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.
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.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Reynolds, E. H. Translation and analysis of a cuneiform text forming part of a Babylonian treatise on epilepsy. Med. History 34, 185–198 (1990).
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.
Temkin, O. The falling sickness: a history of epilepsy from the Greeks to the beginnings of modern neurology (The Johns Hopkins University Press, 1994).
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).
Tissot, S. A. D. Œuvres de Monsieur Tissot, Nouvelle Édition. Tome Douzième Contenant le Traité de l’epilepsie [French] (Chez Francois Grasset & Comp., 1784).
Echeverria, M. E. De l’épilepsie nocturne [French]. Ann. Med. Psych. 6, 177 (1879).
Fere, C. Les Epilepsies et les Epileptiques [French]. (Alcan, 1890).
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).
Leuret, M. Recherches sur l’epilepsie. Arch. Gen. Med. 2, 32–50 (1843).
Gowers, W. R. Epilepsy and other chronic convulsive diseases: their causes, symptoms, & treatment. London: J. & A. Churchill, New Burlington street, 1881.
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.
Reynolds, J. R. Epilepsy: its symptoms, treatment, and relation to other chronic convulsive diseases. Br. Foreign Med. Chir. Rev. 30, 309–312 (1862).
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.
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.
Berger, H. Uber das Elektrenkephalogramm des Menschen [German]. Arch. fur Psychiatrie und Nervenkrankheiten 87, 527–570 (1929).
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).
Jasper, H. H. Electrical signs of epileptic discharge. Electroencephalogr. Clin. Neurophysiol. 1, 11–18 (1949).
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).
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).
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).
Fisher, R. S. et al. Seizure diaries for clinical research and practice: limitations and future prospects. Epilepsy Behav. 24, 304–310 (2012).
Blum, D. E., Eskola, J., Bortz, J. J. & Fisher, R. S. Patient awareness of seizures. Neurology 47, 260–264 (1996).
Hoppe, C., Poepel, A. & Elger, C. E. Epilepsy: accuracy of patient seizure counts. Arch. Neurol. 64, 1595–1599 (2007).
Elger, C. E. & Hoppe, C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol. 17, 279–288 (2018).
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.
Freestone, D. R., Karoly, P. J. & Cook, M. J. A forward-looking review of seizure prediction. Curr. Opin. Neurol. 30, 167–173 (2017).
Baud, M. O. & Rao, V. R. Gauging seizure risk. Neurology 91, 967–973 (2018).
Johnson, K. T. & Picard, R. W. Advancing neuroscience through wearable devices. Neuron 108, 8–12 (2020).
Nasseri, M. et al. Signal quality and patient experience with wearable devices for epilepsy management. Epilepsia 61 (Suppl. 1), S25–S35 (2020).
Halford, J. J. et al. Detection of generalized tonic–clonic seizures using surface electromyographic monitoring. Epilepsia 58, 1861–1869 (2017).
Onorati, F. et al. Multicenter clinical assessment of improved wearable multimodal convulsive seizure detectors. Epilepsia 58, 1870–1879 (2017).
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).
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).
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).
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.
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).
Grone, B. P. & Baraban, S. C. Animal models in epilepsy research: legacies and new directions. Nat. Neurosci. 18, 339–343 (2015).
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).
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).
Frankel, W. N. Genetics of complex neurological disease: challenges and opportunities for modeling epilepsy in mice and rats. Trends Genet. 25, 361–367 (2009).
Noebels, J. Pathway-driven discovery of epilepsy genes. Nat. Neurosci. 18, 344–350 (2015).
Valatx, J. L., Bugat, R. & Jouvet, M. Genetic studies of sleep in mice. Nature 238, 226–227 (1972).
Ashida, H., Takeuchi, N., Mori, A. & Jinnai, D. Anti-convulsive action of gamma-aminobutyryl choline. Nature 206, 514–515 (1965).
Wykes, R. C. et al. WONOEP appraisal: Network concept from an imaging perspective. Epilepsia 60, 1293–1305 (2019).
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).
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).
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).
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).
Leguia, M. G., Rao, R. R., Kleen, J. K., & Baud, M. O. Measuring synchrony in bio-medical timeseries. Chaos 31, 013138 (2021).
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.
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.
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.
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.
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.
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.
