Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Cycles in epilepsy



Epilepsy is among the most dynamic disorders in neurology. A canonical view holds that seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans have looked for patterns of temporal organization in seizure occurrence. Observations that seizures are cyclical date back to antiquity, but recent technological advances have, for the first time, enabled cycles of seizure occurrence to be quantitatively characterized with direct brain recordings. Chronic recordings of brain activity in humans and in animals have yielded converging evidence for the existence of cycles of epileptic brain activity that operate over diverse timescales: daily (circadian), multi-day (multidien) and yearly (circannual). Here, we review this evidence, synthesizing data from historical observational studies, modern implanted devices, electronic seizure diaries and laboratory-based animal neurophysiology. We discuss advances in our understanding of the mechanistic underpinnings of these cycles and highlight the knowledge gaps that remain. The potential clinical applications of a knowledge of cycles in epilepsy, including seizure forecasting and chronotherapy, are discussed in the context of the emerging concept of seizure risk. In essence, this Review addresses the broad question of why seizures occur when they occur.

Key points

  • Cyclical phenomena have long been described in epilepsy, but tools to quantify them in humans have only recently become available.

  • Chronic recordings of brain activity in rodents, canines and humans have yielded converging evidence that robust cycles in epilepsy exist across species.

  • Cycles of epileptic brain activity exist over multiple timescales, including circadian, multidien and circannual.

  • Critical phases of these cycles help determine periods of highest seizure risk, opening the possibility of forecasting seizures over long horizons.

  • Unanswered questions involve the mechanistic basis of cycles in epilepsy and how to leverage these cycles for clinical applications such as chronotherapy.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Circadian seizure cycles.
Fig. 2: Multidien seizure cycles.
Fig. 3: Circannual seizure cycles.
Fig. 4: Deterministic and probabilistic seizure forecasting.
Fig. 5: Probabilistic seizure forecasting in two individuals.


  1. 1.

    Reynolds, E. H. Translation and analysis of a cuneiform text forming part of a Babylonian treatise on epilepsy. Med. History 34, 185–198 (1990).

    Article  Google Scholar 

  2. 2.

    Bercel, N. A. The periodic features of some seizure states. Ann. NY Acad. Sci. 117, 555–563 (1964). Landmark historical study that revealed periodicity in seizure diaries kept by patients.

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Temkin, O. The falling sickness: a history of epilepsy from the Greeks to the beginnings of modern neurology (The Johns Hopkins University Press, 1994).

  4. 4.

    Mead, R. & Stack, T. A treatise concerning the influence of the sun and moon upon human bodies, and the diseases thereby produced 36–47 (J. Brindley, 1748).

  5. 5.

    Tissot, S. A. D. Œuvres de Monsieur Tissot, Nouvelle Édition. Tome Douzième Contenant le Traité de l’epilepsie [French] (Chez Francois Grasset & Comp., 1784).

  6. 6.

    Echeverria, M. E. De l’épilepsie nocturne [French]. Ann. Med. Psych. 6, 177 (1879).

    Google Scholar 

  7. 7.

    Fere, C. Les Epilepsies et les Epileptiques [French]. (Alcan, 1890).

  8. 8.

    Moreau, J. J. De l’étiologie de l’épilepsie: et des indications que l’étude des causes peut fournir pour le traitement de cette maladie [French] 94 (Bailliere, 1854).

  9. 9.

    Leuret, M. Recherches sur l’epilepsie. Arch. Gen. Med. 2, 32–50 (1843).

    Google Scholar 

  10. 10.

    Gowers, W. R. Epilepsy and other chronic convulsive diseases: their causes, symptoms, & treatment. London: J. & A. Churchill, New Burlington street, 1881.

  11. 11.

    Langdon-Down, M. B. & Brain, W. R. Time of day in relation to convulsions in epilepsy. Lancet 213, 1029–1032 (1929). Landmark study in the Lingfield epilepsy colony that revealed circadian peak seizure times in nocturnal and diurnal epilepsies.

    Article  Google Scholar 

  12. 12.

    Reynolds, J. R. Epilepsy: its symptoms, treatment, and relation to other chronic convulsive diseases. Br. Foreign Med. Chir. Rev. 30, 309–312 (1862).

    Google Scholar 

  13. 13.

    Griffiths, G. & Fox, J. T. Rhythm in epilepsy. Lancet 232, 409–416 (1938). Landmark study in the Lingfield epilepsy colony that confirmed circadian peak seizure times and described multidien cycles of seizures with patient-specific periodicity.

    Article  Google Scholar 

  14. 14.

    Patry, F. L. The relationship of time of day, sleep and other factors to the incidence of epileptic seizures. Am. J. Psych. 87, 789–813 (1931). Confirmatory study in the Utica epilepsy colony showing circadian peak seizure times in nocturnal and diurnal epilepsies.

    Article  Google Scholar 

  15. 15.

    Berger, H. Uber das Elektrenkephalogramm des Menschen [German]. Arch. fur Psychiatrie und Nervenkrankheiten 87, 527–570 (1929).

    Article  Google Scholar 

  16. 16.

    Gibbs, F. A., Davis, H. & Lennox, W. G. The electro-encephalogram in epilepsy and in conditions of impaired consciousness. Arch. Neurol. Psychiatry 34, 1133 (1935).

    Article  Google Scholar 

  17. 17.

    Jasper, H. H. Electrical signs of epileptic discharge. Electroencephalogr. Clin. Neurophysiol. 1, 11–18 (1949).

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Herman, S. T., Walczak, T. S. & Bazil, C. W. Distribution of partial seizures during the sleep–wake cycle: differences by seizure onset site. Neurology 56, 1453–1459 (2001).

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Hofstra, W. A., Grootemarsink, B. E., Dieker, R., van der Palen, J. & de Weerd, A. W. Temporal distribution of clinical seizures over the 24-h day: a retrospective observational study in a tertiary epilepsy clinic. Epilepsia 50, 2019–2026 (2009).

    PubMed  Article  Google Scholar 

  20. 20.

    Chiang, S., Moss, R., Patel, A. D. & Rao, V. R. Seizure detection devices and health-related quality of life: a patient- and caregiver-centered evaluation. Epilepsy Behav. 105, 106963 (2020).

    PubMed  Article  Google Scholar 

  21. 21.

    Fisher, R. S. et al. Seizure diaries for clinical research and practice: limitations and future prospects. Epilepsy Behav. 24, 304–310 (2012).

    PubMed  Article  Google Scholar 

  22. 22.

