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Neurology

Physiology of functional and effective networks in epilepsy

Profile image of Sridevi SarmaSridevi SarmaProfile image of Sabato SantanielloSabato SantanielloProfile image of Pierre MégevandPierre MégevandProfile image of Ashesh MehtaAshesh Mehta

2014, Clinical Neurophysiology

https://doi.org/10.1016/J.CLINPH.2014.09.009
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Abstract

The relationships between functional and structural networks provide insights into brain abnormalities that are observed in epilepsy. Functional and effective connectivity methods have been used to identify the ictal onset zone as well as to characterize the onset, spread, and termination of seizures. Studies of the dynamics of epileptic networks suggest mechanisms that may explain the sudden onset and termination of seizures.

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<title>Large scale brain networks in epilepsy</title>
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References (109)

  1. Ansari-Asl K, Senhadji L, Bellanger JJ, Wendling F. Quantitative evaluation of linear and nonlinear methods characterizing interdependencies between brain signals. Phys Rev E Stat Nonlin Soft Matter Phys 2006;74:031916.
  2. Aoki T, Aoyagi T. Self-organized network of phase oscillators coupled by activity- dependent interactions. Phys Rev E Stat Nonlin Soft Matter Phys 2011;84:066109.
  3. Bak P, Paczuski M. Complexity, contingency, and criticality. Proc Natl Acad Sci USA 1995;92:6689-96.
  4. Bak P, Tang C, Wiesenfeld K. Self-organized criticality: an explanation of the 1/f noise. Phys Rev Lett 1987;59:381-4.
  5. Beggs JM, Plenz D. Neuronal avalanches in neocortical circuits. J Neurosci 2003;23:11167-77.
  6. Beggs JM, Plenz D. Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures. J Neurosci 2004;24:5216-29.
  7. Bernhardt BC, Hong S, Bernasconi A, Bernasconi N. Imaging structural and functional brain networks in temporal lobe epilepsy. Front Hum Neurosci 2013;7:624.
  8. Bettus G, Bartolomei F, Confort-Gouny S, Guedj E, Chauvel P, Cozzone PJ, et al. Role of resting state functional connectivity MRI in presurgical investigation of mesial temporal lobe epilepsy. J Neurol Neurosurg Psychiatry 2010;81:1147-54.
  9. Bialonski S, Horstmann MT, Lehnertz K. From brain to earth and climate systems: small-world interaction networks or not? Chaos 2010;20:013134.
  10. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995;34:537-41.
  11. Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, et al. Toward discovery science of human brain function. Proc Natl Acad Sci USA 2010;107:4734-9.
  12. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU. Complex networks: structure and dynamics. Phys Rep 2006;424:175-308.
  13. Bogaard A, Parent J, Zochowski M, Booth V. Interaction of cellular and network mechanisms in spatiotemporal pattern formation in neuronal networks. J Neurosci 2009;29:1677-87.
  14. Brazier MA. Spread of seizure discharges in epilepsy: anatomical and electrophysiological considerations. Exp Neurol 1972;36:263-72.
  15. Breakspear M, Stam CJ. Dynamics of a neural system with a multiscale architecture. Philos Trans R Soc Lond B Biol Sci 2005;360:1051-74.
  16. Brovelli A, Ding M, Ledberg A, Chen Y, Nakamura R, Bressler SL. Beta oscillations in a large-scale sensorimotor cortical network: directional influences revealed by Granger causality. Proc Natl Acad Sci USA 2004;101:9849-54.
  17. Buchel C, Friston K. Assessing interactions among neuronal systems using functional neuroimaging. Neural Netw 2000;13:871-82.
