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Transition of brain networks from an interictal to a preictal state preceding a seizure revealed by scalp EEG network analysis

  • Fali Li
  • Yi Liang
  • Luyan Zhang
  • Chanlin Yi
  • Yuanyuan Liao
  • Yuanling Jiang
  • Yajing Si
  • Yangsong Zhang
  • Dezhong Yao
  • Liang YuEmail author
  • Peng XuEmail author
Research Article
  • 54 Downloads

Abstract

Epilepsy is a neurological disorder in the brain that is characterized by unprovoked seizures. Epileptic seizures are attributed to abnormal synchronous neuronal activity in the brain. To detect the seizure as early as possible, the identification of specific electroencephalogram (EEG) dynamics is of great importance in investigating the transition of brain activity as the epileptic seizure approaches. In this study, we investigated the transition of brain activity from interictal to preictal states preceding a seizure by combining EEG network and clustering analyses together in different frequency bands. The findings of this study demonstrated the best clustering performance of k-medoids in the beta band; in addition, compared to the interictal state, the preictal state experienced increased synchronization of EEG network connectivity, characterized by relatively higher network properties. These findings can provide helpful insight into the mechanism of epilepsy, which can also be used in the prediction of epileptic seizures and subsequent intervention.

Keywords

Epileptic seizure EEG network K-medoids Preictal state Synchronization 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (#61522105, #61603344, #81330032, #71601136, and #81771925), the Open Foundation of Henan Key Laboratory of Brain Science and Brain–Computer Interface Technology (No. HNBBL17001), and the Longshan academic talent research supporting program of SWUST (#17LZX692).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Blanco S, Garay A, Coulombie D (2013) Comparison of frequency bands using spectral entropy for epileptic seizure prediction. ISRN Neurol 2013:287327.  https://doi.org/10.1155/2013/287327 CrossRefGoogle Scholar
  2. Blumenfeld H (2012) Impaired consciousness in epilepsy. Lancet Neurol 11:814–826.  https://doi.org/10.1016/S1474-4422(12)70188-6 CrossRefGoogle Scholar
  3. Bou Assi E, Nguyen DK, Rihana S, Sawan M (2017) Towards accurate prediction of epileptic seizures: a review. Biomed Signal Process 34:144–157.  https://doi.org/10.1016/j.bspc.2017.02.001 CrossRefGoogle Scholar
  4. Burns SP et al (2014) Network dynamics of the brain and influence of the epileptic seizure onset zone. Proc Natl Acad Sci USA 111:E5321–E5330.  https://doi.org/10.1073/pnas.1401752111 CrossRefGoogle Scholar
  5. Chavez M, Van Quyen ML, Navarro V, Baulac M (2003) Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings. IEEE Trans Biomed Eng 50:571–583CrossRefGoogle Scholar
  6. Chen T, Hong Ren W (2001) Adaptive impulse detection using center-weighted median filters. IEEE Signal Proc Lett 8:1–3.  https://doi.org/10.1109/97.889633 CrossRefGoogle Scholar
  7. Cheung MC, Chan AS, Chan YL, Lam JMK, Lam W (2006) Effects of illness duration on memory processing of patients with temporal lobe epilepsy. Epilepsia 47:1320–1328.  https://doi.org/10.1111/j.1528-1167.2006.00556.x CrossRefGoogle Scholar
  8. Dasdemir Y, Yildirim E, Yildirim S (2017) Analysis of functional brain connections for positive–negative emotions using phase locking value. Cogn Neurodyn 11:487–500CrossRefGoogle Scholar
  9. Drury I, Smith B, Li DZ, Savit R (2003) Seizure prediction using scalp electroencephalogram. Exp Neurol 184:S9–S18.  https://doi.org/10.1016/S0014-4886(03)00354-6 CrossRefGoogle Scholar
