Journal of Neuro-Oncology

, Volume 128, Issue 3, pp 437–444 | Cite as

Exploratory study of the effect of brain tumors on the default mode network

  • Sukhmanjit Ghumman
  • D. Fortin
  • M. Noel-Lamy
  • S. C. Cunnane
  • K. Whittingstall
Clinical Study


Resting state functional magnetic resonance imaging (RS-fMRI) is a popular method of visualizing functional networks in the brain. One of these networks, the default mode network (DMN), has exhibited altered connectivity in a variety of pathological states, including brain tumors. However, very few studies have attempted to link the effect of tumor localization, type and size on DMN connectivity. We collected RS-fMRI data in 73 patients with various brain tumors and attempted to characterize the different effects these tumors had on DMN connectivity based on their location, type and size. This was done by comparing the tumor patients with healthy controls using independent component analysis (ICA) and seed based analysis. We also used a multi-seed approach described in the paper to account for anatomy distortion in the tumor patients. We found that tumors in the left hemisphere had the largest effect on DMN connectivity regardless of their size and type, while this effect was not observed for right hemispheric tumors. Tumors in the cerebellum also had statistically significant effects on DMN connectivity. These results suggest that DMN connectivity in the left side of the brain may be more fragile to insults by lesions.


Default mode network Glioma Functional connectivity Posterior cingulate cortex fMRI 



The authors are grateful for the help given by Russell Butler in the writing of this paper.


K.W. is supported by a Canada Research Chair in Neurovascular Coupling and the Natural Sciences and Engineering Council of Canada (NSERC). S.G. is supported by a research grant from the Faculty of Medicine and Health Sciences of Université de Sherbrooke.


