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Seizure Prediction: Science Fiction or Soon to Become Reality?

  • Epilepsy (CW Bazil, Section Editor)
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Abstract

This review highlights recent developments in the field of epileptic seizure prediction. We argue that seizure prediction is possible; however, most previous attempts have used data with an insufficient amount of information to solve the problem. The review discusses four methods for gaining more information above standard clinical electrophysiological recordings. We first discuss developments in obtaining long-term data that enables better characterisation of signal features and trends. Then, we discuss the usage of electrical stimulation to probe neural circuits to obtain robust information regarding excitability. Following this, we present a review of developments in high-resolution micro-electrode technologies that enable neuroimaging across spatial scales. Finally, we present recent results from data-driven model-based analyses, which enable imaging of seizure generating mechanisms from clinical electrophysiological measurements. It is foreseeable that the field of seizure prediction will shift focus to a more probabilistic forecasting approach leading to improvements in the quality of life for the millions of people who suffer uncontrolled seizures. However, a missing piece of the puzzle is devices to acquire long-term high-quality data. When this void is filled, seizure prediction will become a reality.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Matsuo Y. Prediction, forecasting, and chance discovery. In: Ohsawa Y, McBurney P, editors. Chance discovery. Berlin: Springer; 2003. p. 30–43.

    Chapter  Google Scholar 

  2. Cook MJ, O’Brien TJ, Berkovic SF, Murphy M, Morokoff A, Fabinyi G, et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol. 2013;12(6):563–71. Cook conducted the first clinical trial of a device dedicated to forecasting the likelihood of seizure occurrences in 15 patients. It demonstrated for the first time that seizure prediction is possible.

    Article  PubMed  Google Scholar 

  3. Blum D, Eskola J, Bortz J, Fisher R. Patient awareness of seizures. Neurology. 1996;47(1):260–4.

    Article  CAS  PubMed  Google Scholar 

  4. Alotaiby TN, Alshebeili SA, Alshawi T, Ahmad I, El-Samie FEA. EEG seizure detection and prediction algorithms: a survey. EURASIP J Adv Signal Process. 2014;1:183.

    Article  Google Scholar 

  5. Mormann F, Andrzejak R, Elger CE, Lehnertz K. Seizure prediction: the long and winding road. Brain. 2007;130(2):314–33.

    Article  PubMed  Google Scholar 

  6. Freestone DR, Kuhlmann L, Grayden DB, Burkitt AN, Lai A, Nelson TS, et al. Electrical probing of cortical excitability in patients with epilepsy. Epilepsy Behav. 2011;22:110–8.

    Article  Google Scholar 

  7. Kalitzin S, Velis D, Suffczynski P, Parra J, da Silva FL. Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy. Clin Neurophysiol. 2005;116(3):718–28.

    Article  CAS  PubMed  Google Scholar 

  8. Badawy R, Macdonell R, Jackson G, Berkovic S. The peri-ictal state: cortical excitability changes within 24 h of a seizure. Brain. 2009;132(4):1013–21.

    Article  PubMed  Google Scholar 

  9. 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(5):635–41.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Howbert JJ, Patterson EE, Stead SM, Brinkmann B, Vasoli V, Crepeau D, et al. Forecasting seizures in dogs with naturally occurring epilepsy. PLoS ONE. 2014;9(1):e81920.

    Article  PubMed Central  PubMed  Google Scholar 

  11. Heck CN, King-Stephens D, Massey AD, Nair DR, Jobst BC, Barkley GL, et al. Two-year seizure reduction in adults with medically intractable partial onset epilepsy treated with responsive neurostimulation: final results of the RNS System Pivotal trial. Epilepsia. 2014;55(3):432–41.

    Article  PubMed Central  PubMed  Google Scholar 

  12. Stypulkowski PH, Stanslaski SR, Denison TJ, Giftakis JE. Chronic evaluation of a clinical system for deep brain stimulation and recording of neural network activity. Stereotact Funct Neurosurg. 2012;91(4):220–32.

    Article  Google Scholar 

  13. Freestone DR, Long SN, Frey S, Stypulkowski PH, Giftakis JE, Cook MJ. A method for actively tracking excitability of brain networks using a fully implantable monitoring system. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:6151–4.

    PubMed  Google Scholar 

  14. McLaughlin BL, Mariano LJ, Prakash SR, Kindle AL, Czarnecki A, Modarres MH, Rotenberg A, Loddenkemper T, Shoeb A, Schachter SC. An electroencephalographic recording platform for real-time seizure detection. in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012.