Janz, D. The grand mal epilepsies and the sleeping-waking cycle. Epilepsia 3, 69–109 (1962).
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.
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.
Winawer, M. R. et al. Genetic effects on sleep/wake variation of seizures. Epilepsia 57, 557–565 (2016).
Mirzoev, A. et al. Circadian profiles of focal epileptic seizures: a need for reappraisal. Seizure 21, 412–416 (2012).
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).
Tinuper, P. et al. Definition and diagnostic criteria of sleep-related hypermotor epilepsy. Neurology 86, 1834–1842 (2016).
Licchetta, L. et al. Sleep-related hypermotor epilepsy: long-term outcome in a large cohort. Neurology 88, 70–77 (2017).
Guerrini, R., Marini, C. & Barba, C. Generalized epilepsies. Handb. Clin. Neurol. 161, 3–15 (2019).
Xu, L. et al. Juvenile myoclonic epilepsy and sleep. Epilepsy Behav. 80, 326–330 (2018).
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).
Gibbs, E. L. Diagnostic and localizing value of electroencephalographic studies in sleep. Res. Publ. Assoc. Nerv. Ment. Dis. 26, 366–376 (1947).
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.
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.
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.
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).
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).
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).
Rao, V. R., M, G. L., Tcheng, T. K. & Baud, M. O. Cues for seizure timing. Epilepsia https://doi.org/10.1111/epi.16611 (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.
Goldenholz, D. M. et al. Different as night and day: Patterns of isolated seizures, clusters, and status epilepticus. Epilepsia 59, e73–e77 (2018).
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).
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).
Schapel, G. J., Beran, R. G., Kennaway, D. L., McLoughney, J. & Matthews, C. D. Melatonin response in active epilepsy. Epilepsia 36, 75–78 (1995).
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).
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).
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).
Dabak, O. et al. Evaluation of plasma melatonin levels in children with afebrile and febrile seizures. Pediatr. Neurol. 57, 51–55 (2016).
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).
van Campen, J. S. et al. Cortisol fluctuations relate to interictal epileptiform discharges in stress sensitive epilepsy. Brain 139, 1673–1679 (2016).
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).
Gotman, J. & Marciani, M. G. Electroencephalographic spiking activity, drug levels, and seizure occurrence in epileptic patients. Ann. Neurol. 17, 597–603 (1985).
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).
Janszky, J. et al. Spatiotemporal relationship between seizure activity and interictal spikes in temporal lobe epilepsy. Epilepsy Res. 47, 179–188 (2001).
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).
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).
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).
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).
Pitsch, J. et al. Circadian clustering of spontaneous epileptic seizures emerges after pilocarpine-induced status epilepticus. Epilepsia 58, 1159–1171 (2017).
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).
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).
Matzen, J., Buchheim, K. & Holtkamp, M. Circadian dentate gyrus excitability in a rat model of temporal lobe epilepsy. Exp. Neurol. 234, 105–111 (2012).
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.
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.
Buhr, E. D. & Takahashi, J. S. in Handbook of Experimental Pharmacology 217 (eds Kramer, A, & Merrow, M.) 3–27 (Springer, 2013)
Bass, J. & Lazar, M. A. Circadian time signatures of fitness and disease. Science 354, 994–999 (2016).
Reppert, S. M. & Weaver, D. R. Coordination of circadian clocks in mammals. Nature 418, 935–941 (2002).
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).
Noya, S. B. et al. The forebrain synaptic transcriptome is organized by clocks but its proteome is driven by sleep. Science 366, eaav2642 (2019).
Bruning, F. et al. Sleep-wake cycles drive daily dynamics of synaptic phosphorylation. Science 366, eaav3617 (2019).
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.
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.
Bernard, C. Circadian/multidien molecular oscillations and rhythmicity of epilepsy (MORE). Epilepsia 62, S49–S68 (2021).
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).
Herzog, A. G. Catamenial epilepsy: definition, prevalence pathophysiology and treatment. Seizure 17, 151–159 (2008).
Laidlaw, J. Catamenial epilepsy. Lancet 268, 1235–1237 (1956).
Cook, M. J. et al. The dynamics of the epileptic brain reveal long-memory processes. Front. Neurol. 5, 217 (2014).
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).
