Brain Imaging and Behavior

, Volume 13, Issue 5, pp 1333–1351 | Cite as

Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks

  • Luyan Liu
  • Han Zhang
  • Jinsong Wu
  • Zhengda Yu
  • Xiaobo Chen
  • Islem Rekik
  • Qian WangEmail author
  • Junfeng LuEmail author
  • Dinggang ShenEmail author
Original Research


High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning.


Survival Prognosis Glioma Functional connectivity Brain network Connectomics Machine learning 



This work is partially supported by National Natural Science Foundation of China (NSFC) Grants (61473190, 61401271, 81471733, 81201156, and 81401395), the National Key Technology R&D Program of China (2014BAI04B05), and NIH grant (EB022880).

Compliance with ethical standards


The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Huashan Institutional Review Board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11682_2018_9949_MOESM1_ESM.docx (4.1 mb)
ESM 1 (DOCX 4163 kb)


  1. Aerts, H., Fias, W., Caeyenberghs, K., & Marinazzo, D. (2016). Brain networks under attack: Robustness properties and the impact of lesions. Brain, 139(12), 3063–3083.Google Scholar
  2. Agarwal, S., Sair, H. I., & Pillai, J. J. (2017). The resting state fMRI regional homogeneity (ReHo) metrics KCC-ReHo & Cohe-ReHo are valid indicators of tumor-related neurovascular uncoupling. Brain Connectivity, 7(4), 228–235.Google Scholar
  3. Aibaidula, A., Lu, J.-F., Wu, J.-S., Zou, H.-J., Chen, H., Wang, Y.-Q., et al. (2015). Establishment and maintenance of a standardized glioma tissue bank: Huashan experience. Cell and Tissue Banking, 16(2), 271–281.Google Scholar
  4. Alakorkko, T., Saarimaki, H., Glerean, E., Saramaki, J., & Korhonen, O. (2017). Effects of spatial smoothing on functional brain networks. The European Journal of Neuroscience, 46, 2471–2480.Google Scholar
  5. Alexander, A. L., Hurley, S. A., Samsonoy, A. A., Adluru, N., Hosseinbor, A. P., Mossahebi, P., et al. (2011). Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain Connectivity, 1, 423–446.Google Scholar
  6. Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38(1), 95–113.Google Scholar
  7. Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839–851.Google Scholar
  8. Balleine, B. W., & O'doherty, J. P. (2010). Human and rodent homologies in action control: Corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology, 35(1), 48–69.Google Scholar
  9. Bartolomei, F., Bosma, I., Klein, M., Baayen, J. C., Reijneveld, J. C., Postma, T. J., et al. (2006). Disturbed functional connectivity in brain tumour patients: Evaluation by graph analysis of synchronization matrices. Clinical Neurophysiology, 117(9), 2039–2049.Google Scholar
  10. Bisdas, S., Kirkpatrick, M., Giglio, P., Welsh, C., Spampinato, M., & Rumboldt, Z. (2009). Cerebral blood volume measurements by perfusion-weighted MR imaging in gliomas: Ready for prime time in predicting short-term outcome and recurrent disease? American Journal of Neuroradiology, 30(4), 681–688.Google Scholar
  11. Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537–541.Google Scholar
  12. Bosma, I., Reijneveld, J. C., Klein, M., Douw, L., Van Dijk, B. W., Heimans, J. J., et al. (2009). Disturbed functional brain networks and neurocognitive function in low-grade glioma patients: A graph theoretical analysis of resting-state MEG. Nonlinear Biomedical Physics, 3(1), 9.Google Scholar
  13. Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.Google Scholar
  14. Buzsaki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304, 1926–1929.Google Scholar
  15. Cairncross, J. G., Ueki, K., Zlatescu, M. C., Lisle, D. K., Finkelstein, D. M., Hammond, R. R., et al. (1998). Specific genetic predictors of chemotherapeutic response and survival in patients with anaplastic oligodendrogliomas. Journal of the National Cancer Institute, 90(19), 1473–1479.Google Scholar
