基于图论的胶质瘤患者脑结构网络分析

刘明航; 攸娜; 杨晨轩; 赵恺; 赵悦; 许百男

doi:10.3969/j.issn.2095-5227.2023.05.008

基于图论的胶质瘤患者脑结构网络分析

doi: 10.3969/j.issn.2095-5227.2023.05.008

刘明航^{1, 2,},
攸娜^{1, 2},
杨晨轩^{1, 2},
赵恺²,
赵悦²,
许百男^2, ,

1.
解放军医学院，北京　100853
2.
解放军总医院第一医学中心神经外科医学部，北京　100853

详细信息

作者简介:
刘明航，男，在读硕士。研究方向：神经外科学。Email: liumhdoctor@163.com

通讯作者:
许百男，男，博士，主任医师，学术主任。Email: xubn301@aliyun.com

中图分类号: R739.4
计量
- 文章访问数: 75
- HTML全文浏览量: 27
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2022-09-19
- 网络出版日期: 2023-05-09
- 刊出日期: 2023-05-28

Graph theoretical analysis of structural brain network in patients with glioma

LIU Minghang^{1, 2
,},
YOU Na^{1, 2},
YANG Chenxuan^{1, 2},
ZHAO Kai²,
ZHAO Yue²,
XU Bainan^{2
, ,}

1.
Chinese PLA Medical School, Beijing 100853, China
2.
Department of Neurosurgery, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China

More Information

Corresponding author: XU Bainan. Email: xubn301@aliyun.com

摘要

摘要: 背景胶质瘤是颅内十分常见的恶性肿瘤，具有很强的侵袭性，既往研究表明胶质瘤的存在不仅会对病变周围造成损害，还会对肿瘤范围以外的其他远隔部位造成功能损害。对大脑网络的研究，可帮助临床医师对胶质瘤患者的手术预期及预后作出更精准的判断。目的研究不同部位胶质瘤患者脑网络拓扑属性的改变，探讨由肿瘤病变引起与大脑功能变化相关的结构网络重塑。方法纳入解放军总医院第一医学中心神经外科2018年1月 - 2020年12月收治的113例胶质瘤患者，采集所有受试者核磁共振扫描的弥散张量成像后，采用Mrtrix3软件对数据进行处理并构建大脑结构网络，通过GRETNA软件基于图论方法计算网络拓扑属性，观察不同位置肿瘤对脑结构网络的影响。结果额叶、颞叶、顶叶胶质瘤三组间的全局最短路径长度(the shortest path length，Lp)、聚类系数(clustering coefficient，Cp)、全局网络效率(network efficiency，Eg)、局部效率(local efficiency，Eloc)表现出现统计学差异，与额叶、颞叶胶质瘤组相比，顶叶胶质瘤组的全局拓扑属性表现为升高(P＜0.05)，与其他脑区节点网络属性指标相比，肿瘤所在脑区的节点介数中心性(betweenness centrality，NBc)、度中心性(degree centrality，NDc)、全局效率(nodular global efficiency，NEg)升高，节点最短路径长度(nodular shortest path length，NLp)下降，差异有统计学意义(P＜0.05)。结论不同脑叶位置胶质瘤的网络拓扑属性发生了变化，全局属性和局部节点属性均表现出脑结构网络连接性改变。
- 脑结构网络 /
- 胶质瘤 /
- 肿瘤位置 /
- 神经重塑
Abstract: Background Glioma is the most common intracranial malignant tumor, which often shows strong invasiveness. Previous studies have shown that the existence of gliomas will not only cause damage around the lesions, but also distant sites outside the scope of the tumor. The study of structural brain network can help surgeons to make better judgment about the surgical expectation and prognosis for glioma patients. Objective To investigate the changes of topological properties of brain network in patients with gliomas at different sites, and reveal the structural network remodeling related to brain function changes caused by tumor lesions. Methods Totally 113 glioma patients were enrolled from 2018 to 2020 at the Department of Neurosurgery, Chinese PLA General Hospital. The diffusion tensor imaging data from MRI were processed by Mrtrix3 software and the brain structure network was constructed. The topological properties of the network were calculated by GRETNA software using graph theory. The effects of tumors in different locations on brain structural network were observed. Results There were significant differences in global shortest path length (Lp), clustering coefficient (Cp), global network efficiency (Eg) and local efficiency (Eloc) among frontal lobe, temporal lobe and parietal lobe gliomas. The global topological property of parietal lobe glioma group was higher than that of frontal lobe and temporal lobe glioma group (P<0.05). Compared with other brain region node network attribute indexes, betweenness centrality (NBc), degree centrality (NDc), nodular global efficiency (NEg) increased and nodular shortest path length (NLp) decreased in the brain region where the tumor was located, and the difference was statistically significant (P< 0.05). Conclusion The network topology attributes of glioma at different lobe locations have changed, and both the global attributes and local node attributes show changes in the connectivity of the brain structure network.
- brain structural network /
- glioma /
- tumor location /
- neuroplasticity

