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| The Large-Scale Brain Network Dysfunction of Juvenile Myoclonus Epilepsy Patients |
| Ke Ming1*, Cui Jiping1, Liu Guangyao2* |
1(College of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China)
2(Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou 730030, China) |
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Abstract Resting-state functional magnetic resonance imaging (fMRI) data were used to investigate the changes of large-scale brain networks in juvenile myoclonus epilepsy (JME) patients. The brain rs-fMRI data of 17 JME patients and 15 normal volunteers were collected. The partial correlation coefficient was used to construct resting state brain networks in both groups. Thresholds were independently calculated for the JME group and the normal control group. A binary brain network was built. The betweenness values of each brain region of the two groups were calculated. Two-sample T test was used to compare the differences in the betweenness values of brain network between the two groups (Bonferroni correction, P<0.01). The brain regions with significant changes of betweenness values were identified. The brain network was constructed by the partial correlation coefficient, and the brain network showed small-world property. The betweenness values of brain regions of JME patients group was significantly different from that of normal control group. Compared with the normal control group, in the JME patient group, there were 2 brain regions with significantly lower betweenness values and 17 brain regions with significantly higher betweenness values. Among them, 8 brain regions belong to the default mode network (DMN), and 5 brain regions belong to the salience network (SN). The betweenness values of the right paracentral lobule and the right posterior cingulate gyrus were significantly reduced in JME group. The regions of significantly increased betweenness values were mainly distributed in the right dorsolateral superior frontal gyrus, the left middle occipital gyrus, the right precuneus, the right lingual gyrus. The brain regions where the betweenness value of JME patients had significantly changed mainly belong to the default mode network and the salience network. It can be inferred that the connections between internal brain regions had changed in default mode network and salience network separately. These changes showed that JME patients' brain functions had been influenced and changed, resulting in impaired cognitive and executive functions.
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Received: 28 August 2021
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Corresponding Authors:
*E-mail: keming@lut.edu.cn;lgy362263779@163.com
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[1] Bullmore E, Sporns O. The economy of brain network organization [J]. Nature Reviews Neuroscience, 2012, 13(13): 336-349.
[2] Liao Ruizhi, Dou Weibei, Zhang Mingyu, et al. A framework for mapping scalable human brain anatomical networks via diffusion MRI[C] // IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). Las Vegas, NV, USA, IEEE, 2016: 49-52.
[3] 和梦鑫,平亮亮,许秀峰.基于图论的复杂脑网络分析在精神疾病中的研究进展[J].昆明医科大学学报,2019,40(5):129-134.
[4] Guye M, Bettus G, Bartolomei F, et al. Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks [J]. MAGMA. 2010, 23(5-6): 409-421.
[5] Wang Lifeng, Dai Zhengjia, Peng Hongjun, et al. Overlapping and segregated resting-state functional connectivity in patients with major depressive disorder with and without childhood neglect [J]. Human Brain Mapping, 2014, 35(4):1154-1166.
[6] Jallon P, Latour P. Epidemiology of idiopathic generalized epilepsies [J]. Epilepsia, 2005, 46: 10-14.
[7] Baykan B, Martínez-Juárez IE, Altindag EA, et al. Lifetime prognosis of juvenile myoclonic epilepsy[J]. Epilepsy & Behavior, 2013, 28: S18-S24.
[8] Genton P, Gelisse P. The history of juvenile myoclonic epilepsy [J] .Epilepsy Behav, 2013, 28 (Suppl 1) :S2-S7.
[9] Wolf P, Yacubian EMT, Avanzini G, et al. Juvenile myoclonic epilepsy: a system disorder of the brain [J]. Epilepsy Research, 2015, 114: 2-12.
[10] Piazzini A, Turner K, Vignoli A, et al. Frontal cognitive dysfunction in juvenile myoclonic epilepsy[J]. Epilepsia, 2008, 49(4): 657-662.
[11] Giorgi FS, Guida M, Caciagli L, et al. Social cognition in juvenile myoclonic epilepsy [J]. Epilepsy Res, 2016, 128:61-67.
[12] Schmitz B, Yacubian EM, Feucht M, et al. Neuropsychology and behavior in juvenile myoclonic epilepsy[J]. Epilepsy & Behavior, 2013, 28: S72-S73.
[13] Walsh J, Thomas RH, Church C, et al. Executive functions and psychiatric symptoms in drug-refractory juvenile myoclonic epilepsy[J]. Epilepsy & Behavior, 2014, 35: 72-77.
