, Volume 58, Issue 3, pp 311–320 | Cite as

Alteration of long-distance functional connectivity and network topology in patients with supratentorial gliomas

  • Ji Eun Park
  • Ho Sung KimEmail author
  • Sang Joon Kim
  • Jeong Hoon Kim
  • Woo Hyun Shim
Functional Neuroradiology



The need for information regarding functional alterations in patients with brain gliomas is increasing, but little is known about the functional consequences of focal brain tumors throughout the entire brain. Using resting-state functional MR imaging (rs-fMRI), this study assessed functional connectivity in patients with supratentorial brain gliomas with possible alterations in long-distance connectivity and network topology.


Data from 36 patients with supratentorial brain gliomas and 12 healthy subjects were acquired using rs-fMRI. The functional connectivity matrix (FCM) was created using 32 pairs of cortical seeds on Talairach coordinates in each individual subject. Local and distant connectivity were calculated using z-scores in the individual patient’s FCM, and the averaged FCM of patients was compared with that of healthy subjects. Weighted network analysis was performed by calculating local efficiency, global efficiency, clustering coefficient, and small-world topology, and compared between patients and healthy controls.


When comparing the averaged FCM of patients with that of healthy controls, the patients showed decreased long-distance, inter-hemispheric connectivity (0.32 ± 0.16 in patients vs. 0. 42 ± 0.15 in healthy controls, p = 0.04). In network analysis, patients showed increased local efficiency (p < 0.05), but global efficiency, clustering coefficient, and small-world topology were relatively preserved compared to healthy subjects.


Patients with supratentorial brain gliomas showed decreased long-distance connectivity while increased local efficiency and preserved small-world topology. The results of this small case series may provide a better understanding of the alterations of functional connectivity in patients with brain gliomas across the whole brain scale.


Resting state functional magnetic resonance imaging Functional connectivity Supratentorial gliomas Network analysis 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant Number: NRF-2009-0076988).

Compliance with ethical standards

We declare that all human and animal studies have been approved by the Institutional Review Board of Asan Medical Center and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. We declare that all patients gave informed consent prior to inclusion in this study.

Conflict of Interest

We declare that we have no conflict of interest.

Supplementary material

234_2015_1621_MOESM1_ESM.docx (18 kb)
ESM 1 (DOCX 28 kb)


