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Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Brain tumor patients are usually accompanied by impairments in cognitive functions, and these dysfunctions arise from the altered diffusion tensor of water molecules and disrupted neuronal conduction in white matter. Diffusion tensor imaging (DTI) is a powerful noninvasive imaging technique that can reflect diffusion anisotropy of water and brain white matter neural connectivity in vivo. This study was aimed to analyze the topological properties and connection densities of the brain anatomical networks in brain tumor patients based on DTI and provide new insights into the investigation of the structural plasticity and compensatory mechanism of tumor patient’s brain.

Methods

In this study, the brain anatomical networks of tumor patients and healthy controls were constructed using the tracking of white matter fiber bundles based on DTI and the topological properties of these networks were described quantitatively. The statistical comparisons were performed between two groups with six DTI parameters: degree, regional efficiency, local efficiency, clustering coefficient, vulnerability, and betweenness centrality. In order to localize changes in structural connectivity to specific brain regions, a network-based statistic approach was utilized. By comparing the edge connection density of brain network between two groups, the edges with greater difference in connection density were associated with three functional systems.

Results

Compared with controls, tumor patients show a significant increase in small-world feature of cerebral structural network. Two-sample two-tailed t test indicates that the regional properties are altered in 17 regions (\(p<0.05\)). Study reveals that the positive and negative changes in vulnerability take place in the 14 brain areas. In addition, tumor patients lose 3 hub regions and add 2 new hubs when compared to normal controls. Eleven edges show much significantly greater connection density in the patients than in the controls. Most of the edges with greater connection density are linked to regions located in the limbic/subcortical and other systems. Besides, most of the edges connect the two hemispheres of the brains.

Conclusion

The stronger small-world property in the tumor patients proves the existence of compensatory mechanism. The changes in the regional properties, especially the betweenness centrality and vulnerability, aid in understanding the brain structural plasticity. The increased connection density in the tumor group suggests that tumors may induce reorganization in the structural network.

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References

  1. Taphoorn MJ, Klein M (2004) Cognitive deficits in adult patients with brain tumours. Lancet Neurol 3(3):159–168

    Article  PubMed  Google Scholar 

  2. Bartolomei F, Bosma I, Klein M, Baayen JC, Reijneveld JC, Postma TJ, Heimans JJ, van Dijk BW, de Munck JC, de Jongh A, Cover KS, Stam CJ (2006) Disturbed functional connectivity in brain tumour patients: evaluation by graph analysis of synchronization matrices. Clin Neurophysiol 117(9):2039–2049

    Article  PubMed  Google Scholar 

  3. Correa DD, Shi W, Thaler HT, Cheung AM, DeAngelis LM, Abrey LE (2008) Longitudinal cognitive follow-up in low-grade gliomas. J Neurooncol 86(3):321e327

    Article  Google Scholar 

  4. Duffau H (2012) The “frontal syndrome” revisited: lessons from electrostimulation mapping studies. Cortex 48(1):120–131. doi:10.1016/j.cortex.2011.04.029

    Article  PubMed  Google Scholar 

  5. Duffau H (2007) Contribution of cortical and subcortical electrostimulation in brain glioma surgery: methodological and functional considerations. Clin Neurophysiol 37(6):373–382

    Article  CAS  Google Scholar 

  6. Palmer SL, Glass JO, Li Y, Ogg R, Qaddoumi I, Armstrong GT, Wright K, Wetmore C, Broniscer A, Gajjar A, Reddick WE (2012) White matter integrity is associated with cognitive processing in patients treated for a posterior fossa brain tumor. Neuro-oncology 14:1185–1193

    Article  PubMed  PubMed Central  Google Scholar 

  7. Mori S, Frederiksen K, Zijl PC, Stieltjes B, Kraut MA (2002) Brain white matter anatomy of tumor patients evaluated with diffusion tensor imaging. Ann Neurol 51:377–380

    Article  PubMed  Google Scholar 

  8. Bai F, Shu N, Yuan Y, Shi Y, Yu H, Wu D, Wang J, Xia M, He Y, Zhang Z (2012) Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. J Neurosci 32(12):4307–4318

