The Transformation of Dynamic Network State in Ischemic Stroke
DOI:
https://doi.org/10.62051/ijcsit.v3n3.44Keywords:
Ischemic stroke, EEG, Resting state fMRI, Brain networkAbstract
Ischemic stroke is a common disease of the nervous system, which poses a serious threat to human health due to its high disability rate and mortality rate, among which ischemic stroke occupies the first place. According to the Chinese National Stroke Registry Study, the crude and standardized prevalence of stroke in people over 40 years of age is 2.0% and 1.9% [1]. Because of its high disability rate and mortality, it is very important to accurately evaluate the disease condition and prognosis in the early stage of the disease. There are many methods to judge the severity of stroke patients, and the most widely used ones are clinical scale and imaging examination. In order to detect the onset of ischemic stroke early, it is important to identify specific electroencephalogram (EEG) dynamics and resting state fMRI for the transformation of brain activity during the onset of ischemic stroke. In this study, we investigated the transition of brain activity from interseizure to preseizure states, combining EEG networks at different frequency bands and cluster analysis combined with resting state fMRI to consider changes in patients' brain networks. These findings may provide useful insights into the pathogenesis of ischemic stroke and can also be used for prediction and subsequent intervention of ischemic stroke onset.
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[1] Gao Huili, Huang Xishun. Multivariate analysis of refractory epilepsy. Clinical Review, 2012, 27 (10) :888-890.
[2] Siegel JS, Seitzman BA, Ramsey LE, Ortega M, Gordon EM, Dosenbach NUF, et al.Re-emergence of modular brain networks in stroke recovery. Cortex.2018; 101:4459. doi:10.1016/j.cortex.2017.12.019.
[3] Li A, Cornelius SP, Liu YY, Wang L, Barabasi AL. The fundamental advantages of temporal networks. Science. 2017; 358(6366): 1042–1046. doi:10.1126/science.aai7488.
[4] Holme P, Saram ¨aki J. Temporal networks. Physics Reports. 2012; 519(3): 97–125. doi:10.1016/j.physrep.2012.03.001.
[5] Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST. Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences. 2011; 108(18): 7641–7646. doi:10.1073/pnas.1018985108.
[6] Tang J, Scellato S, Musolesi M, Mascolo C, Latora V. Small-world behavior in time varying graphs. Physical Review E. 2010; 81(5). doi:10.1103/physreve.81.055101.
[7] Thompson WH, Brantefors P, Fransson P. From static to temporal network theory Applications to functional brain connectivity. Network Neuroscience. 2017; 1(2):69–99. doi:10.1162/netn00011.
[8] Braun U, Sch¨afer A, Bassett DS, Rausch F, Schweiger JI, Bilek E, et al. Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function. Proceedings of the National Academy of Sciences. 2016; 113(44):12568–12573. doi:10.1073/pnas.1608819113.
[9] Cole MW, Reynolds JR, Power JD, Repovs G, Anticevic A, Braver TS. Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience. 2013; 16(9):13481355. doi:10.1038/nn.3470.
[10] Ekman M, Derrfuss J, Tittgemeyer M, Fiebach CJ. Predicting errors from reconfiguration patterns in human brain networks. Proceedings of the National Academy of Sciences. 2012;109(41): 16714–16719. doi:10.1073/pnas.1207523109.
[11] Li F et al (2015) Relationships between the resting-state network and the P3: evidence from a scalp EEG study. Sci Rep 5: 15129. https://doi.org/10.1038/Srep15129
[12] Dasdemir Y, Yildirim E, Yildirim S (2017) Analysis of functional brain connections for positive–negative emotions using phase locking value. Cogn Neurodyn 11:487–500
[13] Dasdemir Y, Yildirim E, Yildirim S (2017) Analysis of functional brain connections for positive–Wang C, Qin W, Zhang J, et al. Altered functional organization within and between resting- state networks in chronic subcortical infarction [J]. J Cereb Blood Flow Metab, 2014, 34(4):597—605.
[14] Sun JR. Introduction of Brain Sciences [M]. Beijing: PekingUniversity Press, 2001: 180—196.
[15] Park CH, Chang WH, Ohn SH, et al. Longitudinal changes of resting- state functional connectivity during motor recovery after stroke [J]. Stroke, 2011, 42(5):1357—1362.
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