Sustained Effects of Acupuncture Stimulation Investigated with Centrality Mapping Analysis

Xiangyu Long, Wenjing Huang, Vitaly Napadow, Fanrong Liang, Burkhard Pleger, Arno Villringer, Claudia M Witt, Till Nierhaus, Daniel Pach, Xiangyu Long, Wenjing Huang, Vitaly Napadow, Fanrong Liang, Burkhard Pleger, Arno Villringer, Claudia M Witt, Till Nierhaus, Daniel Pach

Abstract

Acupuncture can have instant and sustained effects, however, its mechanisms of action are still unclear. Here, we investigated the sustained effect of acupuncture by evaluating centrality changes in resting-state functional magnetic resonance imaging after manually stimulating the acupuncture point ST36 at the lower leg or two control point locations (CP1 same dermatome, CP2 different dermatome). Data from a previously published experiment evaluating instant BOLD effects and S2-seed-based resting state connectivity was re-analyzed using eigenvector centrality mapping and degree centrality mapping. These data-driven methods might add new insights into sustained acupuncture effects on both global and local inter-region connectivity (centrality) by evaluating the summary of connections of every voxel. We found higher centrality in parahippocampal gyrus and middle temporal gyrus after ST36 stimulation in comparison to the two control points. These regions are positively correlated to major hubs of the default mode network, which might be the primary network affected by chronic pain. The stronger integration of both regions within the whole-brain connectome after stimulation of ST36 might be a potential contributor to pain modulation by acupuncture. These findings highlight centrality mapping as a valuable analysis for future imaging studies investigating clinically relevant outcomes associated with physiological response to acupuncture stimulation.

Clinical trial registration: NCT01079689, ClinicalTrials.gov.

Keywords: acupuncture; centrality; functional connectivity; pain; resting-state fMRI.

Figures

FIGURE 1
FIGURE 1
Locations of acupuncture point ST36 and the control points on the right leg [view from the front and from the back, figure adapted (Drake et al., 2009)]. ST36 is located on the anterior aspect of the right leg, on the line connecting ST35 with ST41, 3 B-cun inferior to ST35. The location of ST36 belongs to the dermatome L5. Control point 1 (CP1) is located lateral to ST36, at the middle line between Bladder meridian and Gallbladder meridian, in the same dermatome L5. Control point 2 (CP2) is located 2 B-cun dorsally of GB31, the location of CP2 belongs to the dermatome L2 (Nierhaus et al., 2015).
FIGURE 2
FIGURE 2
The paradigm of the experiment included needle interventions and resting-state scans. The upper part represents the needle stimulation sequence. The lower part represents the whole run of the experiment. Four resting-state scans including one baseline resting-state scan and three scans after needle stimulation were performed. For the Interventions I1/I2/I3 the sequence of the stimulated points (ST36/CP1/CP2) was randomized across participants (Nierhaus et al., 2015).
FIGURE 3
FIGURE 3
The centrality changes of the post-stimulation of all three points against each other. L means the left hemisphere. The warm color means increased centrality and the cold color means decreased centrality. All images were in the Talairach space. P < 0.05, corrected. The average centrality value (mean ± SEM) of the related centrality measures from each resting-state scan within the selected ROIs which were detected in the conjunction analysis were displayed. No significant result was found in the comparison between CP1 and CP2 for both ECM and DCM analysis. Abbreviations: PHG, parahippocampus gyrus; MTG, middle temporal gyrus.