HTML全文

图 1 额叶胶质瘤、颞叶胶质瘤、顶叶胶质瘤三组间全局参数比较(组别1：顶叶胶质瘤组；组别2：颞叶胶质瘤组；组别3：额叶胶质瘤组)

A：三组间聚类系数指标比较；B：三组间最短路径长度比较；C：三组间全局效率比较；D：三组间局部效率比较

Figure 1. Comparison of global parameters among frontal lobe glioma, temporal lobe glioma and parietal lobe glioma (Group 1: parietal lobe glioma group; Group 2: temporal lobe glioma group; Group 3: frontal lobe glioma group)

A: cluster coefficient index comparison among the three groups; B: shortest path length comparison among the three groups; C: global efficiency comparison among the three groups; D: local efficiency comparison among the three groups

下载: 全尺寸图片幻灯片

图 2 额叶胶质瘤、颞叶胶质瘤、顶叶胶质瘤三组间节点的介数中心性比较(红色小球表示组间比较升高的脑区，绿色小球表示组间比较降低的脑区。小球越大，表示升高或降低越显著；小球间连接线越粗，表示两节点间连接强度越强)

A：额叶胶质瘤组与颞叶胶质瘤组的比较；B：额叶胶质瘤组与顶叶胶质瘤组的比较；C：颞叶胶质瘤组与顶叶胶质瘤组的比较

Figure 2. Comparison of the centrality of nodes among frontal lobe glioma, temporal lobe glioma and parietal lobe glioma (The red ball represents the elevated brain area of comparison between the groups, and the green ball indicates the decreased brain area of comparison between the groups. The larger the ball, the more obvious it is to rise or decrease. The thicker the connection line between the balls, the stronger the connection between the two nodes)

A: comparison between frontal lobe glioma group and temporal lobe glioma group; B: comparison between frontal lobe glioma group and parietal lobe glioma group; C: comparison between temporal lobe glioma group and parietal lobe glioma group

下载: 全尺寸图片幻灯片

图 3 额叶胶质瘤、颞叶胶质瘤、顶叶胶质瘤三组间节点的度中心性比较(红色小球表示组间比较升高的脑区，绿色小球表示组间比较降低的脑区。小球越大，表示升高或降低越显著；小球间连接线越粗，表示两节点间连接强度越强)

A：额叶胶质瘤组与颞叶胶质瘤组的比较；B：额叶胶质瘤组与顶叶胶质瘤组的比较；C：颞叶胶质瘤组与顶叶胶质瘤组的比较

Figure 3. Comparison of degree centrality of nodes among frontal lobe glioma, temporal lobe glioma and parietal lobe glioma (The red ball represents the elevated brain area of comparison between the groups, and the green ball indicates the decreased brain area of comparison between the groups. The larger the ball, the more obvious it is to rise or decrease. The thicker the connection line between the balls, the stronger the connection between the two nodes)