    Blum, D. E., Eskola, J., Bortz, J. J. & Fisher, R. S. Patient awareness of seizures. Neurology 47, 260–264 (1996).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Hoppe, C., Poepel, A. & Elger, C. E. Epilepsy: accuracy of patient seizure counts. Arch. Neurol. 64, 1595–1599 (2007).

    PubMed  Article  Google Scholar 

  24. 24.

    Elger, C. E. & Hoppe, C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol. 17, 279–288 (2018).

    PubMed  Article  Google Scholar 

  25. 25.

    Karoly, P. J. et al. Circadian and circaseptan rhythms in human epilepsy: a retrospective cohort study. Lancet Neurol. 17, 977–985 (2018). Retrospective study that used an online, self-reported seizure diary (“Seizure Tracker”) to show the high prevalence of circadian cycles across epilepsy syndromes and identify patients who have an influence of calendar days on reported seizures.

    PubMed  Article  Google Scholar 

  26. 26.

    Freestone, D. R., Karoly, P. J. & Cook, M. J. A forward-looking review of seizure prediction. Curr. Opin. Neurol. 30, 167–173 (2017).

    PubMed  Article  Google Scholar 

  27. 27.

    Baud, M. O. & Rao, V. R. Gauging seizure risk. Neurology 91, 967–973 (2018).

    PubMed  Article  Google Scholar 

  28. 28.

    Johnson, K. T. & Picard, R. W. Advancing neuroscience through wearable devices. Neuron 108, 8–12 (2020).

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    Nasseri, M. et al. Signal quality and patient experience with wearable devices for epilepsy management. Epilepsia 61 (Suppl. 1), S25–S35 (2020).

    PubMed  Google Scholar 

  30. 30.

    Halford, J. J. et al. Detection of generalized tonic–clonic seizures using surface electromyographic monitoring. Epilepsia 58, 1861–1869 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Onorati, F. et al. Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors. Epilepsia 58, 1870–1879 (2017).

    PubMed  Article  Google Scholar 

  32. 32.

    Hinrichs, H. et al. Comparison between a wireless dry electrode EEG system with a conventional wired wet electrode EEG system for clinical applications. Sci. Rep. 10, 5218 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Morrell, M. J. & RNS System in Epilepsy Study Group. Responsive cortical stimulation for the treatment of medically intractable partial epilepsy. Neurology 77, 1295–1304 (2011).

    PubMed  Article  Google Scholar 

  34. 34.

    Kremen, V. et al. Integrating brain implants with local and distributed computing devices: a next generation epilepsy management system. IEEE J. Transl. Eng. Health Med. 6, 2500112 (2018).

    PubMed  Article  Google Scholar 

  35. 35.

    Cook, M. J. 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. 12, 563–571 (2013). First and only truly prospective study of real-time seizure warnings in epilepsy patients implanted with a seizure advisory system.

    PubMed  Article  Google Scholar 

  36. 36.

    Duun-Henriksen, J., Baud, M., Richardson, M. P. & Cook, M. A new era in EEG monitoring? Sub-scalp devices for ultra long-term recordings. Epilepsia 61, 1805–1817 (2020).

    PubMed  Article  Google Scholar 

  37. 37.

    Grone, B. P. & Baraban, S. C. Animal models in epilepsy research: legacies and new directions. Nat. Neurosci. 18, 339–343 (2015).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Nitz, D. A., Van Swinderen, B., Tononi, G. & Greenspan, R. J. Electrophysiological correlates of rest and activity in Drosophila melanogaster. Curr. Biol. 12, 1934–1940 (2002).

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Lyamin, O. I. et al. Unihemispheric slow wave sleep and the state of the eyes in a white whale. Behav. Brain Res. 129, 125–129 (2002).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Frankel, W. N. Genetics of complex neurological disease: challenges and opportunities for modeling epilepsy in mice and rats. Trends Genet. 25, 361–367 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Noebels, J. Pathway-driven discovery of epilepsy genes. Nat. Neurosci. 18, 344–350 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Valatx, J. L., Bugat, R. & Jouvet, M. Genetic studies of sleep in mice. Nature 238, 226–227 (1972).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Ashida, H., Takeuchi, N., Mori, A. & Jinnai, D. Anti-convulsive action of gamma-aminobutyryl choline. Nature 206, 514–515 (1965).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Wykes, R. C. et al. WONOEP appraisal: Network concept from an imaging perspective. Epilepsia 60, 1293–1305 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Khoshkhoo, S., Vogt, D. & Sohal, V. S. Dynamic, cell-type-specific roles for GABAergic Interneurons in a mouse model of optogenetically inducible seizures. Neuron 93, 291–298 (2017).

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Heske, L., Nødtvedt, A., Jäderlund, K. H., Berendt, M. & Egenvall, A. A cohort study of epilepsy among 665,000 insured dogs: incidence, mortality and survival after diagnosis. Vet. J. 202, 471–476 (2014).

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Berendt, M., Hogenhaven, H., Flagstad, A. & Dam, M. Electroencephalography in dogs with epilepsy: similarities between human and canine findings. Acta Neurol. Scand. 99, 276–283 (1999).

    CAS  PubMed  Article  Google Scholar 

  48. 48.

    Mormann, F., Lehnertz, K., David, P. & Elger, C. E. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Phys. D. Nonlinear Phenom. 144, 358–369 (2000).

    Article  Google Scholar 

  49. 49.

    Leguia, M. G., Rao, R. R., Kleen, J. K., & Baud, M. O. Measuring synchrony in bio-medical timeseries. Chaos 31, 013138 (2021).

    PubMed  Article  Google Scholar 

  50. 50.

    Maturana, M. I. et al. Critical slowing down as a biomarker for seizure susceptibility. Nat. Commun. 11, 2172 (2020). Retrospective study that used cycles at different scales (circadian and multidien) extracted from various EEG-based biomarkers to partition time into high, medium and low risk.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Baud, M. O. et al. Multi-day rhythms modulate seizure risk in epilepsy. Nat. Commun. 9, 88 (2018). First study to characterize the phasic relationship between IEA and seizures on the scale of multiple days using chronic EEG recordings.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52.

    Karoly, P. J. et al. Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity. Brain 139, 1066–1078 (2016). First study to characterize circadian and longer cycles in IEA and seizures using chronic EEG recordings.

    PubMed  Article  Google Scholar 

  53. 53.

    Gregg, N. M. et al. Circadian and multiday seizure periodicities, and seizure clusters in canine epilepsy. Brain Commun. 2, fcaa008 (2020). First study to characterize circadian and multidien cycles of seizures in dogs with epilepsy.