  15. Badawy R, Freestone D, Lai A, Cook M. Epilepsy: ever-changing states of cortical excitability. Neuroscience. 2012;222:89–99. This review organizes evidence highlighting that hyper-excitability associated with seizures is governed by predictable slow processes. It promotes a critical rethink from using passive EEG to active measurement techniques involving electrical stimulation and TMS to solve the problem of seizure prediction.

    Article  CAS  PubMed  Google Scholar 

  16. Dai L, Vorselen D, Korolev KS, Gore J. Generic indicators for loss of resilience before a tipping point leading to population collapse. Science. 2012;336(6085):1175–7.

    Article  CAS  PubMed  Google Scholar 

  17. Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR, Dakos V, et al. Early-warning signals for critical transitions. Nature. 2009;461(7260):53–9.

    Article  CAS  PubMed  Google Scholar 

  18. Suffczynski P, Kalitzin S, Lopez da Silva F, Parra J, Velis D, Wendling F. Active paradigms of seizure anticipation: computer model evidence for necessity of stimulation. Phys Rev E. 2008;78:051917.

    Article  Google Scholar 

  19. O’Sullivan-Greene E, Mareels IMY, Freestone DR, Kuhlmann L, Burkitt AN. A paradigm for epileptic seizure prediction using a coupled oscillator model of the brain, in Proceedings of the 31st IEEE Engineering in Medicine & Biology Conference, Minneapolis. 2009.

  20. Penfield W, Boldrey E. Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain J Neurol vol. Ix, p. 389. 1937.

  21. 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. 2012;123(2):252–60.

    Article  PubMed  Google Scholar 

  22. 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(4):292–302.

    PubMed  Google Scholar 

  23. Alarcon G, Valentin A. Cortical stimulation with single electrical pulses in human epilepsy. Clin Neurophysiol. 2012;123(2):223–4.

    Article  PubMed  Google Scholar 

  24. Racine RJ, Gartner JG, Burnham WM. Epileptiform activity and neural plasticity in limbic structures. Brain Res. 1972;47(1):262–8.

    Article  CAS  PubMed  Google Scholar 

  25. Medeiros DC, Oliveira LB, Mourão FAG, Bastos CP, Cairasco NG, Pereira GS, et al. Temporal rearrangement of pre-ictal PTZ induced spike discharges by low frequency electrical stimulation to the amygdaloid complex. Brain Stimulation. 2014;7(2):170–8.

    Article  PubMed  Google Scholar 

  26. Stypulkowski PH, Stanslaski SR, Jensen RM, Denison TJ, Giftakis JE. Brain stimulation for epilepsy—local and remote modulation of network excitability. Brain Stimulation. 2014;7(3):350–8.

    Article  PubMed  Google Scholar 

  27. Long S, Frey S, Freestone DR, LeChevoir M, Stypulkowski P, Giftakis J, et al. Placement of deep brain electrodes in the dog using the brainsight frameless stereotactic system: a pilot feasibility study. J Vet Intern Med. 2014;28(1):189–97.

    Article  CAS  PubMed  Google Scholar 

  28. Schevon CA, Ng SK, Cappell J, Goodman RR, McKhann Jr G, Waziri A, et al. Microphysiology of epileptiform activity in human neocortex. J Clin Neurophysiol. 2008;25(6):321.

    Article  PubMed Central  PubMed  Google Scholar 

  29. Schevon CA, Trevelyan A, Schroeder C, Goodman R, McKhann G, Emerson R. Spatial characterization of interictal high frequency oscillations in epileptic neocortex. Brain, p. awp222. 2009.

  30. Schevon C, Goodman R, McKhann Jr G, Emerson R. Propagation of epileptiform activity on a submillimeter scale. J Clin Neurophysiol. 2010;27(6):406.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  31. Stead M, Bower M, Brinkmann B, Lee K, Marsh R, Meyer F, et al. Microseizures and the spatiotemporal scales of human partial epilepsy. Brain. 2010;133(9):2789–97.

    Article  PubMed Central  PubMed  Google Scholar 

  32. Truccolo W, Ahmed OJ, Harrison MT, Eskandar EN, Cosgrove GR, Madsen JR, et al. Neuronal ensemble synchrony during human focal seizures. J Neurosci. 2014;34(30):9927–44.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  33. Buzsaki G, Anastassiou CA, Koch C. The origin of extracellular fields and currents—EEG, ECoG, LFP and spikes. Nat Rev Neurosci. 2012;13(6):407–20.

