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Original Article

Connectome analysis for pre-operative brain mapping in neurosurgery

, &
Pages 506-517 | Received 19 Apr 2016, Accepted 23 Jun 2016, Published online: 22 Jul 2016

References

  • Greenblatt SH, Dagi TF, Epstein MH, eds. A history of neurosurgery: in its scientific and professional contexts. Park Ridge, IL: AANS; 1997.
  • Penfield W, Rasmussen T. The cerebral cortex of man. New York: Macmillan; 1950.
  • Fornito A, Zalesky A, Breakspear M. Graph analysis of the human connectome: promise, progress, and pitfalls. NeuroImage 2013;80:426–44.
  • Smith SM, Beckmann CF, Andersson J, et al. Resting-state fMRI in the human connectome project. NeuroImage 2013;80:144–68.
  • Sporns O, ed. Discovering the human connectome. USA: MIT Press; 2012.
  • Sporns O, ed. Networks of the brain. 1st ed. USA: MIT Press; 2011.
  • Sporns O, Tononi G, Kötter R. The human connectome: a structural description of the human brain. PLoS Comput Biol 2005;1:e42.
  • Hart MG, Ypma RJ, Romero-Garcia R, et al. Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery. J Neurosurg 2015;6:1–14.
  • Watts DJ, Strogatz SH. Collective dynamics of “small-world” networks. Nature 1998;493:440–2.
  • Achard S, Salvador R, Whitcher B, et al. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 2006;26:63–72.
  • Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009;10:186–98.
  • Newman M, ed. Networks: an introduction. Oxford: OUP; 2010.
  • NIH Blueprint. Available from: https://neuroscienceblueprint.nih.gov/connectome [last accessed 12 Jul 2016].
  • Fornito A, Zalesky A, Breakspear M. The connectomics of brain disorders. Nat Rev Neurosci 2015;16:159–72.
  • Stam CJ. Modern network science of neurological disorders. Nat Rev Neurosci 2014;15:683–95.
  • Louis DN, Ohgaki H, Wiestler OD, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 2007;114:97–109.
  • Wen PY, Macdonald DR, Reardon DA, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010;28:1963–72.
  • Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996;29:162–73.
  • Kundu P, Inati SJ, Evans JW, et al. Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. NeuroImage 2012;102:861–74.
  • Kundu P, Brenowitz ND, Voon V, et al. Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proc Natl Acad Sci USA 2013;110:16187–92.
  • Lutkenhoff ES, Rosenberg M, Chiang J, et al. Optimized brain extraction for pathological brains (optiBET). PLoS One 2014;9:e115551.
  • Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal 2001;5:143–56.
  • Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 2002;17:825–41.
  • Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 2002;15:273–89.
  • Bullmore E, Fadili J, Breakspear M, et al. Wavelets and statistical analysis of functional magnetic resonance images of the human brain. Stat Methods Med Res 2003;12:375–99.
  • Bullmore E, Fadili J, Maxim V, et al. Wavelets and functional magnetic resonance imaging of the human brain. NeuroImage 2004;23:S234–49.
  • Achard S, Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol 2007;3:e17.
  • R Development Core Team. (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available from: http://www.R-project.org.
  • Brainwaver: basic wavelet analysis of multivariate time series with a visualisation and parametrisation using graph theory. R package version 1.6. Available from: http://CRAN.R-project.org/package=brainwaver.
  • Alstott J, Breakspear M, Hagmann P, Cammoun L, Sporns O. Modelling the impact of lesions in the human brain. PLoS Comput Biol 2009;5:e1000408.
  • Kaiser M, Martin R, Andras P, Young MP. Simulation of robustness against lesions of cortical networks. Eur J Neurosci 2007;25:3185–92.
  • Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett 2001;87:198701–4.
  • Latora V, Marchiori M. A measure of centrality based on network efficiency. New J Phys 2007;9:188–99.
  • Akaite H. A new look at the statistical model identification. IEEE Trans Automat Control 1974;19:716–23.
  • Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One 2013;8:e68910.
  • Krzywinski MI, Schein JE, Birol I, et al. Circos: an information aesthetic for comparative genomics. Genome Res 2009;19:1639–45.
  • Irimia A, Chambers MC, Torgerson CM, Van Horn JD. Circular representation of human cortical networks for subject and population-level connectomic visualization. NeuroImage 2012;60:1340–51.