  18. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 2008;1124:1-38.
  19. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 2009;10:186-98.
  20. Burns SP, Santaniello S, Anderson WS, Sarma SV. State dynamics of the epileptic brain. In: Proceedings of the 6th ASME annual dynamic systems and control conference (DSCC), Palo Alto, CA; 2013. p. V002T22A001.
  21. Burns SP, Sritharan D, Jouny C, Bergey G, Crone N, Anderson WS, et al. A network analysis of the dynamics of seizure. Conf Proc IEEE Eng Med Biol Soc 2012;2012:4684-7.
  22. Ching S, Brown EN, Kramer MA. Distributed control in a mean-field cortical network model: implications for seizure suppression. Phys Rev E Stat Nonlin Soft Matter Phys 2012;86:021920.
  23. Conner CR, Ellmore TM, DiSano MA, Pieters TA, Potter AW, Tandon N. Anatomic and electro-physiologic connectivity of the language system: a combined DTI-CCEP study. Comput Biol Med 2011;41:1100-9.
  24. Constable RT, Scheinost D, Finn ES, Shen X, Hampson M, Winstanley FS, et al. Potential use and challenges of functional connectivity mapping in intractable epilepsy. Front Neurol 2013;4:39.
  25. Craddock RC, James GA, Holtzheimer 3rd PE, Hu XP, Mayberg HS. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum Brain Mapp 2012;33:1914-28.
  26. David O, Bastin J, Chabardes S, Minotti L, Kahane P. Studying network mechanisms using intracranial stimulation in epileptic patients. Front Syst Neurosci 2010;4:148.
  27. David O, Job AS, De Palma L, Hoffmann D, Minotti L, Kahane P. Probabilistic functional tractography of the human cortex. Neuroimage 2013;80:307-17.
  28. Deco G, Jirsa VK, Robinson PA, Breakspear M, Friston K. The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Comput Biol 2008;4:e1000092.
  29. Deco G, Senden M, Jirsa V. How anatomy shapes dynamics: a semi-analytical study of the brain at rest by a simple spin model. Front Comput Neurosci 2012;6:68.
  30. Duncan JS, Sander JW, Sisodiya SM, Walker MC. Adult epilepsy. Lancet 2006;367:1087-100.
  31. Enatsu R, Bulacio J, Nair DR, Bingaman W, Najm I, Gonzalez-Martinez J. Posterior cingulate epilepsy: clinical and neurophysiological analysis. J Neurol Neurosurg Psychiatry 2014;85:44-50.
  32. Enatsu R, Jin K, Elwan S, Kubota Y, Piao Z, O'Connor T, et al. Correlations between ictal propagation and response to electrical cortical stimulation: a cortico- cortical evoked potential study. Epilepsy Res 2012a;101:76-87.
  33. Enatsu R, Piao Z, O'Connor T, Horning K, Mosher J, Burgess R, et al. Cortical excitability varies upon ictal onset patterns in neocortical epilepsy: a cortico- cortical evoked potential study. Clin Neurophysiol 2012b;123:252-60.
  34. Entz L, Toth E, Keller CJ, Bickel S, Groppe DM, Fabo D, et al. Evoked effective connectivity of the human neocortex. Hum Brain Mapp 2014. http://dx.doi.org/ 10.1002/hbm.22581.
  35. Fisher RS, van Emde Boas W, Blume W, Elger C, Genton P, Lee P, et al. Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia 2005;46:470-2.
  36. Friston KJ. Functional and effective connectivity in neuroimaging: a synthesis. Hum Brain Mapp 1994;2:56-78.
  37. Friston KJ. Functional and effective connectivity: a review. Brain Connect 2011;1:13-36.
  38. Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Functional connectivity: the principal- component analysis of large (PET) data sets. J Cereb Blood Flow Metab 1993;13:5-14.
  39. Gomez-Gardenes J, Gomez S, Arenas A, Moreno Y. Explosive synchronization transitions in scale-free networks. Phys Rev Lett 2011;106:128701.
  40. Gotman J. Measurement of small time differences between EEG channels: method and application to epileptic seizure propagation. Electroencephalogr Clin Neurophysiol 1983;56:501-14.
