Reconfigured functional network dynamics in adult moyamoya disease: a resting-state fMRI study

  • Yu Lei
  • Benshen Song
  • Liang Chen
  • Jiabin Su
  • Xin Zhang
  • Wei Ni
  • Yuguo Yu
  • Bin Xu
  • Lianchun YuEmail author
  • Yuxiang GuEmail author
  • Ying Mao


Treatment of vascular cognitive impairment (VCI) in adult moyamoya disease (MMD) is still unclear because of its unveiled neural synchronization. This study introduced a dynamic measurement of connectivity number entropy (CNE) to characterize both spatial and temporal dimensions of network interactions. Fifty-one patients with MMD were recruited (27 with VCI and 24 with intact cognition), as well as 26 normal controls (NCs). Static network properties were first examined to confirm its aberrance in MMD with VCI. Then, the dynamic measurement of CNE was used to detect the deteriorated flexibility of MMD with VCI at global, regional, and network levels. Finally, dynamic reconfiguration of flexible and specialized regions was traced across the three groups. Graph theory analysis indicated that MMD exhibited “small-world” network topology but presented with a deviating pattern from NC as the disease progressed in all topologic metrics of integration, segregation, and small-worldness. Subsequent dynamic analysis showed significant CNE differences among the three groups at both global (p < 0.001) and network levels (default mode network, p = 0.004; executive control network, p = 0.001). Specifically, brain regions related to key aspects of information processing exhibited significant CNE changes across the three groups. Furthermore, CNE values of both flexible and specialized regions changed with impaired cognition. This study not only sheds light on both the static and dynamic organizational principles behind network changes in adult MMD for the first time, but also provides a new methodologic viewpoint to acquire more knowledge of its pathophysiology and treatment direction.


Functional flexibility Moyamoya disease Network topology Resting-state fMRI Vascular cognitive impairment 



This study was supported by the National Natural Science Foundation of China (No. 81771237, 81801155 & 11105062); the National Key Research and Development Program (No. SQ2016YFSF110141); the Fundamental Research Funds for the Central Universities (No. lzujbky-2015-119); the Natural Science Foundation and Major Basic Research Program of Shanghai (No. 16JC1420100); the “Dawn” Program of Shanghai Education Commission (No. 16SG02); and the Scientific Research Project of Huashan Hospital, Fudan University (No. 2016QD082).

Compliance with ethical standards

This manuscript has been read and approved by all authors, who acknowledge due care in ensuring the integrity of the work. All authors have made substantial contributions to the design, collection, analysis and/or interpretation of data, and many have contributed to the writing and intellectual content of the article.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study involving human participants were approved by the Institutional Ethics Committee of Huashan Hospital of Fudan University, and were conducted in accordance with the 1964 Helsinki declaration and its later amendments.

Informed consent

All participants gave written informed consent after totally understanding the purposes of our study.

Supplementary material

11682_2018_9_MOESM1_ESM.docx (97 kb)
ESM 1 (DOCX 97 kb)