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Baud, M. O., Ghestem, A., Benoliel, J. J., Becker, C. & Bernard, C. Endogenous multidien rhythm of epilepsy in rats. Exp. Neurol. 315, 82–87 (2019). First animal study to confirm the existence of multidien IEA cycles organizing clusters of seizures in male rodents and to suggest their endogenous nature.

    PubMed  Article  Google Scholar 

  55. 55.

    Pavlova, M. K., Shea, S. A., Scheer, F. A. & Bromfield, E. B. Is there a circadian variation of epileptiform abnormalities in idiopathic generalized epilepsy? Epilepsy Behav. 16, 461–467 (2009). Small-scope study proposing a constant environment paradigm in humans with generalized epilepsy to disentangle sleep and circadian modulations.

    PubMed  Article  Google Scholar 

  56. 56.

    Janz, D. The grand mal epilepsies and the sleeping-waking cycle. Epilepsia 3, 69–109 (1962).

    CAS  PubMed  Article  Google Scholar 

  57. 57.

    Khan, S. et al. Circadian rhythm and epilepsy. Lancet Neurol. 17, 1098–1108 (2018). Review focusing on the circadian modulation of seizures and possible mechanisms.

    PubMed  Article  Google Scholar 

  58. 58.

    Leguia, M. G. et al. Seizure cycles in focal epilepsy. JAMA Neurol. (2021). Retrospective study in 222 participants in the NeuroPace trials with up to 10 years of chronic EEG data, that investigated patterns and strength of circadian, multidien and circannual cycles with 89%, 60% and 12% prevalence, respectively.

  59. 59.

    Winawer, M. R. et al. Genetic effects on sleep/wake variation of seizures. Epilepsia 57, 557–565 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Mirzoev, A. et al. Circadian profiles of focal epileptic seizures: a need for reappraisal. Seizure 21, 412–416 (2012).

    PubMed  Article  Google Scholar 

  61. 61.

    Thomas, R. H., King, W. H., Johnston, J. A. & Smith, P. E. Awake seizures after pure sleep-related epilepsy: a systematic review and implications for driving law. J. Neurol. Neurosurg. Psychiatry 81, 130–135 (2010).

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Tinuper, P. et al. Definition and diagnostic criteria of sleep-related hypermotor epilepsy. Neurology 86, 1834–1842 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Licchetta, L. et al. Sleep-related hypermotor epilepsy: long-term outcome in a large cohort. Neurology 88, 70–77 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  64. 64.

    Guerrini, R., Marini, C. & Barba, C. Generalized epilepsies. Handb. Clin. Neurol. 161, 3–15 (2019).

    PubMed  Article  Google Scholar 

  65. 65.

    Xu, L. et al. Juvenile myoclonic epilepsy and sleep. Epilepsy Behav. 80, 326–330 (2018).

    PubMed  Article  Google Scholar 

  66. 66.

    Rossi, K. C., Joe, J., Makhija, M. & Goldenholz, D. M. Insufficient sleep, electroencephalogram activation, and seizure risk: Re-evaluating the evidence. Ann. Neurol. 87, 798–806 (2020).

    PubMed  Article  Google Scholar 

  67. 67.

    Gibbs, E. L. Diagnostic and localizing value of electroencephalographic studies in sleep. Res. Publ. Assoc. Nerv. Ment. Dis. 26, 366–376 (1947).

    Google Scholar 

  68. 68.

    Frauscher, B. & Gotman, J. Sleep, oscillations, interictal discharges, and seizures in human focal epilepsy. Neurobiol. Dis. 127, 545–553 (2019). Review of studies on influences of specific sleep stages on seizures, epileptic spikes and high-frequency oscillations.

    PubMed  Article  Google Scholar 

  69. 69.

    Ng, M. & Pavlova, M. Why are seizures rare in rapid eye movement sleep? Review of the frequency of seizures in different sleep stages. Epilepsy Res. Treat. 2013, 932790 (2013). Meta-analysis of nine studies that investigated the occurrence of seizures in different stages of sleep.

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Frauscher, B. et al. Facilitation of epileptic activity during sleep is mediated by high amplitude slow waves. Brain 138, 1629–1641 (2015). First study to show the effect of phases of slow-waves on the emergence of interictal epileptiform discharges during sleep.

    PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Schwarz, J. R. & Zangemeister, W. H. The diagnostic value of the short sleep EEG and other provocative methods following sleep deprivation. J. Neurol. 218, 179–186 (1978).

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    Fountain, N. B., Kim, J. S. & Lee, S. I. Sleep deprivation activates epileptiform discharges independent of the activating effects of sleep. J. Clin. Neurophysiol. 15, 69–75 (1998).

    CAS  PubMed  Article  Google Scholar 

  73. 73.

    Anderson, C. T., Tcheng, T. K., Sun, F. T. & Morrell, M. J. Day-night patterns of epileptiform activity in 65 patients with long-term ambulatory electrocorticography. J. Clin. Neurophysiol. 32, 406–412 (2015).

    PubMed  Article  Google Scholar 

  74. 74.

    Rao, V. R., M, G. L., Tcheng, T. K. & Baud, M. O. Cues for seizure timing. Epilepsia (2020). Study that specifically investigated the role of environmental cues in seizure cycles and suggested that multidien rhythms were free-running in humans with epilepsy.

    Article  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Goldenholz, D. M. et al. Different as night and day: Patterns of isolated seizures, clusters, and status epilepticus. Epilepsia 59, e73–e77 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    Hofstra, W. A. & de Weerd, A. W. The circadian rhythm and its interaction with human epilepsy: a review of literature. Sleep. Med. Rev. 13, 413–420 (2009).

    PubMed  Article  Google Scholar 

  77. 77.

    Molina-Carballo, A., Muñóz-Hoyos, A., Rodríguez-Cabezas, T. & Acuña-Castroviejo, D. Day-night variations in melatonin secretion by the pineal gland during febrile and epileptic convulsions in children. Psychiatry Res. 52, 273–283 (1994).

    CAS  PubMed  Article  Google Scholar 

  78. 78.

    Schapel, G. J., Beran, R. G., Kennaway, D. L., McLoughney, J. & Matthews, C. D. Melatonin response in active epilepsy. Epilepsia 36, 75–78 (1995).

    CAS  PubMed  Article  Google Scholar 

  79. 79.