  16. Carbo, E. W., Hillebrand, A., Van Dellen, E., Tewarie, P., de Witt Hamer, P. C., Baayen, J. C., et al. (2017). Dynamic hub load predicts cognitive decline after resective neurosurgery. Scientific Reports, 7, 42117.Google Scholar
  17. Carlson, N. R. (2010). Physiology of behavior. Allyn & Bacon Boston.Google Scholar
  18. Cavada, C., Tejedor, J., Cruz-Rizzolo, R. J., & Reinoso-Suárez, F. (2000). The anatomical connections of the macaque monkey orbitofrontal cortex. A review. Cereb Cortex, 10(3), 220–242.Google Scholar
  19. Chen, W., Delaloye, S., Silverman, D. H., Geist, C., Czernin, J., Sayre, J., et al. (2007). Predicting treatment response of malignant gliomas to bevacizumab and irinotecan by imaging proliferation with [18F] fluorothymidine positron emission tomography: A pilot study. Journal of Clinical Oncology, 25(30), 4714–4721.Google Scholar
  20. Chen, X., Zhang, H., Gao, Y., Wee, C. Y., Li, G., & Shen, D. (2016a). High-order resting-state functional connectivity network for MCI classification. Human Brain Mapping, 37(9), 3282–3296.Google Scholar
  21. Chen, X., Zhang, H., & Shen, D. (2016b) Ensemble hierarchical high-order functional connectivity networks for MCI classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 18-25.Google Scholar
  22. Chen, X., Zhang, H., Lee, S.-W., & Shen, D. (2017). Hierarchical high-order functional connectivity networks and selective feature fusion for MCI classification. Neuroinformatics, 15(3), 271–284.Google Scholar
  23. Cochereau, J., Deverdun, J., Herbet, G., Charroud, C., Boyer, A., Moritz-Gasser, S., et al. (2016). Comparison between resting state fMRI networks and responsive cortical stimulations in glioma patients. Human Brain Mapping, 37(11), 3721–3732.Google Scholar
  24. Collet, S., Valable, S., Constans, J., Lechapt-Zalcman, E., Roussel, S., Delcroix, N., et al. (2015). [18 F]-fluoro-l-thymidine PET and advanced MRI for preoperative grading of gliomas. NeuroImage: Clinical, 8, 448–454.Google Scholar
  25. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.Google Scholar
  26. Cui, Y., Tha, K. K., Terasaka, S., Yamaguchi, S., Wang, J., Kudo, K., et al. (2015). Prognostic imaging biomarkers in glioblastoma: Development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology, 278(2), 546–553.Google Scholar
  27. Damaraju, E., Allen, E., Belger, A., Ford, J., McEwen, S., Mathalon, D., et al. (2014). Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia. NeuroImage: Clinical, 5, 298–308.Google Scholar
  28. Davis, F. G., Freels, S., Grutsch, J., Barlas, S., & Brem, S. (1998). Survival rates in patients with primary malignant brain tumors stratified by patient age and tumor histological type: An analysis based on surveillance, epidemiology, and end results (SEER) data, 1973–1991. Journal of Neurosurgery, 88(1), 1–10.Google Scholar
  29. Desmurget, M., Bonnetblanc, F., & Duffau, H. (2007). Contrasting acute and slow-growing lesions: A new door to brain plasticity. Brain, 130(4), 898–914.Google Scholar
  30. Duffau, H. (2017). A two-level model of interindividual anatomo-functional variability of the brain and its implications for neurosurgery. Cortex, 86, 303–313.Google Scholar
  31. Fan, Y., Gur, R. E., Gur, R. C., Wu, X., Shen, D., Calkins, M. E., & Davatzikos, C. (2008). Unaffected family members and schizophrenia patients share brain structure patterns: A high-dimensional pattern classification study. Biological Psychiatry, 63(1), 118–124.Google Scholar
  32. Fornito, A., Zalesky, A., & Bullmore, E. T. (2010). Network scaling effects in graph analytic studies of human resting-state FMRI data. Frontiers in Systems Neuroscience, 4, 22.Google Scholar