References

    1. Absinta M., Rocca M. A., Colombo B., Falini A., Comi G., Filippi M. (2012). Selective decreased grey matter volume of the pain-matrix network in cluster headache. Cephalalgia 32 109–115. 10.1177/0333102411431334
    1. Bai L., Qin W., Tian J., Dong M., Pan X., Chen P., et al. (2009). Acupuncture modulates spontaneous activities in the anticorrelated resting brain networks. Brain Res. 1279 37–49. 10.1016/j.brainres.2009.04.056
    1. Baliki M. N., Mansour A. R., Baria A. T., Apkarian A. V. (2014). Functional reorganization of the default mode network across chronic pain conditions. PLoS ONE 9:e106133 10.1371/journal.pone.0106133
    1. Behzadi Y., Restom K., Liau J., Liu T. T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37 90–101. 10.1016/j.neuroimage.2007.04.042
    1. Buckner R. L., Andrews-Hanna J. R., Schacter D. L. (2008). The brain’s default network: anatomy, function, and relevance to disease. Ann. N. Y. Acad. Sci. 1124 1–38. 10.1196/annals.1440.011
    1. Buckner R. L., Sepulcre J., Talukdar T., Krienen F. M., Liu H., Hedden T., et al. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J. Neurosci. 29 1860–1873. 10.1523/JNEUROSCI.5062-08.2009
    1. Cabeza R., Nyberg L. (2000). Imaging cognition II: an empirical review of 275 PET and fMRI studies. J. Cogn. Neurosci. 12 1–47. 10.1162/08989290051137585
    1. Chae Y., Chang D. S., Lee S. H., Jung W. M., Lee I. S., Jackson S., et al. (2013). Inserting needles into the body: a meta-analysis of brain activity associated with acupuncture needle stimulation. J. Pain 14 215–222. 10.1016/j.jpain.2012.11.011
    1. Chao L. L., Haxby J. V., Martin A. (1999). Attribute-based neural substrates in temporal cortex for perceiving and knowing about objects. Nat. Neurosci. 2 913–919. 10.1038/13217
    1. Cox R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29 162–173. 10.1006/cbmr.1996.0014
    1. Craig A. D. (2009). How do you feel–now? The anterior insula and human awareness. Nat. Rev. Neurosci. 10 59–70. 10.1038/nrn2555
    1. Deng D., Liao H., Duan G., Liu Y., He Q., Liu H., et al. (2016). Modulation of the default mode network in first-episode, drug-naive major depressive disorder via acupuncture at Baihui (GV20) acupoint. Front. Hum. Neurosci. 10:230 10.3389/fnhum.2016.00230
    1. Dhond R. P., Kettner N., Napadow V. (2007). Neuroimaging acupuncture effects in the human brain. J. Altern. Complement. Med. 13 603–616. 10.1089/acm.2007.7040
    1. Dhond R. P., Yeh C., Park K., Kettner N., Napadow V. (2008). Acupuncture modulates resting state connectivity in default and sensorimotor brain networks. Pain 136 407–418. 10.1016/j.pain.2008.01.011
    1. Dice L. R. (1945). Measures of the amount of ecologic association between species. Ecology 26 297–302. 10.2307/1932409
    1. Drake R., Vogl A. W., Mitchell A. W. M. (2009). Gray’s Anatomy for Students. London: Elsevier Health Sciences.
    1. Fang J., Jin Z., Wang Y., Li K., Kong J., Nixon E. E., et al. (2009). The salient characteristics of the central effects of acupuncture needling: limbic-paralimbic-neocortical network modulation. Hum. Brain Mapp. 30 1196–1206. 10.1002/hbm.20583
    1. Fang J. L., Wang X. L., Wang Y., Hong Y., Liu H. S., Liu J., et al. (2012). [Comparison of brain effects of electroacupuncture at Zusanli (ST 36) and Guanyuan (CV 4) shown by fMRI in 21 healthy volunteers]. Zhen Ci Yan Jiu 37 46–52.