    Article  CAS  PubMed  Google Scholar 

  34. Manning JR, Jacobs J, Fried I, Kahana MJ. Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans. J Neurosci. 2009;29(43):13613–20.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Stacey W. Recording from over 1,000 cells: a new toy in place for epilepsy research? Epilepsy Curr. 2014;14(2):95–6.

    Article  PubMed Central  PubMed  Google Scholar 

  36. Szabo GG, Schneider CJ, Soltesz I. Resolution revolution: epilepsy dynamics at the microscale. Curr Opin Neurobiol. 2015;31:239–43.

    Article  CAS  PubMed  Google Scholar 

  37. Worrell GA, Parish L, Cranstoun SD, Jonas R, Baltuch G, Litt B. High‐frequency oscillations and seizure generation in neocortical epilepsy. Brain. 2004;127(7):1496–506.

    Article  PubMed  Google Scholar 

  38. Van Gompel JJ, Worrell GA, Bell ML, Patrick TA, Cascino GD, Raffel C, et al. Intracranial electroencephalography with subdural grid electrodes: techniques, complications, and outcomes. Neurosurgery. 2008;63(3):498–506.

    Article  PubMed  Google Scholar 

  39. Worrell GA, Gardner AB, Stead SM, Hu S, Goerss S, Cascino GJ, et al. High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings. Brain. 2008;131(4):928–37.

    Article  PubMed Central  PubMed  Google Scholar 

  40. Van Gompel JJ, Stead SM, Giannini C, Meyer FB, Marsh WR, Fountain T, et al. Phase I trial: safety and feasibility of intracranial electroencephalography using hybrid subdural electrodes containing macro- and microelectrode arrays. Neurosurg Focus. 2008;25(3):E23.

    Article  PubMed Central  PubMed  Google Scholar 

  41. Bower MR, Stead M, Meyer FB, Marsh WR, Worrell GA. Spatiotemporal neuronal correlates of seizure generation in focal epilepsy. Epilepsia. 2012;53(5):807–16.

    Article  PubMed Central  PubMed  Google Scholar 

  42. Brinkmann BH, Bower MR, Stengel KA, Worrell GA, Stead M. Large-scale electrophysiology: acquisition, compression, encryption, and storage of big data. J Neurosci Methods. 2009;180(1):185–92.

    Article  PubMed Central  PubMed  Google Scholar 

  43. Shoaran M, Pollo C, Leblebici Y, Schmid A. Design techniques and analysis of high-resolution neural recording systems targeting epilepsy focus localization, in Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, IEEE, pp. 5150--5153. 2012.

  44. Waziri A, Schevon CA, Cappell J, Emerson RG, McKhann 2nd G, Goodman RR. Initial surgical experience with a dense cortical microarray in epileptic patients undergoing craniotomy for subdural electrode implantation. Neurosurgery. 2009;64(3):540.

    Article  PubMed Central  PubMed  Google Scholar 

  45. Weiss S, Connors R, Banks G, McKhann G, Zhao B, Filippi C, et al. Resection of ictal phase locked HFOs is correlated with outcome following epilepsy surgery. Neurology. 2014;82(10S):S50–003.

    Google Scholar 

  46. Kim D-H, Viventi J, Amsden JJ, Xiao J, Vigeland L, Kim Y-S, et al. Dissolvable films of silk fibroin for ultrathin conformal bio-integrated electronics. Nat Mater. 2010;9(6):511–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  47. Viventi J, Blanco J, Litt B. Mining terabytes of submillimeter-resolution ECoG datasets for neurophysiologic biomarkers, in Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, Buenos Aires. 2010.

  48. Viventi J, Kim D-H, Moss JD, Kim Y-S, Blanco JA, Annetta N, et al. A conformal, bio-interfaced class of silicon electronics for mapping cardiac electrophysiology. Sci Transl Med. 2010;2(24):24ra22.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Viventi J, Kim D-H, Vigeland L, Frechette ES, Blanco JA, Kim Y-S, et al. Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo. Nat Neurosci. 2011;14(12):1599–605.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  50. Kim T, Artan NS, Viventi J, Chao HJ. Spatiotemporal compression for efficient storage and transmission of high-resolution electrocorticography data, in Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, IEEE, pp. 1012–1015. 2012.

  51. Escabi MA, Read HL, Viventi J, Kim D-H, Higgins NC, Storace DA, et al. A high-density, high-channel count, multiplexed μECoG array for auditory-cortex recordings. J Neurophysiol. 2014;112(6):1566–83.