    Laakso, M. L., Leinonen, L., Hätönen, T., Alila, A. & Heiskala, H. Melatonin, cortisol and body temperature rhythms in Lennox-Gastaut patients with or without circadian rhythm sleep disorders. J. Neurol. 240, 410–416 (1993).

    CAS  PubMed  Article  Google Scholar 

  80. 80.

    Bazil, C. W., Short, D., Crispin, D. & Zheng, W. Patients with intractable epilepsy have low melatonin, which increases following seizures. Neurology 55, 1746–1748 (2000).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  81. 81.

    Yalýn, Ö., Arman, F., Erdoǧan, F. & Kula, M. A comparison of the circadian rhythms and the levels of melatonin in patients with diurnal and nocturnal complex partial seizures. Epilepsy Behav. 8, 542–546 (2006).

    PubMed  Article  Google Scholar 

  82. 82.

    Dabak, O. et al. Evaluation of plasma melatonin levels in children with afebrile and febrile seizures. Pediatr. Neurol. 57, 51–55 (2016).

    PubMed  Article  Google Scholar 

  83. 83.

    Molina-Carballo, A. et al. Melatonin increases following convulsive seizures may be related to its anticonvulsant properties at physiological concentrations. Neuropediatrics 38, 122–125 (2007).

    CAS  PubMed  Article  Google Scholar 

  84. 84.

    van Campen, J. S. et al. Cortisol fluctuations relate to interictal epileptiform discharges in stress sensitive epilepsy. Brain 139, 1673–1679 (2016).

    PubMed  Article  Google Scholar 

  85. 85.

    den Heijer, J. M. et al. The relation between cortisol and functional connectivity in people with and without stress-sensitive epilepsy. Epilepsia 59, 179–189 (2018).

    Article  CAS  Google Scholar 

  86. 86.

    Gotman, J. & Marciani, M. G. Electroencephalographic spiking activity, drug levels, and seizure occurrence in epileptic patients. Ann. Neurol. 17, 597–603 (1985).

    CAS  PubMed  Article  Google Scholar 

  87. 87.

    Krishnan, B. et al. A novel spatiotemporal analysis of peri-ictal spiking to probe the relation of spikes and seizures in epilepsy. Ann. Biomed. Eng. 42, 1606–1617 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Janszky, J. et al. Spatiotemporal relationship between seizure activity and interictal spikes in temporal lobe epilepsy. Epilepsy Res. 47, 179–188 (2001).

    CAS  PubMed  Article  Google Scholar 

  89. 89.

    Spencer, S. S., Goncharova, I. I., Duckrow, R. B., Novotny, E. J. & Zaveri, H. P. Interictal spikes on intracranial recording: behavior, physiology, and implications. Epilepsia 49, 1881–1892 (2008).

    PubMed  Article  Google Scholar 

  90. 90.

    Quigg, M., Clayburn, H., Straume, M., Menaker, M. & Bertram, E. H. 3rd. Effects of circadian regulation and rest-activity state on spontaneous seizures in a rat model of limbic epilepsy. Epilepsia 41, 502–509 (2000).

    CAS  PubMed  Article  Google Scholar 

  91. 91.

    Quigg, M., Straume, M., Menaker, M. & Bertram, E. H. 3rd. Temporal distribution of partial seizures: comparison of an animal model with human partial epilepsy. Ann. Neurol. 43, 748–755 (1998).

    CAS  PubMed  Article  Google Scholar 

  92. 92.

    Danesi, M. A. Seasonal variations in the incidence of photoparoxysmal response to stimulation among photosensitive epileptic patients: evidence from repeated EEG recordings. J. Neurol. Neurosurg. Psychiatry 51, 875–877 (1988).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  93. 93.

    Pitsch, J. et al. Circadian clustering of spontaneous epileptic seizures emerges after pilocarpine-induced status epilepticus. Epilepsia 58, 1159–1171 (2017).

    CAS  PubMed  Article  Google Scholar 

  94. 94.

    Gerstner, J. R. et al. BMAL1 controls the diurnal rhythm and set point for electrical seizure threshold in mice. Front. Syst. Neurosci. 8, 121 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  95. 95.

    Stewart, L. S., Leung, L. S. & Persinger, M. A. Diurnal variation in pilocarpine-induced generalized tonic-clonic seizure activity. Epilepsy Res. 44, 207–212 (2001).

    CAS  PubMed  Article  Google Scholar 

  96. 96.

    Matzen, J., Buchheim, K. & Holtkamp, M. Circadian dentate gyrus excitability in a rat model of temporal lobe epilepsy. Exp. Neurol. 234, 105–111 (2012).

    PubMed  Article  Google Scholar 

  97. 97.

    Ly, J. Q. M. et al. Circadian regulation of human cortical excitability. Nat. Commun. 7, 11828 (2016). Study in healthy humans showing a circadian regulation of cortical excitability using transcranial magnetic stimulation.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  98. 98.

    Huber, R. et al. Human cortical excitability increases with time awake. Cereb. Cortex 23, 332–338 (2013). Study in healthy humans showing the effect of prolonged wakefulness on cortical excitability using transcranial magnetic stimulation.

    PubMed  Article  Google Scholar 

  99. 99.

    Buhr, E. D. & Takahashi, J. S. in Handbook of Experimental Pharmacology 217 (eds Kramer, A, & Merrow, M.) 3–27 (Springer, 2013)

  100. 100.

    Bass, J. & Lazar, M. A. Circadian time signatures of fitness and disease. Science 354, 994–999 (2016).

    CAS  PubMed  Article  Google Scholar 

  101. 101.

    Reppert, S. M. & Weaver, D. R. Coordination of circadian clocks in mammals. Nature 418, 935–941 (2002).

    CAS  PubMed  Article  Google Scholar 

  102. 102.

    Teichman, E. M., O’Riordan, K. J., Gahan, C. G. M., Dinan, T. G. & Cryan, J. F. When rhythms meet the blues: circadian interactions with the microbiota-gut-brain axis. Cell Metab. 31, 448–471 (2020).

    CAS  PubMed  Article  Google Scholar 

  103. 103.

    Noya, S. B. et al. The forebrain synaptic transcriptome is organized by clocks but its proteome is driven by sleep. Science 366, eaav2642 (2019).

    CAS  PubMed  Article  Google Scholar 

  104. 104.

    Bruning, F. et al. Sleep-wake cycles drive daily dynamics of synaptic phosphorylation. Science 366, eaav3617 (2019).

    PubMed  Article  CAS  Google Scholar 

  105. 105.

