Topological Measures of Connectomics for Low Grades Glioma

  • Benjamin Amoah
  • Alessandro CrimiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)


Recent advancements in neuroimaging have allowed the use of network analysis to study the brain in a system-based approach. In fact, several neurological disorders have been investigated from a network perspective. These include Alzheimer’s disease, autism spectrum disorder, stroke, and traumatic brain injury. So far, few studies have been conducted on glioma by using connectome techniques. A connectome-based approach might be useful in quantifying the status of patients, in supporting surgical procedures, and ultimately shedding light on the underlying mechanisms and the recovery process.

In this manuscript, by using graph theoretical methods of segregation and integration, topological structural connectivity is studied comparing patients with low grade glioma to healthy control. These measures suggest that it is possible to quantify the status of patients pre- and post-surgical intervention to evaluate the condition.


Autism Spectrum Disorder Autism Spectrum Disorder Fractional Anisotropy Functional Connectivity Cluster Coefficient 
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  1. 1.
    Berman, J.I., Berger, M.S., et al.: Diffusion-tensor imaging-guided tracking of fibers of the pyramidal tract combined with intraoperative cortical stimulation mapping in patients with gliomas. J. Neurosurg. 101(1), 66–72 (2004)CrossRefGoogle Scholar
  2. 2.
    Briganti, C., Sestieri, C., Mattei, P., Esposito, R., Galzio, R., Tartaro, A., Romani, G., Caulo, M.: Reorganization of functional connectivity of the language network in patients with brain gliomas. Am. J. Neuroradiol. 33(10), 1983–1990 (2012)CrossRefGoogle Scholar
  3. 3.
    Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10(3), 186–198 (2009)CrossRefGoogle Scholar
  4. 4.
    Deco, G., Tononi, G., Boly, M., Kringelbach, M.L.: Rethinking segregation and integration: contributions of whole-brain modelling. Nat. Rev. Neurosci. 16(7), 430–439 (2015)CrossRefGoogle Scholar
  5. 5.
    Drakesmith, M., Caeyenberghs, K., Dutt, A., Zammit, S., Evans, C.J., Reichenberg, A., Lewis, G., David, A.S., Jones, D.K.: Schizophrenia-like topological changes in the structural connectome of individuals with subclinical psychotic experiences. Hum. Brain Mapp. 36(7), 2629–2643 (2015)CrossRefGoogle Scholar
  6. 6.
    Fandino, J., et al.: Intraoperative validation of functional magnetic resonance imaging and cortical reorganization patterns in patients with brain tumors involving the primary motor cortex. J. Neurosurg. 91(2), 238–250 (1999)CrossRefGoogle Scholar
  7. 7.
    Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., Van Der Walt, S., Descoteaux, M., Nimmo-Smith, I.: Contributors, Dipy, a library for the analysis of diffusion MRI data. Frontiers Neuroinformatics 8 (2014)Google Scholar
  8. 8.
    Gordon, E.M., et al.: Generation and evaluation of a cortical area parcellation from resting-state correlations. Cerebral cortex, p. bhu239 (2014)Google Scholar
  9. 9.
    Griffa, A., Baumann, P.S., Thiran, J.P., Hagmann, P.: Structural connectomics in brain diseases. Neuroimage 80, 515–526 (2013)CrossRefGoogle Scholar
  10. 10.
    Griffa, A., Baumann, P.S., Ferrari, C., Do, K.Q., Conus, P., Thiran, J.P., Hagmann, P.: Characterizing the connectome in schizophrenia with diffusion spectrum imaging. Hum. Brain Mapp. 36(1), 354–366 (2015)CrossRefGoogle Scholar
  11. 11.
    Guggisberg, A.G., Honma, S.M., Findlay, A.M., Dalal, S.S., Kirsch, H.E., Berger, M.S., Nagarajan, S.S.: Mapping functional connectivity in patients with brain lesions. Ann. Neurol. 63(2), 193–203 (2008)CrossRefGoogle Scholar
  12. 12.
    Harris, R.J., Bookheimer, S.Y., Cloughesy, T.F., Kim, H.J., Pope, W.B., Lai, A., Nghiemphu, P.L., Liau, L.M., Ellingson, B.M.: Altered functional connectivity of the default mode network in diffuse gliomas measured with pseudo-resting state fmri. J. Neurooncol. 116(2), 373–379 (2014)CrossRefGoogle Scholar
  13. 13.
    van den Heuvel, M.P., Fornito, A.: Brain networks in Schizophrenia. Neuropsychol. Rev. 24(1), 32–48 (2014)CrossRefGoogle Scholar
  14. 14.
    Hou, B., et al.: Quantitative comparisons on hand motor functional areas determined by resting state and task bold fmri and anatomical mri for pre-surgical planning of patients with brain tumors. Neuroimage Clin. 11, 378–387 (2016)CrossRefGoogle Scholar
  15. 15.
    Kapsalakis, I.Z., et al.: Preoperative evaluation with FMRI of patients with intracranial gliomas. Radiology research and practice 2012 (2012)Google Scholar
  16. 16.
    Kinno, R., Ohta, S., Muragaki, Y., Maruyama, T., Sakai, K.L.: Differential reorganization of three syntax-related networks induced by a left frontal glioma. Brain 137(4), 1193–1212 (2014)CrossRefGoogle Scholar
  17. 17.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059–1069 (2010)CrossRefGoogle Scholar
  18. 18.
    Rudie, J.D., Brown, J., Beck-Pancer, D., Hernandez, L., Dennis, E., Thompson, P., Bookheimer, S., Dapretto, M.: Altered functional and structural brain network organization in autism. NeuroImage Clin. 2, 79–94 (2013)CrossRefGoogle Scholar
  19. 19.
    Sanai, N., Berger, M.S.: Glioma extent of resection and its impact on patient outcome. Neurosurgery 62(4), 753–766 (2008)CrossRefGoogle Scholar
  20. 20.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2016. CA: A Cancer J. Clin. 66(1), 7–30 (2016)Google Scholar
  21. 21.
    Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., et al.: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31(4), 1487–1505 (2006)CrossRefGoogle Scholar
  22. 22.
    Sporns, O., Tononi, G., Kötter, R.: The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1(4), e42 (2005)CrossRefGoogle Scholar
  23. 23.
    Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273–289 (2002)CrossRefGoogle Scholar
  24. 24.
    Varoquaux, G., Craddock, R.C.: Learning and comparing functional connectomes across subjects. NeuroImage 80, 405–415 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Lancaster UniversityLancasterUK
  2. 2.Istituto Italiano di TecnologiaGenoaItaly

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