Brain network theory, the significance and practice in clinical epileptology_Journal of Epilepsy

Authors：

WANG Weiwei ,  WU Xun

Department of Neurology, First Hospital of Peking University , Beijing 100034, China;

Corresponding author：

WU Xun, Email: bxtong@163.com

Keywords：

Epilepsy; Brain network; Node; Hub; Graph theory; Connetivity

DOI：

10.7507/2096-0247.202311016

Video：

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Abstract Full text Figures/Tables Video References Cited by

Currently, about one-third of patients with anti-epilepsy drug or resective surgery continue to have sezure, the mechanism remin unknown. Up to date, the main target for presurgical evaluation is to determene the EZ and SOZ. Since the early nineties of the last century network theory was introduct into neurology, provide new insights into understanding the onset, propagation and termination. Focal seizure can impact the function of whole brain, but the abnormal pattern is differet to generalized seizure. Brain network is a conception of mathematics. According to the epilepsy, network node and hub are related to the treatment. Graphy theory and connectivity are main algorithms. Understanding the mechanism of epilepsy deeply, since study the theory of epilepsy network, can improve the planning of surgery, resection epileptogenesis zone, seizure onset zone and abnormal node of hub simultaneously, increase the effect of resectiv surgery and predict the surgery outcome. Eventually, develop new drugs for correct the abnormal network and increase the effect. Nowadays, there are many algorithms for the brain network. Cooperative study by the clinicans and biophysicists instituted standard and extensively applied algorithms is the precondition of widely used clinically.

Citation： WANG Weiwei, WU Xun. Brain network theory, the significance and practice in clinical epileptology. Journal of Epilepsy, 2024, 10(1): 66-72. doi: 10.7507/2096-0247.202311016 Copy