下载: 全尺寸图片幻灯片

图 4 额叶胶质瘤、颞叶胶质瘤、顶叶胶质瘤三组间节点的全局效率比较(红色小球表示组间比较升高的脑区，绿色小球表示组间比较降低的脑区。小球越大，表示升高或降低越显著；小球间连接线越粗，表示两节点间连接强度越强)

A：额叶胶质瘤组与颞叶胶质瘤组的比较；B：额叶胶质瘤组与顶叶胶质瘤组的比较；C：颞叶胶质瘤组与顶叶胶质瘤组的比较

Figure 4. Comparison of global efficiency of nodes among frontal lobe glioma, temporal lobe glioma and parietal lobe glioma (The red ball represents the elevated brain area of comparison between the groups, and the green ball indicates the decreased brain area of comparison between the groups. The larger the ball, the more obvious it is to rise or decrease. The thicker the connection line between the balls, the stronger the connection between the two nodes)

下载: 全尺寸图片幻灯片

图 5 额叶胶质瘤、颞叶胶质瘤、顶叶胶质瘤三组间节点的最短路径长度比较(红色小球表示组间比较升高的脑区，绿色小球表示组间比较降低的脑区。小球越大，表示升高或降低越显著；小球间连接线越粗，表示两节点间连接强度越强)

A：额叶胶质瘤组与颞叶胶质瘤组的比较；B：额叶胶质瘤组与顶叶胶质瘤组的比较；C：颞叶胶质瘤组与顶叶胶质瘤组的比较

Figure 5. Comparison of the shortest path length among frontal lobe glioma, temporal lobe glioma and parietal lobe glioma (The red ball represents the elevated brain area of comparison between the groups, and the green ball indicates the decreased brain area of comparison between the groups. The larger the ball, the more obvious it is to rise or decrease. The thicker the connection line between the balls, the stronger the connection between the two nodes)

下载: 全尺寸图片幻灯片

表 1 全局拓扑属性比较

Table 1. Comparison of global topology attribute

全局参数	额叶肿瘤组	颞叶肿瘤组	顶叶肿瘤组	F值	P值
聚类系数	0.638 ± 0.013	0.636 ± 0.017	0.652 ± 0.009	14.400	0.001
最短路径长度	1.541 ± 0.032	1.548 ± 0.026	1.546 ± 0.015	15.532	0.001
全局效率	0.649 ± 0.012	0.646 ± 0.010	0.647 ± 0.006	15.582	0.001
局部效率	0.818 ± 0.007	0.816 ± 0.009	0.825 ± 0.005	15.399	0.001
λ	1.015 ± 0.009	1.017 ± 0.007	1.018 ± 0.005	1.459	0.237
γ	1.612 ± 0.130	1.581 ± 0.069	1.595 ± 0.043	1.042	0.356
σ	1.587 ± 0.110	1.555 ± 0.062	1.566 ± 0.039	1.649	0.197

下载: 导出CSV

参考文献(50)