Binnie, C. et al. Temporal characteristics of seizures and epileptiform discharges. Electroencephalogr. Clin. Neurophysiol. 58, 498–505 (1984).
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).
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.
Wehr, T. A. Bipolar mood cycles and lunar tidal cycles. Mol. Psychiatry 23, 923–931 (2018).
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).
Coventry, B. J. et al. CRP identifies homeostatic immune oscillations in cancer patients: a potential treatment targeting tool? J. Transl Med. 7, 102 (2009).
Zoghi, M. et al. Circadian and infradian rhythms of vasovagal syncope in young and middle-aged subjects. Pacing Clin. Electrophysiol. 31, 1581–1584 (2008).
Li, K. et al. Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data. NPJ Digital Med. 3, 79 (2020).
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).
Harden, C. L. & Pennell, P. B. Neuroendocrine considerations in the treatment of men and women with epilepsy. Lancet Neurol. 12, 72–83 (2013).
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).
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).
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.
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.
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).
Celec, P. et al. Infradian rhythmic variations of salivary estradiol and progesterone in healthy men. Biol. Rhythm. Res. 37, 37–44 (2006).
Rakova, N. et al. Long-term space flight simulation reveals infradian rhythmicity in human Na+ balance. Cell Metab. 17, 125–131 (2013).
Jozsa, R. et al. Circadian and extracircadian exploration during daytime hours of circulating corticosterone and other endocrine chronomes. Biomed. Pharmacother. 59, S109–S116 (2005).
Haut, S. R., Vouyiouklis, M. & Shinnar, S. Stress and epilepsy: a patient perception survey. Epilepsy Behav. 4, 511–514 (2003).
Pritchard, P. B. III. The effect of seizures on hormones. Epilepsia 32, S46–S50 (1991).
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).
Wright, K. E. et al. How might tissue glucose influence responsive neurostimulation detection? Epilepsy Behav. Rep. 12, 100331 (2019).
Gruenbaum, S. E. et al. Branched-chain amino acids and seizures: a systematic review of the literature. CNS Drugs 33, 755–770 (2019).
Allen, C. N. Circadian rhythms, diet, and neuronal excitability. Epilepsia 49, 124–126 (2008).
Dash, M. B., Bellesi, M., Tononi, G. & Cirelli, C. Sleep/wake dependent changes in cortical glucose concentrations. J. Neurochem. 124, 79–89 (2013).
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).
Pappas, A. et al. Does glucose influence multidien cycles of interictal and/or ictal activities? Seizure 85, 145–150 (2021).
Leloup, J. C. & Goldbeter, A. Modeling the circadian clock: from molecular mechanism to physiological disorders. Bioessays 30, 590–600 (2008).
Foster, R. G. & Roenneberg, T. Human responses to the geophysical daily, annual and lunar cycles. Curr. Biol. 18, R784–R794 (2008).
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).
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).
Baxendale, S. Seeing the light? Seizures and sunlight. Epilepsy Res. 84, 72–76 (2009).
Ünsal, M. A., Atmaca, M. M. & Özbey, Y. Seasonal clustering in epilepsy. Med. Sci. Discov. 7, 419–421 (2020).
Alexandratou, I. et al. Seasonal pattern of epileptic seizures: a single-center experience. Sci. Repos. 3, 1–4 (2020).
Clemens, Z. et al. Seasonality in epileptic seizures. J. Neurol. Transl Neurosci. 1, 1–3 (2013).
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).
Meyer, C. et al. Seasonality in human cognitive brain responses. Proc. Natl Acad. Sci. USA 113, 3066–3071 (2016).
Tendler, A. et al. Hormone seasonality in medical records suggests circannual endocrine circuits. Proc. Natl Acad. Sci. USA 118, e2003926118 (2021).
Rakers, F. et al. Weather as a risk factor for epileptic seizures: a case-crossover study. Epilepsia 58, 1287–1295 (2017).
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).
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 https://doi.org/10.1523/ENEURO.0349-17.2017 (2017).
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.
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.
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.
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).
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).
Baud, M. O., Proix, T., Rao, V. R. & Schindler, K. Chance and risk in epilepsy. Curr. Opin. Neurol. 33, 163–172 (2020).
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).
Litt, B. & Lehnertz, K. Seizure prediction and the preseizure period. Curr. Opin. Neurol. 15, 173–177 (2002).
