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

Abstract

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.

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References

  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.

  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.

  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.

  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.

  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.

  6. Ashburner, J. (2007). A fast diffeomorphic image registration algorithm. Neuroimage, 38(1), 95–113.

  7. Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839–851.

  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.

  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.

  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.

  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.

  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.

  13. Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198.

  14. Buzsaki, G., & Draguhn, A. (2004). Neuronal oscillations in cortical networks. Science, 304, 1926–1929.

  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.

  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.

  17. Carlson, N. R. (2010). Physiology of behavior. Allyn & Bacon Boston.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  25. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  33. Fox, M. D., & Greicius, M. (2010). Clinical applications of resting state functional connectivity. Frontiers in Systems Neuroscience, 4, 19.

  34. Fuster, J. M. (1988). Prefrontal cortex. In Comparative Neuroscience and Neurobiology (pp. 107-109): Springer.

  35. Gazzaniga, M. S. (2009). The cognitive neurosciences IV. Cambridge: MA.

  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.

  37. Gillies, R. J., Kinahan, P. E., & Hricak, H. (2015). Radiomics: Images are more than pictures, they are data. Radiology, 278(2), 563–577.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  46. Hart, M. G., Price, S. J., & Suckling, J. (2016b). Functional connectivity networks for preoperative brain mapping in neurosurgery. Journal of Neurosurgery, 1–10.

  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.

  48. Horwitz, B. (2003). The elusive concept of brain connectivity. NeuroImage, 19(2), 466–470.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  64. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

  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.

  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.

  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.

  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.

  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.

  70. Meinshausen, N., & Bühlmann, P. (2006). High-dimensional graphs and variable selection with the lasso. The Annals of Statistics, 1436–1462.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  78. Ratey, J. J. (2001). A user's guide to the brain: Perception, attention, and the four theatres of the brain. Vintage.

  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.

  80. Rosazza, C., & Minati, L. (2011). Resting-state brain networks: Literature review and clinical applications. Neurological Sciences, 32(5), 773–785.

  81. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069.

  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.

  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.

  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.

  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.

  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.

  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.

  88. Squire, L., Berg, D., Bloom, F. E., Du Lac, S., Ghosh, A., & Spitzer, N. C. (2012). Fundamental Neuroscience. Academic Press.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  100. Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  109. Xia, M., Wang, J., & He, Y. (2013). BrainNet viewer: A network visualization tool for human brain connectomics. PLoS One, 8(7), e68910.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

  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.

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Acknowledgments

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).

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Correspondence to Qian Wang or Junfeng Lu or Dinggang Shen.

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

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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.

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

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Liu, L., Zhang, H., Wu, J. et al. Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks. Brain Imaging and Behavior 13, 1333–1351 (2019). https://doi.org/10.1007/s11682-018-9949-2

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Keywords

  • Survival
  • Prognosis
  • Glioma
  • Functional connectivity
  • Brain network
  • Connectomics
  • Machine learning