  41. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA 2003;100:253-8.
  42. Guye M, Regis J, Tamura M, Wendling F, McGonigal A, Chauvel P, et al. The role of corticothalamic coupling in human temporal lobe epilepsy. Brain 2006;129:1917-28.
  43. Hagmann P. From diffusion MRI to brain connectomics [Ph.D. dissertation].
  44. Lausanne, CH: EPFL; 2005.
  45. Haneef Z, Lenartowicz A, Yeh HJ, Levin HS, Engel Jr J, Stern JM. Functional connectivity of hippocampal networks in temporal lobe epilepsy. Epilepsia 2014;55:137-45.
  46. He B, Liu Z. Multimodal functional neuroimaging: integrating functional MRI and EEG/MEG. IEEE Rev Biomed Eng 2008;1:23-40.
  47. Hobbs JP, Smith JL, Beggs JM. Aberrant neuronal avalanches in cortical tissue removed from juvenile epilepsy patients. J Clin Neurophysiol 2010;27:380-6.
  48. Honey CJ, Kotter R, Breakspear M, Sporns O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci USA 2007;104:10240-5.
  49. Iwasaki M, Enatsu R, Matsumoto R, Novak E, Thankappen B, Piao Z, et al. Accentuated cortico-cortical evoked potentials in neocortical epilepsy in areas of ictal onset. Epileptic Disord 2010;12:292-302.
  50. Keller CJ, Bickel S, Entz L, Ulbert I, Milham MP, Kelly C, et al. Intrinsic functional architecture predicts electrically evoked responses in the human brain. Proc Natl Acad Sci USA 2011;108:10308-13.
  51. Keller CJ, Bickel S, Honey CJ, Groppe DM, Entz L, Craddock RC, et al. Neurophysiological investigation of spontaneous correlated and anticorrelated fluctuations of the BOLD signal. J Neurosci 2013;33:6333-42.
  52. Keller CJ, Honey CJ, Entz L, Bickel S, Groppe DM, Toth E, et al. Corticocortical evoked potentials reveal projectors and integrators in human brain networks. J Neurosci 2014a;34:9152-63.
  53. Keller CJ, Honey CJ, Mégevand P, Entz L, Ulbert I, Mehta AD. Mapping human brain networks with cortico-cortical evoked potentials. Philos Trans R Soc B 2014. http://dx.doi.org/10.1098/rstb.2013.0528.
  54. Kiebel SJ, Garrido MI, Moran R, Chen CC, Friston KJ. Dynamic causal modeling for EEG and MEG. Hum Brain Mapp 2009;30:1866-76.
  55. Kokkonen SM, Nikkinen J, Remes J, Kantola J, Starck T, Haapea M, et al. Preoperative localization of the sensorimotor area using independent component analysis of resting-state fMRI. Magn Reson Imaging 2009;27:733-40.
  56. Kramer MA, Cash SS. Epilepsy as a disorder of cortical network organization. Neuroscientist 2012;18:360-72.
  57. Kramer MA, Eden UT, Kolaczyk ED, Zepeda R, Eskandar EN, Cash SS. Coalescence and fragmentation of cortical networks during focal seizures. J Neurosci 2010;30:10076-85.
  58. Kramer MA, Kolaczyk ED, Kirsch HE. Emergent network topology at seizure onset in humans. Epilepsy Res 2008;79:173-86.
  59. Lacruz ME, Garcia Seoane JJ, Valentin A, Selway R, Alarcon G. Frontal and temporal functional connections of the living human brain. Eur J Neurosci 2007;26:1357-70.
  60. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 2001;13:534-46.
  61. Lehnertz K, Bialonski S, Horstmann MT, Krug D, Rothkegel A, Staniek M, et al. Synchronization phenomena in human epileptic brain networks. J Neurosci Methods 2009;183:42-8.
  62. Lepage KQ, Kramer MA, Chu CJ. A statistically robust EEG re-referencing procedure to mitigate reference effect. J Neurosci Methods 2014;235c:101-16.
  63. Lieb JP, Hoque K, Skomer CE, Song XW. Inter-hemispheric propagation of human mesial temporal lobe seizures: a coherence/phase analysis. Electroencephalogr Clin Neurophysiol 1987;67:101-19.