  10. Epstein CM, Adhikari BM, Gross R, Willie J, Dhamala M (2014) Application of high-frequency Granger causality to analysis of epileptic seizures and surgical decision making. Epilepsia 55:2038–2047.  https://doi.org/10.1111/epi.12831 CrossRefGoogle Scholar
  11. Fields C, Glazebrook JF (2017) Disrupted development and imbalanced function in the global neuronal workspace: a positive-feedback mechanism for the emergence of ASD in early infancy. Cogn Neurodyn 11:1–21CrossRefGoogle Scholar
  12. Fisher RS, Boas WVE, Blume W, Elger C, Genton P, Lee P, Engel JJE Jr (2005) Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 46:470–472CrossRefGoogle Scholar
  13. Fisher RS et al (2014) ILAE official report: a practical clinical definition of epilepsy. Epilepsia 55:475–482.  https://doi.org/10.1111/epi.12550 CrossRefGoogle Scholar
  14. Gadhoumi K, Lina JM, Mormann F, Gotman J (2016) Seizure prediction for therapeutic devices: a review. J Neurosci Methods 260:270–282.  https://doi.org/10.1016/j.jneumeth.2015.06.010 CrossRefGoogle Scholar
  15. Gao ZK, Cai Q, Yang YX, Dong N, Zhang SS (2017) Visibility graph from adaptive optimal kernel time-frequency representation for classification of epileptiform EEG. Int J Neural Syst 27:1750005.  https://doi.org/10.1142/S0129065717500058 CrossRefGoogle Scholar
  16. Hommet C, Sauerwein HC, De Toffol B, Lassonde M (2006) Idiopathic epileptic syndromes and cognition. Neurosci Biobehav Rev 30:85–96.  https://doi.org/10.1016/j.neubiorev.2005.06.004 CrossRefGoogle Scholar
  17. Hussain L (2018) Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach. Cogn Neurodyn 12:271–294.  https://doi.org/10.1007/s11571-018-9477-1 CrossRefGoogle Scholar
  18. Iasemidis LD, Sackellares JC, Zaveri HP, Williams WJ (1990) Phase space topography and the Lyapunov exponent of electrocorticograms in partial seizures. Brain Topogr 2:187–201CrossRefGoogle Scholar
  19. Le Van Quyen M et al (2001) Anticipation of epileptic seizures from standard EEG recordings. Lancet 357:183–188.  https://doi.org/10.1016/S0140-6736(00)03591-1 CrossRefGoogle Scholar
  20. Le VQM, Martinerie J, Navarro V, Baulac M, Varela FJ (2001) Characterizing neurodynamic changes before seizures. J Clin Neurophysiol 18:191–208CrossRefGoogle Scholar
  21. Lehnertz K et al (2001) Nonlinear EEG analysis in epilepsy: its possible use for interictal focus localization, seizure anticipation, and prevention. J Clin Neurophysiol 18:209–222.  https://doi.org/10.1097/00004691-200105000-00002 CrossRefGoogle Scholar
  22. 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 CrossRefGoogle Scholar
  23. Li F et al (2016) The time-varying networks in P300: a task-evoked EEG study. IEEE Trans Neural Syst Rehabil Eng 24:725–733.  https://doi.org/10.1109/Tnsre.2016.2523678 CrossRefGoogle Scholar
  24. Li F et al (2018) Top-down disconnectivity in schizophrenia during P300 tasks. Front Comput Neurosci.  https://doi.org/10.3389/fncom.2018.00033 Google Scholar
  25. Mateos DM, Guevara Erra R, Wennberg R, Perez Velazquez JL (2018) Measures of entropy and complexity in altered states of consciousness. Cogn Neurodyn 12:73–84.  https://doi.org/10.1007/s11571-017-9459-8 CrossRefGoogle Scholar
  26. Mormann F, Elger CE, Lehnertz K (2006) Seizure anticipation: from algorithms to clinical practice. Curr Opin Neurol 19:187–193.  https://doi.org/10.1097/01.wco.0000218237.52593.bc CrossRefGoogle Scholar
  27. Mormann F, Andrzejak RG, Elger CE, Lehnertz K (2007) Seizure prediction: the long and winding road. Brain 130:314–333.  https://doi.org/10.1093/brain/awl241 CrossRefGoogle Scholar
  28. Mumtaz W, Vuong P, Xia LK, Malik A, Bin Abd Rashid R (2017) An EEG-based machine learning method to screen alcohol use disorder. Cogn Neurodyn 11:161–171CrossRefGoogle Scholar