1.	Lehnutz K, Bialoneki S, Horstmann M-T, et al. Synchronization phenomena in human brain network. Neuroscience Methods, 2009, 183: 42-48.
2.	Niso G, Carrasco S, Gudií M, et al. What graph theory actually tells us about resting state interictal MEG epileptic activity. NeuroImage:Clinical, 2015, 8: 503-515.
3.	Nahvi M, Ardeshir G, Ezoji M, et al. An application of dynamical directed connectivity of ictal intracranial EEG recording in seizure onset zone localization. J Neuroscience Methods, 2023, 386: 109775.
4.	Kramer MA, Cash SS. Epilepsy as a disorder of cortical network organization. Neuroscientist, 2012, 18(4): 360-370.
5.	Goodfellow M, Rummel C, Abela E, et al. Estimation of brain network ictogenicity predicts outcome from epilepsy surgery. Scientific Reports, 2016, 6(1): 29215.
6.	Iandlo G, Chaurasi N, Ntolkeras G, et al. Children undergoing repeated epilepsy surgery: An EEG saurce connectivity study. Diagnostics, 2011, 11: 1234.
7.	Guo D, Feng L, Yang Z, et al. Altered temporal variations of functional connectivity associaded with surgical outcomes in drug-resistant temporal lobe epilepsy. Frontiers in Neuroscience. 2022, 10: 840481.
8.	Bernabei JM, Sinha N, Arnold TC, et al. Normative intracranial EEG maps epileptogenic tissue in focul epilepsy. Brain, 2022, 145: 1949-1961.
9.	Weiss SA, Postore T, Orosz I, et al. Graph theoretical measures of fast ripples support the epileptic network hypothesis. Brain Communication, 2022, 20,4(3): fcac101.
10.	Douw L, de Graot M, von Dellen E, et al. “Functional connectivity” is a sensitive predictor of epilepsy diagnosis after the first seizure. PLOS One, 2010, 5(5): elo839.
11.	Stam CJ, von Struaten ECW. The organization of physiological brain networks. Clin Neurophysiol, 2012, 123: 1067-1087.
12.	Otsubo H, Ogawa H, Pang E, et al. A pediatrics epilepsy: an update on best practice. Expert Review of Neurotherupeutics. 2021, 21(11): 1225-1240.
13.	Chari A, Seumarine KK, He X, et al. Drug-resistant focal epilepsy in children is associated with increased mode controllability of the whole brain and epileptogenesis regions. Communication Biology, 2022, 5: 394.
14.	徐翠萍, 杜薇, 李勇杰. 癫痫患者大脑网络研究进展. 临床神经外科杂志, 2014, 11(1): 232-234.
15.	Nissen IA, Stam CJ, Reineveld JC, et al. Identifying the epileptogenic zone in interictal resting-state MEG source-space networks. Epilepsia, 2017, 58(1): 134-148.
16.	Ofer I, LeRose C, Mast H, et al. Association between seizure freedom and default mode network reorganization in patients with unilateral temporal lobe epilepsy. Epilepsy & Behavior, 2019, 90: 238-246.
17.	van Dissen E, Otte WH, Stam CJ, et al. Electroencephalography based functional networks in newly diagnosed childhood epilepsies. Clinical Neurophysiology, 2016, 127: 2325-2332.
18.	Lariviere S, Rodriguez-Cruces R, Royer J, et al. Network-based atrophy modeling in the commen epilepsies: a worldwide ENIGMA study. Science Adv, 2020, 6: eabc6457.
19.	Engel JrJ, Thampson PM, Sterm JM, et al. Connectomics and epilepsy. Curr Opin Neurol, 2015, 26(2): 186-194.
20.	Nie L, Jiang Y, Lv Z, et al. A study of brain functional network and alterness changes in temporal lobe epilepsy with and without focal to bilateral tonic-clonic seizure. BMC Neurology, 2022, 22: 4.
21.	Vega Zeloya L, Pastor JE, de Sola RG, et al. Inhomogeneous cortical synchronization and partial epileptic seizure. Frontiers in Neurology, 2014, 10: 00187.
22.	van Dellen E, Hillebrand A, Douw L, et al. Local polymorephic delta activity in cortical lesions causes global decreases in functional connectivity. Neuroimage, 2013, 83: 524-532.
23.	Hontmann MT, Bialonskis, Noenning N, et al. State dependent properties of epileptic brain networks: Comparative graph-theoretical analysis of simultaneousty recorded EEG and MEG. Clin Neurophysiology, 2010, 121: 172-185.