  64. Matsumoto R, Nair DR, LaPresto E, Najm I, Bingaman W, Shibasaki H, et al. Functional connectivity in the human language system: a cortico-cortical evoked potential study. Brain 2004;127:2316-30.
  65. Matsuzaki N, Juhasz C, Asano E. Cortico-cortical evoked potentials and stimulation- elicited gamma activity preferentially propagate from lower-to higher-order visual areas. Clin Neurophysiol 2013;124:1290-6.
  66. McCormick C, Quraan M, Cohn M, Valiante TA, McAndrews MP. Default mode network connectivity indicates episodic memory capacity in mesial temporal lobe epilepsy. Epilepsia 2013;54:809-18.
  67. Meisel C, Kuehn C. Scaling effects and spatio-temporal multilevel dynamics in epileptic seizures. PLoS One 2012;7:e30371.
  68. Meisel C, Storch A, Hallmeyer-Elgner S, Bullmore E, Gross T. Failure of adaptive self- organized criticality during epileptic seizure attacks. PLoS Comput Biol 2012;8:e1002312.
  69. Minati L, Aquino D. Probing neural connectivity through diffusion tensor imaging DTI. In: Trappl R, editor. Cybernetics and systems 2006. Austrian Society for Cybernetics Studies; 2006. p. 263-8.
  70. Mitchell TJ, Hacker CD, Breshears JD, Szrama NP, Sharma M, Bundy DT, et al. A novel data-driven approach to preoperative mapping of functional cortex using resting-state functional magnetic resonance imaging. Neurosurgery 2013;73:969-82. discussion 982-3.
  71. Morgan RJ, Soltesz I. Nonrandom connectivity of the epileptic dentate gyrus predicts a major role for neuronal hubs in seizures. Proc Natl Acad Sci USA 2008;105:6179-84.
  72. Negishi M, Martuzzi R, Novotny EJ, Spencer DD, Constable RT. Functional MRI connectivity as a predictor of the surgical outcome of epilepsy. Epilepsia 2011;52:1733-40.
  73. Netoff TI, Clewley R, Arno S, Keck T, White JA. Epilepsy in small-world networks. J Neurosci 2004;24:8075-83.
  74. Netoff TI, Schiff SJ. Decreased neuronal synchronization during experimental seizures. J Neurosci 2002;22:7297-307.
  75. Newman MEJ. Networks: an introduction. Oxford, New York: Oxford University Press; 2010.
  76. Percha B, Dzakpasu R, Zochowski M, Parent J. Transition from local to global phase synchrony in small world neural network and its possible implications for epilepsy. Phys Rev E Stat Nonlin Soft Matter Phys 2005;72:031909.
  77. Pereira FR, Alessio A, Sercheli MS, Pedro T, Bilevicius E, Rondina JM, et al. Asymmetrical hippocampal connectivity in mesial temporal lobe epilepsy: evidence from resting state fMRI. BMC Neurosci 2010;11:66.
  78. Perucca P, Dubeau F, Gotman J. Intracranial electroencephalographic seizure-onset patterns: effect of underlying pathology. Brain 2014;137:183-96.
  79. Ponten SC, Bartolomei F, Stam CJ. Small-world networks and epilepsy: graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures. Clin Neurophysiol 2007;118:918-27.
  80. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 2012;59:2142-54.
  81. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci USA 2001;98:676-82.
  82. Raichle ME, Snyder AZ. A default mode of brain function: a brief history of an evolving idea. Neuroimage 2007;37:1083-90. discussion 1097-9.
  83. Richardson MP. New observations may inform seizure models: very fast and very slow oscillations. Prog Biophys Mol Biol 2011;105:5-13.
  84. Richardson MP. Large scale brain models of epilepsy: dynamics meets connectomics. J Neurol Neurosurg Psychiatry 2012;83:1238-48.
  85. Schevon CA, Cappell J, Emerson R, Isler J, Grieve P, Goodman R, et al. Cortical abnormalities in epilepsy revealed by local EEG synchrony. Neuroimage 2007;35:140-8.
  86. Schindler K, Leung H, Elger CE, Lehnertz K. Assessing seizure dynamics by analysing the correlation structure of multichannel intracranial EEG. Brain 2007;130:65-77.