    Article  PubMed Central  PubMed  Google Scholar 

  52. Wang J, Trumpis M, Insanally M, Froemke R, Viventi J. A low-cost, multiplexed electrophysiology system for chronic μECoG recordings in rodents, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, IEEE, pp. 5256–5259. 2014.

  53. Khodagholy D, Gelinas JN, Thesen T, Doyle W, Devinsky O, Malliaras GG, et al. NeuroGrid: recording action potentials from the surface of the brain. Nat Neurosci. 2014;18(2):310–5.

    Article  PubMed Central  PubMed  Google Scholar 

  54. Ferrea E, Maccione A, Medrihan L, Nieus T, Ghezzi D, Baldelli P, et al. Large-scale, high-resolution electrophysiological imaging of field potentials in brain slices with microelectronic multielectrode arrays. Front Neural Circ. 2012;6:80.

    CAS  Google Scholar 

  55. Sillay KA, Rutecki P, Cicora K, Worrell G, Drazkowski J, Shih JJ, et al. Long-term measurement of impedance in chronically implanted depth and subdural electrodes during responsive neurostimulation in humans. Brain Stimulation. 2013;6(5):718–26.

    Article  PubMed  Google Scholar 

  56. Kramer MA, Truccolo W, Eden UT, Lepage KQ, Hochberg L, Eskandar EN, et al. Human seizures self-terminate across spatial scales via a critical transition. Proc Natl Acad Sci. 2012;109(51):21116–21.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  57. Schevon CA, Weiss SA, McKhann Jr G, Goodman RR, Yuste R, Emerson RG, et al. Evidence of an inhibitory restraint of seizure activity in humans. Nat Commun. 2012;3:1060.

    Article  PubMed Central  PubMed  Google Scholar 

  58. Wagner FBP, Truccolo W, Wang J, Nurmikko A. Spatiotemporal dynamics of optogenetically-induced and spontaneous seizure transitions in primary generalized epilepsy. J Neurophysiol pp. jn--01040. 2014.

  59. Krook-Magnuson E, Armstrong C, Oijala M, Soltesz I. On-demand optogenetic control of spontaneous seizures in temporal lobe epilepsy. Nat Commun. 2013;4:1376.

    Article  PubMed Central  PubMed  Google Scholar 

  60. Sornette D, Osorio I. Prediction, in epilepsy: the intersection of neurosciences, biology, mathematics, engineering, and physics, CRC Press, pp. 203–241. 2011.

  61. Freeman W Topological properties, in mass action in the nervous system, London, Academic Press. 1975.

  62. Deco G, Jirsa V, Robinson P, Breakspear M, Friston K. The dynamic brain: from spiking neurons to neural masses and cortical fields. PLoS Comput Biol. 2008;4(8):e1000092.

    Article  PubMed Central  PubMed  Google Scholar 

  63. Wang Y, Goodfellow M, Taylor PN, Baier G. Dynamic mechanisms of neocortical focal seizure onset. PLoS Comput Biol. 2014;10(8):e1003787.

    Article  PubMed Central  PubMed  Google Scholar 

  64. Kim J, Roberts J, Robinson P. Dynamics of epileptic seizures: evolution, spreading, and suppression. J Theor Biol. 2009;257(4):527–32.

    Article  CAS  PubMed  Google Scholar 

  65. Wendling F, Hernandez A, Bellanger J-J, Chauvel P, Bartolomei F. Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG. J Clin Neurophysiol. 2005;22:345.

    Google Scholar 

  66. Wendling F, Bartolomei F, Bellanger J, Chauvel P. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur J Neurosci. 2002;15:1499–508.

    Article  CAS  PubMed  Google Scholar 

  67. Baier GAGM, Taylor PN, Wang Y, Garry DJ. The importance of modeling epileptic seizure dynamics as spatio-temporal patterns. Front Physiol vol. 3. 2012.

  68. Freestone DR, Nesic D, Jafarian A, Cook MJ, Grayden DB A neural mass model of spontaneous burst suppression and epileptic seizures, in Engineering in Medicine & Biology Conference. 2013.

  69. Timofeev I, Steriade M. Neocortical seizures: initiation, development and cessation. Neuroscience. 2004;123(2):299–336.

    Article  CAS  PubMed  Google Scholar 

  70. Freestone DR, Aram P, Dewar M, Scerri K, Grayden DB, Kadirkamanathan V. A data-driven framework for neural field modeling. NeuroImage. 2011;56(3):1043–58.

