “The role of the posterior cingulate cortex in cognition and disease,” Brain, vol. 137, no. 1, pp. 12–32, 2014. View at Publisher · View at Google Scholar · View at Scopus
  89. J. S. Damoiseaux, C. F. Beckmann, E. J. S. Arigita et al., “Reduced resting-state brain activity in the ‘default network’ in normal aging,” Cerebral Cortex, vol. 18, no. 8, pp. 1856–1864, 2008. View at Publisher · View at Google Scholar · View at Scopus
  90. B. A. Vogt, L. Vogt, and S. Laureys, “Cytology and functionally correlated circuits of human posterior cingulate areas,” NeuroImage, vol. 29, no. 2, pp. 452–466, 2006. View at Publisher · View at Google Scholar · View at Scopus
  91. D. S. Margulies, J. L. Vincent, C. Kelly et al., “Precuneus shares intrinsic functional architecture in humans and monkeys,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 47, pp. 20069–20074, 2009. View at Publisher · View at Google Scholar · View at Scopus
  92. V. Napadow, L. LaCount, K. Park, S. As-Sanie, D. J. Clauw, and R. E. Harris, “Intrinsic brain connectivity in fibromyalgia is associated with chronic pain intensity,” Arthritis & Rheumatism, vol. 62, no. 8, pp. 2545–2555, 2010. View at Publisher · View at Google Scholar · View at Scopus
  93. M. N. Baliki, P. Y. Geha, A. V. Apkarian, and D. R. Chialvo, “Beyond feeling: chronic pain hurts the brain, disrupting the default-mode network dynamics,” Journal of Neuroscience, vol. 28, no. 6, pp. 1398–1403, 2008. View at Publisher · View at Google Scholar · View at Scopus
  94. A. Kucyi, T. V. Salomons, and K. D. Davis, “Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks,” Proceedings of the National Academy of Sciences of the United States of America, vol. 110, no. 46, pp. 18692–18697, 2013. View at Publisher · View at Google Scholar · View at Scopus
  95. M.-H. Chang, J.-W. Hsu, K.-L. Huang et al., “Bidirectional association between depression and fibromyalgia syndrome: a nationwide longitudinal study,” The Journal of Pain, vol. 16, no. 9, pp. 895–902, 2015. View at Publisher · View at Google Scholar · View at Scopus
  96. P. C. M. P. Koolschijn, N. E. M. van Haren, G. J. L. M. Lensvelt-Mulders, H. E. Hulshoff Pol, and R. S. Kahn, “Brain volume abnormalities in major depressive disorder: a meta-analysis of magnetic resonance imaging studies,” Human Brain Mapping, vol. 30, no. 11, pp. 3719–3735, 2009. View at Publisher · View at Google Scholar · View at Scopus
  97. E. Bora, A. Fornito, C. Pantelis, and M. Yücel, “Gray matter abnormalities in Major Depressive Disorder: a meta-analysis of voxel based morphometry studies,” Journal of Affective Disorders, vol. 138, no. 1-2, pp. 9–18, 2012. View at Publisher · View at Google Scholar · View at Scopus
  98. R. F. Smallwood, A. R. Laird, A. E. Ramage et al., “Structural brain anomalies and chronic pain: a quantitative meta-analysis of gray matter volume,” The Journal of Pain, vol. 14, no. 7, pp. 663–675, 2013. View at Publisher · View at Google Scholar · View at Scopus
  99. A. May, “Structural brain imaging: a window into chronic pain,” Neuroscientist, vol. 17, no. 2, pp. 209–220, 2011. View at Publisher · View at Google Scholar · View at Scopus