Acupuncture modulates cortical thickness and functional connectivity in knee osteoarthritis patients

Scientific Reports

Volumn 4, Issue , 2014, Pages

  Chen, Xiaoyan ^a   Spaeth, Rosa B ^a   Retzepi, Kallirroi ^a   Ott, Daniel ^a   Kong, Jian ^a  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACUPUNCTURE; ADULT; AGED; CLINICAL TRIAL; DIAGNOSTIC IMAGING; FEMALE; HUMAN; MALE; MIDDLE AGED; NUCLEAR MAGNETIC RESONANCE IMAGING; OSTEOARTHRITIS, KNEE; RADIOGRAPHY;

ACUPUNCTURE THERAPY; ADULT; AGED; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; OSTEOARTHRITIS, KNEE; RADIOGRAPHY;

EID: 84923330687     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep06482     Document Type: Article

References (95)

1. Ho-Pham, L. T. et al. Prevalence of radiographic osteoarthritis of the knee and its relationship to self-reported pain. PLoS One 9, e94563, doi:10.1371/ journal.pone.0094563 PONE-D-13-46418 (2014).
2. Nguyen, U. S. et al. Increasing prevalence of knee pain and symptomatic knee osteoarthritis: survey and cohort data. Ann Intern Med 155, 725-732, doi:155/11/ 725 10.7326/0003-4819-155-11-201112060-00004 (2011).
3. Hochman, J. R., Davis, A. M., Elkayam, J., Gagliese, L. & Hawker, G. A. Neuropathic pain symptoms on the modified painDETECT correlate with signs of central sensitization in knee osteoarthritis. Osteoarthritis Cartilage 21, 1236-1242, doi:S1063-4584(13)00859-5 10.1016/j.joca.2013.06.023 (2013).
4. Arendt-Nielsen, L. et al. Sensitization in patients with painful knee osteoarthritis. Pain 149, 573-581 (2010).
5. Finan, P. H. et al. Discordance between pain and radiographic severity in knee osteoarthritis: findings from quantitative sensory testing of central sensitization. Arthritis Rheum 65, 363-372, doi:10.1002/art.34646 (2013).
6. Gwilym, S. E. et al. Psychophysical and functional imaging evidence supporting the presence of central sensitization in a cohort of osteoarthritis patients. Arthritis Rheum 61, 1226-1234 (2009).
7. Howard, M. A. et al. Alterations in resting-state regional cerebral blood flow demonstrate ongoing pain in osteoarthritis: An arterial spin-labeled magnetic resonance imaging study. Arthritis Rheum 64, 3936-3946, doi:10.1002/art.37685 (2012).
8. Gay, M. C., Philippot, P. & Luminet, O. Differential effectiveness of psychological interventions for reducing osteoarthritis pain: a comparison of Erikson [correction of Erickson] hypnosis and Jacobson relaxation. Eur J Pain 6, 1-16, doi:10.1053/eujp.2001.0263 S1090380101903143 (2002).
9. Berman, B. M. et al. Effectiveness of acupuncture as adjunctive therapy in osteoarthritis of the knee: a randomized, controlled trial. Ann Intern Med 141, 901-910 (2004).
10. Felson, D. T. et al. Osteoarthritis: new insights. Part 2: treatment approaches. Ann Intern Med 133, 726-737 (2000).
11. Witt, C. et al. Acupuncture in patients with osteoarthritis of the knee: a randomised trial. Lancet 366, 136-143 (2005).
12. Scharf, H. P. et al. Acupuncture and knee osteoarthritis: a three-armed randomized trial. Ann Intern Med 145, 12-20 (2006).
13. Mavrommatis, C. I., Argyra, E., Vadalouka, A. & Vasilakos, D. G. Acupuncture as an adjunctive therapy to pharmacological treatment in patients with chronic pain due to osteoarthritis of the knee: a 3-armed, randomized, placebo-controlled trial. Pain 153, 1720-1726 (2012).
14. Han, J. S. Acupuncture analgesia: areas of consensus and controversy. Pain 152, S41-48 (2011).
15. Hans, J. S. Physiology of acupuncture: review of thirty years of research. J Altern Complement Med (Suppl 1), S101-108 (1997).
16. Zhao, Z. Q. Neural mechanism underlying acupuncture analgesia. Prog Neurobiol 85, 355-375 (2008).
17. Chae, Y. et al. Inserting needles into the body: a meta-analysis of brain activity associated with acupuncture needle stimulation. J Pain 14, 215-222 (2013).
18. Huang, W. et al. Characterizing acupuncture stimuli using brain imaging with FMRI -a systematic review and meta-analysis of the literature. PLoS One 7, e32960 (2012).