24.	Seung 著, 孙天齐译. 神奇的连接组你的大脑可以改变. 人民邮电出版社, 北京, 2022, 25: 37-43,230-231.
25.	Wang ZJ, Noh BH, Kim ES, et al. Brain network analysis of interictal epileptiform discharges from ECoG to identify epileptogenic zone in padiatric patients with epilepsy and focal cortical dysplasia typeⅡ: a retrospective study. Frontiers in Neurology, 2022, 33: 901633.
26.	Kreil kamp BAK, McKavanagh A, Alonazi B, et al. Altered structural connectome in nonlessional newly diagnosed focal epilepsy. Relation to pharmacoresistence. NeuroImage, 2021, 29: 102564.
27.	van Dellen E, Dauw L, Hillebeand A, et al. Epilepsy surgery outcome and functional network alteration in longitudinal MEG: a minimum spanning analysis. NeuroImage, 2014, 86: 354-363.
28.	Schevon C, Cappsell J, Emerson R, et al. Cortical abnormalities in epilepsy revealed by local EEG synchrony. NeuroImage, 2007, 35(1): 140-159.
29.	Varotto G, Tassi L, Franceschetti S, et al. Epileptogenic networws of typeⅡ focal cortical dysplasia: A stereo-EEG study. NeuroImage, 2012, 61: 591-598.
30.	Lee DA, Lee HJ, Kim HC, et al. Alterations of structural connectivity and structural co-variance network in focal cortical dysplasia. BMC Neurology, 2021, 2: 330.
31.	Ren Y, Cong F, Ristaniemi T, et al. Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analysis of ECoG data in temporal lobe epilepsy. J Neurology, 2019, 266(4): 844-859.
32.	Wilke C, WorrellG, He B. Graph analysis of epileptogenic network in humon partial epilepsy. Epilepsia, 2011, 52(1): 84-92.
33.	Ortega GJ, Sola RG, Pastor J. Complex network analysis of humon ECoG data. Neurosience Letters, 2008, 447: 129-133.
34.	郭舒雯, 郝锋丽, 喻良, 等. 基于EEG信号的卷积神经网络在癫痫检测中的应用价值研究. 卒中与神经疾病, 2023, 30(2): 193-197.
35.	Mormann F, Kreuz T, Rieke C, et al. On the predictability of epileptic seizure. Clin Neurophysiol, 2005, 116: 569-587.
36.	Pourmotabbed H, Wheless JW, Babajami-Feremi A. Lateralization of epilepsy using intrahemspheric brain networks based on resting-state MEG data. Hum Brain Mapp, 2020, 41: 2964-2979.
37.	González HFJ, Narasimkan S, Gooddale SE, et al. Arousal and salience network connectivity alterations in surgical temporal lobe epilepsy. J Neurosurg, 2023, 138: 810-820.
38.	Sinha N, Wang Y, de Silva NM, et al. Strectural brain network abnormatities and the probability of seizure recurrence after epilepsy surgery. Neurology, 2021, 96: e758-e771.
39.	Li W, Jiang Y, Qin Y, et al. Altered resting state networks before and after temporal lobe epilepsy surgery. Brain Topography, 2022, 35: 692-701.
40.	Sala-Padro J, Gifreu-Faixino A, Miro J, et al. Verbral learning and longitudinal hippocampal network connectivity in temporal lobe epilepsy surgery. Frontiers in Neurology, 2022, 13: 854313.
41.	de Silva NM, Forsyth R, McEvay A, et al. Network reorganization following temporal lobe resection and relation with post-surgery seizure relapse: a longitudinal study. Neuro Image:Clinical, 2020, 27: 102320.
42.	Jeong W, Jin S-H, Kim M, et al. Abnormal functional brain network in epilepsy patients with focal corticol dysplasia. Epilepsy Research, 2014, 108: 1618-1626.
43.	Ramaraju S, Wang Y, Sinka N, et al. Removal of interictal MEG-Derived network hubs is associated with postoperative seizure freedom. Front Neurol, 2020, 11: 563847.
44.	Aydin U, Pallegrimo G, Ali OBK, et al. Magnetoencephalography resting state connectivity patterns as indicatives of surgical outcome in epilepsy patients. J Neural Engineering, 2020, 17(3): 035007.
45.	Sun Y, Song Y, Ren H, et al. Synchronization clusters located on epileptic onset zones in neocortical epilepsy. Acta Epileptologica, 2022, 5(4): 233-224.