  • Van Horn JD, Irimia A, Torgerson CM, et al. Mapping connectivity damage in the case of Phineas Gage. PLoS One 2012;7:e37454.
  • Margulies DS, Böttger J, Watanabe A, Gorgolewski KJ. Visualizing the human connectome. NeuroImage 2013;80:445–61.
  • Bello L, Gallucci M, Fava M, et al. Subcortical language tract mapping guides surgical removal of gliomas involving speech areas. Neurosurgery 2007;60:67–82.
  • Bello L, Gambini A, Castellano A, et al. Motor and language DTI Fiber Tracking combined with intraoperative subcortical mapping for surgical removal of gliomas. NeuroImage 2008;39:369–82.
  • De Benedictis A, Moritz-Gasser S, Duffau H. Awake mapping optimizes the extent of resection for low-grade gliomas in eloquent areas. Neurosurgery 2010;66:1074–84.
  • Duffau H, ed. Neural basis of cognition to surgical applications. In: Brain mapping. Vienna: Springer Science & Business Media; 2011.
  • Duffau H. Stimulation mapping of white matter tracts to study brain functional connectivity. Nat Rev Neurol 2015;11:255–65.
  • Deco G, Tononi G, Boly M, Kringelbach ML. Rethinking segregation and integration: contributions of whole-brain modelling. Nat Rev Neurosci 2015;16:430–9.
  • Castellanos FX, Di Martino A, Craddock RC, et al. Clinical applications of the functional connectome. NeuroImage 2013;80:527–40.
  • van den Heuvel MP, Stam CJ, Kahn RS, Hulshoff Pol HE. Efficiency of functional brain networks and intellectual performance. J Neurosci 2009;29:7619–24.
  • Li Y, Liu Y, Li J, et al. Brain anatomical network and intelligence. PLoS Comput Biol 2009;5:e1000395.
  • Micheloyannis S, Pachou E, Stam CJ, et al. Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis. Neurosci Lett 2006;402:273–7.
  • Huang Q, Zhang R, Hu X, et al. Disturbed small-world networks and neurocognitive function in frontal lobe low-grade glioma patients. PLoS One 2014;9:e94095.
  • Xu H, Ding S, Hu X, et al. Reduced efficiency of functional brain network underlying intellectual decline in patients with low-grade glioma. Neurosci Lett 2013;543:27–31.
  • Smith SM, Fox PT, Miller KL, et al. Correspondence of the brain's functional architecture during activation and rest. Proc Natl Acad Sci USA 2009;106:13040–5.
  • Smith SM, Vidaurre D, Beckmann CF, et al. Functional connectomics from resting-state fMRI. Trends Cogn Sci 2013;17:666–82.
  • Kelly C, Castellanos FX. Strengthening connections: functional connectivity and brain plasticity. Neuropsychol Rev 2014;24:63–76.
  • Duffau H. Diffuse low-grade gliomas and neuroplasticity. Diagn Interven Imaging 2014;10:1–11.
  • Papagno C, Casarotti A, Comi A, et al. Measuring clinical outcomes in neuro-oncology. A battery to evaluate low-grade gliomas (LGG). J Neurooncol 2012;108:269–75.
  • Fernández Coello A, Moritz-Gasser S, Martino J, et al. Selection of intraoperative tasks for awake mapping based on relationships between tumor location and functional networks. J Neurosurg 2013;119:1380–94.
  • De Vico Fallani F, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Philos Trans R Soc Lond B: Biol Sci 2014;369:20130521.
  • Craddock RC, Jbabdi S, Yan C-G, et al. Imaging human connectomes at the macroscale. Nat Methods 2013;10:524–39.
  • Zalesky A, Fornito A, Harding IH, et al. Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage 2010;50:970–83.
  • Zalesky A, Fornito A, Bullmore E. On the use of correlation as a measure of network connectivity. NeuroImage 2012;60:2096–106.
  • Simas T, Rocha LM. Distance closures on complex networks. Network Sci 2015;3;1–42.
  • Gonzalez-Castillo J, Bandettini PA. What cascade spreading models can teach us about the brain. Neuron 2015;86:1327–9.
  • Mišić B, Betzel RF, Nematzadeh A, et al. Cooperative and competitive spreading dynamics on the human connectome. Neuron 2015;86:1518–29.
  • Motter AE, Lai Y-C. Cascade-based attacks on complex networks. Phys Rev E: Stat Nonlin Soft Matter Phys 2002;66:065102.
  • Huang X, Vodenska I, Havlin S, Stanley HE. Cascading failures in bi-partite graphs: model for systemic risk propagation. Sci Rep 2013;3:1219.
  • Daqing L, Yinan J, Rui K, Havlin S. Spatial correlation analysis of cascading failures: congestions and blackouts. Sci Rep 2014;4:5381.
  • Sanai N, Mirzadeh Z, Berger MS. Functional outcome after language mapping for glioma resection. N Engl J Med 2008;358:18–27.