19. Kong, J. et al. Test-retest study of fMRI signal change evoked by electroacupuncture stimulation. Neuroimage 34, 1171-1181 PMCID: PMC1994822 (2007).
20. Leung, A., Zhao, Y. & Shukla, S. The effect of acupuncture needle combination on central pain processing -an fMRI study. Mol Pain 10, 23, doi:1744-8069-10-23 (2014).
21. Kong, J. et al. A pilot study of functional magnetic resonance imaging of the brain during manual and electroacupuncture stimulation of acupuncture point (LI-4 Hegu) in normal subjects reveals differential brain activation between methods. J Altern Complement Med 8, 411-419 (2002).
22. Zyloney, C. E. et al. Imaging the functional connectivity of the Periaqueductal Gray during genuine and sham electroacupuncture treatment. Mol Pain 6, 80 (2010).
23. Dhond, R. P., Yeh, C., Park, K., Kettner, N. & Napadow, V. Acupuncture modulates resting state connectivity in default and sensorimotor brain networks. Pain 136, 407-418 (2008).
24. Liu, P. et al. Combining spatial and temporal information to explore functionguide action of acupuncture using fMRI. JMagn Reson Imaging 30, 41-46 (2009).
25. Bai, L. et al. Acupuncture modulates spontaneous activities in the anticorrelated resting brain networks. Brain Res 1279, 37-49, doi:S0006-8993(09)00899-3 (2009).
26. Qin, W. et al. FMRI connectivity analysis of acupuncture effects on an amygdalaassociated brain network. Mol Pain 4, 55 (2008).
27. Liu, B. et al. Altered small-world efficiency of brain functional networks in acupuncture at ST36: a functional MRI study. PLoS One 7, e39342, doi:10.1371/ journal.pone.0039342 PONE-D-11-22310 (2012).
28. Sun, R. et al. Connectomics: A New Direction in Research to Understand the Mechanism of Acupuncture. Evid Based Complement Alternat Med 2014, 568429, doi:10.1155/2014/568429 (2014).
29. Zhang, Y. et al. An fMRI study of acupuncture using independent component analysis. Neurosci Lett 449, 6-9, doi:S0304-3940(08)01450-X 10.1016/ j.neulet.2008.10.071 (2009).
30. Apkarian, A. V. et al. Chronic back pain is associated with decreased prefrontal and thalamic gray matter density. J Neurosci 24, 10410-10415 (2004).
31. Flor, H. Cortical reorganisation and chronic pain: implications for rehabilitation. J Rehabil Med 41 Suppl, 66-72 (2003).
32. Schmidt-Wilcke, T. et al. Affective components and intensity of pain correlate with structural differences in gray matter in chronic back pain patients. Pain 125, 89-97 (2006).
33. Baliki, M. N., Schnitzer, T. J., Bauer, W. R. & Apkarian, A. V. Brain morphological signatures for chronic pain. PLoS One 6, e26010 (2011).
34. May, A. Chronic pain may change the structure of the brain. Pain 137, 7-15 (2008).
35. Henry, D. E., Chiodo, A. E. & Yang, W. Central nervous system reorganization in a variety of chronic pain states: a review. Pm R 3, 1116-1125 (2011).
36. Kong, J. et al. S1 is associated with chronic low back pain: a functional and structural MRI study. Molecular pain 9, doi: 10.1186/1744-8069-1189-1143 (2013).
37. Jensen, K. et al. Overlapping structural and functional brain changes in patients with long-term exposure to fibromyalgia. Arthritis& Rheumatism 65, 3293-3303 (2013).
38. Apkarian, A.V., Baliki, M. N. & Geha, P. Y. Towards a theory of chronic pain. Prog Neurobiol 87, 81-97 (2009).
39. Bushnell, M. C. How neuroimaging studies have challenged us to rethink: is chronic pain a disease? J Pain 10, 1113-1120 (2009).
40. Davis, K. D. & Moayedi, M. Central Mechanisms of Pain Revealed Through Functional and Structural MRI. J Neuroimmune Pharmacol 8, 518-534 (2013).
41. Baliki, M. N. et al. Corticostriatal functional connectivity predicts transition to chronic back pain. Nat Neurosci, doi:10.1038/nn.3153 (2012).
42. Loggia, M. L. et al. Default mode network connectivity encodes clinical pain: An arterial spin labeling study. Pain 154, 24-33 (2013).
43. Seminowicz, D. A. et al. Effective treatment of chronic low back pain in humans reverses abnormal brain anatomy and function. J Neurosci 31, 7540-7550 (2011).