    1. Farmer M. A., Baliki M. N., Apkarian A. V. (2012). A dynamic network perspective of chronic pain. Neurosci. Lett. 520 197–203. 10.1016/j.neulet.2012.05.001
    1. Feng Y., Bai L., Ren Y., Wang H., Liu Z., Zhang W., et al. (2011). Investigation of the large-scale functional brain networks modulated by acupuncture. Magn. Reson. Imaging 29 958–965. 10.1016/j.mri.2011.04.009
    1. Fox M. D., Zhang D., Snyder A. Z., Raichle M. E. (2009). The global signal and observed anticorrelated resting state brain networks. J. Neurophysiol. 101 3270–3283. 10.1152/jn.90777.2008
    1. Fransson P., Aden U., Blennow M., Lagercrantz H. (2011). The functional architecture of the infant brain as revealed by resting-state fMRI. Cereb. Cortex 21 145–154. 10.1093/cercor/bhq071
    1. Goffaux P., Girard-Tremblay L., Marchand S., Daigle K., Whittingstall K. (2014). Individual differences in pain sensitivity vary as a function of precuneus reactivity. Brain Topogr. 27 366–374. 10.1007/s10548-013-0291-0
    1. Gottlich M., Kramer U. M., Kordon A., Hohagen F., Zurowski B. (2014). Decreased limbic and increased fronto-parietal connectivity in unmedicated patients with obsessive-compulsive disorder. Hum. Brain Mapp. 35 5617–5632. 10.1002/hbm.22574
    1. Greicius M. D., Krasnow B., Reiss A. L., Menon V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. U.S.A. 100 253–258. 10.1073/pnas.0135058100
    1. Han J. (1994). Some factors affecting acupuncture-indeced analgesia. Acupunct. Res. 1–3.
    1. Huang S. (2006). The aftereffect, tolerence and frequecy in acupuncture analgesia. Chin. J. Pain Med. 12 360–362.
    1. Huang W., Pach D., Napadow V., Park K., Long X., Neumann J., et al. (2012). Characterizing acupuncture stimuli using brain imaging with FMRI - a systematic review and meta-analysis of the literature. PLoS ONE 7:e32960 10.1371/journal.pone.0032960
    1. Hui K. K., Liu J., Marina O., Napadow V., Haselgrove C., Kwong K. K., et al. (2005). The integrated response of the human cerebro-cerebellar and limbic systems to acupuncture stimulation at ST 36 as evidenced by fMRI. Neuroimage 27 479–496. 10.1016/j.neuroimage.2005.04.037
    1. Hui K. K., Marina O., Claunch J. D., Nixon E. E., Fang J., Liu J., et al. (2009). Acupuncture mobilizes the brain’s default mode and its anti-correlated network in healthy subjects. Brain Res. 1287 84–103. 10.1016/j.brainres.2009.06.061
    1. Jiang Y., Liu J., Liu J., Han J., Wang X., Cui C. (2014). Cerebral blood flow-based evidence for mechanisms of low- versus high-frequency transcutaneous electric acupoint stimulation analgesia: a perfusion fMRI study in humans. Neuroscience 268 180–193. 10.1016/j.neuroscience.2014.03.019
    1. Jiang Y., Wang H., Liu Z., Dong Y., Xiang X., Bai L., et al. (2013). Manipulation of and sustained effects on the human brain induced by different modalities of acupuncture: an FMRI study. PLoS ONE 8:e66815 10.1371/journal.pone.0066815
    1. Krause T., Asseyer S., Taskin B., Floel A., Witte A. V., Mueller K., et al. (2016). The cortical signature of central poststroke pain: gray matter decreases in somatosensory, insular, and prefrontal cortices. Cereb. Cortex 26 80–88. 10.1093/cercor/bhu177
    1. Laird A. R., Eickhoff S. B., Li K., Robin D. A., Glahn D. C., Fox P. T. (2009). Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. J. Neurosci. 29 14496–14505. 10.1523/JNEUROSCI.4004-09.2009