  1. Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3(2), e17.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Calhoun, V. D., Miller, R., Pearlson, G., & Adalı, T. (2014). The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron, 84(2), 262–274.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Calviere, L., Ssi Yan Kai, G., Catalaa, I., Marlats, F., Bonneville, F., & Larrue, V. (2012). Executive dysfunction in adults with moyamoya disease is associated with increased diffusion in frontal white matter. Journal of Neurology, Neurosurgery, and Psychiatry, 83(6), 591–593.CrossRefPubMedGoogle Scholar
  4. Chang, C., & Glover, G. H. (2010). Time–frequency dynamics of resting-state brain connectivity measured with fMRI. Neuroimage, 50(1), 81–98.CrossRefPubMedGoogle Scholar
  5. Chao-Gan, Y., & Yu-Feng, Z. (2010). DPARSF: A MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13.PubMedPubMedCentralGoogle Scholar
  6. Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 70(6 Pt2), 066111.CrossRefPubMedGoogle Scholar
  7. Cocchi, L., Gollo, L. L., Zalesky, A., & Breakspear, M. (2017). Criticality in the brain: A synthesis of neurobiology, models and cognition. Progress in Neurobiology, 158, 132–152.CrossRefPubMedGoogle Scholar
  8. Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16, 1348–1355.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011). Emerging concepts for the dynamical organization of resting-state activity in the brain. Nature Reviews. Neuroscience, 12(1), 43–56.CrossRefPubMedGoogle Scholar
  10. Deco, G., Mclntosh, A. R., Shen, K., Hutchison, R. M., Menon, R. S., Everling, S., et al. (2014). Identification of optimal structural connectivity using functional connectivity and neural modeling. The Journal of Neuroscience, 34(23), 7910–7916.CrossRefPubMedGoogle Scholar
  11. Fang, L., Huang, J., Zhang, Q., Chan, R. C., Wang, R., & Wan, W. (2016). Different aspects of dysexecutive syndrome in patients with moyamoya disease and its clinical subtypes. Journal of Neurosurgery, 125(2), 299–307.CrossRefPubMedGoogle Scholar
  12. Fedorenko, E., Duncan, J., & Kanwisher, N. (2013). Broad domain generality in focal regions of frontal and parietal cortex. Proceedings of the National Academy of Sciences of the United States of America, 110(41), 16616–16621.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Festa, J. R., Schwarz, L. R., Pliskin, N., Cullum, C. M., Lacritz, L., Charbel, F. T., Mathews, D., Starke, R. M., Connolly, E. S., Marshall, R. S., & Lazar, R. M. (2010). Neurocognitive dysfunction in adult moyamoya disease. Journal of Neurology, 257(5), 806–815.CrossRefPubMedGoogle Scholar
  14. Gorelick, P. B., Scuteri, A., Black, S. E., Decarli, C., Greenberg, S. M., Iadecola, C., Launer, L. J., Laurent, S., Lopez, O. L., Nyenhuis, D., Petersen, R. C., Schneider, J. A., Tzourio, C., Arnett, D. K., Bennett, D. A., Chui, H. C., Higashida, R. T., Lindquist, R., Nilsson, P. M., Roman, G. C., Sellke, F. W., Seshadri, S., & American Heart Association Stroke Council, Council on Epidemiology and Prevention, Council on Cardiovascular Nursing, Council on Cardiovascular Radiology and Intervention, and Council on Cardiovascular Surgery and Anesthesia. (2011). Vascular contributions to cognitive impairment and dementia: A statement for healthcare professionals from the american heart association/american stroke association. Stroke, 42(9), 2672–2713.CrossRefPubMedPubMedCentralGoogle Scholar
  15. Haglund, M. M., Ojemann, G. A., Schwartz, T. W., & Lettich, E. (1994). Neuronal activity in human lateral temporal cortex during serial retrieval from short-term memory. The Journal of Neuroscience, 14(3), 1507–1515.CrossRefPubMedGoogle Scholar
  16. Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., Della Penna, S., Duyn, J. H., Glover, G. H., Gonzalez-Castillo, J., Handwerker, D. A., Keilholz, S., Kiviniemi, V., Leopold, D. A., de Pasquale, F., Sporns, O., Walter, M., & Chang, C. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. Neuroimage, 80, 360–378.CrossRefPubMedGoogle Scholar
