The Characteristics of Brain Structural Network in Patients with Low Grade Glioma Revealed by Diffusion Tensor Imaging
Paper #3154 received 24 Jan 2017; revised manuscript received 5 May 2017; accepted for publication 10 May 2017; published online 15 Jun 2017.
Purpose: At present, few studies have investigated the functional performance in patients with low grade glioma (LGG) from the perspective of brain structural characteristics. This study aimed to analyze the topological properties of brain structural networks in LGG patients and investigate the influence of operation and lesion location on whole-brain and hemisphere networks. On this basis, the structural plasticity and function compensatory mechanism of LGG patient were discussed. Method: We constructed whole-brain and hemisphere anatomical networks for 20 LGG patients and 20 normal controls (NC). The structural connections were established by selecting an appropriate connectivity threshold. Brain hub nodes and random nodes between LGG and NC were compared. Statistical analyses were performed to reveal the significant differences of various network characteristics between LGG patients and NC as well as between LGG patients before and after operation. The two-factor variance analysis was used to investigate the influence of operation and lesion location on whole-brain and hemisphere networks. Results: From the graph-based topological metrics of the constructed network, various global parameters changed significantly in LGG patients. Meanwhile, altered regions in the LGG patients was obtained and various nodal parameters were further calculated in each region. Most of hub nodes were identical between LGG and NC groups, while the betweenness centrality values of those hub nodes and some random nodes were higher in the LGG patients. By comparing LGG patients pre- and post- operation, more significantly altered network metrics were obtained from hemisphere network analysis than that from whole-brain network analysis, and network features of dominant hemisphere changed more drastically when the lesion is located in the same hemisphere. Conclusion: To conclude, the present study indicates the existence of compensatory mechanism in LGG patients to adapt to cognitive requirements. Function reorganization and the rewiring of neuronal circuits allow signal transmission bypassing the lesion area or surgical trauma. It also suggests that graph-based topological metrics analysis could become a useful method to provide valuable indices of brain function in the evaluation of the LGG.
1. E. G. Shaw, B. W. Scheithauer, and J. R. OFallon, “Management of supratentorial low-grade gliomas,” Seminars in Radiation Oncology 1(1), 23-31 (1993).
2. E. F. Chang, A. Clark, J. S. Smith, M.-Y. Polley, S. M. Chang, N. M. Barbaro, A. T. Parsa, M. W. McDermott, and M. S. Berger, “Functional mapping-guided resection of lowgrade gliomas in eloquent areas of the brain: improvement of long-term survival,” Journal of Neurosurgery 114(3), 566-573 (2011). Crossref
3. J. R. Petrella, L. M. Shah, K. M. Harris, A. H. Friedman, T. M. George, J. H. Sampson, J. S. Pekala, and J. T. Voyvodic, “Preoperative functional MR imaging localization of language and motor areas: effect on therapeutic decision making in patients with potentially resectable brain tumors,” Radiology 240(3), 793-802 (2006). Crossref
4. S. M. Smirnakis, M. C. Schmid, B. Weber, A. S. Tolias, M. Augath, and N. K. Logothetis, “Spatial specificity of BOLD versus cerebral blood volume fMRI for mapping cortical organization,” Journal of Cerebral Blood Flow & Metabolism 27(6), 1248-1261 (2007). Crossref
5. A. G. Goloshevsky, C. W.-H. Wu, S. J. Dodd, and A. P. Koretsky, “Mapping cortical representations of the rodent forepaw and hindpaw with BOLD fMRI reveals two spatial boundaries,” Neuroimage 57(2), 526-538 (2011). Crossref
6. C. M. Filley (ed.), The Behavioral Neurology of White Matter, Oxford University Press, New York (2001). ISBN: 978-0-1951-3561-9.
7. P. J. Basser, and D. K. Jones, “Diffusion-tensor MRI: theory, experimental design and data analysis – a technical review,” NMR Biomed 15(7-8), 456–467 (2002).
8. E. Bullmore, and O. Sporns, “Complex brain networks: graph theoretical analysis of structural and functional systems,” Nature Reviews Neuroscience 10(3), 186-198 (2009). Crossref
9. M. R. Del Bigio, M. J. Wilson, and T. Enno, “Chronic hydrocephalus in rats and humans: white matter loss and behavior changes,” Ann Neurol 53(3), 337-346 (2003). Crossref
10. Y. Ding, J. P. McAllister, B. Yao, N. Yan, and A. I. Canady, “Axonal damage associated with enlargement of ventricles during hydrocephalus: a silver impregnation study,” Neurol Res 23(6), 581-587 (2001). Crossref
11. M. Lazar, A. L. Alexander, P. J. Thottakara, B. Badie, and A. S. Field, “White matter reorganization after surgical resection of brain tumors and vascular malformations,” American journal of neuroradiology 27(6), 1258-1271 (2006).
12. G. Gong, Y. He, L. Concha, C. Lebel, D. W. Gross, A. C. Evans, and C. Beaulieu, “Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography,” Cerebral cortex 19(3), 524-536 (2009).
13. B. Wang, Y. Fan, M. Lu, S. Li, Z. Song, X. Peng, R. Zhang, Q. Lin, Y. He, J. Wang, and R. Huang, “Brain anatomical networks in world class gymnasts: A DTI tractography study,” NeuroImage 65, 476-487 (2013).