  33. Fox, M. D., & Greicius, M. (2010). Clinical applications of resting state functional connectivity. Frontiers in Systems Neuroscience, 4, 19.Google Scholar
  34. Fuster, J. M. (1988). Prefrontal cortex. In Comparative Neuroscience and Neurobiology (pp. 107-109): Springer.Google Scholar
  35. Gazzaniga, M. S. (2009). The cognitive neurosciences IV. Cambridge: MA.Google Scholar
  36. Ghumman, S., Fortin, D., Noel-Lamy, M., Cunnane, S., & Whittingstall, K. (2016). Exploratory study of the effect of brain tumors on the default mode network. Journal of Neuro-Oncology, 128(3), 437–444.Google Scholar
  37. Gillies, R. J., Kinahan, P. E., & Hricak, H. (2015). Radiomics: Images are more than pictures, they are data. Radiology, 278(2), 563–577.Google Scholar
  38. Giovagnoli, A., Silvani, A., Colombo, E., & Boiardi, A. (2005). Facets and determinants of quality of life in patients with recurrent high grade glioma. Journal of Neurology, Neurosurgery & Psychiatry, 76(4), 562–568.Google Scholar
  39. Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L., Auerbach, E. J., Behrens, T. E., et al. (2016). The human connectome project's neuroimaging approach. Nature Neuroscience, 19(9), 1175–1187.Google Scholar
  40. Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016). Generation and evaluation of a cortical area Parcellation from resting-state correlations. Cerebral Cortex, 26, 288–303.Google Scholar
  41. Grabner, G., Kiesel, B., Wöhrer, A., Millesi, M., Wurzer, A., Göd, S., et al. (2016). Local image variance of 7 tesla SWI is a new technique for preoperative characterization of diffusely infiltrating gliomas: Correlation with tumour grade and IDH1 mutational status. European Radiology, 27(4), 1556–1567.Google Scholar
  42. Grandjean, J., Preti, M. G., Bolton, T. A., Buerge, M., Seifritz, E., Pryce, C. R., et al. (2017). Dynamic reorganization of intrinsic functional networks in the mouse brain. NeuroImage, 152, 497–508.Google Scholar
  43. Grossman, S. A., Ye, X., Piantadosi, S., Desideri, S., Nabors, L. B., Rosenfeld, M., et al. (2010). Survival of patients with newly diagnosed glioblastoma treated with radiation and temozolomide in research studies in the United States. Clinical Cancer Research, 16(8), 2443–2449.Google Scholar
  44. Gu, S., Yang, M., Medaglia, J. D., Gur, R. C., Gur, R. E., Satterthwaite, T. D., et al. (2017). Functional hypergraph uncovers novel covariant structures over neurodevelopment. Human Brain Mapping, 38(8), 3823–3835.Google Scholar
  45. Hart, M. G., Price, S. J., & Suckling, J. (2016a). Connectome analysis for pre-operative brain mapping in neurosurgery. British Journal of Neurosurgery, 30(5), 506–517.Google Scholar
  46. Hart, M. G., Price, S. J., & Suckling, J. (2016b). Functional connectivity networks for preoperative brain mapping in neurosurgery. Journal of Neurosurgery, 1–10.Google Scholar
  47. He, S.-Q., Dum, R. P., & Strick, P. (1995). Topographic organization of corticospinal projections from the frontal lobe: Motor areas on the medial surface of the hemisphere. Journal of Neuroscience, 15(5), 3284–3306.Google Scholar
  48. Horwitz, B. (2003). The elusive concept of brain connectivity. NeuroImage, 19(2), 466–470.Google Scholar
  49. Horwitz, B., Grady, C. L., Schlageter, N., Duara, R., & Rapoport, S. (1987). Intercorrelations of regional cerebral glucose metabolic rates in Alzheimer's disease. Brain Research, 407(2), 294–306.Google Scholar
  50. Huang, Q., Zhang, R., Hu, X., Ding, S., Qian, J., Lei, T., et al. (2014). Disturbed small-world networks and neurocognitive function in frontal lobe low-grade glioma patients. PLoS One, 9(4), e94095.Google Scholar
  51. Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., et al. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage, 80, 360–378.Google Scholar