  87. Schindler KA, Bialonski S, Horstmann MT, Elger CE, Lehnertz K. Evolving functional network properties and synchronizability during human epileptic seizures. Chaos 2008;18:033119.
  88. Sporns O. Networks of the brain. Cambridge, MA: MIT Press; 2011.
  89. Sporns O. The human connectome: origins and challenges. Neuroimage 2013;80:53-61.
  90. Sporns O, Tononi G, Kotter R. The human connectome: a structural description of the human brain. PLoS Comput Biol 2005;1:e42.
  91. Sritharan D, Sarma SV. Fragility in dynamic networks: application to neural networks in the epileptic cortex. Neural Comput 2014. http://dx.doi.org/ 10.1162/NECO_a_00644:1-34.
  92. Stefan H, Lopes da Silva FH. Epileptic neuronal networks: methods of identification and clinical relevance. Front Neurol 2013;4:8.
  93. Strogatz SH. Exploring complex networks. Nature 2001;410:268-76.
  94. Stufflebeam SM, Liu H, Sepulcre J, Tanaka N, Buckner RL, Madsen JR. Localization of focal epileptic discharges using functional connectivity magnetic resonance imaging. J Neurosurg 2011;114:1693-7.
  95. Timofeev I, Steriade M. Neocortical seizures: initiation, development and cessation. Neuroscience 2004;123:299-336.
  96. Topolnik L, Steriade M, Timofeev I. Hyperexcitability of intact neurons underlies acute development of trauma-related electrographic seizures in cats in vivo. Eur J Neurosci 2003;18:486-96.
  97. Truccolo W, Donoghue JA, Hochberg LR, Eskandar EN, Madsen JR, Anderson WS, et al. Single-neuron dynamics in human focal epilepsy. Nat Neurosci 2011;14:635-41.
  98. Valentin A, Anderson M, Alarcon G, Seoane JJ, Selway R, Binnie CD, et al. Responses to single pulse electrical stimulation identify epileptogenesis in the human brain in vivo. Brain 2002;125:1709-18.
  99. van den Heuvel MP, Mandl RC, Kahn RS, Hulshoff Pol HE. Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp 2009;30:3127-41.
  100. Vogels TP, Rajan K, Abbott LF. Neural network dynamics. Annu Rev Neurosci 2005;28:357-76.
  101. Warren CP, Hu S, Stead M, Brinkmann BH, Bower MR, Worrell GA. Synchrony in normal and focal epileptic brain: the seizure onset zone is functionally disconnected. J Neurophysiol 2010;104:3530-9.
  102. Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature 1998;393:440-2.
  103. Wendling F, Ansari-Asl K, Bartolomei F, Senhadji L. From EEG signals to brain connectivity: a model-based evaluation of interdependence measures. J Neurosci Methods 2009;183:9-18.
  104. Whalen AJ, Brennan SN, Sauer TD, Schiff SJ. Observability of neuronal network motifs. In: 46th annual conference on information sciences and systems (CISS); 2012. p. 1-5.
  105. Wurina, Zang YF, Zhao SG. Resting-state fMRI studies in epilepsy. Neurosci Bull 2012;28:449-55.
  106. Yaffe R, Burns S, Gale J, Park HJ, Bulacio J, Gonzalez-Martinez J, et al. Brain state evolution during seizure and under anesthesia: a network-based analysis of stereotaxic eeg activity in drug-resistant epilepsy patients. Conf Proc IEEE Eng Med Biol Soc 2012;2012:5158-61.
  107. Zapperi S, Baekgaard Lauritsen K, Stanley HE. Self-organized branching processes: mean-field theory for avalanches. Phys Rev Lett 1995;75:4071-4.
  108. Zhang D, Johnston JM, Fox MD, Leuthardt EC, Grubb RL, Chicoine MR, et al. Preoperative sensorimotor mapping in brain tumor patients using spontaneous fluctuations in neuronal activity imaged with functional magnetic resonance imaging: initial experience. Neurosurgery 2009;65:226-36.
  109. Zhang Z, Lu G, Zhong Y, Tan Q, Liao W, Wang Z, et al. Altered spontaneous neuronal activity of the default-mode network in mesial temporal lobe epilepsy. Brain Res 2010;1323:152-60.