  1. 1.
    Mevel K, Grassiot B, Chetelat G, Defer G, Desgranges B, Eustache F (2010) The default mode network: cognitive role and pathological disturbances. Rev Neurol 166:859–872. doi: 10.1016/j.neurol.2010.01.008 CrossRefPubMedGoogle Scholar
  2. 2.
    Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. Proc Natl Acad Sci USA 98:676–682. doi: 10.1073/pnas.98.2.676 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJ (2009) Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev 33:279–296. doi: 10.1016/j.neubiorev.2008.09.002 CrossRefPubMedGoogle Scholar
  4. 4.
    Maesawa S, Bagarinao E, Fujii M, Futamura M, Motomura K, Watanabe H, Mori D, Sobue G, Wakabayashi T (2015) Evaluation of resting state networks in patients with gliomas: connectivity changes in the unaffected side and its relation to cognitive function. PLoS ONE 10:e0118072. doi: 10.1371/journal.pone.0118072 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Harris RJ, Bookheimer SY, Cloughesy TF, Kim HJ, Pope WB, Lai A, Nghiemphu PL, Liau LM, Ellingson BM (2014) Altered functional connectivity of the default mode network in diffuse gliomas measured with pseudo-resting state fMRI. J Neurooncol 116:373–379. doi: 10.1007/s11060-013-1304-2 CrossRefPubMedGoogle Scholar
  6. 6.
    Liu J, Qin W, Wang H, Zhang J, Xue R, Zhang X, Yu C (2014) Altered spontaneous activity in the default-mode network and cognitive decline in chronic subcortical stroke. J Neurol Sci 347:193–198. doi: 10.1016/j.jns.2014.08.049 CrossRefPubMedGoogle Scholar
  7. 7.
    Dacosta-Aguayo R, Grana M, Iturria-Medina Y, Fernandez-Andujar M, Lopez-Cancio E, Caceres C, Bargallo N, Barrios M, Clemente I, Toran P, Fores R, Davalos A, Auer T, Mataro M (2015) Impairment of functional integration of the default mode network correlates with cognitive outcome at three months after stroke. Hum Brain Mapp 36:577–590. doi: 10.1002/hbm.22648 CrossRefPubMedGoogle Scholar
  8. 8.
    Rorden C, Brett M (2000) Stereotaxic display of brain lesions. Behavioural neurology 12:191–200CrossRefPubMedGoogle Scholar
  9. 9.
    Hafkemeijer A, van der Grond J, Rombouts SA (2012) Imaging the default mode network in aging and dementia. Biochim Biophys Acta 1822:431–441. doi: 10.1016/j.bbadis.2011.07.008 CrossRefPubMedGoogle Scholar
  10. 10.
    Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res Int J 29:162–173CrossRefGoogle Scholar
  11. 11.
    Cox RW, Hyde JS (1997) Software tools for analysis and visualization of fMRI data. NMR Biomed 10:171–178CrossRefPubMedGoogle Scholar
  12. 12.
    Murphy K, Birn RM, Bandettini PA (2013) Resting-state fMRI confounds and cleanup. NeuroImage 80:349–359. doi: 10.1016/j.neuroimage.2013.04.001 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(Suppl 1):S208–S219. doi: 10.1016/j.neuroimage.2004.07.051 CrossRefPubMedGoogle Scholar
  14. 14.
    Dipasquale O, Griffanti L, Clerici M, Nemni R, Baselli G, Baglio F (2015) High-dimensional ICA analysis detects within-network functional connectivity damage of default-mode and sensory-motor networks in Alzheimer’s disease. Front Hum Neurosci 9:43. doi: 10.3389/fnhum.2015.00043 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Shirer WR, Ryali S, Rykhlevskaia E, Menon V, Greicius MD (2012) Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex 22:158–165. doi: 10.1093/cercor/bhr099 CrossRefPubMedGoogle Scholar
  16. 16.
    Filippini N, MacIntosh BJ, Hough MG, Goodwin GM, Frisoni GB, Smith SM, Matthews PM, Beckmann CF, Mackay CE (2009) Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci USA 106:7209–7214. doi: 10.1073/pnas.0811879106 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Smith SM (2012) The future of FMRI connectivity. NeuroImage 62:1257–1266. doi: 10.1016/j.neuroimage.2012.01.022 CrossRefPubMedGoogle Scholar
  18. 18.
    Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE (2014) Permutation inference for the general linear model. NeuroImage 92:381–397. doi: 10.1016/j.neuroimage.2014.01.060 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15:1–25CrossRefPubMedGoogle Scholar
  20. 20.
    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:e40231. doi: 10.1371/journal.pone.0040231 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Ribolsi M, Daskalakis ZJ, Siracusano A, Koch G (2014) Abnormal asymmetry of brain connectivity in schizophrenia. Front Hum Neurosci 8:1010. doi: 10.3389/fnhum.2014.01010 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Jalili M (2014) Hemispheric asymmetry of electroencephalography-based functional brain networks. NeuroReport 25:1266–1271. doi: 10.1097/WNR.0000000000000256 CrossRefPubMedGoogle Scholar
  23. 23.
    Thatcher RW, Biver CJ, North D (2007) Spatial-temporal current source correlations and cortical connectivity. Clin EEG Neurosci 38:35–48CrossRefPubMedGoogle Scholar
  24. 24.
    Tucker DM, Roth DL, Bair TB (1986) Functional connections among cortical regions: topography of EEG coherence. Electroencephalogr Clin Neurophysiol 63:242–250CrossRefPubMedGoogle Scholar
  25. 25.
    Medvedev AV (2014) Does the resting state connectivity have hemispheric asymmetry? A near-infrared spectroscopy study. NeuroImage 85(Pt 1):400–407. doi: 10.1016/j.neuroimage.2013.05.092 CrossRefPubMedGoogle Scholar
  26. 26.
    Yan H, Zuo XN, Wang D, Wang J, Zhu C, Milham MP, Zhang D, Zang Y (2009) Hemispheric asymmetry in cognitive division of anterior cingulate cortex: a resting-state functional connectivity study. NeuroImage 47:1579–1589. doi: 10.1016/j.neuroimage.2009.05.080 CrossRefPubMedGoogle Scholar
  27. 27.
    Middleton FA, Strick PL (2001) Cerebellar projections to the prefrontal cortex of the primate. J Neurosci 21:700–712PubMedGoogle Scholar
  28. 28.
    Middleton FA, Strick PL (1994) Anatomical evidence for cerebellar and basal ganglia involvement in higher cognitive function. Science 266:458–461CrossRefPubMedGoogle Scholar
  29. 29.
    Habas C, Kamdar N, Nguyen D, Prater K, Beckmann CF, Menon V, Greicius MD (2009) Distinct cerebellar contributions to intrinsic connectivity networks. J Neurosci 29:8586–8594. doi: 10.1523/JNEUROSCI.1868-09.2009 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Krienen FM, Buckner RL (2009) Segregated fronto-cerebellar circuits revealed by intrinsic functional connectivity. Cereb Cortex 19:2485–2497. doi: 10.1093/cercor/bhp135 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Departments of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health ScienceUniversité de SherbrookeSherbrookeCanada
  2. 2.Department of Surgery, Faculty of Medicine and Health ScienceUniversité de SherbrookeSherbrookeCanada
  3. 3.Department of Medicine, Faculty of Medicine and Health ScienceUniversité de SherbrookeSherbrookeCanada
  4. 4.Department of Diagnostic Radiology, Faculty of Medicine and Health ScienceUniversité de SherbrookeSherbrookeCanada

Personalised recommendations