    Article  CAS  PubMed  Google Scholar 

  71. Negahbani E, Steyn-Ross DA, Steyn-Ross ML, Wilson MT, Sleigh JW. Noise-induced precursors of state transitions in the stochastic Wilson–Cowan model. J Math Neurosci. 2015;5(1):1–27.

    Article  Google Scholar 

  72. Aarabi A, He B. Seizure prediction in hippocampal and neocortical epilepsy using a model-based approach. Clin Neurophysiol. 2014;125(5):930–40.

    Article  PubMed Central  PubMed  Google Scholar 

  73. Freestone D, Kuhlmann L, Chong M, Nesic D, Grayden D, Aram P, Postoyan R, Cook M. Patient-specific neural mass modelling: stochastic and deterministic methods, in Recent Advances in Predicting and Preventing Epileptic Seizures, pp. 63–82. 2013.

  74. Terry JR, Benjamin O, Richardson MP. Seizure generation: the role of nodes and networks. Epilepsia. 2012;53(9):166–9.

    Article  Google Scholar 

  75. Kramer MA, Cash SS. Epilepsy as a disorder of cortical network organization. Neuroscientist. 2012;18(4):360–72.

    Article  PubMed Central  PubMed  Google Scholar 

  76. Einstein A. Investigations on the theory of the Brownian movement. New York: Dover Publications; 1956.

    Google Scholar 

  77. Freestone D, Karoly P, Nesic D, Aram P, Cook MJ, Grayden D. Estimation of effective connectivity via data-driven neural modeling. Front Neurosci. 2014;8:383. The authors developed a framework for creating patient specific mathematical models from clinical data. This technique will enable imaging of the physiological mechanisms that govern seizure dynamics. Tracking such physiological variables is the ideal scenario for seizure anticipation.

    Article  PubMed Central  PubMed  Google Scholar 

  78. Zaytsev YV, Morrison A, Deger M. Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity, arXiv preprint arXiv:1502.04993. 2015.

  79. Buesing L, Machado TA, Cunningham JP, Paninski L Clustered factor analysis of multineuronal spike data, Montreal. 2014.

  80. Potjans TC, Diesmann M. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cereb Cortex. 2014;24(3):785–806.

    Article  PubMed Central  PubMed  Google Scholar 

  81. Thomson AM, Lamy C. Functional maps of neocortical local circuitry. Front Neurosci. 2007;1(1):19–42.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  82. Prevedel R, Yoon Y-G, Hoffmann M, Pak N, Wetzstein G, Kato S, et al. Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy. Nat Methods. 2014;11(7):727–30.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  83. Gosden M, Fox JT, Brain WR. The cholesterol of the blood plasma in epilepsy. Lancet. 1929;214(5523):12–6.

    Article  Google Scholar 

  84. Griffiths GM, Fox JT. Rhythm in epilepsy. Lancet. 1938;232(5999):409–16.

    Article  Google Scholar 

  85. Langdon-Down M, Russell Brain W. Time of day in relation to convulsions in epilepsy. Lancet. 1929;213(5516):1029–32.

    Article  Google Scholar 

  86. Loddenkemper T, Lockley SW, Kaleyias J, Kothare SV. Chronobiology of epilepsy: diagnostic and therapeutic implications of chrono-epileptology. J Clin Neurophysiol. 2011;28(2):146–53.

    Article  PubMed  Google Scholar 

  87. David O, Friston K. A neural mass model for MEG/EEG: coupling and neuronal dynamics. NeuroImage. 2003;20:1743–55.

    Article  PubMed  Google Scholar 

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Acknowledgments

The authors acknowledge the support from the Australian National Health and Medical Research Council Project Grant (APP1065638). Dr Freestone acknowledges the support of the Australian-American Fulbright Commission and would also like to thank Professor Liam Paninski for his support. The authors would also like to thank Associate Professor Bruce Gluckmann for insightful discussions and pointing out that sometimes you have to look backward to go forward.

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Conflict of Interest

Dean R. Freestone, Philippa J. Karoly, Andre D. H. Peterson, Levin Kuhlmann, Alan Lai, and Farhad Goodarzy declare that they have no conflict of interest. Mark J. Cook was lead investigator in the NeuroVista study cited in the publication. However, Dr. Cook had no financial relationship with the sponsors.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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This article is part of the Topical Collection on Epilepsy

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Freestone, D.R., Karoly, P.J., Peterson, A.D.H. et al. Seizure Prediction: Science Fiction or Soon to Become Reality?. Curr Neurol Neurosci Rep 15, 73 (2015). https://doi.org/10.1007/s11910-015-0596-3

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