    Article  CAS  PubMed  Google Scholar 

  9. Zalesky A, Fornito A, Seal ML, Cocchi L, Westin CF, Bullmore ET, Egan GF, Pantelis C (2011) Disrupted axonal fiber connectivity in schizophrenia. Biol Psychiatry 69(1):80–89

    Article  PubMed  Google Scholar 

  10. Shu N, Liu Y, Li J, Li Y, Yu C, Jiang T (2009) Altered anatomical network in early blindness revealed by diffusion tensor tractography. PloS ONE 4(9):e7228

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198

    Article  CAS  PubMed  Google Scholar 

  12. Yao Z, Zhang Y, Lin L, Zhou Y, Xu C, Jiang T (2010) Alzheimer’s Disease Neuroimaging Initiative. Abnormal cortical networks in mild cognitive impairment and Alzheimer’s disease. PLoS Comp Biol 6:e1001006

    Article  Google Scholar 

  13. Stam CJ, Haan W, Daffertshofer A, Jones BF, Manshanden I, Cappellen AM, Montez T, Verbunt JP, Munck JC, Dijk BW, Berendse HW, Scheltens P (2009) Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain 132:213e224

    Google Scholar 

  14. Singh MK, Kesle SR, Hosseini SM, Kelley RG, Amatya D, Hamilton JP, Chen MC, Gotlib IH (2013) Anomalous gray matter structural networks in major depressive disorder. Biol Psychiatry 74(10):777–785

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wang B, Fan Y, Lu M, Li S, Song Z, Peng X, Zhang R, Lin Q, He Y, Wang J, Huang R (2013) Brain anatomical networks in world class gymnasts: a DTI tractography study. NeuroImage 65:476–487

    Article  PubMed  Google Scholar 

  16. Gol’dshtein V, Koganov GA, Surdutovich GI (2004) Vulnerability and hierarchy of complex networks. arXiv:cond-mat/0409298 (preprint)

  17. Costa LDF, Rodrigues FA, Travieso G, Villas Boas PR (2007) Characterization of complex networks: a survey of measurements. Adv Phys 56(1):167–242

    Article  Google Scholar 

  18. Bosma I, Reijneveld JC, Klein M, Douw L, Van Dijk BW, Heimans JJ, Stam CJ (2009) Disturbed functional brain networks and neurocognitive function in low-grade glioma patients: a graph theoretical analysis of resting-state MEG. Nonlinear Biomed Phys 3(1):9

    Article  PubMed  PubMed Central  Google Scholar 

  19. Huang Q, Zhang R, Hu X, Ding S, Qian J, Lei T, Cao X, Tao L, Qian Z, Liu H (2014) Disturbed small-world networks and neurocognitive function in frontal lobe low-grade glioma patients. PloS ONE 9(4):e94095

    Article  PubMed  PubMed Central  Google Scholar 

  20. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (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

    Article  CAS  PubMed  Google Scholar 

  21. Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC, Beaulieu C (2009) Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 19(3):524–536

    Article  PubMed  Google Scholar 

  22. Holme P, Kim BJ, Yoon CN, Han SK (2002) Attack vulnerability of complex networks. Phys Rev E 65(5):056109

    Article  Google Scholar 

  23. Humphries MD, Gurney K (2008) Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PloS ONE 3(4):e0002051

    Article  PubMed  Google Scholar 

  24. Zalesky A, Fornito A, Bullmore ET (2010) Network-based statistic: identifying differences in brain networks. Neuroimage 53(4):1197–1207

    Article  PubMed  Google Scholar 

  25. He Y, Wang J, Wang L, Chen ZJ, Yan C, Yang H, Tang H, Zhu C, Gong Q, Zang Y, Evans AC (2009) Uncovering intrinsic modular organization of spontaneous brain activity in humans. PloS ONE 4(4):e5226

  26. Zhang J, Wang J, Wu Q, Kuang W, Huang X, He Y, Gong Q (2011) Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry 70(4):334–342

    Article  PubMed  Google Scholar 

  27. He Y, Dagher A, Chen Z, Charil A, Zijdenbos A, Worsley K, Evans A (2009) Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain 132:3366–3379