  17. Karzmark, P., Zeifert, P. D., Bell-Stephens, T. E., Steinberg, G. K., & Dorfman, L. J. (2012). Neurocognitive impairment in adults with moyamoya disease without stroke. Neurosurgery, 70(3), 634–638.CrossRefPubMedGoogle Scholar
  18. Kazumata, K., Tha, K. K., Narita, H., Kusumi, I., Shichinohe, H., Ito, M., Nakayama, N., & Houkin, K. (2015). Chronic ischemia alters brain microstructural integrity and cognitive performance in adult moyamoya disease. Stroke, 46(2), 354–360.CrossRefPubMedGoogle Scholar
  19. Kazumata, K., Tha, K. K., Narita, H., Shichinohe, H., Ito, M., Uchino, H., & Abumiya, T. (2016). Investigating brain network characteristics interrupted by covert white matter injury in patients with moyamoya disease: Insights from graph theoretical analysis. World Neurosurgery, 89, 654–665.CrossRefPubMedGoogle Scholar
  20. Kazumata, K., Tha, K. K., Uchino, H., Ito, M., Nakayama, N., & Abumiya, T. (2017). Mapping altered brain connectivity and its clinical associations in adult moyamoya disease: A resting-state functional MRI study. PLoS One, 12(8), e0182759.CrossRefPubMedPubMedCentralGoogle Scholar
  21. Kiviniemi, V., Vire, T., Remes, J., Elseoud, A. A., Starck, T., Tervonen, O., & Nikkinen, J. (2011). A sliding time-window ICA reveals spatial variability of the default mode network in time. Brain Connectivity, 1(4), 339–347.CrossRefPubMedGoogle Scholar
  22. Kringelbach, M. L. (2005). The human orbitofrontal cortex: Linking reward to hedonic experience. Nature Reviews. Neuroscience, 6(9), 691–702.CrossRefPubMedGoogle Scholar
  23. Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.CrossRefPubMedPubMedCentralGoogle Scholar
  24. Lei, Y., Li, Y., Ni, W., Jiang, H., Yang, Z., Guo, Q., Gu, Y., & Mao, Y. (2014). Spontaneous brain activity in adult patients with moyamoya disease: A resting-state fMRI study. Brain Research, 1546, 27–33.CrossRefPubMedGoogle Scholar
  25. Lei, Y., Su, J., Jiang, H., Guo, Q., Ni, W., Yang, H., Gu, Y., & Mao, Y. (2017). Aberrant regional homogeneity of resting-state executive control, default mode, and salience networks in adult patients with moyamoya disease. Brain Imaging and Behavior, 11(1), 176–184.CrossRefPubMedGoogle Scholar
  26. Liang, X., Zou, Q., He, Y., & Yang, Y. (2016). Topologically reorganized connectivity architecture of default-mode, executive-control, and salience networks across working memory task loads. Cerebral Cortex, 26(4), 1501–1511.CrossRefPubMedPubMedCentralGoogle Scholar
  27. Liu, X., & Duyn, J. H. (2013). Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proceedings of the National Academy of Sciences of the United States of America, 110(11), 4392–4397.CrossRefPubMedPubMedCentralGoogle Scholar
  28. Liu, Y., Liang, M., Zhou, Y., He, Y., Hao, Y., Song, M., Yu, C., Liu, H., Liu, Z., & Jiang, T. (2008). Disrupted small-world networks in schizophrenia. Brain, 131(Pt 4), 945–961.CrossRefPubMedGoogle Scholar
  29. Massobrio, P., de Arcangelis, L., Pasquale, V., Jensen, H. J., & Plenz, D. (2015). Criticality as a signature of healthy neural systems. Frontiers in Systems Neuroscience, 9, 22.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Mohr, H., Wolfensteller, U., Betzel, R. F., Mišić, B., Sporns, O., Richiardi, J., & Ruge, H. (2016). Integration and segregation of large-scale brain networks during short-term task automatization. Nature Communications, 7, 13217.CrossRefPubMedPubMedCentralGoogle Scholar
  31. Paulus, M. P., Hozack, N. E., Zauscher, B. E., Frank, L., Brown, G. G., Braff, D. L., & Schuckit, M. A. (2002). Behavioral and functional neuroimaging evidence for prefrontal dysfunction in methamphetamine-dependent subjects. Neuropsychopharmacology, 26(1), 53–63.CrossRefPubMedGoogle Scholar
  32. Pfurtscheller, G., & Aranibar, A. (1977). Event-related cortical desynchronization detected by power measurements of scalp EEG. Electroencephalography and Clinical Neurophysiology, 42(6), 817–826.CrossRefPubMedGoogle Scholar
  33. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059–1069.CrossRefGoogle Scholar