Velmurugan, J. et al. Magnetoencephalographic imaging of ictal high-frequency oscillations (80-200Hz) in pharmacologically resistant focal epilepsy. Epilepsia 59, 190–202 (2018).
Jacobs, J. et al. High frequency oscillations (80-500Hz) in the preictal period in patients with focal seizures. Epilepsia 50, 1780–1792 (2009).
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).
Richardson, M. P. & Jefferys, J. G. Introduction–Epilepsy Research U. K. Workshop 2010 on “Preictal Phenomena”. Epilepsy Res. 97, 229–230 (2011).
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).
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).
Pigorini, A. et al. Bistability breaks-off deterministic responses to intracortical stimulation during non-REM sleep. Neuroimage 112, 105–113 (2015).
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.
Freestone, D. R. et al. Electrical probing of cortical excitability in patients with epilepsy. Epilepsy Behav. 22, S110–S118 (2011).
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).
Donaire, A. et al. Identifying the structures involved in seizure generation using sequential analysis of ictal-fMRI data. Neuroimage 47, 173–183 (2009).
Tyvaert, L., LeVan, P., Dubeau, F. & Gotman, J. Noninvasive dynamic imaging of seizures in epileptic patients. Hum. Brain Mapp. 30, 3993–4011 (2009).
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).
Haut, S. R., Hall, C. B., LeValley, A. J. & Lipton, R. B. Can patients with epilepsy predict their seizures? Neurology 68, 262–266 (2007).
Haut, S. R., Hall, C. B., Masur, J. & Lipton, R. B. Seizure occurrence: precipitants and prediction. Neurology 69, 1905–1910 (2007).
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.
Scaramelli, A. et al. Prodromal symptoms in epileptic patients: clinical characterization of the pre-ictal phase. Seizure 18, 246–250 (2009).
Sanchez Fernandez, I., Loddenkemper, T., Galanopoulou, A. S. & Moshe, S. L. Should epileptiform discharges be treated? Epilepsia 56, 1492–1504 (2015).
Ung, H. et al. Interictal epileptiform activity outside the seizure onset zone impacts cognition. Brain 140, 2157–2168 (2017).
Kleen, J. K. et al. Hippocampal interictal epileptiform activity disrupts cognition in humans. Neurology 81, 18–24 (2013).
Kuhlmann, L. et al. Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain 141, 2619–2630 (2018).
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.
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.
Winterhalder, M. et al. The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy Behav. 4, 318–325 (2003).
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).
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).
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).
Karoly, P. J. et al. Forecasting cycles of seizure likelihood. Epilepsia 61, 776–786 (2020).
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.
Baud, M. O., Schindler, K. & Rao, V. R. Under-sampling in epilepsy: Limitations of conventional EEG. Clin. Neurophysiol. Pract. 6, 41–49 (2021).
Ramgopal, S., Thome-Souza, S. & Loddenkemper, T. Chronopharmacology of anti-convulsive therapy. Curr. Neurol. Neurosci. Rep. 13, 339 (2013).
Sanchez Fernandez, I. & Loddenkemper, T. Chronotherapeutic implications of cyclic seizure patterns. Nat. Rev. Neurol. 14, 696–697 (2018).
Thome-Souza, S. et al. Clobazam higher-evening differential dosing as an add-on therapy in refractory epilepsy. Seizure 40, 1–6 (2016).
Goldenholz, D. M. et al. Is seizure frequency variance a predictable quantity? Ann. Clin. Transl Neurol. 5, 201–207 (2018).
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).
Cremers, J. & Klugkist, I. One Direction? A tutorial for circular data analysis using R with examples in cognitive psychology. Front. Psychol. 9, 2040 (2018).
Berens, P. CircStat: A MATLAB toolbox for circular statistics. J. Stat. Soft 31, 1–21 (2009).
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.
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).
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.
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.
About this article
Cite this article
Karoly, P.J., Rao, V.R., Gregg, N.M. et al. Cycles in epilepsy. Nat Rev Neurol 17, 267–284 (2021). https://doi.org/10.1038/s41582-021-00464-1
Growing role of S100B protein as a putative therapeutic target for neurological -and nonneurological- disorders
Neuroscience & Biobehavioral Reviews (2021)