    Debski, K. J. et al. The circadian dynamics of the hippocampal transcriptome and proteome is altered in experimental temporal lobe epilepsy. Sci. Adv. 6, eaat5979 (2020). First study to investigate circadian molecular oscillations in epileptic and control tissue.

    CAS  PubMed  Article  Google Scholar 

  106. 106.

    Li, P. et al. Loss of CLOCK results in dysfunction of brain circuits underlying focal epilepsy. Neuron 96, 387–401 (2017). Study showing the emergence of epilepsy after the deletion of clock gene in a subpopulation of neurons.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. 107.

    Bernard, C. Circadian/multidien molecular oscillations and rhythmicity of epilepsy (MORE). Epilepsia 62, S49–S68 (2021).

    CAS  PubMed  Google Scholar 

  108. 108.

    Quigg, M., Fowler, K. M., Herzog, A. G. & NIH Progesterone Trial Study Group. Circalunar and ultralunar periodicities in women with partial seizures. Epilepsia 49, 1081–1085 (2008).

    PubMed  Article  Google Scholar 

  109. 109.

    Herzog, A. G. Catamenial epilepsy: definition, prevalence pathophysiology and treatment. Seizure 17, 151–159 (2008).

    PubMed  Article  Google Scholar 

  110. 110.

    Laidlaw, J. Catamenial epilepsy. Lancet 268, 1235–1237 (1956).

    Article  Google Scholar 

  111. 111.

    Cook, M. J. et al. The dynamics of the epileptic brain reveal long-memory processes. Front. Neurol. 5, 217 (2014).

    PubMed  PubMed Central  Google Scholar 

  112. 112.

    Osorio, I., Frei, M. G., Sornette, D. & Milton, J. Pharmaco-resistant seizures: self-triggering capacity, scale-free properties and predictability? Eur. J. Neurosci. 30, 1554–1558 (2009).

    PubMed  Article  Google Scholar 

  113. 113.

    Binnie, C. et al. Temporal characteristics of seizures and epileptiform discharges. Electroencephalogr. Clin. Neurophysiol. 58, 498–505 (1984).

    CAS  PubMed  Article  Google Scholar 

  114. 114.

    Milton, J. G., Gotman, J., Remillard, G. M. & Andermann, F. Timing of seizure recurrence in adult epileptic patients: a statistical analysis. Epilepsia 28, 471–478 (1987).

    CAS  PubMed  Article  Google Scholar 

  115. 115.

    Ferastraoaru, V. et al. Characteristics of large patient-reported outcomes: where can one million seizures get us? Epilepsia Open 3, 364–373 (2018). Retrospective study of the very large Seizure Tracker cohort that shows tight clusters of seizures (≥3 in 24 hours) in a majority of patients (~50%), morning and evening peaks of seizure incidence as well as a trend towards more seizures during weekdays than weekends.

    PubMed  PubMed Central  Article  Google Scholar 

  116. 116.

    Wehr, T. A. Bipolar mood cycles and lunar tidal cycles. Mol. Psychiatry 23, 923–931 (2018).

    CAS  PubMed  Article  Google Scholar 

  117. 117.

    Benedetti, F., Barbini, B., Colombo, C., Campori, E. & Smeraldi, E. Infradian mood fluctuations during a major depressive episode. J. Affect. Disord. 41, 81–87 (1996).

    CAS  PubMed  Article  Google Scholar 

  118. 118.

    Coventry, B. J. et al. CRP identifies homeostatic immune oscillations in cancer patients: a potential treatment targeting tool? J. Transl Med. 7, 102 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  119. 119.

    Zoghi, M. et al. Circadian and infradian rhythms of vasovagal syncope in young and middle-aged subjects. Pacing Clin. Electrophysiol. 31, 1581–1584 (2008).

    PubMed  Article  Google Scholar 

  120. 120.

    Li, K. et al. Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data. NPJ Digital Med. 3, 79 (2020).

    Article  Google Scholar 

  121. 121.

    Herzog, A. G. Catamenial epilepsy: update on prevalence, pathophysiology and treatment from the findings of the NIH Progesterone Treatment Trial. Seizure 28, 18–25 (2015).

    PubMed  Article  Google Scholar 

  122. 122.

    Harden, C. L. & Pennell, P. B. Neuroendocrine considerations in the treatment of men and women with epilepsy. Lancet Neurol. 12, 72–83 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  123. 123.

    Majewska, M. D., Harrison, N. L., Schwartz, R. D., Barker, J. L. & Paul, S. M. Steroid hormone metabolites are barbiturate-like modulators of the GABA receptor. Science 232, 1004–1007 (1986).

    CAS  PubMed  Article  Google Scholar 

  124. 124.

    D’Amour, J. et al. Interictal spike frequency varies with ovarian cycle stage in a rat model of epilepsy. Exp. Neurol. 269, 102–119 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  125. 125.

    Maguire, J. L., Stell, B. M., Rafizadeh, M. & Mody, I. Ovarian cycle-linked changes in GABA(A) receptors mediating tonic inhibition alter seizure susceptibility and anxiety. Nat. Neurosci. 8, 797–804 (2005). Study that proposes a causal role of ovarian steroids in modulating seizures, as shown by the loss of modulation after ovariectomy.

    CAS  PubMed  Article  Google Scholar 

  126. 126.

    Herzog, A. G. et al. Progesterone vs placebo therapy for women with epilepsy: a randomized clinical trial. Neurology 78, 1959–1966 (2012). Landmark clinical trial of progesterone that did not show the expected effect on seizure rates.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  127. 127.

    Celec, P., Ostatniková, D., Putz, Z. & Kudela, M. The circalunar cycle of salivary testosterone and the visual-spatial performance. Bratisl. Lek. Listy 103, 59–69 (2002).

    CAS  PubMed  Google Scholar 

  128. 128.

    Celec, P. et al. Infradian rhythmic variations of salivary estradiol and progesterone in healthy men. Biol. Rhythm. Res. 37, 37–44 (2006).

    CAS  Article  Google Scholar 

  129. 129.

    Rakova, N. et al. Long-term space flight simulation reveals infradian rhythmicity in human Na+ balance. Cell Metab. 17, 125–131 (2013).

    CAS  PubMed  Article  Google Scholar 

  130. 130.

    Jozsa, R. et al. Circadian and extracircadian exploration during daytime hours of circulating corticosterone and other endocrine chronomes. Biomed. Pharmacother. 59, S109–S116 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  131. 131.

    Haut, S. R., Vouyiouklis, M. & Shinnar, S. Stress and epilepsy: a patient perception survey. Epilepsy Behav. 4, 511–514 (2003).