[14] de Carvalho KC, Uchida CGP, Guaranha MSB, et al. Cognitive performance in juvenile myoclonic epilepsy patients with specific endophenotypes[J]. Seizure, 2016, 40: 33-41.
[15] Moschetta S, Valente KD. Impulsivity and seizure frequency, but not cognitive deficits, impact social adjustment in patients with juvenile myoclonic epilepsy[J]. Epilepsia, 2013, 54(5): 866-870.
[16] Kim J, Lee W G, Park S, et al. Can we predict drug response by functional connectivity in patients with juvenile myoclonic epilepsy? [J]. Clinical Neurology and Neurosurgery, 2020, 198: 106119.
[17] Lee HJ, Seo SA, Lee BI, et al. Thalamic nuclei volumes and network in juvenile myoclonic epilepsy[J]. Acta Neurologica Scandinavica, 2020, 141(4): 271-278.
[18] Cai Huanhuan, Wang Chunli, Qian Yinfeng, et al. Large‐scale functional network connectivity mediate the associations of gut microbiota with sleep quality and executive functions [J]. Human Brain Mapping, 2021, 42(10): 3088-3101.
[19] 辛斐,谢超,王丽君,等.大尺度脑网络交互支持内外部指向认知的研究进展[J].中国生物医学工程学报,2020,39(6):736-746.
[20] Zhang Zhe, Liu Guangyao, Zheng Weibo, et al. Altered dynamic effective connectivity of the default mode network in newly diagnosed drug-naïve juvenile myoclonic epilepsy [J]. NeuroImage: Clinical, 2020, 28: 102431.
[21] Seeley WW, Menon V, Schatzberg AF, et al. Dissociable intrinsic connectivity networks for salience processing and executive control [J]. Journal of Neuroscience, 2007, 27(9): 2349-2356.
[22] Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks [J]. Proceedings of the National Academy of Sciences, 2008, 105(34): 12569-12574.
[23] 尹宁,代扬杨,生晖,等.基于复杂网络的磁刺激内关穴脑皮层功能连接分析[J].中国生物医学工程学报,2019,38(6):695-701.
[24] Hagmann P, Cammoun L, Gigandet X, et al. Mapping the structural core of human cerebral cortex[J]. PLoS Biology, 2008, 6(7): e159.
[25] 何高奇,胡云奉,杨宇,等. 基于rs-fMRI数据的脑功能网络构建与分析[J].华东理工大学学报(自然科学版),2015,41(6):821-827.
[26] Achard S, Bullmore E. Efficiency and cost of economical brain functional networks [J]. PLoS Computational Biology, 2007, 3(2): e17.
[27] Chen Zhiyi, Hu Xingwang, Chen Qi, et al. Altered structural and functional brain network overall organization predict human intertemporal decision‐making [J]. Human Brain Mapping, 2019, 40(1): 306-328.
[28] 薛绍伟,唐一源,李健,等. 一种基于fMRI数据的脑功能网络构建方法[J].计算机应用研究,2010,27(11):4055-4057.
[29] 范晶,秦卓琼,张国清.基于中介中心性提高复杂网络容量的方法[J].计算机仿真,2008(3):167-170.
[30] Fransson P, Marrelec G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis [J]. Neuroimage, 2008, 42(3): 1178-1184.
[31] Shaw SB, McKinnon MC, Heisz J, et al. Dynamic task-linked switching between brain networks-A tri-network perspective [J]. Brain and cognition, 2021, 151: 105725.
[32] Smith SM, Miller KL, Salimi-Khorshidi G, et al. Network modelling methods for FMRI [J]. Neuroimage, 2011, 54(2): 875-891.
[33] Jiang Sisi, Luo Cheng, Liu Zhixuan, et al. Altered local spontaneous brain activity in juvenile myoclonic epilepsy: a preliminary resting-state fMRI study[J]. Neural Plasticity, 2015, 2016(5):1-7.
[34] 刘永宏,杨旭红,廖伟,等. 青少年肌阵挛癫痫发作间期EEG-fMRI的研究[J].生物医学工程学杂志,2007, 24(4):748-751.
[35] Li Dong, Luo Cheng, Zhu Yutian, et al. Complex discharge‐affecting networks in juvenile myoclonic epilepsy: a simultaneous EEG‐fMRI study [J]. Human Brain Mapping, 2016, 37(10): 3515-3529. |
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