44. Gwilym, S. E., Filippini, N., Douaud, G., Carr, A. J. & Tracey, I. Thalamic atrophy associated with painful osteoarthritis of the hip is reversible after arthroplasty: a longitudinal voxel-based morphometric study. Arthritis Rheum 62, 2930-2940 (2010).
45. Roos, E. M. & Toksvig-Larsen, S. Knee injury and Osteoarthritis Outcome Score (KOOS) - validation and comparison to theWOMAC in total knee replacement. Health and Quality of Life Outcomes 1, 1-10 (2003).
46. Cheng, X. N. Chinese acupuncture and moxibustion. (Foreign Language Press, 1987).
47. Streitberger, K. & Kleinhenz, J. Introducing a placebo needle into acupuncture research. Lancet 352, 364-365 (1998).
48. Kong, J. et al. Psychophysical outcomes from a randomized pilot study of manual, electro, and sham acupuncture treatment on experimentally induced thermal pain. J Pain 6, 55-64 (2005).
49. Kong, J. et al. Brain activity associated with expectancy-enhanced placebo analgesia as measured by functional magnetic resonance imaging. J Neurosci 26, 381-388 (2006).
50. Zhu, D.,Gao, Y., Chang, J. & Kong, J. Placebo acupuncture devices: considerations for acupuncture research. Evid Based Complement Alternat Med 2013, 628907 (2013).
51. Spaeth, R. B. et al. A longitudinal study of the reliability of acupuncture deqi sensations in knee osteoarthritis. Evid Based Complement Alternat Med 2013, 204259 (2013).
52. Kong, J. et al. Acupuncture de qi, from qualitative history to quantitative measurement. J Altern Complement Med 13, 1059-1070 (2007).
53. Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179-194 (1999).
54. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341-355 (2002).
55. Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195-207 (1999).
56. Reuter, M., Schmansky, N. J., Rosas, H. D. & Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402-1418, doi:S1053-8119(12)00276-5 10.1016/j.neuroimage.2012.02.084 (2012).
57. Reuter, M. & Fischl, B. Avoiding asymmetry-induced bias in longitudinal image processing. Neuroimage 57, 19-21, doi:S1053-8119(11)00253-9 10.1016/ j.neuroimage.2011.02.076 (2011).
58. Reuter, M., Rosas, H. D. & Fischl, B. Highly accurate inverse consistent registration: a robust approach. Neuroimage 53, 1181-1196, doi:S1053- 8119(10)00971-7 10.1016/j.neuroimage.2010.07.020 (2010).
59. Chao-Gan, Y. & Yu-Feng, Z. DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Front Syst Neurosci 4, 13 (2010).
60. Yan, C. G. et al. Acomprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76, 183-201, doi:S1053-8119(13)00212-7 10.1016/j.neuroimage.2013.03.004 (2013).
61. Song, X. W. et al. REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One 6, e25031, doi:10.1371/journal.pone.0025031 PONE-D-11-09456 (2011).
62. Yu, R. et al. Placebo analgesia and reward processing: Integrating genetics, personality, and intrinsic brain activity. Hum Brain Mapp, doi: 10.1002/ hbm.22496 (2014).
63. Bai, L. et al. Time-varied characteristics of acupuncture effects in fMRI studies. Hum Brain Mapp 30, 3445-3460, doi:10.1002/hbm.20769 (2009).
64. Bai, L. et al. Acupuncture modulates temporal neural responses in wide brain networks: evidence from fMRI study. Mol Pain 6, 73, doi:1744-8069-6-73 (2010).
65. Fairhurst, M.,Wiech, K., Dunckley, P. & Tracey, I. Anticipatory brainstem activity predicts neural processing of pain in humans. Pain 128, 101-110, doi:S0304- 3959(06)00458-1 10.1016/j.pain.2006.09.001 (2007).
66. Kucyi, A., Salomons, T. V. & Davis, K. D. Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks. Proc Natl Acad Sci U S A 110, 18692-18697, doi:1312902110 10.1073/pnas.1312902110 (2013).
67. Bingel, U., Schoell, E., Herken, W., Buchel, C. & May, A. Habituation to painful stimulation involves the antinociceptive system. Pain 131, 21-30, doi:S0304- 3959(06)00670-1 10.1016/j.pain.2006.12.005 (2007).
68. Kong, J. et al. Functional connectivity of frontoparietal network predicts cognitive modulation of pain. Pain 154, 459-467 (2013).
69. Lui, F. et al. Neural bases of conditioned placebo analgesia. Pain 151, 816-824 (2010).