  52. Itakura, H., Achrol, A. S., Mitchell, L. A., Loya, J. J., Liu, T., Westbroek, E. M., et al. (2015). Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Science Translational Medicine, 7(303), 138.Google Scholar
  53. Jain, R., Poisson, L. M., Gutman, D., Scarpace, L., Hwang, S. N., Holder, C. A., et al. (2014). Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: Focus on the nonenhancing component of the tumor. Radiology, 272(2), 484–493.Google Scholar
  54. Jeremic, B., Grujicis, D., Antunovic, V., Djuric, L., et al. (1994). Influence of extent of surgery and tumor location on treatment outcome of patients with glioblastoma multiforme treated combined modality approach. Journal of Neuro-Oncology, 21(2), 177–185.Google Scholar
  55. Kickingereder, P., Bonekamp, D., Nowosielski, M., Kratz, A., Sill, M., Burth, S., et al. (2016a). Radiogenomics of glioblastoma: Machine learning–based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiology, 281(3), 907–918.Google Scholar
  56. Kickingereder, P., Burth, S., Wick, A., Götz, M., Eidel, O., Schlemmer, H.-P., et al. (2016b). Radiomic profiling of glioblastoma: Identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology, 280(3), 880–889.Google Scholar
  57. Klein, M., . Taphoorn, M. J., Heimans, J. J., van der Ploeg, H. M., Vandertop, W. P., Smit, E. F., et al. (2001). Neurobehavioral status and health-related quality of life in newly diagnosed high-grade glioma patients. Journal of Clinical Oncology, 19(20), 4037–4047.Google Scholar
  58. Klein, M., Heimans, J., Aaronson, N., Van der Ploeg, H., Grit, J., Muller, M., et al. (2002). Effect of radiotherapy and other treatment-related factors on mid-term to long-term cognitive sequelae in low-grade gliomas: A comparative study. The Lancet, 360(9343), 1361–1368.Google Scholar
  59. Lacroix, M., Abi-Said, D., Fourney, D. R., Gokaslan, Z. L., Shi, W., DeMonte, F., et al. (2001). A multivariate analysis of 416 patients with glioblastoma multiforme: Prognosis, extent of resection, and survival. Journal of Neurosurgery, 95(2), 190–198.Google Scholar
  60. Law, M., Young, R. J., Babb, J. S., Peccerelli, N., Chheang, S., Gruber, M. L., et al. (2008). Gliomas: Predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging 1. Radiology, 247(2), 490–498.Google Scholar
  61. Legler, J. M., Ries, L. A. G., Smith, M. A., Warren, J. L., Heineman, E. F., Kaplan, R. S., et al. (1999). Brain and other central nervous system cancers: Recent trends in incidence and mortality. Journal of the National Cancer Institute, 91(16), 1382–1390.Google Scholar
  62. Liu, L., Zhang, H., Rekik, I., Chen, X., Wang, Q., & Shen, D. (2016) Outcome prediction for patient with high-grade gliomas from brain functional and structural networks. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 26-34.Google Scholar
  63. Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet, A., et al. (2007). The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathologica, 114(2), 97–109.Google Scholar
  64. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.Google Scholar
  65. Lynall, M.-E., Bassett, D. S., Kerwin, R., McKenna, P. J., Kitzbichler, M., Muller, U., et al. (2010). Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience, 30(28), 9477–9487.Google Scholar
  66. Macyszyn, L., Akbari, H., Pisapia, J. M., Da, X., Attiah, M., Pigrish, V., et al. (2016). Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro-Oncology, 18(3), 417–425.Google Scholar
  67. Maesawa, S., Bagarinao, E., Fujii, M., Futamura, M., Motomura, K., Watanabe, H., et al. (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(2), e0118072.Google Scholar
  68. Maldaun, M. V., Suki, D., Lang, F. F., Prabhu, S., Shi, W., Fuller, G. N., et al. (2004). Cystic glioblastoma multiforme: Survival outcomes in 22 cases. Journal of Neurosurgery, 100(1), 61–67.Google Scholar