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Network dynamics of the brain and influence of the epileptic seizure onset zone
Sridevi Sarma

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.

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Abnormal binding and disruption in large scale networks involved in human partial seizures
Maxime Guye, Fabrice Bartolomei

There is a marked increase in the amount of electrophysiological and neuroimaging works dealing with the study of large scale brain connectivity in the epileptic brain. Our view of the epileptogenic process in the brain has largely evolved over the last twenty years from the historical concept of " epileptic focus " to a more complex description of " Epileptogenic networks " involved in the genesis and " propagation " of epileptic activities. In particular, a large number of studies have been dedicated to the analysis of intracerebral EEG signals to characterize the dynamic of interactions between brain areas during temporal lobe seizures. These studies have reported that large scale functional connectivity is dramatically altered during seizures, particularly during temporal lobe seizure genesis and development. Dramatic changes in neural synchrony provoked by epileptic rhythms are also responsible for the production of ictal symptoms or changes in patient's behaviour such as automatisms, emotional changes or consciousness alteration. Beside these studies dedicated to seizures, large-scale network connectivity during the interictal state has also been investigated not only to define biomarkers of epileptogenicity but also to better understand the cognitive impairments observed between seizures. Review Approximately 30% of focal epilepsies are resistant to antiepileptic drugs. In this situation , surgical resection of the epileptogenic zone (EZ) is the only therapeutic option able to suppress seizures. The localisation and the definition of the EZ are therefore crucial issues that can be addressed through detailed analysis of anatomo-functional data acquired in epileptic patients during pre-surgical evaluation. From a theoretical viewpoint, the EZ is a highly illustrative example of complex system exhibiting nonlinear dynamics as well as ruptures (more or less abrupt) between these dynamics (typically during the transition from interictal to ictal activity) as reflected by signals directly recorded from involved brain structures. Several reviews dealing with " neural networks " and epilepsy or with synchrony and epilepsy [1,2] are available in the literature. With regard to these reviews, our objectives were more specifically to focus on works studying functional connectivity from stereotactic EEG (SEEG) signals, to propose a general framework (" the epileptogenic

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Perspectives on Understanding Aberrant Brain Networks in Epilepsy
Theoden Netoff

Frontiers in Network Physiology, 2022

Epilepsy is a neurological disorder affecting approximately 70 million people worldwide. It is characterized by seizures that are complex aberrant dynamical events typically treated with drugs and surgery. Unfortunately, not all patients become seizure-free, and there is an opportunity for novel approaches to treat epilepsy using a network view of the brain. The traditional seizure focus theory presumed that seizures originated within a discrete cortical area with subsequent recruitment of adjacent cortices with seizure progression. However, a more recent view challenges this concept, suggesting that epilepsy is a network disease, and both focal and generalized seizures arise from aberrant activity in a distributed network. Changes in the anatomical configuration or widespread neural activities spanning lobes and hemispheres could make the brain more susceptible to seizures. In this perspective paper, we summarize the current state of knowledge, address several important challenges that could further improve our understanding of the human brain in epilepsy, and invite novel studies addressing these challenges.

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Dynamic Network Drivers of Seizure Generation, Propagation and Termination in Human Neocortical Epilepsy
Kathryn Davis, Brian Litt