    Article  PubMed  PubMed Central  Google Scholar 

  28. Stam CJ (2004) Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’ network? Neurosci Lett 355(1):25–28

    Article  CAS  PubMed  Google Scholar 

  29. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore ED (2006) A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 26(1):63–72

    Article  CAS  PubMed  Google Scholar 

  30. Micheloyannis S, Pachou E, Stam CJ, Vourkas M, Erimaki S, Tsirka V (2006) Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis. Neurosci Lett 402(3):273–277

    Article  CAS  PubMed  Google Scholar 

  31. Mesulam MM (1998) From sensation to cognition. Brain 121(6):1013–1052

    Article  PubMed  Google Scholar 

  32. Li W, Qin W, Liu H, Fan L, Wang J, Jiang T, Yu C (2013) Subregions of the human superior frontal gyrus and their connections. Neuroimage 78:46–58

    Article  PubMed  Google Scholar 

  33. Planetta PJ, Servos P (2012) The postcentral gyrus shows sustained fMRI activation during the tactile motion aftereffect. Exp Brain Res 216(4):535–544

    Article  PubMed  Google Scholar 

  34. 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(7):e40231

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Salloway S, Malloy P, Cummings JL (eds) (1997) The neuropsychiatry of limbic and subcortical disorders. American Psychiatric Press, Washington, DC

    Google Scholar 

  36. Ingalhalikar M, Smith A, Parker D, Satterthwaite TD, Elliott MA, Ruparel K, Hakonarson H, Gur RE, Gur RC, Verma R (2014) Sex differences in the structural connectome of the human brain. Proc Natl Acad Sci 111(2):823–828

    Article  CAS  PubMed  Google Scholar 

  37. Liu J, Zhao L, Li G, Xiong S, Nan J, Li J, Yuan K, von Deneen KM, Liang F, Qin W, Tian J (2012) Hierarchical alteration of brain structural and functional networks in female migraine sufferers. PloS ONE 7(12):e51250

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wen W, Zhu W, He Y, Kochan NA, Reppermund S, Slavin MJ, Brodaty H, Crawford J, Xia A, Sachdev P (2011) Discrete neuroanatomical networks are associated with specific cognitive abilities in old age. J Neurosci 31(4):1204–1212

    Article  CAS  PubMed  Google Scholar 

  39. Yan C, Gong G, Wang J, Wang D, Liu D, Zhu C, Chen ZJ, Evans A, Zang Y, He Y (2011) Sex- and brain size-related small-world structural cortical networks in young adults: a DTI tractography study. Cereb Cortex 21(2):449–458

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the Natural Science Foundation of China (Grant Number: 61075107) and the Clinical medical science Special Foundation in Jiangsu Province (Grant Number: SBL201230215).

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Authors and Affiliations

  1. Department of Biomedical Engineering, College of Automation, Nanjing University of Aeronautics and Astronautics, No. 29, Yudao Street, Qinhuai District, Nanjing, 210016, Jiangsu Province, China

    Zhou Yu, Ling Tao, Zhiyu Qian, Jiangfen Wu, Yun Yu, Jiantai Song, Shaobo Wang & Jinyang Sun

  2. Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, No. 264, Guangzhou Road, Gulou District, Nanjing, 210029, Jiangsu Province, China

    Hongyi Liu

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  1. Zhou Yu
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  2. Ling Tao
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Correspondence to Ling Tao.

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Conflict of interest

Zhou Yu, Ling Tao, Zhiyu Qian, Jiangfen Wu, Cuihua Zhao, Yun Yu, Jiantai Song, Shaobo Wang, and Jinyang Sun declare that they have no conflict of interest.

Ethical standard

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Declaration of Helsinki 1975, as revised in 2008 (5).

Informed consent

Informed consent was obtained from all patients for being included in the study.

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Yu, Z., Tao, L., Qian, Z. et al. Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography. Int J CARS 11, 2007–2019 (2016). https://doi.org/10.1007/s11548-015-1330-y

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  • DOI: https://doi.org/10.1007/s11548-015-1330-y

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