  34. Rudie, J. D., Brown, J. A., Beck-Pancer, D., Hernandez, L. M., Dennis, E. L., Thompson, P. M., Bookheimer, S. Y., & Dapretto, M. (2012). Altered functional and structural brain network organization in autism. Neuroimage Clin, 2, 79–94.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Sakoğlu, U., Pearlson, G. D., Kiehl, K. A., Wang, Y. M., Michael, A. M., & Calhoun, V. D. (2010). A method for evaluating dynamic functional network connectivity and task-modulation: Application to schizophrenia. MAGMA, 23(5–6), 351–366.CrossRefPubMedPubMedCentralGoogle Scholar
  36. Salvador, R., Suckling, J., Schwarzbauer, C., & Bullmore, E. (2005). Undirected graphs of frequency-dependent functional connectivity in whole brain networks. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360(1457), 937–946.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Shew, W. L., Yang, H., Petermann, T., Roy, R., & Plenz, D. (2009). Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. The Journal of Neuroscience, 29(49), 15595–15600.CrossRefPubMedPubMedCentralGoogle Scholar
  38. Shew, W. L., Yang, H., Yu, S., Roy, R., & Plenz, D. (2011). Information capacity and transmission are maximized in balanced cortical networks with neuronal avalanches. The Journal of Neuroscience, 31(1), 55–63.CrossRefPubMedPubMedCentralGoogle Scholar
  39. Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D. (2012). Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cerebral Cortex, 22(1), 158–165.CrossRefGoogle Scholar
  40. Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276.CrossRefPubMedPubMedCentralGoogle Scholar
  41. Suzuki, J., & Kodama, N. (1983). Moyamoya disease--a review. Stroke, 14(1), 104–109.CrossRefPubMedGoogle Scholar
  42. Tagliazucchi, E., Balenzuela, P., Fraiman, D., & Chialvo, D. R. (2012). Criticality in large-scale brain FMRI dynamics unveiled by a novel point process analysis. Frontiers in Physiology, 3, 15.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 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(1), 273–289.CrossRefGoogle Scholar
  44. van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. The Journal of Neuroscience, 29(23), 7619–7624.CrossRefPubMedGoogle Scholar
  45. van Wijk, B. C., Stam, C. J., & Daffertshofer, A. (2010). Comparing brain networks of different size and connectivity density using graph theory. PLoS One, 5(10), e13701.CrossRefPubMedPubMedCentralGoogle Scholar
  46. Vecchio, F., Miraglia, F., Piludu, F., Granata, G., Romanello, R., Caulo, M., Onofrj, V., Bramanti, P., Colosimo, C., & Rossini, P. M. (2017). “Small world” architecture in brain connectivity and hippocampal volume in Alzheimer’s disease: A study via graph theory from EEG data. Brain Imaging and Behavior, 11(2), 473–485.CrossRefPubMedGoogle Scholar
  47. Vincent, J. L., Kahn, I., Snyder, A. Z., Raichle, M. E., & Buckner, R. L. (2008). Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. Journal of Neurophysiology, 100(6), 3328–3342.CrossRefPubMedPubMedCentralGoogle Scholar
  48. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of "small-world" networks. Nature, 393(6684), 440–442.CrossRefPubMedPubMedCentralGoogle Scholar
  49. Xin, F., & Lei, X. (2015). Competition between frontoparietal control and default networks supports social working memory and empathy. Social Cognitive and Affective Neuroscience, 10(8), 1144–1152.CrossRefPubMedPubMedCentralGoogle Scholar
  50. Yin, D., Liu, W., Zeljic, K., Wang, Z., Lv, Q., Fan, M., Cheng, W., & Wang, Z. (2016). Dissociable changes of frontal and parietal cortices in inherent functional flexibility across the human life span. The Journal of Neuroscience, 36(39), 10060–10074.CrossRefPubMedGoogle Scholar
  51. Zhang, J., Wang, J., Wu, Q., Kuang, W., Huang, X., He, Y., & Gong, Q. (2011). Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biological Psychiatry, 70(4), 334–342.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Neurosurgery, Huashan HospitalFudan UniversityShanghaiChina
  2. 2.Institute of Theoretical Physics, Key Laboratory for Magnetism and Magnetic Materials of the Ministry of EducationLanzhou UniversityLanzhouChina
  3. 3.School of Life Science and the State Key Laboratory of Medical Neurobiology, Institutes of Brain Science and the Collaborative Innovation Center for Brain ScienceFudan UniversityShanghaiChina

Personalised recommendations