    1. Li J., Zhang J. H., Yi T., Tang W. J., Wang S. W., Dong J. C. (2014). Acupuncture treatment of chronic low back pain reverses an abnormal brain default mode network in correlation with clinical pain relief. Acupunct. Med. 32 102–108. 10.1136/acupmed-2013-010423
    1. Li Y., Zheng H., Witt C. M., Roll S., Yu S. G., Yan J., et al. (2012). Acupuncture for migraine prophylaxis: a randomized controlled trial. CMAJ 184 401–410. 10.1503/cmaj.110551
    1. Li Z., Fang J., Yi S., Guo Y. (2007). Experimental Acupuncture. Beijing: China Press of Traditional Chinese Medicine.
    1. Liang F., Luo R., Liu Y., Zhao J. (2001). Experimental research of the relationship between analgesia aftereffect by electro-acupuncture and contents of 5-HT, NE, DA in inflamed area. Chin. J. Basic Med. Tradit. Chin. Med. 7 52–55.
    1. Liang P., Wang Z., Qian T., Li K. (2014). Acupuncture stimulation of Taichong (Liv3) and Hegu (LI4) modulates the default mode network activity in alzheimer’s disease. Am. J. Alzheimers Dis. Other Demen. 29 739–748. 10.1177/1533317514536600
    1. Linde K., Allais G., Brinkhaus B., Manheimer E., Vickers A., White A. R. (2009). Acupuncture for migraine prophylaxis. Cochrane Database Syst. Rev. 1:CD001218.
    1. Liu J., Qin W., Guo Q., Sun J., Yuan K., Dong M., et al. (2011). Divergent neural processes specific to the acute and sustained phases of verum and SHAM acupuncture. J. Magn. Reson. Imaging 33 33–40. 10.1002/jmri.22393
    1. Liu J., Qin W., Guo Q., Sun J., Yuan K., Liu P., et al. (2010). Distinct brain networks for time-varied characteristics of acupuncture. Neurosci. Lett. 468 353–358. 10.1016/j.neulet.2009.11.031
    1. Lohmann G., Margulies D. S., Horstmann A., Pleger B., Lepsien J., Goldhahn D., et al. (2010). Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. PLoS ONE 5:e10232 10.1371/journal.pone.0010232
    1. Luchtmann M., Steinecke Y., Baecke S., Lutzkendorf R., Bernarding J., Kohl J., et al. (2014). Structural brain alterations in patients with lumbar disc herniation: a preliminary study. PLoS ONE 9:e90816 10.1371/journal.pone.0090816
    1. Ma T. T., Yu S. Y., Li Y., Liang F. R., Tian X. P., Zheng H., et al. (2012). Randomised clinical trial: an assessment of acupuncture on specific meridian or specific acupoint vs. sham acupuncture for treating functional dyspepsia. Aliment. Pharmacol. Ther. 35 552–561. 10.1111/j.1365-2036.2011.04979.x
    1. Melchart D., Streng A., Hoppe A., Brinkhaus B., Witt C., Wagenpfeil S., et al. (2005). Acupuncture in patients with tension-type headache: randomised controlled trial. BMJ 331 376–382. 10.1136/bmj.38512.405440.8F
    1. Napadow V., Lee J., Kim J., Cina S., Maeda Y., Barbieri R., et al. (2013). Brain correlates of phasic autonomic response to acupuncture stimulation: an event-related fMRI study. Hum. Brain Mapp. 34 2592–2606. 10.1002/hbm.22091
    1. Napadow V., Makris N., Liu J., Kettner N. W., Kwong K. K., Hui K. K. (2005). Effects of electroacupuncture versus manual acupuncture on the human brain as measured by fMRI. Hum. Brain Mapp. 24 193–205. 10.1002/hbm.20081
    1. Nierhaus T., Pach D., Huang W., Long X., Napadow V., Roll S., et al. (2015). Differential cerebral response to somatosensory stimulation of an acupuncture point versus two non-acupuncture points measured with EEG and fMRI. Front. Hum. Neurosci. 9:74 10.3389/fnhum.2015.00074