70. Atlas, L. Y., Bolger, N., Lindquist, M. A. & Wager, T. D. Brain mediators of predictive cue effects on perceived pain. J Neurosci 30, 12964-12977 (2010).
71. Amodio, D. M. & Frith, C. D. Meeting of minds: the medial frontal cortex and social cognition. Nat Rev Neurosci 7, 268-277, doi:nrn1884 10.1038/nrn1884 (2006).
72. Miller, E. K. The prefrontal cortex and cognitive control. Nat Rev Neurosci 1, 59-65 (2000).
73. Corbetta, M. & Shulman, G. L. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 3, 201-215 (2002).
74. Ridderinkhof, K. R.,Ullsperger, M., Crone, E. A. & Nieuwenhuis, S. The role of the medial frontal cortex in cognitive control. Science 306, 443-447 (2004).
75. Matsumoto, K. & Tanaka, K. Neuroscience. Conflict and cognitive control. Science 303, 969-970 (2004).
76. Carter, C. S. & van Veen, V. Anterior cingulate cortex and conflict detection: an update of theory and data. Cogn Affect Behav Neurosci 7, 367-379 (2007).
77. Crone, E. A., Wendelken, C., Donohue, S. E. & Bunge, S. A. Neural evidence for dissociable components of task-switching. Cereb Cortex 16, 475-486 (2006).
78. Sowell, E. R. et al. Mapping cortical change across the human life span. Nat Neurosci 6, 309-315, doi:10.1038/nn1008 nn1008 (2003).
79. Fjell, A. M. et al. High consistency of regional cortical thinning in aging across multiple samples. Cereb Cortex 19, 2001-2012, doi:bhn232 10.1093/cercor/ bhn232 (2009).
80. Salat, D. H. et al. Thinning of the cerebral cortex in aging. Cereb Cortex 14, 721-730, doi:10.1093/cercor/bhh032 bhh032 (2004).
81. Thompson, P. M. et al. Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia. Neuroimage 23 Suppl 1, S2-18, doi:S1053- 8119(04)00399-4 10.1016/j.neuroimage.2004.07.071 (2004).
82. Jernigan, T. L. et al. Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiol Aging 22, 581-594, doi:S0197458001002172 (2001).
83. Chong, C. D., Dodick, D. W., Schlaggar, B. L. & Schwedt, T. J. Atypical age-related cortical thinning in episodic migraine. Cephalalgia, doi:0333102414531157 10.1177/0333102414531157 (2014).
84. Tang, Y. Y., Lu, Q., Fan, M., Yang, Y. & Posner, M. I. Mechanisms of white matter changes induced by meditation. Proc Natl Acad Sci U S A 109, 10570-10574, doi:1207817109 10.1073/pnas.1207817109 (2012).
85. Engvig, A. et al. Effects of memory training on cortical thickness in the elderly. Neuroimage 52, 1667-1676, doi:S1053-8119(10)00773-1 10.1016/ j.neuroimage.2010.05.041 (2010).
86. Liu, H. et al. Connectivity-based parcellation of the human frontal pole with diffusion tensor imaging. J Neurosci, 16, 6782-90 (2013).
87. Fields, H. State-dependent opioid control of pain. Nat Rev Neurosci 5, 565-575 (2004).
88. Tracey, I. et al. Imaging attentional modulation of pain in the periaqueductal gray in humans. J Neurosci 22, 2748-2752 (2002).
89. Jensen, K. B. et al. Patients With Fibromyalgia Display Less Functional Connectivity In The Brain's Pain Inhibitory Network. Mol Pain 8, 32 (2012).
90. Kong, J., Tu, P. C., Zyloney, C. & Su, T. P. Intrinsic functional connectivity of the periaqueductal gray, a resting fMRI study. Behav Brain Res 211, 215-219 (2010).
91. Parks, E. L. et al. Brain activity for chronic knee osteoarthritis: dissociating evoked pain from spontaneous pain. Eur J Pain 15, 843 e841-814, doi:S1090- 3801(11)00019-X 10.1016/j.ejpain.2010.12.007 (2011).
92. Scott, D. J. et al. Individual differences in reward responding explain placeboinduced expectations and effects. Neuron 55, 325-336 (2007).
93. Wanigasekera, V. et al. Baseline reward circuitry activity and trait reward responsiveness predict expression of opioid analgesia in healthy subjects. Proc Natl Acad Sci U S A 109, 17705-17710, doi:1120201109 10.1073/ pnas.1120201109 (2012).
94. Dumville, J., Hahn, S., Miles, J. & Torgerson, D. The use of unequal randomisation ratios in clinical trials: A review. Contemporary Clinical Trials 27, 1-12 (2006).
95. Zuo, X. N. & Xing, X. X. Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective. Neurosci Biobehav Rev 45C, 100-118, doi:S0149-7634(14)00126-2 10.1016/j.neubiorev.2014.05.009 (2014).