1. Lehnutz K, Bialoneki S, Horstmann M-T, et al. Synchronization phenomena in human brain network. Neuroscience Methods, 2009, 183: 42-48.
2. Niso G, Carrasco S, Gudií M, et al. What graph theory actually tells us about resting state interictal MEG epileptic activity. NeuroImage:Clinical, 2015, 8: 503-515.
3. Nahvi M, Ardeshir G, Ezoji M, et al. An application of dynamical directed connectivity of ictal intracranial EEG recording in seizure onset zone localization. J Neuroscience Methods, 2023, 386: 109775.
4. Kramer MA, Cash SS. Epilepsy as a disorder of cortical network organization. Neuroscientist, 2012, 18(4): 360-370.
5. Goodfellow M, Rummel C, Abela E, et al. Estimation of brain network ictogenicity predicts outcome from epilepsy surgery. Scientific Reports, 2016, 6(1): 29215.
6. Iandlo G, Chaurasi N, Ntolkeras G, et al. Children undergoing repeated epilepsy surgery: An EEG saurce connectivity study. Diagnostics, 2011, 11: 1234.
7. Guo D, Feng L, Yang Z, et al. Altered temporal variations of functional connectivity associaded with surgical outcomes in drug-resistant temporal lobe epilepsy. Frontiers in Neuroscience. 2022, 10: 840481.
8. Bernabei JM, Sinha N, Arnold TC, et al. Normative intracranial EEG maps epileptogenic tissue in focul epilepsy. Brain, 2022, 145: 1949-1961.
9. Weiss SA, Postore T, Orosz I, et al. Graph theoretical measures of fast ripples support the epileptic network hypothesis. Brain Communication, 2022, 20,4(3): fcac101.
10. Douw L, de Graot M, von Dellen E, et al. “Functional connectivity” is a sensitive predictor of epilepsy diagnosis after the first seizure. PLOS One, 2010, 5(5): elo839.
11. Stam CJ, von Struaten ECW. The organization of physiological brain networks. Clin Neurophysiol, 2012, 123: 1067-1087.
12. Otsubo H, Ogawa H, Pang E, et al. A pediatrics epilepsy: an update on best practice. Expert Review of Neurotherupeutics. 2021, 21(11): 1225-1240.
13. Chari A, Seumarine KK, He X, et al. Drug-resistant focal epilepsy in children is associated with increased mode controllability of the whole brain and epileptogenesis regions. Communication Biology, 2022, 5: 394.
14. 徐翠萍, 杜薇, 李勇杰. 癫痫患者大脑网络研究进展. 临床神经外科杂志, 2014, 11(1): 232-234.
15. Nissen IA, Stam CJ, Reineveld JC, et al. Identifying the epileptogenic zone in interictal resting-state MEG source-space networks. Epilepsia, 2017, 58(1): 134-148.
16. Ofer I, LeRose C, Mast H, et al. Association between seizure freedom and default mode network reorganization in patients with unilateral temporal lobe epilepsy. Epilepsy & Behavior, 2019, 90: 238-246.
17. van Dissen E, Otte WH, Stam CJ, et al. Electroencephalography based functional networks in newly diagnosed childhood epilepsies. Clinical Neurophysiology, 2016, 127: 2325-2332.
18. Lariviere S, Rodriguez-Cruces R, Royer J, et al. Network-based atrophy modeling in the commen epilepsies: a worldwide ENIGMA study. Science Adv, 2020, 6: eabc6457.
19. Engel JrJ, Thampson PM, Sterm JM, et al. Connectomics and epilepsy. Curr Opin Neurol, 2015, 26(2): 186-194.
20. Nie L, Jiang Y, Lv Z, et al. A study of brain functional network and alterness changes in temporal lobe epilepsy with and without focal to bilateral tonic-clonic seizure. BMC Neurology, 2022, 22: 4.
21. Vega Zeloya L, Pastor JE, de Sola RG, et al. Inhomogeneous cortical synchronization and partial epileptic seizure. Frontiers in Neurology, 2014, 10: 00187.
22. van Dellen E, Hillebrand A, Douw L, et al. Local polymorephic delta activity in cortical lesions causes global decreases in functional connectivity. Neuroimage, 2013, 83: 524-532.
23. Hontmann MT, Bialonskis, Noenning N, et al. State dependent properties of epileptic brain networks: Comparative graph-theoretical analysis of simultaneousty recorded EEG and MEG. Clin Neurophysiology, 2010, 121: 172-185.