  29. Myers MH, Kozma R (2018) Mesoscopic neuron population modeling of normal/epileptic brain dynamics. Cogn Neurodyn 12:211–223.  https://doi.org/10.1007/s11571-017-9468-7 CrossRefGoogle Scholar
  30. Protopapa F, Siettos CI, Myatchin I, Lagae L (2016) Children with well controlled epilepsy possess different spatio-temporal patterns of causal network connectivity during a visual working memory task. Cogn Neurodyn 10:99–111CrossRefGoogle Scholar
  31. Qu H, Gotman J (1995) A seizure warning system for long-term epilepsy monitoring. Neurology 45:2250–2254.  https://doi.org/10.1212/Wnl.45.12.2250 CrossRefGoogle Scholar
  32. Raghu S, Sriraam N, Kumar GP (2017) Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier. Cogn Neurodyn 11:51–66.  https://doi.org/10.1007/s11571-016-9408-y CrossRefGoogle Scholar
  33. Rogowski Z, Gath I, Bental E (1981) On the prediction of epileptic seizures. Biol Cybern 42:9–15CrossRefGoogle Scholar
  34. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52:1059–1069.  https://doi.org/10.1016/j.neuroimage.2009.10.003 CrossRefGoogle Scholar
  35. Salant Y, Gath I, Henriksen O (1998) Prediction of epileptic seizures from two-channel EEG. Med Bio Eng Comput 36:549–556.  https://doi.org/10.1007/Bf02524422 CrossRefGoogle Scholar
  36. Schwartz TH, Hong SB, Bagshaw AP, Chauvel P, Benar CG (2011) Preictal changes in cerebral haemodynamics: review of findings and insights from intracerebral EEG. Epilepsy Res 97:252–266.  https://doi.org/10.1016/j.eplepsyres.2011.07.013 CrossRefGoogle Scholar
  37. Staley K, Hellier JL, Dudek FE (2005) Do interictal spikes drive epileptogenesis? Neuroscientist 11:272–276CrossRefGoogle Scholar
  38. Sun J, Hong X, Tong S (2012) Phase synchronization analysis of EEG signals: an evaluation based on surrogate tests. IEEE Trans Biomed Eng 59:2254–2263CrossRefGoogle Scholar
  39. van Drongelen W et al (2003) Seizure anticipation in pediatric epilepsy: use of Kolmogorov entropy. Pediatr Neurol 29:207–213.  https://doi.org/10.1016/S0887-8994(03)00145-0 CrossRefGoogle Scholar
  40. Wang ZG et al (2011) Altered resting state networks in epileptic patients with generalized tonic-clonic seizures. Brain Res 1374:134–141.  https://doi.org/10.1016/j.brainres.2010.12.034 CrossRefGoogle Scholar
  41. Xu P et al (2013) Cortical network properties revealed by SSVEP in anesthetized rats. Sci Rep 3:2496.  https://doi.org/10.1038/srep02496 CrossRefGoogle Scholar
  42. Zhang ZQ et al (2011) Altered functional-structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain 134:2912–2928.  https://doi.org/10.1093/brain/awr223 CrossRefGoogle Scholar
  43. Zhang Y, Xu P, Huang Y, Cheng K, Yao D (2013) SSVEP response is related to functional brain network topology entrained by the flickering stimulus. PLoS ONE 8:e72654CrossRefGoogle Scholar
  44. Zhang R et al (2015) Efficient resting-state EEG network facilitates motor imagery performance. J Neural Eng 12:066024.  https://doi.org/10.1088/1741-2560/12/6/066024 CrossRefGoogle Scholar
  45. Zhang L et al (2017) Time-varying networks of inter-ictal discharging reveal epileptogenic zone. Front Comput Neurosci.  https://doi.org/10.3389/fncom.2017.00077 Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.Department of NeurologySichuan Academy of Medical Sciences and Sichuan Provincial People’s HospitalChengduChina
  3. 3.Department of NeurologyAffiliated Hospital of University of Electronic Science and Technology of ChinaChengduChina
  4. 4.School of Computer Science and TechnologySouthwest University of Science and TechnologyMianyangChina
  5. 5.School of Life Science and Technology, Center for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduChina

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