    PubMed  Article  Google Scholar 

  132. 132.

    Pritchard, P. B. III. The effect of seizures on hormones. Epilepsia 32, S46–S50 (1991).

    PubMed  Article  Google Scholar 

  133. 133.

    Buchhalter, J. R. et al. The relationship between d-beta-hydroxybutyrate blood concentrations and seizure control in children treated with the ketogenic diet for medically intractable epilepsy. Epilepsia Open 2, 317–321 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  134. 134.

    Wright, K. E. et al. How might tissue glucose influence responsive neurostimulation detection? Epilepsy Behav. Rep. 12, 100331 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  135. 135.

    Gruenbaum, S. E. et al. Branched-chain amino acids and seizures: a systematic review of the literature. CNS Drugs 33, 755–770 (2019).

    CAS  PubMed  Article  Google Scholar 

  136. 136.

    Allen, C. N. Circadian rhythms, diet, and neuronal excitability. Epilepsia 49, 124–126 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  137. 137.

    Dash, M. B., Bellesi, M., Tononi, G. & Cirelli, C. Sleep/wake dependent changes in cortical glucose concentrations. J. Neurochem. 124, 79–89 (2013).

    CAS  PubMed  Article  Google Scholar 

  138. 138.

    Verbeek, M. M., Leen, W. G., Willemsen, M. A., Slats, D. & Claassen, J. Hourly analysis of cerebrospinal fluid glucose shows large diurnal fluctuations. J. Cereb. Blood Flow. Metab. 36, 899–902 (2015).

    Article  Google Scholar 

  139. 139.

    Pappas, A. et al. Does glucose influence multidien cycles of interictal and/or ictal activities? Seizure 85, 145–150 (2021).

    PubMed  Article  Google Scholar 

  140. 140.

    Leloup, J. C. & Goldbeter, A. Modeling the circadian clock: from molecular mechanism to physiological disorders. Bioessays 30, 590–600 (2008).

    CAS  PubMed  Article  Google Scholar 

  141. 141.

    Foster, R. G. & Roenneberg, T. Human responses to the geophysical daily, annual and lunar cycles. Curr. Biol. 18, R784–R794 (2008).

    CAS  PubMed  Article  Google Scholar 

  142. 142.

    Motta, E., Golba, A., Bal, A., Kazibutowska, Z. & Strzala-Orzel, M. Seizure frequency and bioelectric brain activity in epileptic patients in stable and unstable atmospheric pressure and temperature in different seasons of the year–a preliminary report. Neurol. Neurochir. Pol. 45, 561–566 (2011).

    PubMed  Article  Google Scholar 

  143. 143.

    Bras, P. C. et al. Influence of weather on seizure frequency - Clinical experience in the emergency room of a tertiary hospital. Epilepsy Behav. 86, 25–30 (2018).

    PubMed  Article  Google Scholar 

  144. 144.

    Baxendale, S. Seeing the light? Seizures and sunlight. Epilepsy Res. 84, 72–76 (2009).

    PubMed  Article  Google Scholar 

  145. 145.

    Ünsal, M. A., Atmaca, M. M. & Özbey, Y. Seasonal clustering in epilepsy. Med. Sci. Discov. 7, 419–421 (2020).

    Article  Google Scholar 

  146. 146.

    Alexandratou, I. et al. Seasonal pattern of epileptic seizures: a single-center experience. Sci. Repos. 3, 1–4 (2020).

    Google Scholar 

  147. 147.

    Clemens, Z. et al. Seasonality in epileptic seizures. J. Neurol. Transl Neurosci. 1, 1–3 (2013).

    Google Scholar 

  148. 148.

    Lim, A. S. P. et al. Seasonal plasticity of cognition and related biological measures in adults with and without Alzheimer disease: analysis of multiple cohorts. PLoS Med. 15, e1002647 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  149. 149.

    Meyer, C. et al. Seasonality in human cognitive brain responses. Proc. Natl Acad. Sci. USA 113, 3066–3071 (2016).

    CAS  PubMed  Article  Google Scholar 

  150. 150.

    Tendler, A. et al. Hormone seasonality in medical records suggests circannual endocrine circuits. Proc. Natl Acad. Sci. USA 118, e2003926118 (2021).

    PubMed  Article  CAS  Google Scholar 

  151. 151.

    Rakers, F. et al. Weather as a risk factor for epileptic seizures: a case-crossover study. Epilepsia 58, 1287–1295 (2017).

    CAS  PubMed  Article  Google Scholar 

  152. 152.

    Loscher, W. & Fiedler, M. The role of technical, biological and pharmacological factors in the laboratory evaluation of anticonvulsant drugs. VI. Seasonal influences on maximal electroshock and pentylenetetrazol seizure thresholds. Epilepsy Res. 25, 3–10 (1996).

    CAS  PubMed  Article  Google Scholar 

  153. 153.

    Dumanis, S. B., French, J. A., Bernard, C., Worrell, G. A. & Fureman, B. E. Seizure forecasting from idea to reality. outcomes of the my seizure gauge epilepsy innovation institute workshop. eNeuro (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  154. 154.

    Mormann, F., Andrzejak, R. G., Elger, C. E. & Lehnertz, K. Seizure prediction: the long and winding road. Brain 130, 314–333 (2007). Landmark critical review that scrutinized the shortcomings of early studies on seizure forecasting.

    PubMed  Article  Google Scholar 

  155. 155.

    Karoly, P. J. et al. The circadian profile of epilepsy improves seizure forecasting. Brain 140, 2169–2182 (2017). First study to combine seizure precursors from cEEG and seizure likelihood from past seizure circadian distributions to forecast seizure risk and evaluate forecast performance using the Brier skill score.

    PubMed  Article  Google Scholar 

  156. 156.

    Proix, T. et al. Forecasting seizure risk in adults with focal epilepsy: a development and validation study. Lancet Neurol. 20, 127–135 (2021). Large study on existing data that used models to forecast seizure risk over days, thus proposing a radical change of timescale as compared to previous work.

    PubMed  Article  Google Scholar 

  157. 157.

    Snyder, D. E., Echauz, J., Grimes, D. B. & Litt, B. The statistics of a practical seizure warning system. J. Neural Eng. 5, 392–401 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  158. 158.

    Wong, S., Gardner, A. B., Krieger, A. M. & Litt, B. A stochastic framework for evaluating seizure prediction algorithms using hidden Markov models. J. Neurophysiol. 97, 2525–2532 (2007).