24. Seung 著, 孙天齐译. 神奇的连接组你的大脑可以改变. 人民邮电出版社, 北京, 2022, 25: 37-43,230-231.
25. Wang ZJ, Noh BH, Kim ES, et al. Brain network analysis of interictal epileptiform discharges from ECoG to identify epileptogenic zone in padiatric patients with epilepsy and focal cortical dysplasia typeⅡ: a retrospective study. Frontiers in Neurology, 2022, 33: 901633.
26. Kreil kamp BAK, McKavanagh A, Alonazi B, et al. Altered structural connectome in nonlessional newly diagnosed focal epilepsy. Relation to pharmacoresistence. NeuroImage, 2021, 29: 102564.
27. van Dellen E, Dauw L, Hillebeand A, et al. Epilepsy surgery outcome and functional network alteration in longitudinal MEG: a minimum spanning analysis. NeuroImage, 2014, 86: 354-363.
28. Schevon C, Cappsell J, Emerson R, et al. Cortical abnormalities in epilepsy revealed by local EEG synchrony. NeuroImage, 2007, 35(1): 140-159.
29. Varotto G, Tassi L, Franceschetti S, et al. Epileptogenic networws of typeⅡ focal cortical dysplasia: A stereo-EEG study. NeuroImage, 2012, 61: 591-598.
30. Lee DA, Lee HJ, Kim HC, et al. Alterations of structural connectivity and structural co-variance network in focal cortical dysplasia. BMC Neurology, 2021, 2: 330.
31. Ren Y, Cong F, Ristaniemi T, et al. Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analysis of ECoG data in temporal lobe epilepsy. J Neurology, 2019, 266(4): 844-859.
32. Wilke C, WorrellG, He B. Graph analysis of epileptogenic network in humon partial epilepsy. Epilepsia, 2011, 52(1): 84-92.
33. Ortega GJ, Sola RG, Pastor J. Complex network analysis of humon ECoG data. Neurosience Letters, 2008, 447: 129-133.
34. 郭舒雯, 郝锋丽, 喻良, 等. 基于EEG信号的卷积神经网络在癫痫检测中的应用价值研究. 卒中与神经疾病, 2023, 30(2): 193-197.
35. Mormann F, Kreuz T, Rieke C, et al. On the predictability of epileptic seizure. Clin Neurophysiol, 2005, 116: 569-587.
36. Pourmotabbed H, Wheless JW, Babajami-Feremi A. Lateralization of epilepsy using intrahemspheric brain networks based on resting-state MEG data. Hum Brain Mapp, 2020, 41: 2964-2979.
37. González HFJ, Narasimkan S, Gooddale SE, et al. Arousal and salience network connectivity alterations in surgical temporal lobe epilepsy. J Neurosurg, 2023, 138: 810-820.
38. Sinha N, Wang Y, de Silva NM, et al. Strectural brain network abnormatities and the probability of seizure recurrence after epilepsy surgery. Neurology, 2021, 96: e758-e771.
39. Li W, Jiang Y, Qin Y, et al. Altered resting state networks before and after temporal lobe epilepsy surgery. Brain Topography, 2022, 35: 692-701.
40. Sala-Padro J, Gifreu-Faixino A, Miro J, et al. Verbral learning and longitudinal hippocampal network connectivity in temporal lobe epilepsy surgery. Frontiers in Neurology, 2022, 13: 854313.
41. de Silva NM, Forsyth R, McEvay A, et al. Network reorganization following temporal lobe resection and relation with post-surgery seizure relapse: a longitudinal study. Neuro Image:Clinical, 2020, 27: 102320.
42. Jeong W, Jin S-H, Kim M, et al. Abnormal functional brain network in epilepsy patients with focal corticol dysplasia. Epilepsy Research, 2014, 108: 1618-1626.
43. Ramaraju S, Wang Y, Sinka N, et al. Removal of interictal MEG-Derived network hubs is associated with postoperative seizure freedom. Front Neurol, 2020, 11: 563847.
44. Aydin U, Pallegrimo G, Ali OBK, et al. Magnetoencephalography resting state connectivity patterns as indicatives of surgical outcome in epilepsy patients. J Neural Engineering, 2020, 17(3): 035007.
45. Sun Y, Song Y, Ren H, et al. Synchronization clusters located on epileptic onset zones in neocortical epilepsy. Acta Epileptologica, 2022, 5(4): 233-224.

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