  69. Mazurowski, M. A., Zhang, J., Peters, K. B., & Hobbs, H. (2014). Computer-extracted MR imaging features are associated with survival in glioblastoma patients. Journal of Neuro-Oncology, 120(3), 483–488.Google Scholar
  70. Meinshausen, N., & Bühlmann, P. (2006). High-dimensional graphs and variable selection with the lasso. The Annals of Statistics, 1436–1462.Google Scholar
  71. Mikl, M., Marecek, R., Hlustik, P., Pavlicova, M., Drastich, A., Chlebus, P., Brazdil, M., & Krupa, P. (2008). Effects of spatial smoothing on fMRI group inferences. Magnetic Resonance Imaging, 26, 490–503.Google Scholar
  72. Nie, D., Zhang, H., Adeli, E., Liu, L., & Shen, D. (2016). 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 212–220.Google Scholar
  73. Olson, I. R., Plotzker, A., & Ezzyat, Y. (2007). The enigmatic temporal pole: A review of findings on social and emotional processing. Brain, 130(7), 1718–1731.Google Scholar
  74. Ostrom, Q. T., Gittleman, H., Farah, P., Ondracek, A., Chen, Y., Wolinsky, Y., et al. (2013). CBTRUS statistical report: Primary brain and central nervous system tumors diagnosed in the United States in 2006-2010. Neuro-Oncology, 15(suppl 2), ii1–ii56.Google Scholar
  75. Phillips, H. S., Kharbanda, S., Chen, R., Forrest, W. F., Soriano, R. H., Wu, T. D., et al. (2006). Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell, 9(3), 157–173.Google Scholar
  76. Pope, W. B., Sayre, J., Perlina, A., Villablanca, J. P., Mischel, P. S., & Cloughesy, T. F. (2005). MR imaging correlates of survival in patients with high-grade gliomas. American Journal of Neuroradiology, 26(10), 2466–2474.Google Scholar
  77. Rahman, M., Abbatematteo, J., De Leo, E. K., Kubilis, P. S., Vaziri, S., Bova, F., et al. (2017). The effects of new or worsened postoperative neurological deficits on survival of patients with glioblastoma. Journal of Neurosurgery, 127(1), 123–131.Google Scholar
  78. Ratey, J. J. (2001). A user's guide to the brain: Perception, attention, and the four theatres of the brain. Vintage.Google Scholar
  79. Ricard, D., Idbaih, A., Ducray, F., Lahutte, M., Hoang-Xuan, K., & Delattre, J.-Y. (2012). Primary brain tumours in adults. The Lancet, 379(9830), 1984–1996.Google Scholar
  80. Rosazza, C., & Minati, L. (2011). Resting-state brain networks: Literature review and clinical applications. Neurological Sciences, 32(5), 773–785.Google Scholar
  81. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069.Google Scholar
  82. Saksena, S., Jain, R., Narang, J., Scarpace, L., Schultz, L. R., Lehman, N. L., et al. (2010). Predicting survival in glioblastomas using diffusion tensor imaging metrics. Journal of Magnetic Resonance Imaging, 32(4), 788–795.Google Scholar
  83. Sawaya, R., Hammoud, M., Schoppa, D., Hess, K. R., Wu, S. Z., Shi, W.-M., et al. (1998). Neurosurgical outcomes in a modern series of 400 craniotomies for treatment of parenchymal tumors. Neurosurgery, 42(5), 1044–1055.Google Scholar
  84. Simpson, J. R., Horton, J., Scott, C., Curran, W. J., Rubin, P., et al. (1993). Influence of location and extent of surgical resection on survival of patients with glioblastoma multiforme: Results of three consecutive radiation therapy oncology group (RTOG) clinical trials. International Journal of Radiation Oncology, Biology, Physics, 26(2), 239–244.Google Scholar
  85. Smith, J. S., Chang, E. F., Lamborn, K. R., Chang, S. M., Prados, M. D., Cha, S., et al. (2008). Role of extent of resection in the long-term outcome of low-grade hemispheric gliomas. Journal of Clinical Oncology, 26(8), 1338–1345.Google Scholar
  86. Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., et al. (2011). Network modeling methods for FMRI. NeuroImage, 54(2), 875–891.Google Scholar