    PubMed  Article  Google Scholar 

  159. 159.

    Baud, M. O., Proix, T., Rao, V. R. & Schindler, K. Chance and risk in epilepsy. Curr. Opin. Neurol. 33, 163–172 (2020).

    PubMed  Article  Google Scholar 

  160. 160.

    Schelter, B., Feldwisch-Drentrup, H., Schulze-Bonhage, A. & Timmer, J. In Seizure Prediction: An Approach Using Probabilistic Forecasting (eds Osorio I., Zaveri H. P., Frei M. G., Arthurs S.) 249–256 (CRC Press, 2011).

  161. 161.

    Litt, B. & Lehnertz, K. Seizure prediction and the preseizure period. Curr. Opin. Neurol. 15, 173–177 (2002).

    PubMed  Article  Google Scholar 

  162. 162.

    Velmurugan, J. et al. Magnetoencephalographic imaging of ictal high-frequency oscillations (80-200Hz) in pharmacologically resistant focal epilepsy. Epilepsia 59, 190–202 (2018).

    CAS  PubMed  Article  Google Scholar 

  163. 163.

    Jacobs, J. et al. High frequency oscillations (80-500Hz) in the preictal period in patients with focal seizures. Epilepsia 50, 1780–1792 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  164. 164.

    Sato, Y. et al. Preictal surrender of post-spike slow waves to spike-related high-frequency oscillations (80-200Hz) is associated with seizure initiation. Epilepsia 55, 1399–1405 (2014).

    PubMed  Article  Google Scholar 

  165. 165.

    Richardson, M. P. & Jefferys, J. G. Introduction–Epilepsy Research U. K. Workshop 2010 on “Preictal Phenomena”. Epilepsy Res. 97, 229–230 (2011).

    PubMed  Article  Google Scholar 

  166. 166.

    Wright, M. A., Orth, M., Patsalos, P. N., Smith, S. J. & Richardson, M. P. Cortical excitability predicts seizures in acutely drug-reduced temporal lobe epilepsy patients. Neurology 67, 1646–1651 (2006).

    PubMed  Article  Google Scholar 

  167. 167.

    Badawy, R., Macdonell, R., Jackson, G. & Berkovic, S. The peri-ictal state: cortical excitability changes within 24h of a seizure. Brain 132, 1013–1021 (2009).

    PubMed  Article  Google Scholar 

  168. 168.

    Pigorini, A. et al. Bistability breaks-off deterministic responses to intracortical stimulation during non-REM sleep. Neuroimage 112, 105–113 (2015).

    PubMed  Article  Google Scholar 

  169. 169.

    Meisel, C. et al. Intrinsic excitability measures track antiepileptic drug action and uncover increasing/decreasing excitability over the wake/sleep cycle. Proc. Natl Acad. Sci. USA 112, 14694–14699 (2015). Study that proposed the use of active intracranial cortical probing for improved understanding of cortical excitability in epilepsy.

    CAS  PubMed  Article  Google Scholar 

  170. 170.

    Freestone, D. R. et al. Electrical probing of cortical excitability in patients with epilepsy. Epilepsy Behav. 22, S110–S118 (2011).

    PubMed  Article  Google Scholar 

  171. 171.

    Federico, P., Abbott, D. F., Briellmann, R. S., Harvey, A. S. & Jackson, G. D. Functional MRI of the pre-ictal state. Brain 128, 1811–1817 (2005).

    PubMed  Article  Google Scholar 

  172. 172.

    Donaire, A. et al. Identifying the structures involved in seizure generation using sequential analysis of ictal-fMRI data. Neuroimage 47, 173–183 (2009).

    PubMed  Article  Google Scholar 

  173. 173.

    Tyvaert, L., LeVan, P., Dubeau, F. & Gotman, J. Noninvasive dynamic imaging of seizures in epileptic patients. Hum. Brain Mapp. 30, 3993–4011 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  174. 174.

    Schwartz, T. H., Hong, S. B., Bagshaw, A. P., Chauvel, P. & Benar, C. G. Preictal changes in cerebral haemodynamics: review of findings and insights from intracerebral EEG. Epilepsy Res. 97, 252–266 (2011).

    PubMed  Article  Google Scholar 

  175. 175.

    Haut, S. R., Hall, C. B., LeValley, A. J. & Lipton, R. B. Can patients with epilepsy predict their seizures? Neurology 68, 262–266 (2007).

    PubMed  Article  Google Scholar 

  176. 176.

    Haut, S. R., Hall, C. B., Masur, J. & Lipton, R. B. Seizure occurrence: precipitants and prediction. Neurology 69, 1905–1910 (2007).

    PubMed  Article  Google Scholar 

  177. 177.

    Privitera, M., Haut, S. R., Lipton, R. B., McGinley, J. S. & Cornes, S. Seizure self-prediction in a randomized controlled trial of stress management. Neurology 93, e2021–e2031 (2019). Innovative prospective study showing that some patients are able to self-forecast seizures above chance.

    PubMed  Article  Google Scholar 

  178. 178.

    Scaramelli, A. et al. Prodromal symptoms in epileptic patients: clinical characterization of the pre-ictal phase. Seizure 18, 246–250 (2009).

    PubMed  Article  Google Scholar 

  179. 179.

    Sanchez Fernandez, I., Loddenkemper, T., Galanopoulou, A. S. & Moshe, S. L. Should epileptiform discharges be treated? Epilepsia 56, 1492–1504 (2015).

    PubMed  Article  Google Scholar 

  180. 180.

    Ung, H. et al. Interictal epileptiform activity outside the seizure onset zone impacts cognition. Brain 140, 2157–2168 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  181. 181.

    Kleen, J. K. et al. Hippocampal interictal epileptiform activity disrupts cognition in humans. Neurology 81, 18–24 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  182. 182.

    Kuhlmann, L. et al. crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain 141, 2619–2630 (2018).

    PubMed  PubMed Central  Google Scholar 

  183. 183.

    Brinkmann, B. H. et al. Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016). First crowd-sourced machine learning effort to perform seizure forecasting on a subset of the NeuroVista dataset.

    PubMed  PubMed Central  Article  Google Scholar 

  184. 184.

    Kuhlmann, L., Lehnertz, K., Richardson, M. P., Schelter, B. & Zaveri, H. P. Seizure prediction - ready for a new era. Nat. Rev. Neurol. 14, 618–630 (2018). Review of the history and progress in seizure forecasting.

    PubMed  Article  Google Scholar 

  185. 185.