[1]	Weller M,van den Bent M,Tonn JC,et al. European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas[J]. Lancet Oncol,2017,18(6): e315-e329. doi: 10.1016/S1470-2045(17)30194-8
[2]	Zhang HS,Ille S,Sogerer L,et al. Elucidating the structural-functional connectome of language in glioma-induced aphasia using nTMS and DTI[J]. Hum Brain Mapp,2022,43(6): 1836-1849. doi: 10.1002/hbm.25757
[3]	Duffau H. Introducing the concept of brain metaplasticity in glioma:how to reorient the pattern of neural reconfiguration to optimize the therapeutic strategy[J]. J Neurosurg,2021,136(2): 613-617.
[4]	Melgarejo da Rosa M. Communication of Glioma cells with neuronal plasticity:what is the underlying mechanism?[J]. Neurochem Int,2020,141: 104879. doi: 10.1016/j.neuint.2020.104879
[5]	Almairac F,Duffau H,Herbet G. Contralesional macrostructural plasticity of the insular cortex in patients with glioma:a VBM study[J]. Neurology,2018,91(20): e1902-e1908. doi: 10.1212/WNL.0000000000006517
[6]	Lizarazu M,Gil-Robles S,Pomposo I,et al. Spatiotemporal dynamics of postoperative functional plasticity in patients with brain tumors in language areas[J]. Brain Lang,2020,202: 104741. doi: 10.1016/j.bandl.2019.104741
[7]	Cirillo S, Caulo M, Pieri V, et al. Role of functional imaging techniques to assess motor and language cortical plasticity in glioma patients: a systematic review［J/OL］. https://doi.org/10.1155/2019/4056436.
[8]	Li DL,Patel CB,Xu GF,et al. Visualization of diagnostic and therapeutic targets in glioma with molecular imaging[J]. Front Immunol,2020,11: 592389. doi: 10.3389/fimmu.2020.592389
[9]	Luo T, Li YL. Research and analysis of brain glioma imaging based on deep learning［J/OL］. https://doi.org/10.1155/2021/3426080.
[10]	Li GZ,Li L,Li YM,et al. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas[J]. Brain,2022,145(3): 1151-1161. doi: 10.1093/brain/awab340
[11]	Carrete LR,Young JS,Cha S. Advanced imaging techniques for newly diagnosed and recurrent gliomas[J]. Front Neurosci,2022,16: 787755. doi: 10.3389/fnins.2022.787755
[12]	Assaf Y,Pasternak O. Diffusion tensor imaging (DTI)-based white matter mapping in brain research:a review[J]. J Mol Neurosci,2008,34(1): 51-61. doi: 10.1007/s12031-007-0029-0
[13]	Chang XB,Jia XY,Wang YL,et al. Alterations of cerebellar white matter integrity and associations with cognitive impairments in schizophrenia[J]. Front Psychiatry,2022,13: 993866. doi: 10.3389/fpsyt.2022.993866
[14]	Lowry E,Puthusseryppady V,Johnen AK,et al. Cognitive and neuroimaging markers for preclinical vascular cognitive impairment[J]. Cereb Circ Cogn Behav,2021,2: 100029.
[15]	Niu C,Zhang M,Min ZG,et al. Motor network plasticity and low-frequency oscillations abnormalities in patients with brain gliomas:a functional MRI study[J]. PLoS One,2014,9(5): e96850. doi: 10.1371/journal.pone.0096850
[16]	Molendowska M,Matuszewski J,Kossowski B,et al. Temporal dynamics of brain white matter plasticity in sighted subjects during tactile Braille learning:a longitudinal diffusion tensor imaging study[J]. J Neurosci,2021,41(33): 7076-7085. doi: 10.1523/JNEUROSCI.2242-20.2021
[17]	Han K,Chapman SB,Krawczyk DC. Cognitive training reorganizes network modularity in traumatic brain injury[J]. Neurorehabil Neural Repair,2020,34(1): 26-38. doi: 10.1177/1545968319868710
[18]	Freundlieb N,Philipp S,Drabik A,et al. Ipsilesional motor area size correlates with functional recovery after stroke:a 6-month follow-up longitudinal TMS motor mapping study[J]. Restor Neurol Neurosci,2015,33(2): 221-231.