    1. Nierhaus T., Pach D., Huang W., Long X., Napadow V., Roll S., et al. (2016). Difficulties choosing control points in acupuncture research. response: commentary: differential cerebral response, measured with both an EEG and fMRI, to somatosensory stimulation of a single acupuncture point vs. two non-acupuncture points. Front. Hum. Neurosci. 10:404 10.3389/fnhum.2016.00404
    1. Ploghaus A., Narain C., Beckmann C. F., Clare S., Bantick S., Wise R., et al. (2001). Exacerbation of pain by anxiety is associated with activity in a hippocampal network. J. Neurosci. 21 9896–9903.
    1. Power J. D., Barnes K. A., Snyder A. Z., Schlaggar B. L., Petersen S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59 2142–2154. 10.1016/j.neuroimage.2011.10.018
    1. Qin W., Tian J., Bai L., Pan X., Yang L., Chen P., et al. (2008). FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network. Mol. Pain 4:55 10.1186/1744-8069-4-55
    1. Qin W., Tian J., Pan X., Yang L., Zhen Z. (2006). The correlated network of acupuncture effect: a functional connectivity study. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1 480–483.
    1. Ren Y., Bai L., Feng Y., Tian J., Li K. (2010). Investigation of acupoint specificity by functional connectivity analysis based on graph theory. Neurosci. Lett. 482 95–100. 10.1016/j.neulet.2010.06.091
    1. Rocca M. A., Ceccarelli A., Falini A., Colombo B., Tortorella P., Bernasconi L., et al. (2006). Brain gray matter changes in migraine patients with T2-visible lesions: a 3-T MRI study. Stroke 37 1765–1770. 10.1161/01.STR.0000226589.00599.4d
    1. Smallwood R. F., Laird A. R., Ramage A. E., Parkinson A. L., Lewis J., Clauw D. J., et al. (2013). Structural brain anomalies and chronic pain: a quantitative meta-analysis of gray matter volume. J. Pain 14 663–675. 10.1016/j.jpain.2013.03.001
    1. Smith S. M., Miller K. L., Salimi-Khorshidi G., Webster M., Beckmann C. F., Nichols T. E., et al. (2011). Network modelling methods for FMRI. Neuroimage 54 875–891. 10.1016/j.neuroimage.2010.08.063
    1. Taubert M., Lohmann G., Margulies D. S., Villringer A., Ragert P. (2011). Long-term effects of motor training on resting-state networks and underlying brain structure. Neuroimage 57 1492–1498. 10.1016/j.neuroimage.2011.05.078
    1. Tranel D., Damasio H., Damasio A. R. (1997). A neural basis for the retrieval of conceptual knowledge. Neuropsychologia 35 1319–1327. 10.1016/S0028-3932(97)00085-7
    1. Vachon-Presseau E., Roy M., Martel M. O., Caron E., Marin M. F., Chen J., et al. (2013). The stress model of chronic pain: evidence from basal cortisol and hippocampal structure and function in humans. Brain 136 815–827. 10.1093/brain/aws371
    1. Vatansever D., Menon D. K., Manktelow A. E., Sahakian B. J., Stamatakis E. A. (2015). Default mode dynamics for global functional integration. J. Neurosci. 35 15254–15262. 10.1523/JNEUROSCI.2135-15.2015
    1. Vickers A. J. (2004). Statistical reanalysis of four recent randomized trials of acupuncture for pain using analysis of covariance. Clin. J. Pain 20 319–323. 10.1097/00002508-200409000-00006
    1. Visser M., Jefferies E., Embleton K. V., Lambon Ralph M. A. (2012). Both the middle temporal gyrus and the ventral anterior temporal area are crucial for multimodal semantic processing: distortion-corrected fMRI evidence for a double gradient of information convergence in the temporal lobes. J. Cogn. Neurosci. 24 1766–1778. 10.1162/jocn_a_00244