  87. Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C. (2004). Organization, development and function of complex brain networks. Trends in Cognitive Sciences, 8(9), 418–425.Google Scholar
  88. Squire, L., Berg, D., Bloom, F. E., Du Lac, S., Ghosh, A., & Spitzer, N. C. (2012). Fundamental Neuroscience. Academic Press.Google Scholar
  89. Stam, C., Jones, B., Nolte, G., Breakspear, M., & Scheltens, P. (2007). Small-world networks and functional connectivity in Alzheimer's disease. Cerebral Cortex, 17(1), 92–99.Google Scholar
  90. Stensjøen, A. L., Solheim, O., Kvistad, K. A., Håberg, A. K., Salvesen, Ø., & Berntsen, E. M. (2015). Growth dynamics of untreated glioblastomas in vivo. Neuro-Oncology, 17(10), 1402–1411.Google Scholar
  91. Stupp, R., Dietrich, P.-Y., Kraljevic, S. O., Pica, A., Maillard, I., Maeder, P., et al. (2002). Promising survival for patients with newly diagnosed glioblastoma multiforme treated with concomitant radiation plus temozolomide followed by adjuvant temozolomide. Journal of Clinical Oncology, 20(5), 1375–1382.Google Scholar
  92. Stylli, S. S., Kaye, A. H., MacGregor, L., Howes, M., & Rajendra, P. (2005). Photodynamic therapy of high grade glioma–long term survival. Journal of Clinical Neuroscience, 12(4), 389–398.Google Scholar
  93. Taphoorn, M., Schiphorst, A. K., Snoek, F., Lindeboom, J., Wolbers, J., Karim, A., et al. (1994). Cognitive functions and quality of life in patients with low-grade gliomas: The impact of radiotherapy. Annals of Neurology, 36(1), 48–54.Google Scholar
  94. Tijms, B. M., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., et al. (2013). Alzheimer's disease: Connecting findings from graph theoretical studies of brain networks. Neurobiology of Aging, 34(8), 2023–2036.Google Scholar
  95. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (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.Google Scholar
  96. van Dellen, E., Douw, L., Hillebrand, A., de Witt Hamer, P. C., Baayen, J. C., Heimans, J. J., et al. (2014). Epilepsy surgery outcome and functional network alterations in longitudinal MEG: A minimum spanning tree analysis. NeuroImage, 86, 354–363.Google Scholar
  97. van Dellen, E., Douw, L., Hillebrand, A., Ris-Hilgersom, I. H., Schoonheim, M. M., Baayen, J. C., et al. (2012). MEG network differences between low-and high-grade glioma related to epilepsy and cognition. PLoS One, 7(11), e50122.Google Scholar
  98. van Dellen, E., Hillebrand, A., Douw, L., Heimans, J. J., Reijneveld, J. C., & Stam, C. J. (2013). Local polymorphic delta activity in cortical lesions causes global decreases in functional connectivity. NeuroImage, 83, 524–532.Google Scholar
  99. Van Den Heuvel, M. P., & Pol, H. E. H. (2010). Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519–534.Google Scholar
  100. Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.Google Scholar
  101. Wang, H., Douw, L., Hernandez, J. M., Reijneveld, J., Stam, C., & Van Mieghem, P. (2010). Effect of tumor resection on the characteristics of functional brain networks. Physical Review E, 82(2), 021924.Google Scholar
  102. Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., & He, Y. (2015a). GRETNA: A graph theoretical network analysis toolbox for imaging connectomics. Frontiers in Human Neuroscience, 9, 386.Google Scholar
  103. Wang, J., Wang, L., Zang, Y., Yang, H., Tang, H., Gong, Q., Chen, Z., Zhu, C., & He, Y. (2009a). Parcellation-dependent small-world brain functional networks: A resting-state fMRI study. Human Brain Mapping, 30(5), 1511–1523.Google Scholar
  104. Wang, L., Zhu, C., He, Y., Zang, Y., Cao, Q., Zhang, H., et al. (2009b). Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder. Human Brain Mapping, 30(2), 638–649.Google Scholar
  105. Wang, Y., Wang, K., Li, S., Wang, J., Ma, J., Jiang, T., et al. (2015b). Patterns of tumor contrast enhancement predict the prognosis of anaplastic gliomas with IDH1 mutation. American Journal of Neuroradiology, 36(11), 2023–2029.Google Scholar
