Abstract
Nearly two decades have elapsed since the first article of acupuncture fMRI research was published. Since then, the acupuncture neuroimaging researches produce a number of achievements which had been introduced in the previous section. Based on the former studies, there is a regular pattern that the acupuncture neuroimaging studies always move forward along with the development of the imaging technique and data analysis method. Therefore, we introduce a new approach called dynamic functional network connectivity (d-FNC) here and expect to be of utility in the future acupuncture study, especially given the important and diverse time-dependent alterations in brain activity associated with acupuncture therapy. To obtain high-quality acupuncture neuroimaging studies, we also proposed some suggestions for the acupuncture neuroimaging research. Patients with acupuncture indications are more appropriate to recruit as studied object to evaluate the mechanism of acupuncture effect. Multimodal fusion imaging techniques ought to be supported when collecting imaging data to get more comprehensive information. Carefully adopting suitable analysis approaches for the processing of the resultant data will help to improve the reliability of the results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allen EA, Damaraju E, Plis SM, et al. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex. 2014;24(3):663–76.
Bai L, Qin W, Tian J, et al. Detection of dynamic brain networks modulated by acupuncture using a graph theory model. Prog Nat Sci. 2009;19(7):827–35.
Baliki MN, Geha PY, Apkarian AV, et al. Beyond feeling: chronic pain hurts the brain, disrupting the default-mode network dynamics. J Neurosci. 2008;28(6):1398–403.
Biswal B, Yetkin FZ, Haughton VM, et al. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med. 1995;34(4):537–41.
Cauda F, D’Agata F, Sacco K, et al. Functional connectivity of the insula in the resting brain. NeuroImage. 2011;55:8–23.
Cauda F, Costa T, Torta DM, et al. Meta-analytic clustering of the insular cortex: characterizing the meta-analytic connectivity of the insula when involved in active tasks. NeuroImage. 2012;62:343–55.
Calhoun VD, Adali T. Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery. IEEE Rev Biomed Eng. 2012;5:60–73.
Calhoun VD, Kiehl KA, Pearlson GD. Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Hum Brain Mapp. 2008;29(7):828–38.
Chen X, Zhang H, Zou Y. A functional magnetic resonance imaging study on the effect of acupuncture at GB34 (Yanglingquan) on motor-related network in hemiplegic patients. Brain Res. 2015;1601:64–72.
Deng D, Duan G, Liao H, et al. Changes in regional brain homogeneity induced by electro-acupuncture stimulation at the baihui acupoint in healthy subjects: a functional magnetic resonance imaging study. J Altern Complement Med. 2016a;22(10):794–9.
Deng D, Liao H, Duan G, et al. Modulation of the default mode network in first-episode, drug-naive major depressive disorder via acupuncture at Baihui (GV20) acupoint. Front Hum Neurosci. 2016b;10:230.
Fornito A, Zalesky A, Pantelis C, et al. Schizophrenia, neuroimaging and connectomics. NeuroImage. 2012;62(4):2296–314.
Greicius MD, Krasnow B, Reiss AL, et al. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A. 2003;100(1):253–8.
Jia B, Liu Z, Min B, et al. The effects of acupuncture at real or sham acupoints on the intrinsic brain activity in mild cognitive impairment patients. Evid Based Complement Alternat Med. 2015;2015:529675.
Jiang Y, Hao Y, Zhang Y, et al. Thirty minute transcutaneous electric acupoint stimulation modulates resting state brain activities: a perfusion and BOLD fMRI study. Brain Res. 2012;1457:13–25.
Keilholz SD, Magnuson ME, Pan WJ, et al. Dynamic properties of functional connectivity in the rodent. Brain Connect. 2013;3(1):31–40.
Li L, Qin W, Bai L, et al. Exploring vision-related acupuncture point specificity with multivoxel pattern analysis. Magn Reson Imaging. 2010;28(3):380–7.
Liang P, Wang Z, Qian T, et al. Acupuncture stimulation of Taichong (Liv3) and Hegu (LI4) modulates the default mode network activity in Alzheimer’s disease. Am J Alzheimers Dis Other Demen. 2014;29(8):739–48.
Liu P, Zhang Y, Zhou G, et al. Partial correlation investigation on the default mode network involved in acupuncture: an fMRI study. Neurosci Lett. 2009;462(3):183–7.
Liu P, Zhou G, Zhang Y, et al. The hybrid GLM–ICA investigation on the neural mechanism of acupoint ST36: an fMRI study. Neurosci Lett. 2010;479(3):267–71.
Nomi JS, Farrant K, Damaraju E, et al. Dynamic functional network connectivity reveals unique and overlapping profiles of insula subdivisions. Hum Brain Mapp. 2016;37(5):1770–87.
Ptak R. The frontoparietal attention network of the human brain: action, saliency, and a priority map of the environment. Neuroscientist. 2012;18(5):502–15.
Qin W, Tian J, Bai L, et al. FMRI connectivity analysis of acupuncture effects on an amygdala-associated brain network. Mol Pain. 2008;13(4):55.
Seeley WW, Menon V, Schatzberg AF, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci. 2007;27(9):2349–56.
Shen R, Taya F, Yu S, et al. Assessing small-worldness of dynamic functional brain connectivity during complex tasks. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:2904–7.
Shi Y, Zhang S, Li Q, et al. A study of the brain functional network of Deqi via acupuncturing stimulation at BL40 by rs-fMRI. Complement Ther Med. 2016;25:71–7.
Thompson GJ, Merritt MD, Pan WJ, et al. Neural correlates of time-varying functional connectivity in the rat. NeuroImage. 2013;83:826–36.
Thompson GJ, Pan WJ, Magnuson ME, et al. Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity. NeuroImage. 2014;84:1018–31.
Viviani R. Emotion regulation, attention to emotion, and the ventral attentional network. Front Hum Neurosci. 2013;7:746.
Wang Z, Liang P, Zhao Z, et al. Acupuncture modulates resting state hippocampal functional connectivity in Alzheimer disease. PLoS One. 2014;9(3):e91160.
WHO. Acupuncture: review and analysis of reports on controlled clinical trials. Geneva: World Health Organization; 2002.
Wu MT, Sheen JM, Chuang KH, et al. Neuronal specificity of acupuncture response: a fMRI study with electroacupuncture. NeuroImage. 2002;16(4):1028–37.
Yoo J, Kim EY, Ahn YM, et al. Topological persistence vineyard for dynamic functional brain connectivity during resting and gaming stages. J Neurosci Methods. 2016;267:1–13.
Yoo SS, Teh EK, Blinder RA, et al. Modulation of cerebellar activities by acupuncture stimulation: evidence from fMRI study. NeuroImage. 2004;22(2):932–40.
Yu Q, Erhardt EB, Sui J, et al. Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia. NeuroImage. 2015;107:345–55.
Zhang Y, Qin W, Liu P, et al. An fMRI study of acupuncture using independent component analysis. Neurosci Lett. 2009;449(1):6–9.
Zhong C, Bai L, Dai R, et al. 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. 2012;35(3):572–81.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Qin, W., Jin, L., Tian, J. (2018). Prospects of Acupuncture Research in the Future. In: Tian, J. (eds) Multi-Modality Neuroimaging Study on Neurobiological Mechanisms of Acupuncture. Springer, Singapore. https://doi.org/10.1007/978-981-10-4914-9_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-4914-9_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4913-2
Online ISBN: 978-981-10-4914-9
eBook Packages: MedicineMedicine (R0)