    1. Wang W., Liu L., Zhi X., Huang J. B., Liu D. X., Wang H., et al. (2007). Study on the regulatory effect of electro-acupuncture on hegu point (LI4) in cerebral response with functional magnetic resonance imaging. Chin. J. Integr. Med. 13 10–16. 10.1007/s11655-007-0010-3
    1. Wang X., Chan S. T., Fang J., Nixon E. E., Liu J., Kwong K. K., et al. (2013). Neural encoding of acupuncture needling sensations: evidence from a FMRI study. Evid. Based Complement. Alternat. Med. 2013:483105.
    1. Ward A. M., Schultz A. P., Huijbers W., Van Dijk K. R., Hedden T., Sperling R. A. (2014). The parahippocampal gyrus links the default-mode cortical network with the medial temporal lobe memory system. Hum. Brain Mapp. 35 1061–1073. 10.1002/hbm.22234
    1. White A., Foster N. E., Cummings M., Barlas P. (2007). Acupuncture treatment for chronic knee pain: a systematic review. Rheumatology (Oxford) 46 384–390. 10.1093/rheumatology/kel413
    1. Wink A. M., De Munck J. C., Van Der Werf Y. D., Van Den Heuvel O. A., Barkhof F. (2012). Fast eigenvector centrality mapping of voxel-wise connectivity in functional magnetic resonance imaging: implementation, validation, and interpretation. Brain Connect. 2 265–274. 10.1089/brain.2012.0087
    1. Wong Y. M. (2016). Commentary: differential cerebral response, measured with both an EEG and fMRI, to somatosensory stimulation of a single acupuncture point vs. two non-acupuncture points. Front. Hum. Neurosci. 10:63 10.3389/fnhum.2016.00063
    1. Wu M. T., Hsieh J. C., Xiong J., Yang C. F., Pan H. B., Chen Y. C., et al. (1999). Central nervous pathway for acupuncture stimulation: localization of processing with functional MR imaging of the brain–preliminary experience. Radiology 212 133–141. 10.1148/radiology.212.1.r99jl04133
    1. Wu M. T., Sheen J. M., Chuang K. H., Yang P., Chin S. L., Tsai C. Y., et al. (2002). Neuronal specificity of acupuncture response: a fMRI study with electroacupuncture. Neuroimage 16 1028–1037. 10.1006/nimg.2002.1145
    1. Yan B., Li K., Xu J., Wang W., Liu H., Shan B., et al. (2005). Acupoint-specific fMRI patterns in human brain. Neurosci. Lett. 383 236–240. 10.1016/j.neulet.2005.04.021
    1. Yan C. G., Cheung B., Kelly C., Colcombe S., Craddock R. C., Di Martino A., et al. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76C, 183–201. 10.1016/j.neuroimage.2013.03.004
    1. You Y., Bai L., Dai R., Cheng H., Liu Z., Wei W., et al. (2013). Altered hub configurations within default mode network following acupuncture at ST36: a multimodal investigation combining fMRI and MEG. PLoS ONE 8:e64509 10.1371/journal.pone.0064509
    1. Zhao L., Liu J., Zhang F., Dong X., Peng Y., Qin W., et al. (2014). Effects of long-term acupuncture treatment on resting-state brain activity in migraine patients: a randomized controlled trial on active acupoints and inactive acupoints. PLoS ONE 9:e99538 10.1371/journal.pone.0099538
    1. Zhong C., Bai L., Dai R., Xue T., Wang H., Feng Y., et al. (2012). Modulatory effects of acupuncture on resting-state networks: a functional MRI study combining independent component analysis and multivariate Granger causality analysis. J. Magn. Reson. Imaging 35 572–581. 10.1002/jmri.22887
    1. Zuo X. N., Ehmke R., Mennes M., Imperati D., Castellanos F. X., Sporns O., et al. (2012). Network centrality in the human functional connectome. Cereb. Cortex 22 1862–1875. 10.1093/cercor/bhr269

Source: PubMed

3
S'abonner