  1. 1.
    Huang Q, Zhang R, Hu X, Ding S, Qian J, Lei T, Cao X, Tao L, Qian Z, Liu H (2014) Disturbed small-world networks and neurocognitive function in frontal lobe low-grade glioma patients. PLoS ONE 9, e94095CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Esposito R, Mattei PA, Briganti C, Romani GL, Tartaro A, Caulo M (2012) Modifications of default-mode network connectivity in patients with cerebral glioma. PLoS ONE 7, e40231CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Bartolomei F, Bosma I, Klein M, Baayen JC, Reijneveld JC, Postma TJ, Heimans JJ, van Dijk BW, de Munck JC, de Jongh A, Cover KS, Stam CJ (2006) Disturbed functional connectivity in brain tumour patients: evaluation by graph analysis of synchronization matrices. Clin Neurophysiol 117:2039–2049CrossRefPubMedGoogle Scholar
  4. 4.
    Bartolomei F, Bosma I, Klein M, Baayen JC, Reijneveld JC, Postma TJ, Heimans JJ, van Dijk BW, de Munck JC, de Jongh A, Cover KS, Stam CJ (2006) How do brain tumors alter functional connectivity? A magnetoencephalography study. Ann Neurol 59:128–138CrossRefPubMedGoogle Scholar
  5. 5.
    Bressler SL (2002) Understanding cognition through large-scale cortical networks. Curr Dir Psychol Sci 11:58–61CrossRefGoogle Scholar
  6. 6.
    Duffau H, Capelle L, Denvil D, Sichez N, Gatignol P, Lopes M, Mitchell MC, Sichez JP, Van Effenterre R (2003) Functional recovery after surgical resection of low grade gliomas in eloquent brain: hypothesis of brain compensation. J Neurol Neurosurg Psychiatry 74:901–907CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541CrossRefPubMedGoogle Scholar
  8. 8.
    Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, Dogonowski AM, Ernst M, Fair D, Hampson M, Hoptman MJ, Hyde JS, Kiviniemi VJ, Kotter R, Li SJ, Lin CP, Lowe MJ, Mackay C, Madden DJ, Madsen KH, Margulies DS, Mayberg HS, McMahon K, Monk CS, Mostofsky SH, Nagel BJ, Pekar JJ, Peltier SJ, Petersen SE, Riedl V, Rombouts SA, Rypma B, Schlaggar BL, Schmidt S, Seidler RD, Siegle GJ, Sorg C, Teng GJ, Veijola J, Villringer A, Walter M, Wang L, Weng XC, Whitfield-Gabrieli S, Williamson P, Windischberger C, Zang YF, Zhang HY, Castellanos FX, Milham MP (2010) Toward discovery science of human brain function. Proc Natl Acad Sci U S A 107:4734–4739CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Salvador R, Suckling J, Schwarzbauer C, Bullmore E (2005) Undirected graphs of frequency-dependent functional connectivity in whole brain networks. Philos Trans R Soc B-Biol Sci 360:937–946CrossRefGoogle Scholar
  10. 10.
    Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162–173CrossRefPubMedGoogle Scholar
  11. 11.
    Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069CrossRefPubMedGoogle Scholar
  12. 12.
    Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198CrossRefPubMedGoogle Scholar
  13. 13.
    Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87:198701CrossRefPubMedGoogle Scholar
  14. 14.
    Humphries MD, Gurney K (2008) Network 'small-world-ness': a quantitative method for determining canonical network equivalence. PLoS ONE 3, e0002051CrossRefPubMedGoogle Scholar
  15. 15.
    Hou BL, Bradbury M, Peck KK, Petrovich NM, Gutin PH, Holodny AI (2006) Effect of brain tumor neovasculature defined by rCBV on BOLD fMRI activation volume in the primary motor cortex. Neuroimage 32:489–497CrossRefPubMedGoogle Scholar
  16. 16.
    Tarapore PE, Martino J, Guggisberg AG, Owen J, Honma SM, Findlay A, Berger MS, Kirsch HE, Nagarajan SS (2012) Magnetoencephalographic imaging of resting-state functional connectivity predicts postsurgical neurological outcome in brain gliomas. Neurosurgery 71:1012–1022CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Greicius MD, Srivastava G, Reiss AL, Menon V (2004) Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci U S A 101:4637–4642CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Martino J, Honma SM, Findlay AM, Guggisberg AG, Owen JP, Kirsch HE, Berger MS, Nagarajan SS (2011) Resting functional connectivity in patients with brain tumors in eloquent areas. Ann Neurol 69:521–532CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Towle VL, Syed I, Berger C, Grzesczcuk R, Milton J, Erickson RK, Cogen P, Berkson E, Spire JP (1998) Identification of the sensory/motor area and pathologic regions using ECoG coherence. Electroencephalogr Clin Neurophysiol 106:30–39CrossRefPubMedGoogle Scholar
  20. 20.
    Guggisberg AG, Honma SM, Findlay AM, Dalal SS, Kirsch HE, Berger MS, Nagarajan SS (2008) Mapping functional connectivity in patients with brain lesions. Ann Neurol 63:193–203CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    de Jongh A, de Munck JC, Baayen JC, Puligheddu M, Jonkman EJ, Stam CJ (2003) Localization of fast MEG waves in patients with brain tumors and epilepsy. Brain Topogr 15:173–179CrossRefPubMedGoogle Scholar
  22. 22.
    Vincent JL, Patel GH, Fox MD, Snyder AZ, Baker JT, Van Essen DC, Zempel JM, Snyder LH, Corbetta M, Raichle ME (2007) Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447:83–86CrossRefPubMedGoogle Scholar
  23. 23.
    Schreiber A, Hubbe U, Ziyeh S, Hennig J (2000) The influence of gliomas and nonglial space-occupying lesions on blood-oxygen-level-dependent contrast enhancement. Am J Neuroradiol 21:1055–1063PubMedGoogle Scholar
  24. 24.
    Jiang Z, Krainik A, David O, Salon C, Tropres I, Hoffmann D, Pannetier N, Barbier EL, Bombin ER, Warnking J, Pasteris C, Chabardes S, Berger F, Grand S, Segebarth C, Gay E, Le Bas JF (2010) Impaired fMRI activation in patients with primary brain tumors. Neuroimage 52:538–548CrossRefPubMedGoogle Scholar
  25. 25.
    Kalcher K, Boubela RN, Huf W, Bartova L, Kronnerwetter C, Derntl B, Pezawas L, Filzmoser P, Nasel C, Moser E (2014) The spectral diversity of resting-state fluctuations in the human brain. PLoS ONE 9, e93375CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Ji Eun Park
    • 1
  • Ho Sung Kim
    • 1
    Email author
  • Sang Joon Kim
    • 1
  • Jeong Hoon Kim
    • 2
  • Woo Hyun Shim
    • 1
  1. 1.Department of Radiology and Research Institute of Radiology, Asan Medical CenterUniversity of Ulsan College of MedicineSongpa-GuKorea
  2. 2.Department of Neurosurgery, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulKorea

Personalised recommendations