[19]	Bourgeron T. From the genetic architecture to synaptic plasticity in autism spectrum disorder[J]. Nat Rev Neurosci,2015,16(9): 551-563. doi: 10.1038/nrn3992
[20]	Rohlfs Domínguez P. Promoting our understanding of neural plasticity by exploring developmental plasticity in early and adult life[J]. Brain Res Bull,2014,107: 31-36. doi: 10.1016/j.brainresbull.2014.05.006
[21]	Sale A. Molecular mechanisms of neural plasticity:from basic research to implications for visual functional rescue[J]. Int J Mol Sci,2022,23(21): 13183. doi: 10.3390/ijms232113183
[22]	Frizzell TO,Phull E,Khan M,et al. Imaging functional neuroplasticity in human white matter tracts[J]. Brain Struct Funct,2022,227(1): 381-392. doi: 10.1007/s00429-021-02407-4
[23]	Jiao YM,Lin FX,Wu J,et al. Plasticity in language cortex and white matter tracts after resection of dominant inferior parietal lobule arteriovenous malformations:a combined fMRI and DTI study[J]. J Neurosurg,2020,134(3): 953-960.
[24]	Caria A,Dalboni da Rocha JL,Gallitto G,et al. Brain-machine interface induced Morpho-functional remodeling of the neural motor system in severe chronic stroke[J]. Neurotherapeutics,2020,17(2): 635-650. doi: 10.1007/s13311-019-00816-2
[25]	Sinke MR,Otte WM,van Meer MP,et al. Modified structural network backbone in the contralesional hemisphere chronically after stroke in rat brain[J]. J Cereb Blood Flow Metab,2018,38(9): 1642-1653. doi: 10.1177/0271678X17713901
[26]	Baldock AL,Yagle K,Born DE,et al. Invasion and proliferation kinetics in enhancing gliomas predict IDH1 mutation status[J]. Neuro-oncology,2014,16(6): 779-786. doi: 10.1093/neuonc/nou027
[27]	Slegers RJ,Blumcke I. Low-grade developmental and epilepsy associated brain tumors:a critical update 2020[J]. Acta Neuropathol Commun,2020,8(1): 27. doi: 10.1186/s40478-020-00904-x
[28]	Cunha MLVD,Maldaun MVC. Metastasis from glioblastoma multiforme:a meta-analysis[J]. Rev Assoc Med Bras (1992),2019,65(3): 424-433. doi: 10.1590/1806-9282.65.3.424
[29]	Buklina SB,Pitskhelauri DI,Beshplav ST. Clinical and neuropsychological survey of patients with glioma of the corpus callosum[J]. Zh Vopr Neirokhir Im N N Burdenko,2022,86(2): 80-88. doi: 10.17116/neiro20228602180
[30]	Angeli S,Emblem KE,Due-Tonnessen P,et al. Towards patient-specific modeling of brain tumor growth and formation of secondary nodes guided by DTI-MRI[J]. Neuroimage Clin,2018,20: 664-673. doi: 10.1016/j.nicl.2018.08.032
[31]	Yan JL,Li C,van der Hoorn A,et al. A neural network approach to identify the peritumoral invasive areas in glioblastoma patients by using MR radiomics[J]. Sci Rep,2020,10(1): 9748. doi: 10.1038/s41598-020-66691-6
[32]	Zhong LM,Li TF,Shu H,et al. (TS)2WM:tumor segmentation and tract statistics for assessing white matter integrity with applications to glioblastoma patients[J]. NeuroImage,2020,223: 117368. doi: 10.1016/j.neuroimage.2020.117368
[33]	Kocher M,Jockwitz C,Caspers S,et al. Role of the default mode resting-state network for cognitive functioning in malignant glioma patients following multimodal treatment[J]. Neuroimage Clin,2020,27: 102287. doi: 10.1016/j.nicl.2020.102287
[34]	Mair DB,Ames HM,Li R. Mechanisms of invasion and motility of high-grade gliomas in the brain[J]. Mol Biol Cell,2018,29(21): 2509-2515. doi: 10.1091/mbc.E18-02-0123