PLOS Computational Biology, 2015

Drug-resistant epilepsy is traditionally characterized by pathologic cortical tissue comprised of seizure-initiating 'foci'. These 'foci' are thought to be embedded within an epileptic network whose functional architecture dynamically reorganizes during seizures through synchronous and asynchronous neurophysiologic processes. Critical to understanding these dynamics is identifying the synchronous connections that link foci to surrounding tissue and investigating how these connections facilitate seizure generation and termination. We use intracranial recordings from neocortical epilepsy patients undergoing pre-surgical evaluation to analyze functional connectivity before and during seizures. We develop and apply a novel technique to track network reconfiguration in time and to parse these reconfiguration dynamics into distinct seizure states, each characterized by unique patterns of network connections that differ in their strength and topography. Our approach suggests that seizures are generated when the synchronous relationships that isolate seizure 'foci' from the surrounding epileptic network are broken down. As seizures progress, foci reappear as isolated subnetworks, marking a shift in network state that may aid seizure termination. Collectively, our observations have important theoretical implications for understanding the spatial involvement of distributed cortical structures in the dynamics of seizure generation, propagation and termination, and have practical significance in determining which circuits to modulate with implantable devices. epileptic networks | seizure focus | network state | synchrony | graph theory | community detection | dynamic network neuroscience Abbreviations: ECoG, electrocorticography Significance Statement. Localization-related epilepsy affects ≈80% of epilepsy patients and is often resistant to medication. The challenge for treating patients is mapping dynamic connectivity between cortical structures in the epileptic network during seizures. While it is well known that whole-brain functional architecture reconfigures during tasks, we hypothesize that epileptic networks reconfigure at the meso-scale leading to seizure initiation, propagation, and termination. We develop new methods to track dynamic network reconfiguration amongst connections of different strength as seizures evolve. Our results indicate that seizure onset is primarily driven by the breakdown of strong connections that re-surge in an isolated focal sub-network as seizures transition to termination. These findings have practical implications for targeting specific connections with implantable, therapeutic devices to control seizures. Footline Author PNAS Issue Date Volume Issue Number 7 arXiv:1407.5105v1 [q-bio.NC]

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    Nodal Fragility of Intracranial EEG Networks: Towards an EEG Fingerprint for the Epileptogenic Zone
    Iahn Cajigas

    ABSTRACTEpilepsy is a global epidemic and 30% of the 60 million patients do not respond to medication treatment. The only treatment options for patients with medically refractory epilepsy are surgical removal or electrical stimulation of the epileptogenic zone (EZ) i.e. the source of their seizures. Despite extensive evaluations with neuroimaging, visual EEG analysis and clinical testing, surgical success rates vary between 30-70%. Currently, no computational methods have been translated into the clinic to assist in localizing the EZ. Here, we applied a dynamical network model that quantifies the fragility of nodes within a patient’s intracranial EEG (iEEG) brain network. Fragility is quantified as the minimal amount of perturbation that must to be applied to a node’s influence on a “balanced” network to cause imbalance. Here, a balanced network is one in which the connectivity between excitatory and inhibitory nodes render a stable system, and an imbalanced network is unstable and ...

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    Seizure prediction - ready for a new era
    Levin Kuhlmann

    Nature reviews. Neurology, 2018

    Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, se...

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    Epileptic Seizure Detection Using a Convolutional Neural Network
    Hadj Batatia

    Advances in Predictive, Preventive and Personalised Medicine

    The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy.

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    Inhibition-induced explosive synchronization in multiplex networks
    KING ALOK YADAV

    Physical Review E

    To date, explosive synchronization (ES) in a network is shown to be originated from considering either degree-frequency correlation, frequency-coupling strength correlation, inertia, or adaptively controlled phase oscillators. Here we show that ES is a generic phenomenon and can occur in any network by multiplexing it with an appropriate layer without even considering any prerequisite for the emergence of ES. We devise a technique which leads to the occurrence of ES with hysteresis loop in a network upon its multiplexing with a negatively coupled (or inhibitory) layer. The impact of various structural properties of positively coupled (or excitatory) and inhibitory layers along with the strength of multiplexing in gaining control over the induced ES transition is discussed. Analytical prediction for the spread of phase distribution of each layer is provided, which is in good agreement with the numerical assessment. This investigation is a step forward in highlighting the importance of multiplex framework not only in bringing phenomena which are not possible in an isolated network but also in providing more structural control over the induced phenomena.

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    Network dynamics of the brain and influence of the epileptic seizure onset zone
    Christophe Jouny

    Proceedings of the National Academy of Sciences of the United States of America, 2014

    The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is is...

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