14. J. T. Duda, P. A. Cook, and J. C. Gee, “Reproducibility of graph metrics of human brain structural networks,” Frontiers in Neuroinformatics 8(46), (2014).
15. M. Bastiani, N. J. Shah, R. Goebel, and A. Roebroeck, “Human cortical connectome reconstruction from diffusion weighted MRI: the effect of tractography algorithm,” NeuroImage 62(3), 1732-1749 (2012). Crossref
16. C. Thomas, F. Q. Ye, M. O. Irfanoglu, P. Modi, K. S. Saleem, D. A. Leopold, and C. Pierpaoli, “Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited,” Proceedings of the National Academy of Sciences 111(46), 16574-16579 (2014).
17. B. C. M. van Wijk, C. J. Stam, and A. Daffertshofer, “Comparing brain networks of different size and connectivity density using graph theory,” PLoS One 5(10), e13701 (2010).
18. D. J. Watts, and S. H. Strogatz, “Collective dynamics of ‘small-world’ networks,” Nature 393(6684), 440-442 (1998).
19. M. D. Humphries, and K. Gurney, “Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence,” PLoS One 3(4), e0002051 (2008).
20. O. Sporns, and J. D. Zwi, “The small world of the cerebral cortex,” Neuroinformatics 2(2), 145-162 (2004). Crossref
21. R. Albert, and A. Barabási, “Statistical mechanics of complex networks,” Reviews of modern physics 74(1), 47-97 (2002).
22. S. Achard, and E. Bullmore, “Efficiency and cost of economical brain functional networks,” PLoS Comput Biol 3(2), e17 (2007).
23. L. C. Freeman, “A set of measures of centrality based on betweenness,” Sociometry 40(1), 35-41 (1977). Crossref
24. V. Gol'dshtein, G. A. Koganov, and G. I. Surdutovich, “Vulnerability and hierarchy of complex networks,” arXiv preprint, cond-mat/0409298 (2004).
25. P. Holme, B. J. Kim, C. N. Yoon, and S. K. Han, “Attack vulnerability of complex networks,” Physical Review E 65(5), 1-15 (2002). Crossref
26. L. da F. Costa, F. A. Rodrigues, G. Travieso, and P. R. Villas Boas, “Characterization of complex networks: A survey of measurements,” Advances in Physics 56(1), 167-242 (2007).
27. M. Girvan M, and M. E. J. Newman, “Community structure in social and biological networks,” Proceedings of the National Academy of Sciences of the United States of America 99(12), 7821-7826 (2002).
28. V. Latora, and M. Marchiori, “Efficient behavior of small-world networks,” Physical Review Letters 87(19), 1-4 (2001). Crossref
29. M. Filippi, and F. Agosta, “Magnetic resonance techniques to quantify tissue damage, tissue repair, and functional cortical reorganization in multiple sclerosis,” Prog Brain Res 175, 465-482 (2009).
30. C. Sampaio-Baptista, A. A. Khrapitchev, S. Foxley, T. Schlagheck, J. Scholz, S. Jbabdi, G. C. DeLuca, K. L. Miller, A. Taylor, N. Thomas, J. Kleim, N. R. Sibson, D. Bannerman, and H. Johansen-Berg, “Motor skill learning induces changes in white matter microstructure and myelination, “J Neurosci 33(50), 19499-19503 (2013) Crossref
31. S. R. Wainwright, and L. A. M. Galea, “The Neural Plasticity Theory of Depression: Assessing the Roles of Adult Neurogenesis and PSA-NCAM within the Hippocampus,” Neural Plasticity 2013, 1-14 (2013).
32. Healthline Medical Team, Frontal lobe, Medically Reviewed, 2 March 2015.
33. M. Macmillan, “Alfred walter campbell and the visual functions of the occipital cortex,” Cortex 56, 157-181 (2014). Crossref
34. E. J. J. Habets, A. Kloet, R. Walchenbach, C. J. Vecht, M. Klein, and M. J. B. Taphoorn, “Tumour and surgery effects on cognitive functioning in high-grade glioma patients,” Acta Neurochirurgica 156(8), 1451-1459 (2014). Crossref
35. M. Klein, H. Duffau, and P. C. D. W. Hamer, “Cognition and resective surgery for diffuse infiltrative glioma: an overview,” Journal of neuro-oncology 108(2), 309-318 (2012). Crossref
36. K. Caeyenberghs, and A. Leemans, “Hemispheric lateralization of topological organization in structural brain networks,” Human brain mapping 35(9), 4944-4957 (2014).
37. H. Cheng, Y. Wang, J. Sheng, W. G. Kronenberger, V. P. Mathews, T. A. Hummer, and A. J. Saykin, “Characteristics and variability of structural networks derived from diffusion tensor imaging,” Neuroimage 61(4), 1153-1164 (2012). Crossref
38. D. K. Jones, T. R. Knosche, and R. Turner, “White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI,” Neuroimage 73, 239-254 (2013).
39. H. Huang, N. Shu, V. Mishra, T. Jeon, L. Chalak, Z. J. Wang, N. Rollins, G. Gong, H. Cheng, Y. Peng, Q. Dong, and Y. He, “Development of Human Brain Structural Networks Through Infancy and Childhood,” Cerebral Cortex 25(5), 1389-1404 (2015).
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