[35]	D'Souza S,Ormond DR,Costabile J,et al. Fiber-tract localized diffusion coefficients highlight patterns of white matter disruption induced by proximity to glioma[J]. PLoS One,2019,14(11): e0225323. doi: 10.1371/journal.pone.0225323
[36]	Ormond DR,D'Souza S,Thompson JA. Global and targeted pathway impact of gliomas on white matter integrity based on lobar localization[J]. Cureus,2017,9(9): e1660.
[37]	Larsen S,Kikinis R,Talos IF,et al. Quantitative comparison of functional MRI and direct electrocortical stimulation for functional mapping[J]. Int J Med Robot,2007,3(3): 262-270. doi: 10.1002/rcs.149
[38]	Yang J,Gohel S,Zhang Z,et al. Glioma-induced disruption of resting-state functional connectivity and amplitude of low-frequency fluctuations in the salience network[J]. AJNR Am J Neuroradiol,2021,42(3): 551-558. doi: 10.3174/ajnr.A6929
[39]	Maniar YM,Peck KK,Jenabi M,et al. Functional MRI shows altered deactivation and a corresponding decrease in functional connectivity of the default mode network in patients with gliomas[J]. AJNR Am J Neuroradiol,2021,42(8): 1505-1512. doi: 10.3174/ajnr.A7138
[40]	Duffau H. Functional mapping before and after low-grade glioma surgery:a new way to decipher various spatiotemporal patterns of individual neuroplastic potential in brain tumor patients[J]. Cancers,2020,12(9): 2611. doi: 10.3390/cancers12092611
[41]	Nelson CJ,Bonner S. Neuronal graphs:a graph theory primer for microscopic,functional networks of neurons recorded by calcium imaging[J]. Front Neural Circuits,2021,15: 662882. doi: 10.3389/fncir.2021.662882
[42]	Xin ZL,Chen XM,Zhang Q,et al. Alteration in topological properties of brain functional network after 2-year high altitude exposure:a panel study[J]. Brain Behav,2020,10(10): e01656.
[43]	Duffau H. Awake surgery for incidental WHO grade II gliomas involving eloquent areas[J]. Acta Neurochir,2012,154(4): 575-584. doi: 10.1007/s00701-011-1216-x
[44]	Kong NW, Gibb WR, Tate MC. Neuroplasticity: Insights from Patients Harboring Gliomas［J/OL］. https://doi.org/10.1155/2016/2365063.
[45]	Sihvonen AJ,Soinila S,Särkämö T. Post-stroke enriched auditory environment induces structural connectome plasticity:secondary analysis from a randomized controlled trial[J]. Brain Imaging Behav,2022,16(4): 1813-1822. doi: 10.1007/s11682-022-00661-6
[46]	Tavazzi E,Bergsland N,Pirastru A,et al. MRI markers of functional connectivity and tissue microstructure in stroke-related motor rehabilitation:a systematic review[J]. Neuroimage Clin,2022,33: 102931. doi: 10.1016/j.nicl.2021.102931
[47]	Zastron T,Kessner SS,Hollander K,et al. Structural connectivity changes within the basal Ganglia after 8 weeks of sensory-motor training in individuals with chronic stroke[J]. Ann Phys Rehabil Med,2019,62(3): 193-197. doi: 10.1016/j.rehab.2019.02.002
[48]	Southwell DG,Hervey-Jumper SL,Perry DW,et al. Intraoperative mapping during repeat awake craniotomy reveals the functional plasticity of adult cortex[J]. J Neurosurg,2016,124(5): 1460-1469. doi: 10.3171/2015.5.JNS142833
[49]	Sarubbo S,Le Bars E,Moritz-Gasser S,et al. Complete recovery after surgical resection of left Wernicke’s area in awake patient:a brain stimulation and functional MRI study[J]. Neurosurg Rev,2012,35(2): 287-292. doi: 10.1007/s10143-011-0351-4
[50]	Cui WG,Wang YY,Ren JX,et al. Personalized fMRI delineates functional regions preserved within brain tumors[J]. Ann Neurol,2022,91(3): 353-366. doi: 10.1002/ana.26303