  106. Wee, C. Y., Yap, P. T., Denny, K., Browndyke, J. N., Potter, G. G., Welsh-Bohmer, K. A., ... & Shen, D. (2012). Resting-state multi-spectrum functional connectivity networks for identification of MCI patients. PLoS One, 7(5), e37828.Google Scholar
  107. Wee, C.-Y., Yang, S., Yap, P.-T., Shen, D., & Initiative, A. s. D. N. (2016). Sparse temporally dynamic resting-state functional connectivity networks for early MCI identification. Brain Imaging and Behavior, 10(2), 342–356.Google Scholar
  108. Wu, J.-S., Zhou, L.-F., Tang, W.-J., Mao, Y., Hu, J., Song, Y.-Y., et al. (2007). Clinical evaluation and follow-up outcome of diffusion tensor imaging-based functional neuronavigation: A prospective, controlled study in patients with gliomas involving pyramidal tracts. Neurosurgery, 61(5), 935–949.Google Scholar
  109. Xia, M., Wang, J., & He, Y. (2013). BrainNet viewer: A network visualization tool for human brain connectomics. PLoS One, 8(7), e68910.Google Scholar
  110. Xu, H., Ding, S., Hu, X., Yang, K., Xiao, C., Zou, Y., et al. (2013). Reduced efficiency of functional brain network underlying intellectual decline in patients with low-grade glioma. Neuroscience Letters, 543, 27–31.Google Scholar
  111. Yu, Z., Tao, L., Qian, Z., Wu, J., Liu, H., Yu, Y., et al. (2016a). Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography. International Journal of Computer Assisted Radiology and Surgery, 11(11), 2007–2019.Google Scholar
  112. Yu, R., Zhang, H., An, L., Chen, X., Wei, Z., & Shen, D. (2016b). Correlation-weighted sparse group representation for brain network construction in MCI classification. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 37-45.Google Scholar
  113. Zacharaki, E. I., Morita, N., Bhatt, P., O'rourke, D., Melhem, E., & Davatzikos, C. (2012). Survival analysis of patients with high-grade gliomas based on data mining of imaging variables. American Journal of Neuroradiology, 33(6), 1065–1071.Google Scholar
  114. Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010). Whole-brain anatomical networks: Does the choice of nodes matter? NeuroImage, 50(3), 970–983.Google Scholar
  115. Zhang, D., Johnston, J. M., Fox, M. D., Leuthardt, E. C., Grubb, R. L., Chicoine, M. R., et al. (2009). Preoperative sensorimotor mapping in brain tumor patients using spontaneous fluctuations in neuronal activity imaged with fMRI: Initial experience. Neurosurgery, 65(6 Suppl), 226.Google Scholar
  116. Zhang, H., Chen, X., Shi, F., Li, G., Kim, M., Giannakopoulos, P., et al. (2016). Topographical information-based high-order functional connectivity and its application in abnormality detection for mild cognitive impairment. Journal of Alzheimer's Disease, 54(3), 1095–1112.Google Scholar
  117. Zhang, H., Chen, X., Zhang, Y., & Shen, D. (2017a). Test-retest reliability of "high-order" functional connectivity in young healthy adults. Frontiers in Neuroscience, 11, 439.Google Scholar
  118. Zhang, L., Wang, Q., Gao, Y., Li, H., Wu, G., & Shen, D. (2017b). Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images. Neurocomputing, 229, 3–12.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Med-X Research Institute, School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Glioma Surgery Division, Neurosurgery Department of Huashan HospitalFudan UniversityShanghaiChina
  4. 4.Shanghai Key Lab of Medical Image Computing and Computer-Assisted InterventionShanghaiChina
  5. 5.Neurosurgery Department of Huashan HospitalShanghaiChina
  6. 6.BASIRA Lab, CVIP Group, School of Science and Engineering, ComputingUniversity of DundeeDundeeUK
  7. 7.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea

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