    Winterhalder, M. et al. The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy Behav. 4, 318–325 (2003).

    CAS  PubMed  Article  Google Scholar 

  186. 186.

    Jachan, M. et al. Probabilistic forecasts of epileptic seizures and evaluation by the Brier score. 4th European Conference of the International Federation for Medical and Biological Engineering. 1701–1705 (Springer, 2009).

  187. 187.

    Schelter, B. et al. Do false predictions of seizures depend on the state of vigilance? A report from two seizure-prediction methods and proposed remedies. Epilepsia 47, 2058–2070 (2006).

    PubMed  Article  Google Scholar 

  188. 188.

    Sedigh-Sarvestani, M. & Gluckman, B. J. In Recent Advances in Predicting and Preventing Epileptic Seizures (eds Tetzlaff R., Elger C. E. & Lehnertz K.) 264-277 (World Scientific, 2013).

  189. 189.

    Karoly, P. J. et al. Forecasting cycles of seizure likelihood. Epilepsia 61, 776–786 (2020).

    PubMed  Article  Google Scholar 

  190. 190.

    Goldenholz, D. M. et al. Development and validation of forecasting next reported seizure using e-diaries. Ann. Neurol. 88, 588–595 (2020). Retrospective study on the Seizure Tracker dataset that trained a forecaster on a subset of patients to predict daily seizure rates on unseen data.

    PubMed  Article  PubMed Central  Google Scholar 

  191. 191.

    Baud, M. O., Schindler, K. & Rao, V. R. Under-sampling in epilepsy: Limitations of conventional EEG. Clin. Neurophysiol. Pract. 6, 41–49 (2021).

    PubMed  Article  Google Scholar 

  192. 192.

    Ramgopal, S., Thome-Souza, S. & Loddenkemper, T. Chronopharmacology of anti-convulsive therapy. Curr. Neurol. Neurosci. Rep. 13, 339 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  193. 193.

    Sanchez Fernandez, I. & Loddenkemper, T. Chronotherapeutic implications of cyclic seizure patterns. Nat. Rev. Neurol. 14, 696–697 (2018).

    CAS  PubMed  Article  Google Scholar 

  194. 194.

    Thome-Souza, S. et al. Clobazam higher-evening differential dosing as an add-on therapy in refractory epilepsy. Seizure 40, 1–6 (2016).

    PubMed  Article  Google Scholar 

  195. 195.

    Goldenholz, D. M. et al. Is seizure frequency variance a predictable quantity? Ann. Clin. Transl Neurol. 5, 201–207 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  196. 196.

    Karoly, P. J., Romero, J., Cook, M. J., Freestone, D. R. & Goldenholz, D. M. When can we trust responders? Serious concerns when using 50% response rate to assess clinical trials. Epilepsia 60, e99–e103 (2019).

    PubMed  Article  Google Scholar 

  197. 197.

    Cremers, J. & Klugkist, I. One Direction? A tutorial for circular data analysis using R with examples in cognitive psychology. Front. Psychol. 9, 2040 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  198. 198.

    Berens, P. CircStat: A MATLAB toolbox for circular statistics. J. Stat. Soft 31, 1–21 (2009).

    Article  Google Scholar 

Download references


The research of M.O.B. is supported by the Swiss National Science Foundation in the form of an Ambizione grant, number PZ00P3_179929/1, and by the Velux Stiftung, grant #1232. V.R.R. is supported by the Ernest Gallo Foundation Distinguished Professorship in Neurology at the University of California, San Francisco.

Author information




P.J.K., V.R.R., N.M.G., C.B. and M.O.B. researched data for the article, made a substantial contribution to the discussion of content, wrote the article, and reviewed and edited the manuscript before submission. G.A.W. and M.J.C. researched data for the article, made a substantial contribution to discussion of content, and reviewed and edited the manuscript before submission.

Corresponding authors

Correspondence to Philippa J. Karoly or Maxime O. Baud.

Ethics declarations

Competing interests

V.R.R. has served as a consultant for NeuroPace, Inc., manufacturer of the RNS System, a device used in some of the studies referenced here, but NeuroPace, Inc. did not provide targeted funding for this work. M.O.B. reports personal fees from Wyss Center for Bio- and Neuro-engineering as a part-time employee and grants from Wyss Center for Bio- and Neuro-engineering outside the submitted work. M.O.B. has a pending patent under the Patent Cooperation Treaty (#62665486).

Additional information

Peer review information

Nature Reviews Neurology thanks S. Eriksson, S. Kothare, H. Zaveri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



Noting or pertaining to biological cycles of ~24 hours that are generated endogenously in the absence of entrainment by an external cue.


A recently coined term noting or pertaining to biological cycles that are likely to be generated endogenously with a period of >2 days to several weeks.


Noting or pertaining to biological cycles of around 1 year that are likely to be generated endogenously.

Chronic EEG

(cEEG). EEG of long duration (months to years) that requires an implanted device, in contrast to conventional EEG that involves the temporary application of electrodes to the scalp.

Seizure chronotype

One of several discrete temporal patterns based on the observed clustering of seizure occurrence, at particular times or with a particular periodicity, prevalent at the group level in individuals with epilepsy.

Epilepsy colonies

Group housing facilities where people with epilepsy could live and work away from mainstream society, founded in England and elsewhere in the late 1800s owing to the stigma surrounding epilepsy.


The mechanisms and dynamics by which the epileptic brain generates seizures.


The processes that lead the brain to become epileptic.


Noting or pertaining to biological cycles with a period of less than 24 hours (that is, a frequency above once per day) that are generated endogenously.

High-frequency oscillations

High-frequency (>80 Hz) interictal waveforms that can be pathological and relate to epilepsy.

Vigilance states

Different states of alertness and responsiveness to the environment. Specifically defined as wakefulness, non-rapid eye movement sleep (NREM sleep, further subdivided into N1–3) and rapid eye movement (REM) sleep.


From German, “time giver”; rhythmically occurring external or environmental cue that entrains or synchronizes a biological rhythm.

Seizure risk

Stratification of a seizure likelihood into a number of lower and higher risk states.

Pre-ictal state

Retrospectively defined as the period preceding the onset of seizures, often seconds to minutes long, the real-time detection of which would allow warning of imminent seizures.

Pro-ictal state

Consolidated periods of time when seizures are more likely but not certain.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Karoly, P.J., Rao, V.R., Gregg, N.M. et al. Cycles in epilepsy. Nat Rev Neurol 17, 267–284 (2021).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing